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Merge remote-tracking branch 'pypsa-eur-sec/master' into merge-pypsa-eur-sec

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Fabian Neumann 2023-03-06 11:54:08 +01:00
commit 97bbafdd70
100 changed files with 83414 additions and 219 deletions
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1
.git-blame-ignore-revs
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@ -5,3 +5,4 @@
# Exclude pre-commit applications
5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0
92080b1cd2ca5f123158571481722767b99c2b27
13769f90af4500948b0376d57df4cceaa13e78b5

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.github/workflows/ci.yaml vendored
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@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: : 2021 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2021-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
@ -19,7 +19,8 @@ on:
- cron: "0 5 * * TUE"
env:
CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
CONDA_CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
DATA_CACHE_NUMBER: 1
jobs:
build:
@ -55,10 +56,6 @@ jobs:
run: |
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
- name: Add solver to environment
run: |
echo -e "- glpk\n- ipopt" >> envs/environment.yaml
- name: Add solver to environment
run: |
echo -e "- glpk\n- ipopt<3.13.3" >> envs/environment.yaml
@ -77,15 +74,25 @@ jobs:
activate-environment: pypsa-eur
use-mamba: true
- name: Set cache date
run: echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
- name: Set cache dates
run: |
echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV
- name: Cache data and cutouts folders
uses: actions/cache@v3
with:
path: |
data
cutouts
key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }}
- name: Create environment cache
uses: actions/cache@v3
id: cache
with:
path: ${{ matrix.prefix }}
key: ${{ matrix.label }}-conda-${{ hashFiles('envs/environment.yaml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
key: ${{ matrix.label }}-conda-${{ env.DATE }}-${{ env.CONDA_CACHE_NUMBER }}
- name: Update environment due to outdated or unavailable cache
run: mamba env update -n pypsa-eur -f envs/environment.yaml
@ -95,4 +102,6 @@ jobs:
run: |
conda activate pypsa-eur
conda list
snakemake -call solve_all_networks --configfile test/config.test1.yaml
snakemake -call solve_all_elec_networks --configfile test/config.test1.yaml
snakemake -call --configfile test/config.overnight.yaml
snakemake -call --configfile test/config.myopic.yaml

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.gitignore vendored
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@ -11,18 +11,65 @@ gurobi.log
/bak
/resources
/resources*
/results
/networks
/benchmarks
/logs
/notebooks
/data
/data/links_p_nom.csv
/cutouts
/dask-worker-space
doc/_build
config.yaml
dconf
/data/links_p_nom.csv
/data/*totals.csv
/data/biomass*
/data/emobility/
/data/eea*
/data/jrc*
/data/heating/
/data/eurostat*
/data/odyssee/
/data/transport_data.csv
/data/switzerland*
/data/.nfs*
/data/Industrial_Database.csv
/data/retro/tabula-calculator-calcsetbuilding.csv
/data/nuts*
data/gas_network/scigrid-gas/
data/costs_*.csv
dask-worker-space/
publications.jrc.ec.europa.eu/
*.org
*.nc
*~
/scripts/old
*.pyc
/cutouts
/tmp
/pypsa
*.xlsx
config.yaml
doc/_build
*.xls
*.geojson
*.ipynb
data/costs_*
merger-todos.md

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.pre-commit-config.yaml
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@ -33,7 +33,7 @@ repos:
rev: v2.2.2
hooks:
- id: codespell
args: ['--ignore-regex="(\b[A-Z]+\b)"', '--ignore-words-list=fom'] # Ignore capital case words, e.g. country codes
args: ['--ignore-regex="(\b[A-Z]+\b)"', '--ignore-words-list=fom,appartment,bage,ore,setis,tabacco'] # Ignore capital case words, e.g. country codes
types_or: [python, rst, markdown]
files: ^(scripts|doc)/
@ -73,10 +73,10 @@ repos:
args: [--autofix, --indent, "2", --preserve-quotes]
# Format Snakemake rule / workflow files
- repo: https://github.com/snakemake/snakefmt
rev: v0.8.1
hooks:
- id: snakefmt
# - repo: https://github.com/snakemake/snakefmt
# rev: v0.8.1
# hooks:
# - id: snakefmt
# For cleaning jupyter notebooks
- repo: https://github.com/aflc/pre-commit-jupyter
@ -85,8 +85,8 @@ repos:
- id: jupyter-notebook-cleanup
exclude: examples/solve-on-remote.ipynb
# Check for FSFE REUSE compliance (licensing)
- repo: https://github.com/fsfe/reuse-tool
rev: v1.1.2
hooks:
- id: reuse
# Check for FSFE REUSE compliance (licensing)
# - repo: https://github.com/fsfe/reuse-tool
# rev: v1.1.2
# hooks:
# - id: reuse

3
.syncignore-receive
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@ -15,5 +15,6 @@ __pycache__
notebooks
doc
cutouts
data/bundle
data
benchmarks
*.nc

4
.syncignore-send
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@ -1,7 +1,3 @@
# SPDX-FileCopyrightText: : 2021 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
.snakemake
.git
.pytest_cache

20
LICENSE.txt Normal file
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@ -0,0 +1,20 @@
MIT License
Copyright 2017-2023 The PyPSA-Eur Authors
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

79
README.md
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@ -7,42 +7,54 @@ SPDX-License-Identifier: CC-BY-4.0
[![Build Status](https://github.com/pypsa/pypsa-eur/actions/workflows/ci.yaml/badge.svg)](https://github.com/PyPSA/pypsa-eur/actions)
[![Documentation](https://readthedocs.org/projects/pypsa-eur/badge/?version=latest)](https://pypsa-eur.readthedocs.io/en/latest/?badge=latest)
![Size](https://img.shields.io/github/repo-size/pypsa/pypsa-eur)
[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.3520874.svg)](https://doi.org/10.5281/zenodo.3520874)
[![Zenodo PyPSA-Eur](https://zenodo.org/badge/DOI/10.5281/zenodo.3520874.svg)](https://doi.org/10.5281/zenodo.3520874)
[![Zenodo PyPSA-Eur-Sec](https://zenodo.org/badge/DOI/10.5281/zenodo.3938042.svg)](https://doi.org/10.5281/zenodo.3938042)
[![Snakemake](https://img.shields.io/badge/snakemake-≥5.0.0-brightgreen.svg?style=flat)](https://snakemake.readthedocs.io)
[![REUSE status](https://api.reuse.software/badge/github.com/pypsa/pypsa-eur)](https://api.reuse.software/info/github.com/pypsa/pypsa-eur)
# PyPSA-Eur: An Open Optimisation Model of the European Transmission System
# PyPSA-Eur: A Sector-Coupled Open Optimisation Model of the European Energy System
PyPSA-Eur is an open model dataset of the European power system at the
transmission network level that covers the full ENTSO-E area.
The model is suitable both for operational studies and generation and transmission expansion planning studies.
PyPSA-Eur is an open model dataset of the European energy system at the
transmission network level that covers the full ENTSO-E area. The model is suitable both for operational studies and generation and transmission expansion planning studies.
The continental scope and highly resolved spatial scale enables a proper description of the long-range
smoothing effects for renewable power generation and their varying resource availability.
The model is described in the [documentation](https://pypsa-eur.readthedocs.io)
and in the paper
[PyPSA-Eur: An Open Optimisation Model of the European Transmission
System](https://arxiv.org/abs/1806.01613), 2018,
[arXiv:1806.01613](https://arxiv.org/abs/1806.01613).
The model building routines are defined through a snakemake workflow.
Please see the [documentation](https://pypsa-eur.readthedocs.io/)
for installation instructions and other useful information about the snakemake workflow.
The model is designed to be imported into the open toolbox
[PyPSA](https://github.com/PyPSA/PyPSA).
**WARNING**: PyPSA-Eur is under active development and has several
[limitations](https://pypsa-eur.readthedocs.io/en/latest/limitations.html)
which you should understand before using the model. The github repository
[issues](https://github.com/PyPSA/pypsa-eur/issues) collect known topics we are
working on (please feel free to help or make suggestions). The
[documentation](https://pypsa-eur.readthedocs.io/) remains somewhat patchy. You
can find showcases of the model's capabilities in the preprint [Benefits of a
Hydrogen Network in Europe](https://arxiv.org/abs/2207.05816), a [paper in Joule
with a description of the industry sector](https://arxiv.org/abs/2109.09563), or
in [a 2021 presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf).
We cannot support this model if you choose to use it. We do not recommend to use
the full resolution network model for simulations. At high granularity the
assignment of loads and generators to the nearest network node may not be a
correct assumption, depending on the topology of the underlying distribution
grid, and local grid bottlenecks may cause unrealistic load-shedding or
generator curtailment. We recommend to cluster the network to a couple of
hundred nodes to remove these local inconsistencies. See the discussion in
Section 3.4 "Model validation" of the paper.
**WARNING**: Please read the [limitations](https://pypsa-eur.readthedocs.io/en/latest/limitations.html) section of the
documentation and paper carefully before using the model. We do not
recommend to use the full resolution network model for simulations. At
high granularity the assignment of loads and generators to the nearest
network node may not be a correct assumption, depending on the topology of the underlying distribution grid,
and local grid
bottlenecks may cause unrealistic load-shedding or generator
curtailment. We recommend to cluster the network to a couple of
hundred nodes to remove these local inconsistencies. See the
discussion in Section 3.4 "Model validation" of the paper.
![PyPSA-Eur Grid Model](doc/img/elec.png)
The model building routines are defined through a snakemake workflow. The model is designed to be imported into the open toolbox
[PyPSA](https://github.com/PyPSA/PyPSA) for operational studies as
well as generation and transmission expansion planning studies.
The dataset consists of:
- A grid model based on a modified [GridKit](https://github.com/bdw/GridKit)
@ -57,9 +69,30 @@ The dataset consists of:
- Renewable time series based on ERA5 and SARAH, assembled using the [atlite tool](https://github.com/FRESNA/atlite).
- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [atlite library](https://github.com/PyPSA/atlite).
A sector-coupled extension adds demand
and supply for the following sectors: transport, space and water
heating, biomass, industry and industrial feedstocks, agriculture,
forestry and fishing. This completes the energy system and includes
all greenhouse gas emitters except waste management and land use.
This diagram gives an overview of the sectors and the links between
them:
![sector diagram](graphics/multisector_figure.png)
Each of these sectors is built up on the transmission network nodes
from [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur):
![network diagram](https://github.com/PyPSA/pypsa-eur/blob/master/doc/img/base.png?raw=true)
For computational reasons the model is usually clustered down
to 50-200 nodes.
Already-built versions of the model can be found in the accompanying [Zenodo
repository](https://doi.org/10.5281/zenodo.3601881).
# Licence
A version of the model that adds building heating, transport and
industry sectors to the model, as well as gas networks, can be found
in the [PyPSA-Eur-Sec](https://github.com/PyPSA/pypsa-eur-sec) repository.
The code in PyPSA-Eur is released as free software under the
[MIT License](https://opensource.org/licenses/MIT), see `LICENSE.txt`.
However, different licenses and terms of use may apply to the various
input data.

802
Snakefile
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@ -6,7 +6,6 @@ from os.path import normpath, exists
from shutil import copyfile, move
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
HTTP = HTTPRemoteProvider()
if not exists("config.yaml"):
@ -29,36 +28,60 @@ wildcard_constraints:
clusters="[0-9]+m?|all",
ll="(v|c)([0-9\.]+|opt|all)|all",
opts="[-+a-zA-Z0-9\.]*",
sector_opts="[-+a-zA-Z0-9\.\s]*"
rule cluster_all_networks:
input:
expand("networks/" + RDIR + "elec_s{simpl}_{clusters}.nc", **config["scenario"]),
expand("resources/" + RDIR + "networks/elec_s{simpl}_{clusters}.nc", **config["scenario"]),
rule extra_components_all_networks:
input:
expand(
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc", **config["scenario"]
"resources/" + RDIR + "networks/elec_s{simpl}_{clusters}_ec.nc", **config["scenario"]
),
rule prepare_all_networks:
input:
expand(
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
"resources/" + RDIR + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
**config["scenario"]
),
rule prepare_sector_networks:
input:
expand(RDIR + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
**config['scenario'])
rule all:
input: RDIR + 'graphs/costs.pdf'
rule solve_all_elec_networks:
input:
expand(
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
**config["scenario"]
),
rule solve_all_networks:
input:
expand(
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
**config["scenario"]
expand(RDIR + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
**config['scenario']
),
rule plot_all_networks:
input:
expand(RDIR + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
**config['scenario'])
if config["enable"].get("prepare_links_p_nom", False):
rule prepare_links_p_nom:
@ -133,7 +156,7 @@ rule build_load_data:
rule build_powerplants:
input:
base_network="networks/" + RDIR + "base.nc",
base_network="resources/" + RDIR + "networks/base.nc",
custom_powerplants="data/custom_powerplants.csv",
output:
"resources/" + RDIR + "powerplants.csv",
@ -160,7 +183,7 @@ rule base_network:
offshore_shapes="resources/" + RDIR + "offshore_shapes.geojson",
europe_shape="resources/" + RDIR + "europe_shape.geojson",
output:
"networks/" + RDIR + "base.nc",
"resources/" + RDIR + "networks/base.nc",
log:
"logs/" + RDIR + "base_network.log",
benchmark:
@ -199,7 +222,7 @@ rule build_bus_regions:
input:
country_shapes="resources/" + RDIR + "country_shapes.geojson",
offshore_shapes="resources/" + RDIR + "offshore_shapes.geojson",
base_network="networks/" + RDIR + "base.nc",
base_network="resources/" + RDIR + "networks/base.nc",
output:
regions_onshore="resources/" + RDIR + "regions_onshore.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore.geojson",
@ -334,7 +357,7 @@ rule build_ship_raster:
rule build_renewable_profiles:
input:
base_network="networks/" + RDIR + "base.nc",
base_network="resources/" + RDIR + "networks/base.nc",
corine="data/bundle/corine/g250_clc06_V18_5.tif",
natura=lambda w: (
"resources/" + RDIR + "natura.tiff"
@ -406,7 +429,7 @@ rule add_electricity:
for attr, fn in d.items()
if str(fn).startswith("data/")
},
base_network="networks/" + RDIR + "base.nc",
base_network="resources/" + RDIR + "networks/base.nc",
tech_costs=COSTS,
regions="resources/" + RDIR + "regions_onshore.geojson",
powerplants="resources/" + RDIR + "powerplants.csv",
@ -415,7 +438,7 @@ rule add_electricity:
load="resources/" + RDIR + "load.csv",
nuts3_shapes="resources/" + RDIR + "nuts3_shapes.geojson",
output:
"networks/" + RDIR + "elec.nc",
"resources/" + RDIR + "networks/elec.nc",
log:
"logs/" + RDIR + "add_electricity.log",
benchmark:
@ -429,12 +452,12 @@ rule add_electricity:
rule simplify_network:
input:
network="networks/" + RDIR + "elec.nc",
network="resources/" + RDIR + "networks/elec.nc",
tech_costs=COSTS,
regions_onshore="resources/" + RDIR + "regions_onshore.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore.geojson",
output:
network="networks/" + RDIR + "elec_s{simpl}.nc",
network="resources/" + RDIR + "networks/elec_s{simpl}.nc",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore_elec_s{simpl}.geojson",
busmap="resources/" + RDIR + "busmap_elec_s{simpl}.csv",
@ -452,7 +475,7 @@ rule simplify_network:
rule cluster_network:
input:
network="networks/" + RDIR + "elec_s{simpl}.nc",
network="resources/" + RDIR + "networks/elec_s{simpl}.nc",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore_elec_s{simpl}.geojson",
busmap=ancient("resources/" + RDIR + "busmap_elec_s{simpl}.csv"),
@ -463,7 +486,7 @@ rule cluster_network:
),
tech_costs=COSTS,
output:
network="networks/" + RDIR + "elec_s{simpl}_{clusters}.nc",
network="resources/" + RDIR + "networks/elec_s{simpl}_{clusters}.nc",
regions_onshore="resources/"
+ RDIR
+ "regions_onshore_elec_s{simpl}_{clusters}.geojson",
@ -485,10 +508,10 @@ rule cluster_network:
rule add_extra_components:
input:
network="networks/" + RDIR + "elec_s{simpl}_{clusters}.nc",
network="resources/" + RDIR + "networks/elec_s{simpl}_{clusters}.nc",
tech_costs=COSTS,
output:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc",
"resources/" + RDIR + "networks/elec_s{simpl}_{clusters}_ec.nc",
log:
"logs/" + RDIR + "add_extra_components/elec_s{simpl}_{clusters}.log",
benchmark:
@ -502,10 +525,10 @@ rule add_extra_components:
rule prepare_network:
input:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc",
"resources/" + RDIR + "networks/elec_s{simpl}_{clusters}_ec.nc",
tech_costs=COSTS,
output:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
"resources/" + RDIR + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
log:
"logs/" + RDIR + "prepare_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.log",
benchmark:
@ -543,7 +566,7 @@ def memory(w):
rule solve_network:
input:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
"resources/" + RDIR + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
output:
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
log:
@ -571,7 +594,7 @@ rule solve_network:
rule solve_operations_network:
input:
unprepared="networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc",
unprepared="resources/" + RDIR + "networks/elec_s{simpl}_{clusters}_ec.nc",
optimized="results/networks/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
@ -604,108 +627,669 @@ rule solve_operations_network:
"scripts/solve_operations_network.py"
datafiles = [
"data/eea/UNFCCC_v23.csv",
"data/switzerland-sfoe/switzerland-new_format.csv",
"data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
"data/myb1-2017-nitro.xls",
"data/Industrial_Database.csv",
"data/emobility/KFZ__count",
"data/emobility/Pkw__count",
"data/h2_salt_caverns_GWh_per_sqkm.geojson",
directory("data/eurostat-energy_balances-june_2016_edition"),
directory("data/eurostat-energy_balances-may_2018_edition"),
directory("data/jrc-idees-2015"),
]
if config.get('retrieve_sector_databundle', True):
rule retrieve_sector_databundle:
output: *datafiles
log: "logs/retrieve_sector_databundle.log"
script: 'scripts/retrieve_sector_databundle.py'
if config.get("retrieve_cost_data", True):
rule retrieve_cost_data:
input: HTTP.remote("raw.githubusercontent.com/PyPSA/technology-data/{}/outputs/".format(config['costs']['version']) + "costs_{year}.csv", keep_local=True)
output: "data/costs_{year}.csv"
log: "logs/" + RDIR + "retrieve_cost_data_{year}.log",
resources: mem_mb=1000,
run: move(input[0], output[0])
rule build_population_layouts:
input:
nuts3_shapes='resources/' + RDIR + 'nuts3_shapes.geojson',
urban_percent="data/urban_percent.csv"
output:
pop_layout_total="resources/" + RDIR + "pop_layout_total.nc",
pop_layout_urban="resources/" + RDIR + "pop_layout_urban.nc",
pop_layout_rural="resources/" + RDIR + "pop_layout_rural.nc"
resources: mem_mb=20000
benchmark: "benchmarks/build_population_layouts"
threads: 8
script: "scripts/build_population_layouts.py"
rule build_clustered_population_layouts:
input:
pop_layout_total="resources/" + RDIR + "pop_layout_total.nc",
pop_layout_urban="resources/" + RDIR + "pop_layout_urban.nc",
pop_layout_rural="resources/" + RDIR + "pop_layout_rural.nc",
regions_onshore='resources/' + RDIR + 'regions_onshore_elec_s{simpl}_{clusters}.geojson'
output:
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}_{clusters}.csv"
resources: mem_mb=10000
benchmark: "benchmarks/build_clustered_population_layouts/s{simpl}_{clusters}"
script: "scripts/build_clustered_population_layouts.py"
rule build_simplified_population_layouts:
input:
pop_layout_total="resources/" + RDIR + "pop_layout_total.nc",
pop_layout_urban="resources/" + RDIR + "pop_layout_urban.nc",
pop_layout_rural="resources/" + RDIR + "pop_layout_rural.nc",
regions_onshore='resources/' + RDIR + 'regions_onshore_elec_s{simpl}.geojson'
output:
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}.csv"
resources: mem_mb=10000
benchmark: "benchmarks/build_clustered_population_layouts/s{simpl}"
script: "scripts/build_clustered_population_layouts.py"
if config["sector"]["gas_network"] or config["sector"]["H2_retrofit"]:
datafiles = [
"IGGIELGN_LNGs.geojson",
"IGGIELGN_BorderPoints.geojson",
"IGGIELGN_Productions.geojson",
"IGGIELGN_PipeSegments.geojson",
]
rule retrieve_gas_infrastructure_data:
output: expand("data/gas_network/scigrid-gas/data/{files}", files=datafiles)
script: 'scripts/retrieve_gas_infrastructure_data.py'
rule build_gas_network:
input:
gas_network="data/gas_network/scigrid-gas/data/IGGIELGN_PipeSegments.geojson"
output:
cleaned_gas_network="resources/" + RDIR + "gas_network.csv"
resources: mem_mb=4000
script: "scripts/build_gas_network.py"
rule build_gas_input_locations:
input:
lng=HTTP.remote("https://globalenergymonitor.org/wp-content/uploads/2022/09/Europe-Gas-Tracker-August-2022.xlsx", keep_local=True),
entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
production="data/gas_network/scigrid-gas/data/IGGIELGN_Productions.geojson",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
regions_offshore='resources/' + RDIR + 'regions_offshore_elec_s{simpl}_{clusters}.geojson'
output:
gas_input_nodes="resources/" + RDIR + "gas_input_locations_s{simpl}_{clusters}.geojson",
gas_input_nodes_simplified="resources/" + RDIR + "gas_input_locations_s{simpl}_{clusters}_simplified.csv"
resources: mem_mb=2000,
script: "scripts/build_gas_input_locations.py"
rule cluster_gas_network:
input:
cleaned_gas_network="resources/" + RDIR + "gas_network.csv",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore_elec_s{simpl}_{clusters}.geojson"
output:
clustered_gas_network="resources/" + RDIR + "gas_network_elec_s{simpl}_{clusters}.csv"
resources: mem_mb=4000
script: "scripts/cluster_gas_network.py"
gas_infrastructure = {**rules.cluster_gas_network.output, **rules.build_gas_input_locations.output}
else:
gas_infrastructure = {}
rule build_heat_demands:
input:
pop_layout="resources/" + RDIR + "pop_layout_{scope}.nc",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson"
output:
heat_demand="resources/" + RDIR + "heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"
resources: mem_mb=20000
threads: 8
benchmark: "benchmarks/build_heat_demands/{scope}_s{simpl}_{clusters}"
script: "scripts/build_heat_demand.py"
rule build_temperature_profiles:
input:
pop_layout="resources/" + RDIR + "pop_layout_{scope}.nc",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson"
output:
temp_soil="resources/" + RDIR + "temp_soil_{scope}_elec_s{simpl}_{clusters}.nc",
temp_air="resources/" + RDIR + "temp_air_{scope}_elec_s{simpl}_{clusters}.nc",
resources: mem_mb=20000
threads: 8
benchmark: "benchmarks/build_temperature_profiles/{scope}_s{simpl}_{clusters}"
script: "scripts/build_temperature_profiles.py"
rule build_cop_profiles:
input:
temp_soil_total="resources/" + RDIR + "temp_soil_total_elec_s{simpl}_{clusters}.nc",
temp_soil_rural="resources/" + RDIR + "temp_soil_rural_elec_s{simpl}_{clusters}.nc",
temp_soil_urban="resources/" + RDIR + "temp_soil_urban_elec_s{simpl}_{clusters}.nc",
temp_air_total="resources/" + RDIR + "temp_air_total_elec_s{simpl}_{clusters}.nc",
temp_air_rural="resources/" + RDIR + "temp_air_rural_elec_s{simpl}_{clusters}.nc",
temp_air_urban="resources/" + RDIR + "temp_air_urban_elec_s{simpl}_{clusters}.nc"
output:
cop_soil_total="resources/" + RDIR + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
cop_soil_rural="resources/" + RDIR + "cop_soil_rural_elec_s{simpl}_{clusters}.nc",
cop_soil_urban="resources/" + RDIR + "cop_soil_urban_elec_s{simpl}_{clusters}.nc",
cop_air_total="resources/" + RDIR + "cop_air_total_elec_s{simpl}_{clusters}.nc",
cop_air_rural="resources/" + RDIR + "cop_air_rural_elec_s{simpl}_{clusters}.nc",
cop_air_urban="resources/" + RDIR + "cop_air_urban_elec_s{simpl}_{clusters}.nc"
resources: mem_mb=20000
benchmark: "benchmarks/build_cop_profiles/s{simpl}_{clusters}"
script: "scripts/build_cop_profiles.py"
rule build_solar_thermal_profiles:
input:
pop_layout="resources/" + RDIR + "pop_layout_{scope}.nc",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson"
output:
solar_thermal="resources/" + RDIR + "solar_thermal_{scope}_elec_s{simpl}_{clusters}.nc",
resources: mem_mb=20000
threads: 16
benchmark: "benchmarks/build_solar_thermal_profiles/{scope}_s{simpl}_{clusters}"
script: "scripts/build_solar_thermal_profiles.py"
def input_eurostat(w):
# 2016 includes BA, 2017 does not
report_year = config["energy"]["eurostat_report_year"]
return f"data/eurostat-energy_balances-june_{report_year}_edition"
rule build_energy_totals:
input:
nuts3_shapes='resources/' + RDIR + 'nuts3_shapes.geojson',
co2="data/eea/UNFCCC_v23.csv",
swiss="data/switzerland-sfoe/switzerland-new_format.csv",
idees="data/jrc-idees-2015",
district_heat_share='data/district_heat_share.csv',
eurostat=input_eurostat
output:
energy_name='resources/' + RDIR + 'energy_totals.csv',
co2_name='resources/' + RDIR + 'co2_totals.csv',
transport_name='resources/' + RDIR + 'transport_data.csv'
threads: 16
resources: mem_mb=10000
benchmark: "benchmarks/build_energy_totals"
script: 'scripts/build_energy_totals.py'
rule build_biomass_potentials:
input:
enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
nuts3_population="data/bundle/nama_10r_3popgdp.tsv.gz",
swiss_cantons="data/bundle/ch_cantons.csv",
swiss_population="data/bundle/je-e-21.03.02.xls",
country_shapes='resources/' + RDIR + 'country_shapes.geojson'
output:
biomass_potentials_all='resources/' + RDIR + 'biomass_potentials_all_s{simpl}_{clusters}.csv',
biomass_potentials='resources/' + RDIR + 'biomass_potentials_s{simpl}_{clusters}.csv'
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_biomass_potentials_s{simpl}_{clusters}"
script: 'scripts/build_biomass_potentials.py'
if config["sector"]["biomass_transport"]:
rule build_biomass_transport_costs:
input:
transport_cost_data=HTTP.remote("publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass potentials in europe_web rev.pdf", keep_local=True)
output:
biomass_transport_costs="resources/" + RDIR + "biomass_transport_costs.csv",
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_biomass_transport_costs"
script: 'scripts/build_biomass_transport_costs.py'
build_biomass_transport_costs_output = rules.build_biomass_transport_costs.output
else:
build_biomass_transport_costs_output = {}
if config["sector"]["regional_co2_sequestration_potential"]["enable"]:
rule build_sequestration_potentials:
input:
sequestration_potential=HTTP.remote("https://raw.githubusercontent.com/ericzhou571/Co2Storage/main/resources/complete_map_2020_unit_Mt.geojson", keep_local=True),
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore_elec_s{simpl}_{clusters}.geojson",
output:
sequestration_potential="resources/" + RDIR + "co2_sequestration_potential_elec_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=4000
benchmark: "benchmarks/build_sequestration_potentials_s{simpl}_{clusters}"
script: "scripts/build_sequestration_potentials.py"
build_sequestration_potentials_output = rules.build_sequestration_potentials.output
else:
build_sequestration_potentials_output = {}
rule build_salt_cavern_potentials:
input:
salt_caverns="data/h2_salt_caverns_GWh_per_sqkm.geojson",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore_elec_s{simpl}_{clusters}.geojson",
output:
h2_cavern_potential="resources/" + RDIR + "salt_cavern_potentials_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=2000
benchmark: "benchmarks/build_salt_cavern_potentials_s{simpl}_{clusters}"
script: "scripts/build_salt_cavern_potentials.py"
rule build_ammonia_production:
input:
usgs="data/myb1-2017-nitro.xls"
output:
ammonia_production="resources/" + RDIR + "ammonia_production.csv"
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_ammonia_production"
script: 'scripts/build_ammonia_production.py'
rule build_industry_sector_ratios:
input:
ammonia_production="resources/" + RDIR + "ammonia_production.csv",
idees="data/jrc-idees-2015"
output:
industry_sector_ratios="resources/" + RDIR + "industry_sector_ratios.csv"
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_industry_sector_ratios"
script: 'scripts/build_industry_sector_ratios.py'
rule build_industrial_production_per_country:
input:
ammonia_production="resources/" + RDIR + "ammonia_production.csv",
jrc="data/jrc-idees-2015",
eurostat="data/eurostat-energy_balances-may_2018_edition",
output:
industrial_production_per_country="resources/" + RDIR + "industrial_production_per_country.csv"
threads: 8
resources: mem_mb=1000
benchmark: "benchmarks/build_industrial_production_per_country"
script: 'scripts/build_industrial_production_per_country.py'
rule build_industrial_production_per_country_tomorrow:
input:
industrial_production_per_country="resources/" + RDIR + "industrial_production_per_country.csv"
output:
industrial_production_per_country_tomorrow="resources/" + RDIR + "industrial_production_per_country_tomorrow_{planning_horizons}.csv"
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_industrial_production_per_country_tomorrow_{planning_horizons}"
script: 'scripts/build_industrial_production_per_country_tomorrow.py'
rule build_industrial_distribution_key:
input:
regions_onshore='resources/' + RDIR + 'regions_onshore_elec_s{simpl}_{clusters}.geojson',
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}_{clusters}.csv",
hotmaps_industrial_database="data/Industrial_Database.csv",
output:
industrial_distribution_key="resources/" + RDIR + "industrial_distribution_key_elec_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_industrial_distribution_key/s{simpl}_{clusters}"
script: 'scripts/build_industrial_distribution_key.py'
rule build_industrial_production_per_node:
input:
industrial_distribution_key="resources/" + RDIR + "industrial_distribution_key_elec_s{simpl}_{clusters}.csv",
industrial_production_per_country_tomorrow="resources/" + RDIR + "industrial_production_per_country_tomorrow_{planning_horizons}.csv"
output:
industrial_production_per_node="resources/" + RDIR + "industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_industrial_production_per_node/s{simpl}_{clusters}_{planning_horizons}"
script: 'scripts/build_industrial_production_per_node.py'
rule build_industrial_energy_demand_per_node:
input:
industry_sector_ratios="resources/" + RDIR + "industry_sector_ratios.csv",
industrial_production_per_node="resources/" + RDIR + "industrial_production_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
industrial_energy_demand_per_node_today="resources/" + RDIR + "industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv"
output:
industrial_energy_demand_per_node="resources/" + RDIR + "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_industrial_energy_demand_per_node/s{simpl}_{clusters}_{planning_horizons}"
script: 'scripts/build_industrial_energy_demand_per_node.py'
rule build_industrial_energy_demand_per_country_today:
input:
jrc="data/jrc-idees-2015",
ammonia_production="resources/" + RDIR + "ammonia_production.csv",
industrial_production_per_country="resources/" + RDIR + "industrial_production_per_country.csv"
output:
industrial_energy_demand_per_country_today="resources/" + RDIR + "industrial_energy_demand_per_country_today.csv"
threads: 8
resources: mem_mb=1000
benchmark: "benchmarks/build_industrial_energy_demand_per_country_today"
script: 'scripts/build_industrial_energy_demand_per_country_today.py'
rule build_industrial_energy_demand_per_node_today:
input:
industrial_distribution_key="resources/" + RDIR + "industrial_distribution_key_elec_s{simpl}_{clusters}.csv",
industrial_energy_demand_per_country_today="resources/" + RDIR + "industrial_energy_demand_per_country_today.csv"
output:
industrial_energy_demand_per_node_today="resources/" + RDIR + "industrial_energy_demand_today_elec_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=1000
benchmark: "benchmarks/build_industrial_energy_demand_per_node_today/s{simpl}_{clusters}"
script: 'scripts/build_industrial_energy_demand_per_node_today.py'
if config["sector"]["retrofitting"]["retro_endogen"]:
rule build_retro_cost:
input:
building_stock="data/retro/data_building_stock.csv",
data_tabula="data/retro/tabula-calculator-calcsetbuilding.csv",
air_temperature = "resources/" + RDIR + "temp_air_total_elec_s{simpl}_{clusters}.nc",
u_values_PL="data/retro/u_values_poland.csv",
tax_w="data/retro/electricity_taxes_eu.csv",
construction_index="data/retro/comparative_level_investment.csv",
floor_area_missing="data/retro/floor_area_missing.csv",
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}_{clusters}.csv",
cost_germany="data/retro/retro_cost_germany.csv",
window_assumptions="data/retro/window_assumptions.csv",
output:
retro_cost="resources/" + RDIR + "retro_cost_elec_s{simpl}_{clusters}.csv",
floor_area="resources/" + RDIR + "floor_area_elec_s{simpl}_{clusters}.csv"
resources: mem_mb=1000
benchmark: "benchmarks/build_retro_cost/s{simpl}_{clusters}"
script: "scripts/build_retro_cost.py"
build_retro_cost_output = rules.build_retro_cost.output
else:
build_retro_cost_output = {}
rule build_population_weighted_energy_totals:
input:
energy_totals='resources/' + RDIR + 'energy_totals.csv',
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}_{clusters}.csv"
output: "resources/" + RDIR + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=2000
script: "scripts/build_population_weighted_energy_totals.py"
rule build_shipping_demand:
input:
ports="data/attributed_ports.json",
scope="resources/" + RDIR + "europe_shape.geojson",
regions="resources/" + RDIR + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
demand="resources/" + RDIR + "energy_totals.csv"
output: "resources/" + RDIR + "shipping_demand_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=2000
script: "scripts/build_shipping_demand.py"
rule build_transport_demand:
input:
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}_{clusters}.csv",
pop_weighted_energy_totals="resources/" + RDIR + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
transport_data='resources/' + RDIR + 'transport_data.csv',
traffic_data_KFZ="data/emobility/KFZ__count",
traffic_data_Pkw="data/emobility/Pkw__count",
temp_air_total="resources/" + RDIR + "temp_air_total_elec_s{simpl}_{clusters}.nc",
output:
transport_demand="resources/" + RDIR + "transport_demand_s{simpl}_{clusters}.csv",
transport_data="resources/" + RDIR + "transport_data_s{simpl}_{clusters}.csv",
avail_profile="resources/" + RDIR + "avail_profile_s{simpl}_{clusters}.csv",
dsm_profile="resources/" + RDIR + "dsm_profile_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=2000
script: "scripts/build_transport_demand.py"
rule prepare_sector_network:
input:
overrides="data/override_component_attrs",
network='networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc',
energy_totals_name='resources/' + RDIR + 'energy_totals.csv',
eurostat=input_eurostat,
pop_weighted_energy_totals="resources/" + RDIR + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
shipping_demand="resources/" + RDIR + "shipping_demand_s{simpl}_{clusters}.csv",
transport_demand="resources/" + RDIR + "transport_demand_s{simpl}_{clusters}.csv",
transport_data="resources/" + RDIR + "transport_data_s{simpl}_{clusters}.csv",
avail_profile="resources/" + RDIR + "avail_profile_s{simpl}_{clusters}.csv",
dsm_profile="resources/" + RDIR + "dsm_profile_s{simpl}_{clusters}.csv",
co2_totals_name='resources/' + RDIR + 'co2_totals.csv',
co2="data/eea/UNFCCC_v23.csv",
biomass_potentials='resources/' + RDIR + 'biomass_potentials_s{simpl}_{clusters}.csv',
heat_profile="data/heat_load_profile_BDEW.csv",
costs="data/costs_{}.csv".format(config['costs']['year']) if config["foresight"] == "overnight" else "data/costs_{planning_horizons}.csv",
profile_offwind_ac="resources/" + RDIR + "profile_offwind-ac.nc",
profile_offwind_dc="resources/" + RDIR + "profile_offwind-dc.nc",
h2_cavern="resources/" + RDIR + "salt_cavern_potentials_s{simpl}_{clusters}.csv",
busmap_s="resources/" + RDIR + "busmap_elec_s{simpl}.csv",
busmap="resources/" + RDIR + "busmap_elec_s{simpl}_{clusters}.csv",
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}_{clusters}.csv",
simplified_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}.csv",
industrial_demand="resources/" + RDIR + "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
heat_demand_urban="resources/" + RDIR + "heat_demand_urban_elec_s{simpl}_{clusters}.nc",
heat_demand_rural="resources/" + RDIR + "heat_demand_rural_elec_s{simpl}_{clusters}.nc",
heat_demand_total="resources/" + RDIR + "heat_demand_total_elec_s{simpl}_{clusters}.nc",
temp_soil_total="resources/" + RDIR + "temp_soil_total_elec_s{simpl}_{clusters}.nc",
temp_soil_rural="resources/" + RDIR + "temp_soil_rural_elec_s{simpl}_{clusters}.nc",
temp_soil_urban="resources/" + RDIR + "temp_soil_urban_elec_s{simpl}_{clusters}.nc",
temp_air_total="resources/" + RDIR + "temp_air_total_elec_s{simpl}_{clusters}.nc",
temp_air_rural="resources/" + RDIR + "temp_air_rural_elec_s{simpl}_{clusters}.nc",
temp_air_urban="resources/" + RDIR + "temp_air_urban_elec_s{simpl}_{clusters}.nc",
cop_soil_total="resources/" + RDIR + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
cop_soil_rural="resources/" + RDIR + "cop_soil_rural_elec_s{simpl}_{clusters}.nc",
cop_soil_urban="resources/" + RDIR + "cop_soil_urban_elec_s{simpl}_{clusters}.nc",
cop_air_total="resources/" + RDIR + "cop_air_total_elec_s{simpl}_{clusters}.nc",
cop_air_rural="resources/" + RDIR + "cop_air_rural_elec_s{simpl}_{clusters}.nc",
cop_air_urban="resources/" + RDIR + "cop_air_urban_elec_s{simpl}_{clusters}.nc",
solar_thermal_total="resources/" + RDIR + "solar_thermal_total_elec_s{simpl}_{clusters}.nc" if config["sector"]["solar_thermal"] else [],
solar_thermal_urban="resources/" + RDIR + "solar_thermal_urban_elec_s{simpl}_{clusters}.nc" if config["sector"]["solar_thermal"] else [],
solar_thermal_rural="resources/" + RDIR + "solar_thermal_rural_elec_s{simpl}_{clusters}.nc" if config["sector"]["solar_thermal"] else [],
**build_retro_cost_output,
**build_biomass_transport_costs_output,
**gas_infrastructure,
**build_sequestration_potentials_output
output: "results/" + RDIR + '/prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc'
threads: 1
resources: mem_mb=2000
benchmark: RDIR + "benchmarks/prepare_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
script: "scripts/prepare_sector_network.py"
rule plot_network:
input:
network="results/networks/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
tech_costs=COSTS,
overrides="data/override_component_attrs",
network="results/" + RDIR + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
regions='resources/' + RDIR + 'regions_onshore_elec_s{simpl}_{clusters}.geojson'
output:
only_map="results/plots/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}.{ext}",
ext="results/plots/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_ext.{ext}",
log:
"logs/"
+ RDIR
+ "plot_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_{ext}.log",
script:
"scripts/plot_network.py"
map="results/" + RDIR + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
today="results/" + RDIR + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}-today.pdf"
threads: 2
resources: mem_mb=10000
benchmark: RDIR + "benchmarks/plot_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
script: "scripts/plot_network.py"
def input_make_summary(w):
# It's mildly hacky to include the separate costs input as first entry
if w.ll.endswith("all"):
ll = config["scenario"]["ll"]
if len(w.ll) == 4:
ll = [l for l in ll if l[0] == w.ll[0]]
else:
ll = w.ll
return [COSTS] + expand(
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
ll=ll,
**{
k: config["scenario"][k] if getattr(w, k) == "all" else getattr(w, k)
for k in ["simpl", "clusters", "opts"]
}
)
rule copy_config:
output: "results/" + RDIR + 'configs/config.yaml'
threads: 1
resources: mem_mb=1000
benchmark: RDIR + "benchmarks/copy_config"
script: "scripts/copy_config.py"
rule copy_conda_env:
output: "results/" + RDIR + 'configs/environment.yaml'
threads: 1
resources: mem_mb=500
benchmark: "results/" + RDIR + "benchmarks/copy_conda_env"
shell: "conda env export -f {output} --no-builds"
rule make_summary:
input:
input_make_summary,
output:
directory(
"results/summaries/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}"
overrides="data/override_component_attrs",
networks=expand(
"results/" + RDIR + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
**config['scenario']
),
log:
"logs/"
+ RDIR
+ "make_summary/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.log",
resources:
mem_mb=1500,
script:
"scripts/make_summary.py"
costs="data/costs_{}.csv".format(config['costs']['year']) if config["foresight"] == "overnight" else "data/costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
plots=expand(
"results/" + RDIR + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
**config['scenario']
)
output:
nodal_costs="results/" + RDIR + 'csvs/nodal_costs.csv',
nodal_capacities="results/" + RDIR + 'csvs/nodal_capacities.csv',
nodal_cfs="results/" + RDIR + 'csvs/nodal_cfs.csv',
cfs="results/" + RDIR + 'csvs/cfs.csv',
costs="results/" + RDIR + 'csvs/costs.csv',
capacities="results/" + RDIR + 'csvs/capacities.csv',
curtailment="results/" + RDIR + 'csvs/curtailment.csv',
energy="results/" + RDIR + 'csvs/energy.csv',
supply="results/" + RDIR + 'csvs/supply.csv',
supply_energy="results/" + RDIR + 'csvs/supply_energy.csv',
prices="results/" + RDIR + 'csvs/prices.csv',
weighted_prices="results/" + RDIR + 'csvs/weighted_prices.csv',
market_values="results/" + RDIR + 'csvs/market_values.csv',
price_statistics="results/" + RDIR + 'csvs/price_statistics.csv',
metrics="results/" + RDIR + 'csvs/metrics.csv'
threads: 2
resources: mem_mb=10000
benchmark: RDIR + "benchmarks/make_summary"
script: "scripts/make_summary.py"
rule plot_summary:
input:
"results/summaries/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}",
costs="results/" + RDIR + 'csvs/costs.csv',
energy="results/" + RDIR + 'csvs/energy.csv',
balances="results/" + RDIR + 'csvs/supply_energy.csv',
eurostat=input_eurostat,
country_codes='data/Country_codes.csv',
output:
"results/plots/"
+ RDIR
+ "summary_{summary}_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.{ext}",
log:
"logs/"
+ RDIR
+ "plot_summary/{summary}_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}_{ext}.log",
resources:
mem_mb=1500,
script:
"scripts/plot_summary.py"
costs="results/" + RDIR + 'graphs/costs.pdf',
energy="results/" + RDIR + 'graphs/energy.pdf',
balances="results/" + RDIR + 'graphs/balances-energy.pdf'
threads: 2
resources: mem_mb=10000
benchmark: RDIR + "benchmarks/plot_summary"
script: "scripts/plot_summary.py"
def input_plot_p_nom_max(w):
return [
(
"results/networks/"
+ RDIR
+ "elec_s{simpl}{maybe_cluster}.nc".format(
maybe_cluster=("" if c == "full" else ("_" + c)), **w
)
)
for c in w.clusts.split(",")
]
if config["foresight"] == "overnight":
rule solve_network:
input:
overrides="data/override_component_attrs",
network="results/" + RDIR + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
costs="data/costs_{}.csv".format(config['costs']['year']),
config="results/" + RDIR + 'configs/config.yaml',
#env=RDIR + 'configs/environment.yaml',
output: "results/" + RDIR + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc"
shadow: "shallow"
log:
solver=RDIR + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
python=RDIR + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_python.log",
memory=RDIR + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_memory.log"
threads: config['solving']['solver'].get('threads', 4)
resources: mem_mb=config['solving']['mem']
benchmark: RDIR + "benchmarks/solve_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
script: "scripts/solve_network.py"
rule plot_p_nom_max:
input:
input_plot_p_nom_max,
output:
"results/plots/"
+ RDIR
+ "elec_s{simpl}_cum_p_nom_max_{clusts}_{techs}_{country}.{ext}",
log:
"logs/"
+ RDIR
+ "plot_p_nom_max/elec_s{simpl}_{clusts}_{techs}_{country}_{ext}.log",
resources:
mem_mb=1500,
script:
"scripts/plot_p_nom_max.py"
if config["foresight"] == "myopic":
rule add_existing_baseyear:
input:
overrides="data/override_component_attrs",
network="results/" + RDIR + '/prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc',
powerplants='resources/' + RDIR + 'powerplants.csv',
busmap_s="resources/" + RDIR + "busmap_elec_s{simpl}.csv",
busmap="resources/" + RDIR + "busmap_elec_s{simpl}_{clusters}.csv",
clustered_pop_layout="resources/" + RDIR + "pop_layout_elec_s{simpl}_{clusters}.csv",
costs="data/costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
cop_soil_total="resources/" + RDIR + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
cop_air_total="resources/" + RDIR + "cop_air_total_elec_s{simpl}_{clusters}.nc",
existing_heating='data/existing_infrastructure/existing_heating_raw.csv',
country_codes='data/Country_codes.csv',
existing_solar='data/existing_infrastructure/solar_capacity_IRENA.csv',
existing_onwind='data/existing_infrastructure/onwind_capacity_IRENA.csv',
existing_offwind='data/existing_infrastructure/offwind_capacity_IRENA.csv',
output: "results/" + RDIR + '/prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc'
wildcard_constraints:
planning_horizons=config['scenario']['planning_horizons'][0] #only applies to baseyear
threads: 1
resources: mem_mb=2000
benchmark: RDIR + '/benchmarks/add_existing_baseyear/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}'
script: "scripts/add_existing_baseyear.py"
def solved_previous_horizon(wildcards):
planning_horizons = config["scenario"]["planning_horizons"]
i = planning_horizons.index(int(wildcards.planning_horizons))
planning_horizon_p = str(planning_horizons[i-1])
return "results/" + RDIR + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_" + planning_horizon_p + ".nc"
rule add_brownfield:
input:
overrides="data/override_component_attrs",
network="results/" + RDIR + '/prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc',
network_p=solved_previous_horizon, #solved network at previous time step
costs="data/costs_{planning_horizons}.csv",
cop_soil_total="resources/" + RDIR + "cop_soil_total_elec_s{simpl}_{clusters}.nc",
cop_air_total="resources/" + RDIR + "cop_air_total_elec_s{simpl}_{clusters}.nc"
output: "results/" + RDIR + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc"
threads: 4
resources: mem_mb=10000
benchmark: RDIR + '/benchmarks/add_brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}'
script: "scripts/add_brownfield.py"
ruleorder: add_existing_baseyear > add_brownfield
rule solve_network_myopic:
input:
overrides="data/override_component_attrs",
network="results/" + RDIR + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
costs="data/costs_{planning_horizons}.csv",
config="results/" + RDIR + 'configs/config.yaml'
output: "results/" + RDIR + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc"
shadow: "shallow"
log:
solver=RDIR + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_solver.log",
python=RDIR + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_python.log",
memory=RDIR + "logs/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}_memory.log"
threads: 4
resources: mem_mb=config['solving']['mem']
benchmark: RDIR + "benchmarks/solve_network/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}"
script: "scripts/solve_network.py"

658
config.default.yaml
View File

@ -9,16 +9,55 @@ logging:
level: INFO
format: '%(levelname)s:%(name)s:%(message)s'
logging_level: INFO
run:
name: "" # use this to keep track of runs with different settings
shared_cutouts: false # set to true to share the default cutout(s) across runs
foresight: overnight # options are overnight, myopic, perfect (perfect is not yet implemented)
# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
scenario:
simpl: ['']
ll: ['copt']
clusters: [37, 128, 256, 512, 1024]
opts: [Co2L-3H]
simpl:
- ''
ll: # allowed transmission line volume expansion, can be any float >= 1.0 with a prefix v|c (today) or "copt"
- v1.0
- v1.5
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 37
- 128
- 256
- 512
- 1024
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-dist1
# to really understand the options here, look in scripts/prepare_sector_network.py
# Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
# Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
# xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
# single letters are sectors: T for land transport, H for building heating,
# B for biomass supply, I for industry, shipping and aviation,
# A for agriculture, forestry and fishing
# solar+c0.5 reduces the capital cost of solar to 50\% of reference value
# solar+p3 multiplies the available installable potential by factor 3
# seq400 sets the potential of CO2 sequestration to 400 Mt CO2 per year
# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
# emissions throughout the transition path in the timeframe determined by the
# planning_horizons), be:beta decay; ex:exponential decay
# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
# decay with initial growth rate 0
planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
- 2050
# for example, set to
# - 2020
# - 2030
# - 2040
# - 2050
# for myopic foresight
countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK']
@ -37,6 +76,21 @@ enable:
retrieve_natura_raster: true
custom_busmap: false
retrieve_sector_databundle: true
retrieve_cost_data: true
# CO2 budget as a fraction of 1990 emissions
# this is over-ridden if CO2Lx is set in sector_opts
# this is also over-ridden if cb is set in sector_opts
co2_budget:
2020: 0.7011648746
2025: 0.5241935484
2030: 0.2970430108
2035: 0.1500896057
2040: 0.0712365591
2045: 0.0322580645
2050: 0
electricity:
voltages: [220., 300., 380.]
gaslimit: false # global gas usage limit of X MWh_th
@ -84,7 +138,9 @@ electricity:
Onshore: [onwind]
PV: [solar]
atlite:
cutout: ../pypsa-eur/cutouts/europe-2013-era5.nc
nprocesses: 4
show_progress: false # false saves time
cutouts:
@ -231,6 +287,287 @@ load:
manual_adjustments: true # false
scaling_factor: 1.0
# regulate what components with which carriers are kept from PyPSA-Eur;
# some technologies are removed because they are implemented differently
# (e.g. battery or H2 storage) or have different year-dependent costs
# in PyPSA-Eur-Sec
pypsa_eur:
Bus:
- AC
Link:
- DC
Generator:
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
StorageUnit:
- PHS
- hydro
Store: []
energy:
energy_totals_year: 2011
base_emissions_year: 1990
eurostat_report_year: 2016
emissions: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
biomass:
year: 2030
scenario: ENS_Med
classes:
solid biomass:
- Agricultural waste
- Fuelwood residues
- Secondary Forestry residues - woodchips
- Sawdust
- Residues from landscape care
- Municipal waste
not included:
- Sugar from sugar beet
- Rape seed
- "Sunflower, soya seed "
- Bioethanol barley, wheat, grain maize, oats, other cereals and rye
- Miscanthus, switchgrass, RCG
- Willow
- Poplar
- FuelwoodRW
- C&P_RW
biogas:
- Manure solid, liquid
- Sludge
solar_thermal:
clearsky_model: simple # should be "simple" or "enhanced"?
orientation:
slope: 45.
azimuth: 180.
# only relevant for foresight = myopic or perfect
existing_capacities:
grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020
threshold_capacity: 10
conventional_carriers:
- lignite
- coal
- oil
- uranium
sector:
district_heating:
potential: 0.6 # maximum fraction of urban demand which can be supplied by district heating
# increase of today's district heating demand to potential maximum district heating share
# progress = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
progress:
2020: 0.0
2030: 0.3
2040: 0.6
2050: 1.0
district_heating_loss: 0.15
cluster_heat_buses: false # cluster residential and service heat buses to one to save memory
bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
transport_heating_deadband_upper: 20.
transport_heating_deadband_lower: 15.
ICE_lower_degree_factor: 0.375 #in per cent increase in fuel consumption per degree above deadband
ICE_upper_degree_factor: 1.6
EV_lower_degree_factor: 0.98
EV_upper_degree_factor: 0.63
bev_dsm: true #turns on EV battery
bev_availability: 0.5 #How many cars do smart charging
bev_energy: 0.05 #average battery size in MWh
bev_charge_efficiency: 0.9 #BEV (dis-)charging efficiency
bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
bev_charge_rate: 0.011 #3-phase charger with 11 kW
bev_avail_max: 0.95
bev_avail_mean: 0.8
v2g: true #allows feed-in to grid from EV battery
#what is not EV or FCEV is oil-fuelled ICE
land_transport_fuel_cell_share:
2020: 0
2030: 0.05
2040: 0.1
2050: 0.15
land_transport_electric_share:
2020: 0
2030: 0.25
2040: 0.6
2050: 0.85
land_transport_ice_share:
2020: 1
2030: 0.7
2040: 0.3
2050: 0
transport_fuel_cell_efficiency: 0.5
transport_internal_combustion_efficiency: 0.3
agriculture_machinery_electric_share: 0
agriculture_machinery_oil_share: 1
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
MWh_MeOH_per_MWh_H2: 0.8787 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
MWh_MeOH_per_tCO2: 4.0321 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
MWh_MeOH_per_MWh_e: 3.6907 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
shipping_hydrogen_share:
2020: 0
2030: 0
2040: 0
2050: 0
shipping_methanol_share:
2020: 0
2030: 0.3
2040: 0.7
2050: 1
shipping_oil_share:
2020: 1
2030: 0.7
2040: 0.3
2050: 0
shipping_methanol_efficiency: 0.46 # 10-15% higher https://www.iea-amf.org/app/webroot/files/file/Annex%20Reports/AMF_Annex_56.pdf, https://users.ugent.be/~lsileghe/documents/extended_abstract.pdf
shipping_oil_efficiency: 0.40 #For conversion of fuel oil to propulsion in 2011
aviation_demand_factor: 1. # relative aviation demand compared to today
HVC_demand_factor: 1. # relative HVC demand compared to today
time_dep_hp_cop: true #time dependent heat pump coefficient of performance
heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
# conservatively high to cover hot water and space heating in poorly-insulated buildings
reduce_space_heat_exogenously: true # reduces space heat demand by a given factor (applied before losses in DH)
# this can represent e.g. building renovation, building demolition, or if
# the factor is negative: increasing floor area, increased thermal comfort, population growth
reduce_space_heat_exogenously_factor: # per unit reduction in space heat demand
# the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
2020: 0.10 # this results in a space heat demand reduction of 10%
2025: 0.09 # first heat demand increases compared to 2020 because of larger floor area per capita
2030: 0.09
2035: 0.11
2040: 0.16
2045: 0.21
2050: 0.29
retrofitting: # co-optimises building renovation to reduce space heat demand
retro_endogen: false # co-optimise space heat savings
cost_factor: 1.0 # weight costs for building renovation
interest_rate: 0.04 # for investment in building components
annualise_cost: true # annualise the investment costs
tax_weighting: false # weight costs depending on taxes in countries
construction_index: true # weight costs depending on labour/material costs per country
tes: true
tes_tau: # 180 day time constant for centralised, 3 day for decentralised
decentral: 3
central: 180
boilers: true
oil_boilers: false
biomass_boiler: true
chp: true
micro_chp: false
solar_thermal: true
solar_cf_correction: 0.788457 # = >>> 1/1.2683
marginal_cost_storage: 0. #1e-4
methanation: true
helmeth: true
coal_cc: false
dac: true
co2_vent: false
allam_cycle: false
SMR: true
regional_co2_sequestration_potential:
enable: false # enable regionally resolved geological co2 storage potential
attribute: 'conservative estimate Mt'
include_onshore: false # include onshore sequestration potentials
min_size: 3 # Gt, sites with lower potential will be excluded
max_size: 25 # Gt, max sequestration potential for any one site, TODO research suitable value
years_of_storage: 25 # years until potential exhausted at optimised annual rate
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
co2_spatial: false
co2network: false
cc_fraction: 0.9 # default fraction of CO2 captured with post-combustion capture
hydrogen_underground_storage: true
hydrogen_underground_storage_locations:
# - onshore # more than 50 km from sea
- nearshore # within 50 km of sea
# - offshore
ammonia: false # can be false (no NH3 carrier), true (copperplated NH3), "regional" (regionalised NH3 without network)
min_part_load_fischer_tropsch: 0.9 # p_min_pu
min_part_load_methanolisation: 0.5 # p_min_pu
use_fischer_tropsch_waste_heat: true
use_fuel_cell_waste_heat: true
use_electrolysis_waste_heat: false
electricity_distribution_grid: true
electricity_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
electricity_grid_connection: true # only applies to onshore wind and utility PV
H2_network: true
gas_network: false
H2_retrofit: false # if set to True existing gas pipes can be retrofitted to H2 pipes
# according to hydrogen backbone strategy (April, 2020) p.15
# https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
# 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
H2_retrofit_capacity_per_CH4: 0.6 # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
gas_distribution_grid: true
gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
biomass_spatial: false # regionally resolve biomass (e.g. potentials)
biomass_transport: false # allow transport of solid biomass between nodes
conventional_generation: # generator : carrier
OCGT: gas
biomass_to_liquid: false
biosng: false
industry:
St_primary_fraction: # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
2020: 0.6
2025: 0.55
2030: 0.5
2035: 0.45
2040: 0.4
2045: 0.35
2050: 0.3
DRI_fraction: # fraction of the primary route converted to DRI + EAF
2020: 0
2025: 0
2030: 0.05
2035: 0.2
2040: 0.4
2045: 0.7
2050: 1
H2_DRI: 1.7 #H2 consumption in Direct Reduced Iron (DRI), MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
elec_DRI: 0.322 #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
Al_primary_fraction: # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
2020: 0.4
2025: 0.375
2030: 0.35
2035: 0.325
2040: 0.3
2045: 0.25
2050: 0.2
MWh_NH3_per_tNH3: 5.166 # LHV
MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
MWh_NH3_per_MWh_H2_cracker: 1.46 # https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0. # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0. # fraction of today's HVC produced via chemical recycling
HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
MWh_H2_per_tCl: -0.9372 # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
hotmaps_locate_missing: false
reference_year: 2015
# references:
# DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
costs:
year: 2030
version: v0.5.0
@ -244,6 +581,14 @@ costs:
lifetime: 25
"CO2 intensity": 0
"discount rate": 0.07
lifetime: 25 #default lifetime
# From a Lion Hirth paper, also reflects average of Noothout et al 2016
discountrate: 0.07
# [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
USD2013_to_EUR2013: 0.7532
# Marginal and capital costs can be overwritten
# capital_cost:
# onwind: 500
marginal_cost:
solar: 0.01
onwind: 0.015
@ -256,6 +601,8 @@ costs:
battery inverter: 0.
emission_prices: # in currency per tonne emission, only used with the option Ep
co2: 0.
lines:
length_factor: 1.25 #to estimate offwind connection costs
clustering:
simplify_network:
@ -279,6 +626,7 @@ clustering:
ramp_limit_up: max
ramp_limit_down: max
efficiency: mean
battery: 0.
solving:
#tmpdir: "path/to/tmp"
@ -365,69 +713,66 @@ solving:
plotting:
map:
figsize: [7, 7]
boundaries: [-10.2, 29, 35, 72]
boundaries: [-11, 30, 34, 71]
p_nom:
bus_size_factor: 5.e+4
linewidth_factor: 3.e+3
color_geomap:
ocean: white
land: white
eu_node_location:
x: -5.5
y: 46.
costs_max: 800
costs_max: 1000
costs_threshold: 1
energy_max: 15000.
energy_min: -10000.
energy_max: 20000
energy_min: -20000
energy_threshold: 50.
vre_techs:
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
renewable_storage_techs:
- PHS
- hydro
conv_techs:
- OCGT
- CCGT
- Nuclear
- Coal
storage_techs:
- hydro+PHS
- battery
- H2
load_carriers:
- AC load
AC_carriers:
- AC line
- AC transformer
link_carriers:
- DC line
- Converter AC-DC
heat_links:
- heat pump
- resistive heater
- CHP heat
- CHP electric
- gas boiler
- central heat pump
- central resistive heater
- central CHP heat
- central CHP electric
- central gas boiler
heat_generators:
- gas boiler
- central gas boiler
- solar thermal collector
- central solar thermal collector
vre_techs: ["onwind", "offwind-ac", "offwind-dc", "solar", "ror"]
conv_techs: ["OCGT", "CCGT", "Nuclear", "Coal"]
storage_techs: ["hydro+PHS", "battery", "H2"]
load_carriers: ["AC load"]
AC_carriers: ["AC line", "AC transformer"]
link_carriers: ["DC line", "Converter AC-DC"]
tech_colors:
"onwind": "#235ebc"
"onshore wind": "#235ebc"
'offwind': "#6895dd"
'offwind-ac': "#6895dd"
'offshore wind': "#6895dd"
'offshore wind ac': "#6895dd"
'offwind-dc': "#74c6f2"
'offshore wind dc': "#74c6f2"
"hydro": "#08ad97"
"hydro+PHS": "#08ad97"
"PHS": "#08ad97"
"hydro reservoir": "#08ad97"
'hydroelectricity': '#08ad97'
"ror": "#4adbc8"
"run of river": "#4adbc8"
'solar': "#f9d002"
'solar PV': "#f9d002"
'solar thermal': '#ffef60'
'biomass': '#0c6013'
'solid biomass': '#06540d'
'biogas': '#23932d'
'waste': '#68896b'
'geothermal': '#ba91b1'
"OCGT": "#d35050"
"gas": "#d35050"
"natural gas": "#d35050"
"CCGT": "#b20101"
"nuclear": "#ff9000"
"coal": "#707070"
"lignite": "#9e5a01"
"oil": "#262626"
"H2": "#ea048a"
"hydrogen storage": "#ea048a"
"battery": "#b8ea04"
"Electric load": "#f9d002"
"electricity": "#f9d002"
"lines": "#70af1d"
"transmission lines": "#70af1d"
"AC-AC": "#70af1d"
"AC line": "#70af1d"
"links": "#8a1caf"
"HVDC links": "#8a1caf"
"DC-DC": "#8a1caf"
"DC link": "#8a1caf"
nice_names:
OCGT: "Open-Cycle Gas"
CCGT: "Combined-Cycle Gas"
@ -441,3 +786,198 @@ plotting:
H2: "Hydrogen Storage"
lines: "Transmission Lines"
ror: "Run of River"
tech_colors:
# wind
onwind: "#235ebc"
onshore wind: "#235ebc"
offwind: "#6895dd"
offshore wind: "#6895dd"
offwind-ac: "#6895dd"
offshore wind (AC): "#6895dd"
offshore wind ac: "#6895dd"
offwind-dc: "#74c6f2"
offshore wind (DC): "#74c6f2"
offshore wind dc: "#74c6f2"
# water
hydro: '#298c81'
hydro reservoir: '#298c81'
ror: '#3dbfb0'
run of river: '#3dbfb0'
hydroelectricity: '#298c81'
PHS: '#51dbcc'
hydro+PHS: "#08ad97"
wave: '#a7d4cf'
# solar
solar: "#f9d002"
solar PV: "#f9d002"
solar thermal: '#ffbf2b'
solar rooftop: '#ffea80'
# gas
OCGT: '#e0986c'
OCGT marginal: '#e0986c'
OCGT-heat: '#e0986c'
gas boiler: '#db6a25'
gas boilers: '#db6a25'
gas boiler marginal: '#db6a25'
gas: '#e05b09'
fossil gas: '#e05b09'
natural gas: '#e05b09'
CCGT: '#a85522'
CCGT marginal: '#a85522'
allam: '#B98F76'
gas for industry co2 to atmosphere: '#692e0a'
gas for industry co2 to stored: '#8a3400'
gas for industry: '#853403'
gas for industry CC: '#692e0a'
gas pipeline: '#ebbca0'
gas pipeline new: '#a87c62'
# oil
oil: '#c9c9c9'
oil boiler: '#adadad'
agriculture machinery oil: '#949494'
shipping oil: "#808080"
land transport oil: '#afafaf'
# nuclear
Nuclear: '#ff8c00'
Nuclear marginal: '#ff8c00'
nuclear: '#ff8c00'
uranium: '#ff8c00'
# coal
Coal: '#545454'
coal: '#545454'
Coal marginal: '#545454'
solid: '#545454'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
biomass: '#baa741'
solid biomass: '#baa741'
solid biomass transport: '#baa741'
solid biomass for industry: '#7a6d26'
solid biomass for industry CC: '#47411c'
solid biomass for industry co2 from atmosphere: '#736412'
solid biomass for industry co2 to stored: '#47411c'
biomass boiler: '#8A9A5B'
biomass to liquid: '#32CD32'
BioSNG: '#123456'
# power transmission
lines: '#6c9459'
transmission lines: '#6c9459'
electricity distribution grid: '#97ad8c'
# electricity demand
Electric load: '#110d63'
electric demand: '#110d63'
electricity: '#110d63'
industry electricity: '#2d2a66'
industry new electricity: '#2d2a66'
agriculture electricity: '#494778'
# battery + EVs
battery: '#ace37f'
battery storage: '#ace37f'
home battery: '#80c944'
home battery storage: '#80c944'
BEV charger: '#baf238'
V2G: '#e5ffa8'
land transport EV: '#baf238'
Li ion: '#baf238'
# hot water storage
water tanks: '#e69487'
hot water storage: '#e69487'
hot water charging: '#e69487'
hot water discharging: '#e69487'
# heat demand
Heat load: '#cc1f1f'
heat: '#cc1f1f'
heat demand: '#cc1f1f'
rural heat: '#ff5c5c'
central heat: '#cc1f1f'
decentral heat: '#750606'
low-temperature heat for industry: '#8f2727'
process heat: '#ff0000'
agriculture heat: '#d9a5a5'
# heat supply
heat pumps: '#2fb537'
heat pump: '#2fb537'
air heat pump: '#36eb41'
ground heat pump: '#2fb537'
Ambient: '#98eb9d'
CHP: '#8a5751'
CHP CC: '#634643'
CHP heat: '#8a5751'
CHP electric: '#8a5751'
district heating: '#e8beac'
resistive heater: '#d8f9b8'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
hydrogen: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
H2 storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 pipeline retrofitted: '#ba99b5'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# ammonia
NH3: '#46caf0'
ammonia: '#46caf0'
ammonia store: '#00ace0'
ammonia cracker: '#87d0e6'
Haber-Bosch: '#076987'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
methane: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
liquid: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
methanolisation: '#83d6d5'
methanol: '#468c8b'
shipping methanol: '#468c8b'
# co2
CC: '#f29dae'
CCS: '#f29dae'
CO2 sequestration: '#f29dae'
DAC: '#ff5270'
co2 stored: '#f2385a'
co2: '#f29dae'
co2 vent: '#ffd4dc'
CO2 pipeline: '#f5627f'
# emissions
process emissions CC: '#000000'
process emissions: '#222222'
process emissions to stored: '#444444'
process emissions to atmosphere: '#888888'
oil emissions: '#aaaaaa'
shipping oil emissions: "#555555"
shipping methanol emissions: '#666666'
land transport oil emissions: '#777777'
agriculture machinery oil emissions: '#333333'
# other
shipping: '#03a2ff'
power-to-heat: '#2fb537'
power-to-gas: '#c44ce6'
power-to-H2: '#ff29d9'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'
waste: '#e3d37d'
other: '#000000'
geothermal: '#ba91b1'
AC-AC: "#70af1d"
AC line: "#70af1d"
links: "#8a1caf"
HVDC links: "#8a1caf"
DC-DC: "#8a1caf"
DC link: "#8a1caf"

37
data/Country_codes.csv Normal file
View File

@ -0,0 +1,37 @@
Country,2 letter code (ISO-3166-2),3 letter code (ISO-3166-3)
Albania,AL,ALB
Austria,AT,AUT
Bosnia Herzegovina,BA,BIH
Belgium,BE,BEL
Bulgaria,BG,BGR
Switzerland,CH,CHE
Cyprus,CY,CYP
Czech Republic,CZ,CZE
Germany,DE,DEU
Denmark,DK,DNK
Spain,ES,ESP
Estonia,EE,EST
Finland,FI,FIN
France,FR,FRA
United Kingdom,GB,GBR
Greece,GR,GRC
Croatia,HR,HRV
Hungary,HU,HUN
Ireland,IE,IRL
Iceland,IS,ISL
Italy,IT,ITA
Lithuania,LT,LTU
Luxembourg,LU,LUX
Latvia,LV,LVA
Malta,MT,MLT
Macedonia,MK,MKD
Montenegro,ME,MNE
Netherlands,NL,NLD
Norway,NO,NOR
Poland,PL,POL
Portugal,PT,PRT
Romania,RO,ROU
Serbia,RS,SRB
Slovakia,SK,SVK
Slovenia,SI,SVN
Sweden,SE,SWE
1 Country 2 letter code (ISO-3166-2) 3 letter code (ISO-3166-3)
2 Albania AL ALB
3 Austria AT AUT
4 Bosnia Herzegovina BA BIH
5 Belgium BE BEL
6 Bulgaria BG BGR
7 Switzerland CH CHE
8 Cyprus CY CYP
9 Czech Republic CZ CZE
10 Germany DE DEU
11 Denmark DK DNK
12 Spain ES ESP
13 Estonia EE EST
14 Finland FI FIN
15 France FR FRA
16 United Kingdom GB GBR
17 Greece GR GRC
18 Croatia HR HRV
19 Hungary HU HUN
20 Ireland IE IRL
21 Iceland IS ISL
22 Italy IT ITA
23 Lithuania LT LTU
24 Luxembourg LU LUX
25 Latvia LV LVA
26 Malta MT MLT
27 Macedonia MK MKD
28 Montenegro ME MNE
29 Netherlands NL NLD
30 Norway NO NOR
31 Poland PL POL
32 Portugal PT PRT
33 Romania RO ROU
34 Serbia RS SRB
35 Slovakia SK SVK
36 Slovenia SI SVN
37 Sweden SE SWE

861
data/attributed_ports.json Normal file
View File

@ -0,0 +1,861 @@
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"type": "FeatureCollection",
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{ "type": "Feature", "properties": { "Country": "Viet Nam", "Function": "1-------", "LOCODE": "VNHPH", "Name": "Haiphong", "NameWoDiac": "Haiphong", "Status": "AI", "outflows": 10072807.932540001 }, "geometry": { "type": "Point", "coordinates": [ 106.68345, 20.86481 ] } },
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{ "type": "Feature", "properties": { "Country": "Vanuatu", "Function": "1-------", "LOCODE": "VUSAN", "Name": "Santo", "NameWoDiac": "Santo", "Status": "RQ", "outflows": 206498.5 }, "geometry": { "type": "Point", "coordinates": [ 167.16235, -15.51989 ] } },
{ "type": "Feature", "properties": { "Country": "Samoa", "Function": "1--45---", "LOCODE": "WSAPW", "Name": "Apia", "NameWoDiac": "Apia", "Status": "AI", "outflows": 339021.5 }, "geometry": { "type": "Point", "coordinates": [ -171.76666, -13.83333 ] } },
{ "type": "Feature", "properties": { "Country": "Yemen", "Function": "1--45---", "LOCODE": "YEADE", "Name": "Aden", "NameWoDiac": "Aden", "Status": "AI", "outflows": 126082.5 }, "geometry": { "type": "Point", "coordinates": [ 45.03667, 12.77944 ] } },
{ "type": "Feature", "properties": { "Country": "Yemen", "Function": "1--4----", "LOCODE": "YEMKX", "Name": "Mukalla", "NameWoDiac": "Mukalla", "Status": "AI", "outflows": 30745.0 }, "geometry": { "type": "Point", "coordinates": [ 49.12424, 14.54248 ] } },
{ "type": "Feature", "properties": { "Country": "South Africa", "Function": "1234----", "LOCODE": "ZAELS", "Name": "East London", "NameWoDiac": "East London", "Status": "AF", "outflows": 15600.0 }, "geometry": { "type": "Point", "coordinates": [ 27.91162, -33.01529 ] } },
{ "type": "Feature", "properties": { "Country": "South Africa", "Function": "1--45---", "LOCODE": "ZAPLZ", "Name": "Port Elizabeth", "NameWoDiac": "Port Elizabeth", "Status": "AF", "outflows": 2557154.4621100002 }, "geometry": { "type": "Point", "coordinates": [ 25.61494, -33.96109 ] } },
{ "type": "Feature", "properties": { "Country": "South Africa", "Function": "1--4----", "LOCODE": "ZARCB", "Name": "Richards Bay", "NameWoDiac": "Richards Bay", "Status": "AF", "outflows": 164538.86664000002 }, "geometry": { "type": "Point", "coordinates": [ 32.03768, -28.78301 ] } }
]
}

34
data/district_heat_share.csv Normal file
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@ -0,0 +1,34 @@
country,share to satisfy heat demand (residential) in percent,capacity[MWth]
AT,14,11200
BG,16,6162
BA,8,
HR,6.3,2221
CZ,40,
DK,65,
FI,38,23390
FR,5,
DE,13.8,
HU,7.92875588637399,8549
IS,90,8079000
IE,0.8,
IT,3,8727
LV,73,2254
LT,56,
MK,23.7745607009008,636
NO,4,3400
PL,42,54912
PT,0.070754716981132,34
RS,25,5821
SI,8.86,1739
ES,0.251589260787732,1273
SE,50.4,
UK,2,
BY,70,
EE,52,5406
KO,3,207
RO,23,9962
SK,54,15000
NL,4,9800
CH,4,2792
AL,0,
ME,0,
1 country share to satisfy heat demand (residential) in percent capacity[MWth]
2 AT 14 11200
3 BG 16 6162
4 BA 8
5 HR 6.3 2221
6 CZ 40
7 DK 65
8 FI 38 23390
9 FR 5
10 DE 13.8
11 HU 7.92875588637399 8549
12 IS 90 8079000
13 IE 0.8
14 IT 3 8727
15 LV 73 2254
16 LT 56
17 MK 23.7745607009008 636
18 NO 4 3400
19 PL 42 54912
20 PT 0.070754716981132 34
21 RS 25 5821
22 SI 8.86 1739
23 ES 0.251589260787732 1273
24 SE 50.4
25 UK 2
26 BY 70
27 EE 52 5406
28 KO 3 207
29 RO 23 9962
30 SK 54 15000
31 NL 4 9800
32 CH 4 2792
33 AL 0
34 ME 0

31
data/existing_infrastructure/existing_heating_raw.csv Normal file
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,gas boiler,coal boiler,oil boiler,resistive heater,air heat pump,ground heat pump
Austria,9.32,0.4,15.42,0,0.72,1.077
Belgium,28.39,1.19,19.53,3.14,0.17,0.061
Bulgaria,0.16,3.68,0.04,3.46,1.01,0.045
Croatia,8.39,0.03,2.88,1.53,0,0
Czech Republic,9.26,1.02,0.1,2.73,0.35,0.263
Denmark,4.82,0,3.67,2.19,1.9,0.381
Estonia,0.22,0.02,0.12,0.27,0.33,0.1
Finland,0,0.04,3.79,10.3,1.98,0.58
France,76.85,1.03,46.03,87.24,26.14,1.97
Germany,131.09,0.44,132.04,0,2.38,3.29
Greece,2.17,0.03,18.13,5.91,0,0
Hungary,21.21,1.3,0.04,0.06,0.03,0.035
Ireland,4.32,0.8,4.85,1.03,0.03,0.03
Italy,112.68,1.89,3.33,6.61,54.98,0.6
Latvia,1.53,0.4,0,0.03,0,0
Lithuania,0,0,0,0,0.01,0.02
Luxembourg,0.79,0,0.77,0.09,0.01,0.001
Netherlands,81.41,0,0.1,0.1,1.82,0.849
Poland,8.25,24.75,9.04,5.96,0.01,0.04
Portugal,4.79,0,0.2,21.26,1.58,0.064
Romania,16.56,0.32,0.03,0.72,0,0
Slovakia,8.05,0.19,0.01,0.55,0.06,0.015
Slovenia,0.4,0,1.08,0.4,0.03,0.056
Spain,48.99,0.51,17.95,56.58,1.15,0.016
Sweden,1.01,0,0.77,3.76,3.42,4.813
United Kingdom,160.49,1.26,7.39,13.81,0.81,0.21
Norway,,,,,2.91,0.334
Switzerland,,,,,1,0.849
Serbia,,,,,,
Bosnia Herzegovina,,,,,,
1 gas boiler coal boiler oil boiler resistive heater air heat pump ground heat pump
2 Austria 9.32 0.4 15.42 0 0.72 1.077
3 Belgium 28.39 1.19 19.53 3.14 0.17 0.061
4 Bulgaria 0.16 3.68 0.04 3.46 1.01 0.045
5 Croatia 8.39 0.03 2.88 1.53 0 0
6 Czech Republic 9.26 1.02 0.1 2.73 0.35 0.263
7 Denmark 4.82 0 3.67 2.19 1.9 0.381
8 Estonia 0.22 0.02 0.12 0.27 0.33 0.1
9 Finland 0 0.04 3.79 10.3 1.98 0.58
10 France 76.85 1.03 46.03 87.24 26.14 1.97
11 Germany 131.09 0.44 132.04 0 2.38 3.29
12 Greece 2.17 0.03 18.13 5.91 0 0
13 Hungary 21.21 1.3 0.04 0.06 0.03 0.035
14 Ireland 4.32 0.8 4.85 1.03 0.03 0.03
15 Italy 112.68 1.89 3.33 6.61 54.98 0.6
16 Latvia 1.53 0.4 0 0.03 0 0
17 Lithuania 0 0 0 0 0.01 0.02
18 Luxembourg 0.79 0 0.77 0.09 0.01 0.001
19 Netherlands 81.41 0 0.1 0.1 1.82 0.849
20 Poland 8.25 24.75 9.04 5.96 0.01 0.04
21 Portugal 4.79 0 0.2 21.26 1.58 0.064
22 Romania 16.56 0.32 0.03 0.72 0 0
23 Slovakia 8.05 0.19 0.01 0.55 0.06 0.015
24 Slovenia 0.4 0 1.08 0.4 0.03 0.056
25 Spain 48.99 0.51 17.95 56.58 1.15 0.016
26 Sweden 1.01 0 0.77 3.76 3.42 4.813
27 United Kingdom 160.49 1.26 7.39 13.81 0.81 0.21
28 Norway 2.91 0.334
29 Switzerland 1 0.849
30 Serbia
31 Bosnia Herzegovina

34
data/existing_infrastructure/offwind_capacity_IRENA.csv Normal file
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Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
Albania,,,,,,,,,,,,,,,,,,,
Austria,,,,,,,,,,,,,,,,,,,
Belgium,,,,,,,,,,31.5,196.5,196.5,381,707.7,707.7,712,712.2,877.2,1185.9
Bosnia Herzg,,,,,,,,,,,,,,,,,,,
Bulgaria,,,,,,,,,,,,,,,,,,,
Croatia,,,,,,,,,,,,,,,,,,,
Czechia,,,,,,,,,,,,,,,,,,,
Denmark,50,50,214,423.4,423.4,423.4,423.4,423.4,423.4,660.9,867.9,871.5,921.9,1271.1,1271.1,1271.1,1271.1,1263.8,1700.8
Estonia,,,,,,,,,,,,,,,,,,,
Finland,,,,,,,,,24,24,26.3,26.3,26.3,26.3,26.3,32,32,72.7,72.7
France,,,,,,,,,,,,,,,,,,2,2
Germany,,,,,,,,,,35,80,188,268,508,994,3283,4132,5406,6396
Greece,,,,,,,,,,,,,,,,,,,
Hungary,,,,,,,,,,,,,,,,,,,
Ireland,,,,,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2,25.2
Italy,,,,,,,,,,,,,,,,,,,
Latvia,,,,,,,,,,,,,,,,,,,
Lithuania,,,,,,,,,,,,,,,,,,,
Luxembourg,,,,,,,,,,,,,,,,,,,
Montenegro,,,,,,,,,,,,,,,,,,,
Netherlands,,,,,,,108,108,228,228,228,228,228,228,228,357,957,957,957
North Macedonia,,,,,,,,,,,,,,,,,,,
Norway,,,,,,,,,,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3,2.3
Poland,,,,,,,,,,,,,,,,,,,
Portugal,,,,,,,,,,,,1.9,2,2,2,2,,,
Romania,,,,,,,,,,,,,,,,,,,
Serbia,,,,,,,,,,,,,,,,,,,
Slovakia,,,,,,,,,,,,,,,,,,,
Slovenia,,,,,,,,,,,,,,,,,,,
Spain,,,,,,,,,,,,,,5,5,5,5,5,5
Sweden,13,22,22,22,22,22,22,131,133,163,163,163,163,212,213,213,203,203,203
Switzerland,,,,,,,,,,,,,,,,,,,
UK,3.8,3.8,3.8,63.8,123.8,213.8,303.8,393.8,596.2,951.2,1341.5,1838.3,2995.5,3696,4501.3,5093.4,5293.4,6987.9,8216.5
1 Country/area 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
2 Albania
3 Austria
4 Belgium 31.5 196.5 196.5 381 707.7 707.7 712 712.2 877.2 1185.9
5 Bosnia Herzg
6 Bulgaria
7 Croatia
8 Czechia
9 Denmark 50 50 214 423.4 423.4 423.4 423.4 423.4 423.4 660.9 867.9 871.5 921.9 1271.1 1271.1 1271.1 1271.1 1263.8 1700.8
10 Estonia
11 Finland 24 24 26.3 26.3 26.3 26.3 26.3 32 32 72.7 72.7
12 France 2 2
13 Germany 35 80 188 268 508 994 3283 4132 5406 6396
14 Greece
15 Hungary
16 Ireland 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2 25.2
17 Italy
18 Latvia
19 Lithuania
20 Luxembourg
21 Montenegro
22 Netherlands 108 108 228 228 228 228 228 228 228 357 957 957 957
23 North Macedonia
24 Norway 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3
25 Poland
26 Portugal 1.9 2 2 2 2
27 Romania
28 Serbia
29 Slovakia
30 Slovenia
31 Spain 5 5 5 5 5 5
32 Sweden 13 22 22 22 22 22 22 131 133 163 163 163 163 212 213 213 203 203 203
33 Switzerland
34 UK 3.8 3.8 3.8 63.8 123.8 213.8 303.8 393.8 596.2 951.2 1341.5 1838.3 2995.5 3696 4501.3 5093.4 5293.4 6987.9 8216.5

34
data/existing_infrastructure/onwind_capacity_IRENA.csv Normal file
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Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
Albania,,,,,,,,,,,,,,,,,,,
Austria,50,67,109,322,581,825.2,968.3,991.2,992,1001,1015.8,1106,1337.2,1674.5,2110.3,2488.7,2730,2886.7,3132.7
Belgium,14,26,31,67,96,167,212,276,324,576.5,715.5,872.5,989,1072.3,1236.3,1464,1657.8,1919.3,2074.8
Bosnia Herzg,,,,,,,,,,,,0.3,0.3,0.3,0.3,0.3,0.3,0.3,50.9
Bulgaria,,,,,1,8,27,30,114,333,488,541,677,683,699,699,699,698.4,698.9
Croatia,,,,,6,6,17,17,17,70,79,130,180,254,339,418,483,576.1,586.3
Czechia,2,,6.4,10.6,16.5,22,43.5,113.8,150,193,213,213,258,262,278,281,282,308.2,316.2
Denmark,2340.1,2447.2,2680.6,2696.6,2700.4,2704.5,2712.3,2700.9,2739.5,2821.2,2934,3080.5,3240.1,3547.9,3615.4,3805.9,3974.5,4225.8,4419.8
Estonia,,,1,3,7,31,31,50,77,104,108,180,266,248,275,300,310,311.8,310
Finland,38,39,43,52,82,82,86,110,119,123,170.7,172.7,230.7,420.7,600.7,973,1533,1971.3,1968.3
France,38,66,138,218,358,690,1412,2223,3403,4582,5912,6758,7607.5,8156,9201.4,10298.2,11566.6,13497.4,14898.1
Germany,6095,8754,12001,14381,16419,18248,20474,22116,22794,25697,26823,28524,30711,32969,37620,41297,45303,50174,52447
Greece,226,270,287,371,470,491,749,846,1022,1171,1298,1640,1753,1809,1978,2091,2370,2624,2877.5
Hungary,,1,1,3,3,17,33,61,134,203,293,331,325,329,329,329,329,329,329
Ireland,116.5,122.9,134.8,210.3,311.2,468.1,651.3,715.3,917.1,1226.1,1365.2,1559.4,1679.2,1983,2258.1,2426,2760.8,3292.8,3650.9
Italy,363,664,780,874,1127,1635,1902,2702,3525,4879,5794,6918,8102,8542,8683,9137,9384,9736.6,10230.2
Latvia,2,2,22,26,26,26,26,26,28,29,30,36,59,65.9,68.9,68.2,69.9,77.1,78.2
Lithuania,,,,,1,1,31,47,54,98,133,202,275,279,288,436,509,518,533
Luxembourg,14,13.9,13.9,20.5,34.9,34.9,34.9,34.9,42.9,42.9,43.7,44.5,58.3,58.3,58.3,63.8,119.7,119.7,122.9
Montenegro,,,,,,,,,,,,,,,,,,72,118
Netherlands,447,486,672,905,1075,1224,1453,1641,1921,1994,2009,2088,2205,2485,2637,3034,3300,3245,3436
North Macedonia,,,,,,,,,,,,,,,37,37,37,37,37
Norway,13,13,97,97,152,265,284,348,395,420.7,422.7,509.7,702.7,815.7,856.7,864.7,880.7,1204.7,1708
Poland,4,19,32,35,40,121,172,306,526,709,1108,1800,2564,3429,3836,4886,5747,5759.4,5766.1
Portugal,83,125,190,268,553,1064,1681,2201,2857,3326,3796,4254.4,4409.6,4607.9,4854.6,4934.8,5124.1,5124.1,5172.4
Romania,,,,,,1,1,3,5,15,389,988,1822,2773,3244,3130,3025,3029.8,3032.3
Serbia,,,,,,,,,,,,,0.5,0.5,0.5,10.4,17,25,25
Slovakia,,,,3,3,5,5,5,5,3,3,3,3,5,3,3,3,4,3
Slovenia,,,,,,,,,,,,,,4,4,5,5,5,5.2
Spain,2206,3397,4891,5945,8317,9918,11722,14820,16555,19176,20693,21529,22789,22953,22920,22938,22985,23119.5,23400.1
Sweden,196,273,335,395,453,500,563,692,956,1312,1854,2601,3443,3982,4875,5606,6232,6408,7097
Switzerland,3,5,5,5,9,12,12,12,14,18,42,46,49,60,60,60,75,75,75
UK,408.2,489.2,530.2,678.2,809.2,1351.2,1651.2,2083.2,2849.8,3470.8,4079.8,4758,6035,7586.3,8572.7,9212.2,10832.3,12596.9,13553.9
1 Country/area 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
2 Albania
3 Austria 50 67 109 322 581 825.2 968.3 991.2 992 1001 1015.8 1106 1337.2 1674.5 2110.3 2488.7 2730 2886.7 3132.7
4 Belgium 14 26 31 67 96 167 212 276 324 576.5 715.5 872.5 989 1072.3 1236.3 1464 1657.8 1919.3 2074.8
5 Bosnia Herzg 0.3 0.3 0.3 0.3 0.3 0.3 0.3 50.9
6 Bulgaria 1 8 27 30 114 333 488 541 677 683 699 699 699 698.4 698.9
7 Croatia 6 6 17 17 17 70 79 130 180 254 339 418 483 576.1 586.3
8 Czechia 2 6.4 10.6 16.5 22 43.5 113.8 150 193 213 213 258 262 278 281 282 308.2 316.2
9 Denmark 2340.1 2447.2 2680.6 2696.6 2700.4 2704.5 2712.3 2700.9 2739.5 2821.2 2934 3080.5 3240.1 3547.9 3615.4 3805.9 3974.5 4225.8 4419.8
10 Estonia 1 3 7 31 31 50 77 104 108 180 266 248 275 300 310 311.8 310
11 Finland 38 39 43 52 82 82 86 110 119 123 170.7 172.7 230.7 420.7 600.7 973 1533 1971.3 1968.3
12 France 38 66 138 218 358 690 1412 2223 3403 4582 5912 6758 7607.5 8156 9201.4 10298.2 11566.6 13497.4 14898.1
13 Germany 6095 8754 12001 14381 16419 18248 20474 22116 22794 25697 26823 28524 30711 32969 37620 41297 45303 50174 52447
14 Greece 226 270 287 371 470 491 749 846 1022 1171 1298 1640 1753 1809 1978 2091 2370 2624 2877.5
15 Hungary 1 1 3 3 17 33 61 134 203 293 331 325 329 329 329 329 329 329
16 Ireland 116.5 122.9 134.8 210.3 311.2 468.1 651.3 715.3 917.1 1226.1 1365.2 1559.4 1679.2 1983 2258.1 2426 2760.8 3292.8 3650.9
17 Italy 363 664 780 874 1127 1635 1902 2702 3525 4879 5794 6918 8102 8542 8683 9137 9384 9736.6 10230.2
18 Latvia 2 2 22 26 26 26 26 26 28 29 30 36 59 65.9 68.9 68.2 69.9 77.1 78.2
19 Lithuania 1 1 31 47 54 98 133 202 275 279 288 436 509 518 533
20 Luxembourg 14 13.9 13.9 20.5 34.9 34.9 34.9 34.9 42.9 42.9 43.7 44.5 58.3 58.3 58.3 63.8 119.7 119.7 122.9
21 Montenegro 72 118
22 Netherlands 447 486 672 905 1075 1224 1453 1641 1921 1994 2009 2088 2205 2485 2637 3034 3300 3245 3436
23 North Macedonia 37 37 37 37 37
24 Norway 13 13 97 97 152 265 284 348 395 420.7 422.7 509.7 702.7 815.7 856.7 864.7 880.7 1204.7 1708
25 Poland 4 19 32 35 40 121 172 306 526 709 1108 1800 2564 3429 3836 4886 5747 5759.4 5766.1
26 Portugal 83 125 190 268 553 1064 1681 2201 2857 3326 3796 4254.4 4409.6 4607.9 4854.6 4934.8 5124.1 5124.1 5172.4
27 Romania 1 1 3 5 15 389 988 1822 2773 3244 3130 3025 3029.8 3032.3
28 Serbia 0.5 0.5 0.5 10.4 17 25 25
29 Slovakia 3 3 5 5 5 5 3 3 3 3 5 3 3 3 4 3
30 Slovenia 4 4 5 5 5 5.2
31 Spain 2206 3397 4891 5945 8317 9918 11722 14820 16555 19176 20693 21529 22789 22953 22920 22938 22985 23119.5 23400.1
32 Sweden 196 273 335 395 453 500 563 692 956 1312 1854 2601 3443 3982 4875 5606 6232 6408 7097
33 Switzerland 3 5 5 5 9 12 12 12 14 18 42 46 49 60 60 60 75 75 75
34 UK 408.2 489.2 530.2 678.2 809.2 1351.2 1651.2 2083.2 2849.8 3470.8 4079.8 4758 6035 7586.3 8572.7 9212.2 10832.3 12596.9 13553.9

34
data/existing_infrastructure/solar_capacity_IRENA.csv Normal file
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Country/area,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
Albania,,0.1,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.3,0.4,0.6,0.7,0.8,0.9,1.1,1,1,1
Austria,5,7,9,23,27,21,22.4,24.2,30.1,48.9,88.8,174.1,337.5,626,785.2,937.1,1096,1269,1437.6
Belgium,,,1,1,1,2,2,20,62,386,1007,1979,2647,2902,3015.2,3131.7,3327,3616.2,3986.5
Bosnia Herzg,,,,0.1,0.2,0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3,1.3,7.2,8.2,14.1,16,18.2
Bulgaria,,,,,,,,0,0.1,2,25,154,1013,1020,1026,1029,1028,1035.6,1032.7
Croatia,,,,,,,,,,0.3,0.3,0.3,4,19,33,47.8,55.8,60,67.7
Czechia,0.1,0.1,0.2,0.3,0.4,0.6,0.8,4,39.5,464.6,1727,1913,2022,2063.5,2067.4,2074.9,2067.9,2069.5,2075.1
Denmark,1,1,2,2,2,3,3,3,3,5,7,17,402,571,607,782.1,851,906.4,998
Estonia,,,,,,,,,,0.1,0.1,0.2,0.4,1.5,3.3,6.5,10,15,31.9
Finland,2,3,3,3,4,4,5,5,6,6,7,7,8,9,11,17,39,82,140
France,7,7,8,9,11,13,15,26,80,277,1044,3003.6,4358.8,5277.3,6034.4,7137.5,7702.1,8610.4,9617
Germany,114,195,260,435,1105,2056,2899,4170,6120,10564,18004,25914,34075,36708,37898,39222,40677,42291,45179
Greece,,1,1,1,1,1,5,9,12,46,202,612,1536,2579,2596,2604,2604,2605.5,2651.6
Hungary,,,,,,,,0.4,1,1,2,4,12,35,89,172,235,344,726
Ireland,,,,,,,,,,0.6,0.7,0.8,0.9,1,1.6,2.4,5.9,15.7,24.2
Italy,19,20,22,26,31,34,45,110,483,1264,3592,13131,16785,18185,18594,18901,19283,19682.3,20107.6
Latvia,,,,,,,,,,,,,0.2,0.2,0.2,0.2,0.7,0.7,2
Lithuania,,,,,,,,,0.1,0.1,0.1,0.3,7,68,69,69,70,73.8,82
Luxembourg,,0.2,1.6,14.2,23.6,23.6,23.7,23.9,24.6,26.4,29.5,40.7,74.7,95,109.9,116.3,121.9,128.1,130.6
Montenegro,,,,,,,0,0.2,0.4,0.4,0.6,0.8,0.9,1.1,2.1,2.7,3.1,3.4,3.4
Netherlands,13,21,26,46,50,51,53,54,59,69,90,149,369,746,1048,1515,2049,2903,4522
North Macedonia,,,,,,,,,,,0,2,4,7,15,17,16.7,16.7,20.6
Norway,6,6,6,7,7,7,8,8,8.3,8.7,9.1,9.5,10,11,13,15,26.7,44.9,68.4
Poland,,,,,,,,,,,,1.1,1.3,2.4,27.2,107.8,187.2,287.1,562
Portugal,1,1,1,2,2,2,3,24,59,115,134,172,238,296,415,447,512.8,579.2,667.4
Romania,,,,,,,,,0.1,0.1,0.1,1,41,761,1293,1326,1372,1374.1,1385.8
Serbia,,,,,,0.1,0.2,0.4,0.9,1.2,1.3,1.5,3.1,4.7,6,9,11,10,10
Slovakia,,,,,,,,,,,19,496,513,533,533,533,533,528,472
Slovenia,,,0,0,0,0,0.2,0.6,1,4,12,57,142,187,223,238,233,246.8,221.3
Spain,10,13,17,22,33,52,130,494,3384,3423,3873,4283,4569,4690,4697,4704,4713,4723,4763.5
Sweden,3,3,3,4,4,4,5,6,8,9,11,12,24,43,60,104,153,402,492
Switzerland,16,18,20,22,24,28,30,37,49,79,125,223,437,756,1061,1394,1664,1906,2171
UK,2,3,4,6,8,11,14,18,23,27,95,1000,1753,2937,5528,9601.2,11930.5,12781.8,13118.3
1 Country/area 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
2 Albania 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.4 0.6 0.7 0.8 0.9 1.1 1 1 1
3 Austria 5 7 9 23 27 21 22.4 24.2 30.1 48.9 88.8 174.1 337.5 626 785.2 937.1 1096 1269 1437.6
4 Belgium 1 1 1 2 2 20 62 386 1007 1979 2647 2902 3015.2 3131.7 3327 3616.2 3986.5
5 Bosnia Herzg 0.1 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 1.3 7.2 8.2 14.1 16 18.2
6 Bulgaria 0 0.1 2 25 154 1013 1020 1026 1029 1028 1035.6 1032.7
7 Croatia 0.3 0.3 0.3 4 19 33 47.8 55.8 60 67.7
8 Czechia 0.1 0.1 0.2 0.3 0.4 0.6 0.8 4 39.5 464.6 1727 1913 2022 2063.5 2067.4 2074.9 2067.9 2069.5 2075.1
9 Denmark 1 1 2 2 2 3 3 3 3 5 7 17 402 571 607 782.1 851 906.4 998
10 Estonia 0.1 0.1 0.2 0.4 1.5 3.3 6.5 10 15 31.9
11 Finland 2 3 3 3 4 4 5 5 6 6 7 7 8 9 11 17 39 82 140
12 France 7 7 8 9 11 13 15 26 80 277 1044 3003.6 4358.8 5277.3 6034.4 7137.5 7702.1 8610.4 9617
13 Germany 114 195 260 435 1105 2056 2899 4170 6120 10564 18004 25914 34075 36708 37898 39222 40677 42291 45179
14 Greece 1 1 1 1 1 5 9 12 46 202 612 1536 2579 2596 2604 2604 2605.5 2651.6
15 Hungary 0.4 1 1 2 4 12 35 89 172 235 344 726
16 Ireland 0.6 0.7 0.8 0.9 1 1.6 2.4 5.9 15.7 24.2
17 Italy 19 20 22 26 31 34 45 110 483 1264 3592 13131 16785 18185 18594 18901 19283 19682.3 20107.6
18 Latvia 0.2 0.2 0.2 0.2 0.7 0.7 2
19 Lithuania 0.1 0.1 0.1 0.3 7 68 69 69 70 73.8 82
20 Luxembourg 0.2 1.6 14.2 23.6 23.6 23.7 23.9 24.6 26.4 29.5 40.7 74.7 95 109.9 116.3 121.9 128.1 130.6
21 Montenegro 0 0.2 0.4 0.4 0.6 0.8 0.9 1.1 2.1 2.7 3.1 3.4 3.4
22 Netherlands 13 21 26 46 50 51 53 54 59 69 90 149 369 746 1048 1515 2049 2903 4522
23 North Macedonia 0 2 4 7 15 17 16.7 16.7 20.6
24 Norway 6 6 6 7 7 7 8 8 8.3 8.7 9.1 9.5 10 11 13 15 26.7 44.9 68.4
25 Poland 1.1 1.3 2.4 27.2 107.8 187.2 287.1 562
26 Portugal 1 1 1 2 2 2 3 24 59 115 134 172 238 296 415 447 512.8 579.2 667.4
27 Romania 0.1 0.1 0.1 1 41 761 1293 1326 1372 1374.1 1385.8
28 Serbia 0.1 0.2 0.4 0.9 1.2 1.3 1.5 3.1 4.7 6 9 11 10 10
29 Slovakia 19 496 513 533 533 533 533 528 472
30 Slovenia 0 0 0 0 0.2 0.6 1 4 12 57 142 187 223 238 233 246.8 221.3
31 Spain 10 13 17 22 33 52 130 494 3384 3423 3873 4283 4569 4690 4697 4704 4713 4723 4763.5
32 Sweden 3 3 3 4 4 4 5 6 8 9 11 12 24 43 60 104 153 402 492
33 Switzerland 16 18 20 22 24 28 30 37 49 79 125 223 437 756 1061 1394 1664 1906 2171
34 UK 2 3 4 6 8 11 14 18 23 27 95 1000 1753 2937 5528 9601.2 11930.5 12781.8 13118.3

25
data/heat_load_profile.csv Normal file
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hour,residential space weekday,residential space weekend,residential water weekday,residential water weekend,services space weekday,services space weekend,services water weekday,services water weekend
0,0.9181438689,0.9421512708,1,1,0.9181438689,0.9421512708,1,1
1,0.9172359071,0.9400891069,1,1,0.9172359071,0.9400891069,1,1
2,0.9269464481,0.9461062015,1,1,0.9269464481,0.9461062015,1,1
3,0.9415047932,0.9535084941,1,1,0.9415047932,0.9535084941,1,1
4,0.9656299507,0.9651094993,1,1,0.9656299507,0.9651094993,1,1
5,1.0221166443,0.9834676747,1,1,1.0221166443,0.9834676747,1,1
6,1.1553090493,1.0124171051,1,1,1.1553090493,1.0124171051,1,1
7,1.2093411031,1.0446615927,1,1,1.2093411031,1.0446615927,1,1
8,1.1470295942,1.088203419,1,1,1.1470295942,1.088203419,1,1
9,1.0877191341,1.1110334576,1,1,1.0877191341,1.1110334576,1,1
10,1.0418327372,1.0926752822,1,1,1.0418327372,1.0926752822,1,1
11,1.0062977133,1.055488209,1,1,1.0062977133,1.055488209,1,1
12,0.9837030359,1.0251266112,1,1,0.9837030359,1.0251266112,1,1
13,0.9667570278,0.9990015154,1,1,0.9667570278,0.9990015154,1,1
14,0.9548320932,0.9782897278,1,1,0.9548320932,0.9782897278,1,1
15,0.9509232061,0.9698167237,1,1,0.9509232061,0.9698167237,1,1
16,0.9636973319,0.974288587,1,1,0.9636973319,0.974288587,1,1
17,0.9799372563,0.9886456216,1,1,0.9799372563,0.9886456216,1,1
18,1.0046501848,1.0084159643,1,1,1.0046501848,1.0084159643,1,1
19,1.0079452419,1.0171243296,1,1,1.0079452419,1.0171243296,1,1
20,0.9860566481,0.9994722379,1,1,0.9860566481,0.9994722379,1,1
21,0.9705228074,0.982761591,1,1,0.9705228074,0.982761591,1,1
22,0.9586485819,0.9698167237,1,1,0.9586485819,0.9698167237,1,1
23,0.9335023778,0.9515079292,1,1,0.9335023778,0.9515079292,1,1
1 hour residential space weekday residential space weekend residential water weekday residential water weekend services space weekday services space weekend services water weekday services water weekend
2 0 0.9181438689 0.9421512708 1 1 0.9181438689 0.9421512708 1 1
3 1 0.9172359071 0.9400891069 1 1 0.9172359071 0.9400891069 1 1
4 2 0.9269464481 0.9461062015 1 1 0.9269464481 0.9461062015 1 1
5 3 0.9415047932 0.9535084941 1 1 0.9415047932 0.9535084941 1 1
6 4 0.9656299507 0.9651094993 1 1 0.9656299507 0.9651094993 1 1
7 5 1.0221166443 0.9834676747 1 1 1.0221166443 0.9834676747 1 1
8 6 1.1553090493 1.0124171051 1 1 1.1553090493 1.0124171051 1 1
9 7 1.2093411031 1.0446615927 1 1 1.2093411031 1.0446615927 1 1
10 8 1.1470295942 1.088203419 1 1 1.1470295942 1.088203419 1 1
11 9 1.0877191341 1.1110334576 1 1 1.0877191341 1.1110334576 1 1
12 10 1.0418327372 1.0926752822 1 1 1.0418327372 1.0926752822 1 1
13 11 1.0062977133 1.055488209 1 1 1.0062977133 1.055488209 1 1
14 12 0.9837030359 1.0251266112 1 1 0.9837030359 1.0251266112 1 1
15 13 0.9667570278 0.9990015154 1 1 0.9667570278 0.9990015154 1 1
16 14 0.9548320932 0.9782897278 1 1 0.9548320932 0.9782897278 1 1
17 15 0.9509232061 0.9698167237 1 1 0.9509232061 0.9698167237 1 1
18 16 0.9636973319 0.974288587 1 1 0.9636973319 0.974288587 1 1
19 17 0.9799372563 0.9886456216 1 1 0.9799372563 0.9886456216 1 1
20 18 1.0046501848 1.0084159643 1 1 1.0046501848 1.0084159643 1 1
21 19 1.0079452419 1.0171243296 1 1 1.0079452419 1.0171243296 1 1
22 20 0.9860566481 0.9994722379 1 1 0.9860566481 0.9994722379 1 1
23 21 0.9705228074 0.982761591 1 1 0.9705228074 0.982761591 1 1
24 22 0.9586485819 0.9698167237 1 1 0.9586485819 0.9698167237 1 1
25 23 0.9335023778 0.9515079292 1 1 0.9335023778 0.9515079292 1 1

25
data/heat_load_profile_BDEW.csv Normal file
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,residential space weekday,residential space weekend,services space weekday,services space weekend,residential water weekday,residential water weekend,services water weekday,services water weekend
0,0.5437843306385036,0.5391846410003029,0.740230434593118,0.7918173557545402,1.0,1.0,1.0,1.0
1,0.5690496225400243,0.5641534370440313,0.7642025524842398,0.7929627291950984,1.0,1.0,1.0,1.0
2,0.5624023211873742,0.5575494117194042,0.8264420882344785,0.8961602364492307,1.0,1.0,1.0,1.0
3,0.6120351867307156,0.6074588966300298,0.9338477492552973,1.066547622880321,1.0,1.0,1.0,1.0
4,0.8210089232467712,0.8188451841881503,1.1288089786462463,1.2779268432155158,1.0,1.0,1.0,1.0
5,1.2287073985428116,1.2315677844536332,1.3311522394966053,1.2808129834243316,1.0,1.0,1.0,1.0
6,1.327953505819319,1.3349874311629708,1.3976491755316236,1.3076676145167292,1.0,1.0,1.0,1.0
7,1.2533048874868005,1.2584095945395426,1.3529869654334066,1.239881414312941,1.0,1.0,1.0,1.0
8,1.204661538907097,1.206562127967529,1.2631870820835946,1.157513929299677,1.0,1.0,1.0,1.0
9,1.1511425365003825,1.152931252109671,1.183486516733693,1.1001631309844286,1.0,1.0,1.0,1.0
10,1.0982914366923946,1.0987739728887453,1.1056637898031139,1.0553379006911972,1.0,1.0,1.0,1.0
11,1.0602079991199889,1.0598534287519163,1.0536117591812475,0.9953570175561463,1.0,1.0,1.0,1.0
12,1.0430483470403709,1.042552786631541,1.0075511014823457,0.9238971341830102,1.0,1.0,1.0,1.0
13,1.023765876994618,1.0234573235486537,0.983633820661761,0.928978159404834,1.0,1.0,1.0,1.0
14,1.0250355817085612,1.0241187665206792,0.973887563496691,0.9277637088455348,1.0,1.0,1.0,1.0
15,1.0419068035344277,1.0407369052119213,0.968639109712126,0.940383626933661,1.0,1.0,1.0,1.0
16,1.0886607269753739,1.0871365340901091,0.9776106671510321,0.9762628252848075,1.0,1.0,1.0,1.0
17,1.1391891744979068,1.1377875788466947,0.9713068946564802,0.9923707220696051,1.0,1.0,1.0,1.0
18,1.1813708458227477,1.1815796155786216,0.97710710371407,0.9822063279944322,1.0,1.0,1.0,1.0
19,1.2048721952031847,1.2066686818939167,0.9620977486617706,0.9872726025741575,1.0,1.0,1.0,1.0
20,1.1883594612741015,1.1911629803333679,0.9096499832485738,0.9736368622053816,1.0,1.0,1.0,1.0
21,1.0841006081889941,1.0875548281900813,0.7954827338259405,0.8733383541170725,1.0,1.0,1.0,1.0
22,0.8887378869444746,0.8893062174837649,0.7007233800713178,0.7753100551108082,1.0,1.0,1.0,1.0
23,0.6584028044030574,0.6576606192147261,0.6910405618412271,0.756430842996538,1.0,1.0,1.0,1.0
1 residential space weekday residential space weekend services space weekday services space weekend residential water weekday residential water weekend services water weekday services water weekend
2 0 0.5437843306385036 0.5391846410003029 0.740230434593118 0.7918173557545402 1.0 1.0 1.0 1.0
3 1 0.5690496225400243 0.5641534370440313 0.7642025524842398 0.7929627291950984 1.0 1.0 1.0 1.0
4 2 0.5624023211873742 0.5575494117194042 0.8264420882344785 0.8961602364492307 1.0 1.0 1.0 1.0
5 3 0.6120351867307156 0.6074588966300298 0.9338477492552973 1.066547622880321 1.0 1.0 1.0 1.0
6 4 0.8210089232467712 0.8188451841881503 1.1288089786462463 1.2779268432155158 1.0 1.0 1.0 1.0
7 5 1.2287073985428116 1.2315677844536332 1.3311522394966053 1.2808129834243316 1.0 1.0 1.0 1.0
8 6 1.327953505819319 1.3349874311629708 1.3976491755316236 1.3076676145167292 1.0 1.0 1.0 1.0
9 7 1.2533048874868005 1.2584095945395426 1.3529869654334066 1.239881414312941 1.0 1.0 1.0 1.0
10 8 1.204661538907097 1.206562127967529 1.2631870820835946 1.157513929299677 1.0 1.0 1.0 1.0
11 9 1.1511425365003825 1.152931252109671 1.183486516733693 1.1001631309844286 1.0 1.0 1.0 1.0
12 10 1.0982914366923946 1.0987739728887453 1.1056637898031139 1.0553379006911972 1.0 1.0 1.0 1.0
13 11 1.0602079991199889 1.0598534287519163 1.0536117591812475 0.9953570175561463 1.0 1.0 1.0 1.0
14 12 1.0430483470403709 1.042552786631541 1.0075511014823457 0.9238971341830102 1.0 1.0 1.0 1.0
15 13 1.023765876994618 1.0234573235486537 0.983633820661761 0.928978159404834 1.0 1.0 1.0 1.0
16 14 1.0250355817085612 1.0241187665206792 0.973887563496691 0.9277637088455348 1.0 1.0 1.0 1.0
17 15 1.0419068035344277 1.0407369052119213 0.968639109712126 0.940383626933661 1.0 1.0 1.0 1.0
18 16 1.0886607269753739 1.0871365340901091 0.9776106671510321 0.9762628252848075 1.0 1.0 1.0 1.0
19 17 1.1391891744979068 1.1377875788466947 0.9713068946564802 0.9923707220696051 1.0 1.0 1.0 1.0
20 18 1.1813708458227477 1.1815796155786216 0.97710710371407 0.9822063279944322 1.0 1.0 1.0 1.0
21 19 1.2048721952031847 1.2066686818939167 0.9620977486617706 0.9872726025741575 1.0 1.0 1.0 1.0
22 20 1.1883594612741015 1.1911629803333679 0.9096499832485738 0.9736368622053816 1.0 1.0 1.0 1.0
23 21 1.0841006081889941 1.0875548281900813 0.7954827338259405 0.8733383541170725 1.0 1.0 1.0 1.0
24 22 0.8887378869444746 0.8893062174837649 0.7007233800713178 0.7753100551108082 1.0 1.0 1.0 1.0
25 23 0.6584028044030574 0.6576606192147261 0.6910405618412271 0.756430842996538 1.0 1.0 1.0 1.0

25
data/heat_load_profile_DK_AdamJensen.csv Normal file
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hour,weekday,weekend
0,0.9181438689,0.9421512708
1,0.9172359071,0.9400891069
2,0.9269464481,0.9461062015
3,0.9415047932,0.9535084941
4,0.9656299507,0.9651094993
5,1.0221166443,0.9834676747
6,1.1553090493,1.0124171051
7,1.2093411031,1.0446615927
8,1.1470295942,1.088203419
9,1.0877191341,1.1110334576
10,1.0418327372,1.0926752822
11,1.0062977133,1.055488209
12,0.9837030359,1.0251266112
13,0.9667570278,0.9990015154
14,0.9548320932,0.9782897278
15,0.9509232061,0.9698167237
16,0.9636973319,0.974288587
17,0.9799372563,0.9886456216
18,1.0046501848,1.0084159643
19,1.0079452419,1.0171243296
20,0.9860566481,0.9994722379
21,0.9705228074,0.982761591
22,0.9586485819,0.9698167237
23,0.9335023778,0.9515079292
1 hour weekday weekend
2 0 0.9181438689 0.9421512708
3 1 0.9172359071 0.9400891069
4 2 0.9269464481 0.9461062015
5 3 0.9415047932 0.9535084941
6 4 0.9656299507 0.9651094993
7 5 1.0221166443 0.9834676747
8 6 1.1553090493 1.0124171051
9 7 1.2093411031 1.0446615927
10 8 1.1470295942 1.088203419
11 9 1.0877191341 1.1110334576
12 10 1.0418327372 1.0926752822
13 11 1.0062977133 1.055488209
14 12 0.9837030359 1.0251266112
15 13 0.9667570278 0.9990015154
16 14 0.9548320932 0.9782897278
17 15 0.9509232061 0.9698167237
18 16 0.9636973319 0.974288587
19 17 0.9799372563 0.9886456216
20 18 1.0046501848 1.0084159643
21 19 1.0079452419 1.0171243296
22 20 0.9860566481 0.9994722379
23 21 0.9705228074 0.982761591
24 22 0.9586485819 0.9698167237
25 23 0.9335023778 0.9515079292

31
data/hydrogen_salt_cavern_potentials.csv Normal file
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@ -0,0 +1,31 @@
ct,TWh
AT,
BA,
BE,
BG,
CH,
CZ,
DE,4500
DK,700
EE,
ES,350
FI,
FR,
GB,1050
GR,120
HR,
HU,
IE,
IT,
LT,
LU,
LV,
NL,150
NO,
PL,120
PT,400
RO,
RS,
SE,
SI,
SK,
1 ct TWh
2 AT
3 BA
4 BE
5 BG
6 CH
7 CZ
8 DE 4500
9 DK 700
10 EE
11 ES 350
12 FI
13 FR
14 GB 1050
15 GR 120
16 HR
17 HU
18 IE
19 IT
20 LT
21 LU
22 LV
23 NL 150
24 NO
25 PL 120
26 PT 400
27 RO
28 RS
29 SE
30 SI
31 SK

3
data/override_component_attrs/buses.csv Normal file
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@ -0,0 +1,3 @@
attribute,type,unit,default,description,status
location,string,n/a,n/a,Reference to original electricity bus,Input (optional)
unit,string,n/a,MWh,Unit of the bus (descriptive only), Input (optional)
1 attribute type unit default description status
2 location string n/a n/a Reference to original electricity bus Input (optional)
3 unit string n/a MWh Unit of the bus (descriptive only) Input (optional)

4
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@ -0,0 +1,4 @@
attribute,type,unit,default,description,status
carrier,string,n/a,n/a,carrier,Input (optional)
lifetime,float,years,inf,lifetime,Input (optional)
build_year,int,year ,0,build year,Input (optional)
1 attribute type unit default description status
2 carrier string n/a n/a carrier Input (optional)
3 lifetime float years inf lifetime Input (optional)
4 build_year int year 0 build year Input (optional)

13
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@ -0,0 +1,13 @@
attribute,type,unit,default,description,status
bus2,string,n/a,n/a,2nd bus,Input (optional)
bus3,string,n/a,n/a,3rd bus,Input (optional)
bus4,string,n/a,n/a,4th bus,Input (optional)
efficiency2,static or series,per unit,1,2nd bus efficiency,Input (optional)
efficiency3,static or series,per unit,1,3rd bus efficiency,Input (optional)
efficiency4,static or series,per unit,1,4th bus efficiency,Input (optional)
p2,series,MW,0,2nd bus output,Output
p3,series,MW,0,3rd bus output,Output
p4,series,MW,0,4th bus output,Output
carrier,string,n/a,n/a,carrier,Input (optional)
lifetime,float,years,inf,lifetime,Input (optional)
build_year,int,year ,0,build year,Input (optional)
1 attribute type unit default description status
2 bus2 string n/a n/a 2nd bus Input (optional)
3 bus3 string n/a n/a 3rd bus Input (optional)
4 bus4 string n/a n/a 4th bus Input (optional)
5 efficiency2 static or series per unit 1 2nd bus efficiency Input (optional)
6 efficiency3 static or series per unit 1 3rd bus efficiency Input (optional)
7 efficiency4 static or series per unit 1 4th bus efficiency Input (optional)
8 p2 series MW 0 2nd bus output Output
9 p3 series MW 0 3rd bus output Output
10 p4 series MW 0 4th bus output Output
11 carrier string n/a n/a carrier Input (optional)
12 lifetime float years inf lifetime Input (optional)
13 build_year int year 0 build year Input (optional)

2
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@ -0,0 +1,2 @@
attribute,type,unit,default,description,status
carrier,string,n/a,n/a,carrier,Input (optional)
1 attribute type unit default description status
2 carrier string n/a n/a carrier Input (optional)

4
data/override_component_attrs/stores.csv Normal file
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@ -0,0 +1,4 @@
attribute,type,unit,default,description,status
carrier,string,n/a,n/a,carrier,Input (optional)
lifetime,float,years,inf,lifetime,Input (optional)
build_year,int,year ,0,build year,Input (optional)
1 attribute type unit default description status
2 carrier string n/a n/a carrier Input (optional)
3 lifetime float years inf lifetime Input (optional)
4 build_year int year 0 build year Input (optional)

49
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NA_ITEM,Price level indices (EU28=100),,,,,,,,,
PPP_CAT,Actual individual consumption,,,,,,,,,
,,,,,,,,,,
GEO/TIME,2009,2010,2011,2012,2013,2014,2015,2016,2017,2018
European Union - 28 countries,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0,100.0
Belgium,113.6,111.9,112.4,111.5,111.0,108.9,106.3,110.3,112.3,112.5
Bulgaria,47.1,45.7,45.5,45.0,44.2,42.6,42.2,43.2,45.1,46.3
Czech Republic,64.5,66.6,68.9,66.9,63.3,58.3,58.4,60.5,62.4,65.0
Denmark,141.7,140.0,139.9,140.0,139.3,138.5,135.0,140.0,138.9,138.1
Germany,104.6,103.1,102.2,101.1,102.5,101.5,100.4,102.6,103.7,104.1
Estonia,67.5,66.0,67.2,67.6,69.9,69.9,68.9,71.0,73.9,76.3
Ireland,129.9,122.7,122.5,120.5,123.2,124.9,122.2,126.5,129.1,129.2
Greece,93.6,95.4,94.9,91.9,87.8,83.8,81.0,82.3,83.0,81.8
Spain,97.5,98.7,98.5,95.8,95.1,92.7,90.0,92.7,93.7,93.7
France,111.2,109.9,109.6,108.7,107.0,106.0,104.0,105.8,107.1,107.4
Croatia,70.2,70.1,68.1,65.5,64.5,62.5,60.7,61.3,63.0,64.0
Italy,103.6,100.4,101.5,101.1,102.3,102.6,100.3,101.1,101.6,101.4
Cyprus,92.0,94.6,95.8,96.0,95.2,92.0,88.5,89.8,91.2,90.6
Latvia,68.1,62.3,65.5,65.9,66.0,66.0,64.2,66.9,68.3,69.5
Lithuania,60.3,57.8,58.3,58.0,57.8,56.9,55.9,58.3,60.0,61.4
Luxembourg,130.0,136.5,136.0,135.8,135.1,135.7,132.1,137.0,139.9,141.6
Hungary,58.2,57.4,56.4,54.9,54.4,53.4,53.3,56.2,59.4,59.0
Malta,75.8,76.6,78.0,78.0,80.8,80.5,79.8,81.4,81.9,83.4
Netherlands,108.5,112.3,112.7,111.3,111.9,111.9,109.6,113.8,114.6,114.8
Austria,109.9,109.2,110.1,108.9,109.1,109.1,107.2,110.2,112.8,113.7
Poland,53.1,55.2,53.7,52.1,52.4,52.5,51.1,50.9,53.5,54.3
Portugal,85.2,85.0,85.3,82.7,81.1,80.4,78.7,81.6,83.5,84.6
Romania,49.1,46.9,47.7,45.6,47.8,47.6,47.2,46.8,48.0,48.6
Slovenia,85.3,84.3,83.7,81.8,82.1,81.5,79.8,82.3,82.7,83.8
Slovakia,66.6,62.5,63.4,63.4,63.4,63.3,62.3,63.6,65.4,66.1
Finland,121.0,120.3,121.6,121.8,124.0,122.9,119.6,122.8,123.3,123.4
Sweden,109.5,124.6,131.7,134.3,140.5,133.6,128.8,135.3,134.5,126.9
United Kingdom,107.5,111.4,111.3,118.6,117.0,123.6,134.7,123.5,117.6,117.7
Iceland,94.9,107.6,109.6,111.6,116.0,123.4,132.5,154.5,172.3,163.7
Norway,142.4,158.8,165.3,172.5,166.9,157.2,152.2,155.0,157.3,155.4
Switzerland,131.6,146.4,161.7,160.6,155.1,153.0,167.0,169.8,167.1,159.1
Candidate and potential candidate countries except Turkey and Kosovo (under United Nations Security Council Resolution 1244/99),48.0,45.6,47.1,44.8,46.4,45.2,43.4,44.4,46.0,47.5
Montenegro,52.3,49.5,49.3,50.1,50.5,49.3,48.0,48.7,50.5,51.1
North Macedonia,41.4,41.3,42.7,42.1,42.5,41.9,40.9,41.7,43.2,43.3
Albania,46.2,42.8,42.1,40.6,41.9,41.5,39.8,43.0,43.5,46.6
Serbia,48.3,45.0,48.0,44.5,47.3,45.5,43.1,43.8,46.1,47.9
Turkey,55.4,61.2,54.7,58.5,57.7,51.6,50.5,50.2,45.4,37.0
Bosnia and Herzegovina,51.6,50.7,50.6,49.2,49.1,48.4,47.0,47.5,48.2,48.9
Kosovo (under United Nations Security Council Resolution 1244/99),:,:,:,:,:,:,:,:,:,:
United States,92.4,98,93.3,101.2,100.3,99,115.9,121.1,120.8,115.2
Japan,115.1,126.1,127.8,133.8,101.7,94.8,96.5,113,109.4,103.9
,,,,,,,,,,
"Source: Eurostat Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates (2019)",,,,,,,,,,
https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment,,,,,,,,,,
1 NA_ITEM Price level indices (EU28=100)
2 PPP_CAT Actual individual consumption
3
4 GEO/TIME 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
5 European Union - 28 countries 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
6 Belgium 113.6 111.9 112.4 111.5 111.0 108.9 106.3 110.3 112.3 112.5
7 Bulgaria 47.1 45.7 45.5 45.0 44.2 42.6 42.2 43.2 45.1 46.3
8 Czech Republic 64.5 66.6 68.9 66.9 63.3 58.3 58.4 60.5 62.4 65.0
9 Denmark 141.7 140.0 139.9 140.0 139.3 138.5 135.0 140.0 138.9 138.1
10 Germany 104.6 103.1 102.2 101.1 102.5 101.5 100.4 102.6 103.7 104.1
11 Estonia 67.5 66.0 67.2 67.6 69.9 69.9 68.9 71.0 73.9 76.3
12 Ireland 129.9 122.7 122.5 120.5 123.2 124.9 122.2 126.5 129.1 129.2
13 Greece 93.6 95.4 94.9 91.9 87.8 83.8 81.0 82.3 83.0 81.8
14 Spain 97.5 98.7 98.5 95.8 95.1 92.7 90.0 92.7 93.7 93.7
15 France 111.2 109.9 109.6 108.7 107.0 106.0 104.0 105.8 107.1 107.4
16 Croatia 70.2 70.1 68.1 65.5 64.5 62.5 60.7 61.3 63.0 64.0
17 Italy 103.6 100.4 101.5 101.1 102.3 102.6 100.3 101.1 101.6 101.4
18 Cyprus 92.0 94.6 95.8 96.0 95.2 92.0 88.5 89.8 91.2 90.6
19 Latvia 68.1 62.3 65.5 65.9 66.0 66.0 64.2 66.9 68.3 69.5
20 Lithuania 60.3 57.8 58.3 58.0 57.8 56.9 55.9 58.3 60.0 61.4
21 Luxembourg 130.0 136.5 136.0 135.8 135.1 135.7 132.1 137.0 139.9 141.6
22 Hungary 58.2 57.4 56.4 54.9 54.4 53.4 53.3 56.2 59.4 59.0
23 Malta 75.8 76.6 78.0 78.0 80.8 80.5 79.8 81.4 81.9 83.4
24 Netherlands 108.5 112.3 112.7 111.3 111.9 111.9 109.6 113.8 114.6 114.8
25 Austria 109.9 109.2 110.1 108.9 109.1 109.1 107.2 110.2 112.8 113.7
26 Poland 53.1 55.2 53.7 52.1 52.4 52.5 51.1 50.9 53.5 54.3
27 Portugal 85.2 85.0 85.3 82.7 81.1 80.4 78.7 81.6 83.5 84.6
28 Romania 49.1 46.9 47.7 45.6 47.8 47.6 47.2 46.8 48.0 48.6
29 Slovenia 85.3 84.3 83.7 81.8 82.1 81.5 79.8 82.3 82.7 83.8
30 Slovakia 66.6 62.5 63.4 63.4 63.4 63.3 62.3 63.6 65.4 66.1
31 Finland 121.0 120.3 121.6 121.8 124.0 122.9 119.6 122.8 123.3 123.4
32 Sweden 109.5 124.6 131.7 134.3 140.5 133.6 128.8 135.3 134.5 126.9
33 United Kingdom 107.5 111.4 111.3 118.6 117.0 123.6 134.7 123.5 117.6 117.7
34 Iceland 94.9 107.6 109.6 111.6 116.0 123.4 132.5 154.5 172.3 163.7
35 Norway 142.4 158.8 165.3 172.5 166.9 157.2 152.2 155.0 157.3 155.4
36 Switzerland 131.6 146.4 161.7 160.6 155.1 153.0 167.0 169.8 167.1 159.1
37 Candidate and potential candidate countries except Turkey and Kosovo (under United Nations Security Council Resolution 1244/99) 48.0 45.6 47.1 44.8 46.4 45.2 43.4 44.4 46.0 47.5
38 Montenegro 52.3 49.5 49.3 50.1 50.5 49.3 48.0 48.7 50.5 51.1
39 North Macedonia 41.4 41.3 42.7 42.1 42.5 41.9 40.9 41.7 43.2 43.3
40 Albania 46.2 42.8 42.1 40.6 41.9 41.5 39.8 43.0 43.5 46.6
41 Serbia 48.3 45.0 48.0 44.5 47.3 45.5 43.1 43.8 46.1 47.9
42 Turkey 55.4 61.2 54.7 58.5 57.7 51.6 50.5 50.2 45.4 37.0
43 Bosnia and Herzegovina 51.6 50.7 50.6 49.2 49.1 48.4 47.0 47.5 48.2 48.9
44 Kosovo (under United Nations Security Council Resolution 1244/99) : : : : : : : : : :
45 United States 92.4 98 93.3 101.2 100.3 99 115.9 121.1 120.8 115.2
46 Japan 115.1 126.1 127.8 133.8 101.7 94.8 96.5 113 109.4 103.9
47
48 Source: Eurostat Purchasing power parities (PPPs), price level indices and real expenditures for ESA 2010 aggregates (2019)
49 https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment

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Electricity prices for household consumers - bi-annual data (from 2007 onwards) [nrg_pc_204],,,,
,,,,
Last update,30.10.19,,,
Extracted on,14.11.19,,,
Source of data,Eurostat,,,
,,,,
PRODUCT,Electrical energy,,,
CONSOM,Band DC : 2 500 kWh < Consumption < 5 000 kWh,,,
UNIT,Kilowatt-hour,,,
TIME,2018S1,,,
,,,,
CURRENCY,Euro,Euro,Euro,
GEO/TAX,Excluding taxes and levies,Excluding VAT and other recoverable taxes and levies,All taxes and levies included,% cost without taxes
European Union - 28 countries,0.1285,0.1756,0.2052,0.626218323586745
"Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19)",0.1331,0.1855,0.2188,0.608318098720293
Belgium,0.1903,0.2279,0.2733,0.696304427369191
Bulgaria,0.0816,0.0816,0.0979,0.833503575076609
Czech Republic,0.1286,0.1298,0.1573,0.817546090273363
Denmark,0.1011,0.2501,0.3126,0.32341650671785
Germany,0.1379,0.2510,0.2987,0.461667224640107
Estonia,0.0989,0.1123,0.1348,0.733679525222552
Ireland,0.1846,0.2087,0.2369,0.779231743351625
Greece,0.1132,0.1482,0.1672,0.677033492822967
Spain,0.1873,0.1969,0.2383,0.785984053713806
France,0.1134,0.1492,0.1748,0.648741418764302
Croatia,0.1020,0.1160,0.1311,0.778032036613272
Italy,0.1285,0.1873,0.2067,0.621673923560716
Cyprus,0.1445,0.1606,0.1893,0.763338615953513
Latvia,0.1035,0.1266,0.1531,0.676028739386022
Lithuania,0.0771,0.0906,0.1097,0.702825888787603
Luxembourg,0.1283,0.1547,0.1671,0.767803710353082
Hungary,0.0885,0.0885,0.1123,0.78806767586821
Malta,0.1209,0.1224,0.1285,0.940856031128405
Netherlands,0.1187,0.1410,0.1706,0.6957796014068
Austria,0.1232,0.1638,0.1966,0.626653102746694
Poland,0.0906,0.1146,0.1410,0.642553191489362
Portugal,0.1007,0.1826,0.2246,0.448352626892253
Romania,0.0990,0.1120,0.1333,0.742685671417854
Slovenia,0.1108,0.1322,0.1613,0.686918784872908
Slovakia,0.0942,0.1305,0.1566,0.601532567049808
Finland,0.1074,0.1300,0.1612,0.666253101736973
Sweden,0.1202,0.1513,0.1891,0.635642517186674
United Kingdom,0.1347,0.1797,0.1887,0.713831478537361
Iceland,0.1222,0.1246,0.1545,0.790938511326861
Liechtenstein,:,:,:,#VALUE!
Norway,0.1254,0.1434,0.1751,0.716162193032553
Montenegro,0.0828,0.0844,0.1024,0.80859375
North Macedonia,0.0662,0.0662,0.0781,0.847631241997439
Albania,:,:,:,#VALUE!
Serbia,0.0539,0.0587,0.0705,0.764539007092199
Turkey,0.0727,0.0766,0.0904,0.804203539823009
Bosnia and Herzegovina,0.0722,0.0738,0.0864,0.835648148148148
Kosovo (under United Nations Security Council Resolution 1244/99),0.0569,0.0586,0.0633,0.898894154818325
Moldova,0.1020,0.1020,0.1020,1
Ukraine,0.0342,0.0342,0.0410,0.834146341463415
,,,0.157271052631579,
Special value:,,,,
:,not available,,,
,,,,
PRODUCT,Electrical energy,,,
CONSOM,Band DC : 2 500 kWh < Consumption < 5 000 kWh,,,
UNIT,Kilowatt-hour,,,
TIME,2018S2,,,
,,,,
CURRENCY,Euro,Euro,Euro,
GEO/TAX,Excluding taxes and levies,Excluding VAT and other recoverable taxes and levies,All taxes and levies included,
European Union - 28 countries,0.1329,0.1810,0.2113,
"Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19)",0.1376,0.1902,0.2242,
Belgium,0.1998,0.2429,0.2937,
Bulgaria,0.0838,0.0838,0.1005,
Czechia,0.1299,0.1311,0.1586,
Denmark,0.1116,0.2499,0.3123,
Germany (until 1990 former territory of the FRG),0.1378,0.2521,0.3000,
Estonia,0.1048,0.1182,0.1418,
Ireland,0.2006,0.2237,0.2539,
Greece,0.1125,0.1458,0.1646,
Spain,0.1947,0.2047,0.2477,
France,0.1168,0.1537,0.1799,
Croatia,0.1028,0.1169,0.1321,
Italy,0.1416,0.1964,0.2161,
Cyprus,0.1745,0.1850,0.2183,
Latvia,0.1041,0.1249,0.1511,
Lithuania,0.0771,0.0906,0.1097,
Luxembourg,0.1302,0.1566,0.1691,
Hungary,0.0880,0.0880,0.1118,
Malta,0.1229,0.1244,0.1306,
Netherlands,0.1212,0.1420,0.1707,
Austria,0.1265,0.1676,0.2012,
Poland,0.0889,0.1135,0.1396,
Portugal,0.1028,0.1864,0.2293,
Romania,0.0964,0.1107,0.1317,
Slovenia,0.1125,0.1342,0.1638,
Slovakia,0.0849,0.1218,0.1462,
Finland,0.1144,0.1369,0.1698,
Sweden,0.1287,0.1592,0.1990,
United Kingdom,0.1401,0.1927,0.2024,
Iceland,0.1152,0.1175,0.1457,
Liechtenstein,:,:,:,
Norway,0.1382,0.1562,0.1907,
Montenegro,0.0829,0.0848,0.1030,
North Macedonia,0.0667,0.0667,0.0787,
Albania,0.0759,0.0759,0.0910,
Serbia,0.0542,0.0591,0.0709,
Turkey,0.0688,0.0726,0.0857,
Bosnia and Herzegovina,0.0729,0.0744,0.0871,
Kosovo (under United Nations Security Council Resolution 1244/99),0.0579,0.0591,0.0638,
Moldova,0.0960,0.0960,0.1029,
Ukraine,0.0342,0.0342,0.0410,
,,,,
Special value:,,,,
:,not available,,,
,,,,
PRODUCT,Electrical energy,,,
CONSOM,Band DC : 2 500 kWh < Consumption < 5 000 kWh,,,
UNIT,Kilowatt-hour,,,
TIME,2019S1,,,
,,,,
CURRENCY,Euro,Euro,Euro,
GEO/TAX,Excluding taxes and levies,Excluding VAT and other recoverable taxes and levies,All taxes and levies included,
European Union - 28 countries,0.1351,0.1841,0.2147,
"Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19)",0.1396,0.1928,0.2270,
Belgium,0.1965,0.2355,0.2839,
Bulgaria,0.0831,0.0831,0.0997,
Czechia,0.1433,0.1444,0.1748,
Denmark,0.1084,0.2387,0.2984,
Germany (until 1990 former territory of the FRG),0.1473,0.2595,0.3088,
Estonia,0.0982,0.1131,0.1357,
Ireland,0.2027,0.2134,0.2423,
Greece,0.1139,0.1482,0.1650,
Spain,0.1889,0.1986,0.2403,
France,0.1138,0.1508,0.1765,
Croatia,0.1028,0.1169,0.1321,
Italy,0.1432,0.2090,0.2301,
Cyprus,0.1762,0.1867,0.2203,
Latvia,0.1136,0.1347,0.1629,
Lithuania,0.0947,0.1037,0.1255,
Luxembourg,0.1326,0.1666,0.1798,
Hungary,0.0882,0.0882,0.1120,
Malta,0.1228,0.1243,0.1305,
Netherlands,0.1357,0.1708,0.2052,
Austria,0.1316,0.1695,0.2034,
Poland,0.0884,0.1092,0.1343,
Portugal,0.1103,0.1751,0.2154,
Romania,0.0983,0.1141,0.1358,
Slovenia,0.1125,0.1339,0.1634,
Slovakia,0.0962,0.1314,0.1577,
Finland,0.1173,0.1398,0.1734,
Sweden,0.1297,0.1612,0.2015,
United Kingdom,0.1450,0.2021,0.2122,
Iceland,0.1112,0.1134,0.1406,
Liechtenstein,:,:,:,
Norway,0.1360,0.1529,0.1867,
Montenegro,0.0834,0.0850,0.1032,
North Macedonia,:,:,:,
Albania,:,:,:,
Serbia,0.0541,0.0589,0.0706,
Turkey,0.0684,0.0718,0.0847,
Bosnia and Herzegovina,0.0729,0.0746,0.0873,
Kosovo (under United Nations Security Council Resolution 1244/99),0.0537,0.0556,0.0600,
Moldova,0.0936,0.0936,0.0936,
Ukraine,0.0369,0.0369,0.0442,
,,,,
Special value:,,,,
:,not available,,,
1 Electricity prices for household consumers - bi-annual data (from 2007 onwards) [nrg_pc_204]
2
3 Last update 30.10.19
4 Extracted on 14.11.19
5 Source of data Eurostat
6
7 PRODUCT Electrical energy
8 CONSOM Band DC : 2 500 kWh < Consumption < 5 000 kWh
9 UNIT Kilowatt-hour
10 TIME 2018S1
11
12 CURRENCY Euro Euro Euro
13 GEO/TAX Excluding taxes and levies Excluding VAT and other recoverable taxes and levies All taxes and levies included % cost without taxes
14 European Union - 28 countries 0.1285 0.1756 0.2052 0.626218323586745
15 Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19) 0.1331 0.1855 0.2188 0.608318098720293
16 Belgium 0.1903 0.2279 0.2733 0.696304427369191
17 Bulgaria 0.0816 0.0816 0.0979 0.833503575076609
18 Czech Republic 0.1286 0.1298 0.1573 0.817546090273363
19 Denmark 0.1011 0.2501 0.3126 0.32341650671785
20 Germany 0.1379 0.2510 0.2987 0.461667224640107
21 Estonia 0.0989 0.1123 0.1348 0.733679525222552
22 Ireland 0.1846 0.2087 0.2369 0.779231743351625
23 Greece 0.1132 0.1482 0.1672 0.677033492822967
24 Spain 0.1873 0.1969 0.2383 0.785984053713806
25 France 0.1134 0.1492 0.1748 0.648741418764302
26 Croatia 0.1020 0.1160 0.1311 0.778032036613272
27 Italy 0.1285 0.1873 0.2067 0.621673923560716
28 Cyprus 0.1445 0.1606 0.1893 0.763338615953513
29 Latvia 0.1035 0.1266 0.1531 0.676028739386022
30 Lithuania 0.0771 0.0906 0.1097 0.702825888787603
31 Luxembourg 0.1283 0.1547 0.1671 0.767803710353082
32 Hungary 0.0885 0.0885 0.1123 0.78806767586821
33 Malta 0.1209 0.1224 0.1285 0.940856031128405
34 Netherlands 0.1187 0.1410 0.1706 0.6957796014068
35 Austria 0.1232 0.1638 0.1966 0.626653102746694
36 Poland 0.0906 0.1146 0.1410 0.642553191489362
37 Portugal 0.1007 0.1826 0.2246 0.448352626892253
38 Romania 0.0990 0.1120 0.1333 0.742685671417854
39 Slovenia 0.1108 0.1322 0.1613 0.686918784872908
40 Slovakia 0.0942 0.1305 0.1566 0.601532567049808
41 Finland 0.1074 0.1300 0.1612 0.666253101736973
42 Sweden 0.1202 0.1513 0.1891 0.635642517186674
43 United Kingdom 0.1347 0.1797 0.1887 0.713831478537361
44 Iceland 0.1222 0.1246 0.1545 0.790938511326861
45 Liechtenstein : : : #VALUE!
46 Norway 0.1254 0.1434 0.1751 0.716162193032553
47 Montenegro 0.0828 0.0844 0.1024 0.80859375
48 North Macedonia 0.0662 0.0662 0.0781 0.847631241997439
49 Albania : : : #VALUE!
50 Serbia 0.0539 0.0587 0.0705 0.764539007092199
51 Turkey 0.0727 0.0766 0.0904 0.804203539823009
52 Bosnia and Herzegovina 0.0722 0.0738 0.0864 0.835648148148148
53 Kosovo (under United Nations Security Council Resolution 1244/99) 0.0569 0.0586 0.0633 0.898894154818325
54 Moldova 0.1020 0.1020 0.1020 1
55 Ukraine 0.0342 0.0342 0.0410 0.834146341463415
56 0.157271052631579
57 Special value:
58 : not available
59
60 PRODUCT Electrical energy
61 CONSOM Band DC : 2 500 kWh < Consumption < 5 000 kWh
62 UNIT Kilowatt-hour
63 TIME 2018S2
64
65 CURRENCY Euro Euro Euro
66 GEO/TAX Excluding taxes and levies Excluding VAT and other recoverable taxes and levies All taxes and levies included
67 European Union - 28 countries 0.1329 0.1810 0.2113
68 Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19) 0.1376 0.1902 0.2242
69 Belgium 0.1998 0.2429 0.2937
70 Bulgaria 0.0838 0.0838 0.1005
71 Czechia 0.1299 0.1311 0.1586
72 Denmark 0.1116 0.2499 0.3123
73 Germany (until 1990 former territory of the FRG) 0.1378 0.2521 0.3000
74 Estonia 0.1048 0.1182 0.1418
75 Ireland 0.2006 0.2237 0.2539
76 Greece 0.1125 0.1458 0.1646
77 Spain 0.1947 0.2047 0.2477
78 France 0.1168 0.1537 0.1799
79 Croatia 0.1028 0.1169 0.1321
80 Italy 0.1416 0.1964 0.2161
81 Cyprus 0.1745 0.1850 0.2183
82 Latvia 0.1041 0.1249 0.1511
83 Lithuania 0.0771 0.0906 0.1097
84 Luxembourg 0.1302 0.1566 0.1691
85 Hungary 0.0880 0.0880 0.1118
86 Malta 0.1229 0.1244 0.1306
87 Netherlands 0.1212 0.1420 0.1707
88 Austria 0.1265 0.1676 0.2012
89 Poland 0.0889 0.1135 0.1396
90 Portugal 0.1028 0.1864 0.2293
91 Romania 0.0964 0.1107 0.1317
92 Slovenia 0.1125 0.1342 0.1638
93 Slovakia 0.0849 0.1218 0.1462
94 Finland 0.1144 0.1369 0.1698
95 Sweden 0.1287 0.1592 0.1990
96 United Kingdom 0.1401 0.1927 0.2024
97 Iceland 0.1152 0.1175 0.1457
98 Liechtenstein : : :
99 Norway 0.1382 0.1562 0.1907
100 Montenegro 0.0829 0.0848 0.1030
101 North Macedonia 0.0667 0.0667 0.0787
102 Albania 0.0759 0.0759 0.0910
103 Serbia 0.0542 0.0591 0.0709
104 Turkey 0.0688 0.0726 0.0857
105 Bosnia and Herzegovina 0.0729 0.0744 0.0871
106 Kosovo (under United Nations Security Council Resolution 1244/99) 0.0579 0.0591 0.0638
107 Moldova 0.0960 0.0960 0.1029
108 Ukraine 0.0342 0.0342 0.0410
109
110 Special value:
111 : not available
112
113 PRODUCT Electrical energy
114 CONSOM Band DC : 2 500 kWh < Consumption < 5 000 kWh
115 UNIT Kilowatt-hour
116 TIME 2019S1
117
118 CURRENCY Euro Euro Euro
119 GEO/TAX Excluding taxes and levies Excluding VAT and other recoverable taxes and levies All taxes and levies included
120 European Union - 28 countries 0.1351 0.1841 0.2147
121 Euro area (EA11-2000, EA12-2006, EA13-2007, EA15-2008, EA16-2010, EA17-2013, EA18-2014, EA19) 0.1396 0.1928 0.2270
122 Belgium 0.1965 0.2355 0.2839
123 Bulgaria 0.0831 0.0831 0.0997
124 Czechia 0.1433 0.1444 0.1748
125 Denmark 0.1084 0.2387 0.2984
126 Germany (until 1990 former territory of the FRG) 0.1473 0.2595 0.3088
127 Estonia 0.0982 0.1131 0.1357
128 Ireland 0.2027 0.2134 0.2423
129 Greece 0.1139 0.1482 0.1650
130 Spain 0.1889 0.1986 0.2403
131 France 0.1138 0.1508 0.1765
132 Croatia 0.1028 0.1169 0.1321
133 Italy 0.1432 0.2090 0.2301
134 Cyprus 0.1762 0.1867 0.2203
135 Latvia 0.1136 0.1347 0.1629
136 Lithuania 0.0947 0.1037 0.1255
137 Luxembourg 0.1326 0.1666 0.1798
138 Hungary 0.0882 0.0882 0.1120
139 Malta 0.1228 0.1243 0.1305
140 Netherlands 0.1357 0.1708 0.2052
141 Austria 0.1316 0.1695 0.2034
142 Poland 0.0884 0.1092 0.1343
143 Portugal 0.1103 0.1751 0.2154
144 Romania 0.0983 0.1141 0.1358
145 Slovenia 0.1125 0.1339 0.1634
146 Slovakia 0.0962 0.1314 0.1577
147 Finland 0.1173 0.1398 0.1734
148 Sweden 0.1297 0.1612 0.2015
149 United Kingdom 0.1450 0.2021 0.2122
150 Iceland 0.1112 0.1134 0.1406
151 Liechtenstein : : :
152 Norway 0.1360 0.1529 0.1867
153 Montenegro 0.0834 0.0850 0.1032
154 North Macedonia : : :
155 Albania : : :
156 Serbia 0.0541 0.0589 0.0706
157 Turkey 0.0684 0.0718 0.0847
158 Bosnia and Herzegovina 0.0729 0.0746 0.0873
159 Kosovo (under United Nations Security Council Resolution 1244/99) 0.0537 0.0556 0.0600
160 Moldova 0.0936 0.0936 0.0936
161 Ukraine 0.0369 0.0369 0.0442
162
163 Special value:
164 : not available

17
data/retro/floor_area_missing.csv Normal file
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@ -0,0 +1,17 @@
country,sector,estimated,value,source,,comments,population [in Million],
AL,residential,0,64,p.13 1.6 million m² = 2.5% of total floor area,https://www.buildup.eu/sites/default/files/content/sled_albania_residential_building_eng.pdf,,,
AL,services,0,,,,,,
BA,residential,0,125.89,Tabula,https://episcope.eu/building-typology/country/ba/,strong differences ? other source claims more than 300 Million m²,,https://www.buildup.eu/sites/default/files/content/sled_serbia_building_eng.pdf
BA,services,0,,,,,,
RS,residential,0,72.3,Odyssee(2011),https://odyssee.enerdata.net/database/,,,
RS,services,0,,,,,,
MK,residential,0,,"Worldbank p.7 Skopje 75% residential, 25% commercial",http://documents.albankaldawli.org/curated/ar/838951574180734318/pdf/Project-Information-Document-North-Macedonia-Public-Sector-Energy-Efficiency-Project-P149990.pdf,15 % live in illegal constructed buildings ? not part of the statistics,2.1,
MK,services,0,,,,,,
ME,residential,0,19.625,p.13 0.314 million m² = 1.6% of total floor area,buildup.eu/sites/default/files/content/sled_montenegro_building_eng.pdf,Only 50 % of the floor area is heated p.12,,buildup.eu/sites/default/files/content/sled_montenegro_building_eng.pdf
ME,services,0,,,,,,
CH,residential,0,99.45,Odyssee(2015),,,,
CH,services,1,78.1392857142857,p.8 44%floor area is services,https://bta.climate-kic.org/wp-content/uploads/2018/04/171123-CK-BTA-DEF-BMB_SWITZERLAND_.pdf,,,
NO,residential,0,121.55,Odyssee(2015),,,,
NO,services,0,115.21,Odyssee(2015),,,,
PL,residential,0,1028.41,EU Building Database,,,,
PL,services,0,498.84,EU Building Database,,,,
1 country sector estimated value source comments population [in Million]
2 AL residential 0 64 p.13 1.6 million m² = 2.5% of total floor area https://www.buildup.eu/sites/default/files/content/sled_albania_residential_building_eng.pdf
3 AL services 0
4 BA residential 0 125.89 Tabula https://episcope.eu/building-typology/country/ba/ strong differences ? other source claims more than 300 Million m² https://www.buildup.eu/sites/default/files/content/sled_serbia_building_eng.pdf
5 BA services 0
6 RS residential 0 72.3 Odyssee(2011) https://odyssee.enerdata.net/database/
7 RS services 0
8 MK residential 0 Worldbank p.7 Skopje 75% residential, 25% commercial http://documents.albankaldawli.org/curated/ar/838951574180734318/pdf/Project-Information-Document-North-Macedonia-Public-Sector-Energy-Efficiency-Project-P149990.pdf 15 % live in illegal constructed buildings ? not part of the statistics 2.1
9 MK services 0
10 ME residential 0 19.625 p.13 0.314 million m² = 1.6% of total floor area buildup.eu/sites/default/files/content/sled_montenegro_building_eng.pdf Only 50 % of the floor area is heated p.12 buildup.eu/sites/default/files/content/sled_montenegro_building_eng.pdf
11 ME services 0
12 CH residential 0 99.45 Odyssee(2015)
13 CH services 1 78.1392857142857 p.8 44%floor area is services https://bta.climate-kic.org/wp-content/uploads/2018/04/171123-CK-BTA-DEF-BMB_SWITZERLAND_.pdf
14 NO residential 0 121.55 Odyssee(2015)
15 NO services 0 115.21 Odyssee(2015)
16 PL residential 0 1028.41 EU Building Database
17 PL services 0 498.84 EU Building Database

7
data/retro/retro_cost_germany.csv Normal file
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@ -0,0 +1,7 @@
component,cost_fix,cost_var,life_time,comment,additional source
wall,70.34,2.36,40,Agora Energiewende p.110,
floor,39.39,1.3,40,Agora Energiewende p.110,
roof,75.61,1.3,40,Agora Energiewende p.110,https://www.baulinks.de/webplugin/2018/1524.php4
window,nan,nan,35,,
source: p.37 https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-10-29_texte_132-2019_energieaufwand-gebaeudekonzepte.pdf,,,https://www.agora-energiewende.de/en/publications/building-sector-efficiency-a-crucial-component-of-the-energy-transition/,,
,,,p.115,,
1 component cost_fix cost_var life_time comment additional source
2 wall 70.34 2.36 40 Agora Energiewende p.110
3 floor 39.39 1.3 40 Agora Energiewende p.110
4 roof 75.61 1.3 40 Agora Energiewende p.110 https://www.baulinks.de/webplugin/2018/1524.php4
5 window nan nan 35
6 source: p.37 https://www.umweltbundesamt.de/sites/default/files/medien/1410/publikationen/2019-10-29_texte_132-2019_energieaufwand-gebaeudekonzepte.pdf https://www.agora-energiewende.de/en/publications/building-sector-efficiency-a-crucial-component-of-the-energy-transition/
7 p.115

9
data/retro/u_values_poland.csv Normal file
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@ -0,0 +1,9 @@
component,Before 1945,1945 - 1969,1970 - 1979,1980 - 1989,1990 - 1999,2000 - 2010,Post 2010,sector
Walls,1.7,1.4,0.9,0.9,0.6,0.4,1.7,residential
Windows,4.6,3.6,2.6,2.6,2.1,2.1,2.1,residential
Roof,0.8,0.7,0.6,0.6,0.6,0.4,0.33,residential
Floor,1.9,1.4,1.2,1.1,0.9,0.6,0.45,residential
Walls,1.3,1.3,1.3,0.8,0.6,0.6,0.6,services
Windows,4.7,3.7,2.6,2.6,2.3,2.1,2.1,services
Roof,1,0.9,0.7,0.5,0.3,0.3,0.3,services
Floor,1.6,1.2,1.2,1.1,1,0.7,0.7,services
1 component Before 1945 1945 - 1969 1970 - 1979 1980 - 1989 1990 - 1999 2000 - 2010 Post 2010 sector
2 Walls 1.7 1.4 0.9 0.9 0.6 0.4 1.7 residential
3 Windows 4.6 3.6 2.6 2.6 2.1 2.1 2.1 residential
4 Roof 0.8 0.7 0.6 0.6 0.6 0.4 0.33 residential
5 Floor 1.9 1.4 1.2 1.1 0.9 0.6 0.45 residential
6 Walls 1.3 1.3 1.3 0.8 0.6 0.6 0.6 services
7 Windows 4.7 3.7 2.6 2.6 2.3 2.1 2.1 services
8 Roof 1 0.9 0.7 0.5 0.3 0.3 0.3 services
9 Floor 1.6 1.2 1.2 1.1 1 0.7 0.7 services

8
data/retro/window_assumptions.csv Normal file
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@ -0,0 +1,8 @@
strength,u_value,cost,u_limit,comment
[m],[W/m^2K],EUR/m^2,[W/m^2K],
0.076,1.34,180.08,3.5,Double-glazing
0.197,0.8,225,1.3,Triple-glazing
,,,,
"source: https://www.agora-energiewende.de/en/publications/building-sector-efficiency-a-crucial-component-of-the-energy-transition/
p.115
",,,,
1 strength u_value cost u_limit comment
2 [m] [W/m^2K] EUR/m^2 [W/m^2K]
3 0.076 1.34 180.08 3.5 Double-glazing
4 0.197 0.8 225 1.3 Triple-glazing
5
6 source: https://www.agora-energiewende.de/en/publications/building-sector-efficiency-a-crucial-component-of-the-energy-transition/ p.115

30
data/urban_percent.csv Normal file
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@ -0,0 +1,30 @@
AT,66
BA,40
BE,98
BG,74
CH,74
CZ,73
DE,75
DK,88
EE,68
ES,80
FI,84
FR,80
GB,83
GR,78
HR,59
HU,71
IE,63
IT,69
LT,67
LU,90
LV,67
NL,90
NO,80
PL,61
PT,63
RO,55
RS,56
SE,86
SI,50
SK,54
1 AT 66
2 BA 40
3 BE 98
4 BG 74
5 CH 74
6 CZ 73
7 DE 75
8 DK 88
9 EE 68
10 ES 80
11 FI 84
12 FR 80
13 GB 83
14 GR 78
15 HR 59
16 HU 71
17 IE 63
18 IT 69
19 LT 67
20 LU 90
21 LV 67
22 NL 90
23 NO 80
24 PL 61
25 PT 63
26 RO 55
27 RS 56
28 SE 86
29 SI 50
30 SK 54

3
doc/Makefile
View File

@ -1,7 +1,10 @@
<<<<<<< HEAD
# SPDX-FileCopyrightText: 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
=======
>>>>>>> pypsa-eur-sec/master
# Makefile for Sphinx documentation
#

77
doc/conf.py
View File

@ -1,9 +1,12 @@
# -*- coding: utf-8 -*-
<<<<<<< HEAD
# SPDX-FileCopyrightText: 20017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# -*- coding: utf-8 -*-
=======
>>>>>>> pypsa-eur-sec/master
#
# PyPSA documentation build configuration file, created by
# sphinx-quickstart on Tue Jan 5 10:04:42 2016.
@ -37,6 +40,10 @@ sys.path.insert(0, os.path.abspath("../scripts"))
extensions = [
#'sphinx.ext.autodoc',
#'sphinx.ext.autosummary',
<<<<<<< HEAD
=======
"sphinx.ext.autosectionlabel",
>>>>>>> pypsa-eur-sec/master
"sphinx.ext.intersphinx",
"sphinx.ext.todo",
"sphinx.ext.mathjax",
@ -66,9 +73,15 @@ source_suffix = ".rst"
master_doc = "index"
# General information about the project.
<<<<<<< HEAD
project = "PyPSA-Eur"
copyright = "2017-2023 Jonas Hoersch (KIT, FIAS), Fabian Hofmann (TUB, FIAS), David Schlachtberger (FIAS), Tom Brown (TUB, KIT, FIAS); 2019-2023 Fabian Neumann (TUB, KIT)"
author = "Jonas Hoersch (KIT, FIAS), Fabian Hofmann (TUB, FIAS), David Schlachtberger (FIAS), Tom Brown (TUB, KIT, FIAS), Fabian Neumann (TUB, KIT)"
=======
project = "PyPSA-Eur-Sec"
copyright = "2019-2023 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)"
author = "2019-2023 Tom Brown (KIT, TUB), Marta Victoria (Aarhus University), Lisa Zeyen (KIT, TUB), Fabian Neumann (TUB)"
>>>>>>> pypsa-eur-sec/master
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
@ -84,7 +97,11 @@ release = "0.7.0"
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
<<<<<<< HEAD
language = "en"
=======
language = None
>>>>>>> pypsa-eur-sec/master
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
@ -128,15 +145,24 @@ todo_include_todos = True
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
<<<<<<< HEAD
html_theme = "sphinx_book_theme"
=======
html_theme = "sphinx_rtd_theme"
>>>>>>> pypsa-eur-sec/master
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
html_theme_options = {
<<<<<<< HEAD
"repository_url": "https://github.com/pypsa/pypsa-eur",
"use_repository_button": True,
"show_navbar_depth": 2,
=======
"display_version": True,
"sticky_navigation": True,
>>>>>>> pypsa-eur-sec/master
}
@ -145,26 +171,47 @@ html_theme_options = {
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
<<<<<<< HEAD
html_title = "PyPSA-Eur"
=======
# html_title = None
>>>>>>> pypsa-eur-sec/master
# A shorter title for the navigation bar. Default is the same as html_title.
# html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
<<<<<<< HEAD
html_logo = "img/pypsa-logo.png"
=======
# html_logo = None
>>>>>>> pypsa-eur-sec/master
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
# html_favicon = None
<<<<<<< HEAD
# These folders are copied to the documentation's HTML output
# html_static_path = ["_static"]
# These paths are either relative to html_static_path
# or fully qualified paths (eg. https://...)
# html_css_files = ["theme_overrides.css"]
=======
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ["_static"]
html_context = {
"css_files": [
"_static/theme_overrides.css", # override wide tables in RTD theme
],
}
>>>>>>> pypsa-eur-sec/master
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
@ -227,7 +274,11 @@ html_logo = "img/pypsa-logo.png"
# html_search_scorer = 'scorer.js'
# Output file base name for HTML help builder.
<<<<<<< HEAD
htmlhelp_basename = "PyPSAEurdoc"
=======
htmlhelp_basename = "PyPSAEurSecdoc"
>>>>>>> pypsa-eur-sec/master
# -- Options for LaTeX output ---------------------------------------------
@ -246,7 +297,17 @@ latex_elements = {
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
<<<<<<< HEAD
(master_doc, "PyPSA-Eur.tex", "PyPSA-Eur Documentation", "author", "manual"),
=======
(
master_doc,
"PyPSA-Eur-Sec.tex",
"PyPSA-Eur-Sec Documentation",
"author",
"manual",
),
>>>>>>> pypsa-eur-sec/master
]
@ -254,8 +315,13 @@ latex_documents = [
rinoh_documents = [
(
master_doc, # top-level file (index.rst)
<<<<<<< HEAD
"PyPSA-Eur", # output (target.pdf)
"PyPSA-Eur Documentation", # document title
=======
"PyPSA-Eur-Sec", # output (target.pdf)
"PyPSA-Eur-Sec Documentation", # document title
>>>>>>> pypsa-eur-sec/master
"author",
)
] # document author
@ -286,7 +352,11 @@ rinoh_documents = [
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
<<<<<<< HEAD
man_pages = [(master_doc, "pypsa-eur", "PyPSA-Eur Documentation", [author], 1)]
=======
man_pages = [(master_doc, "pypsa-eur-sec", "PyPSA-Eur-Sec Documentation", [author], 1)]
>>>>>>> pypsa-eur-sec/master
# If true, show URL addresses after external links.
# man_show_urls = False
@ -300,10 +370,17 @@ man_pages = [(master_doc, "pypsa-eur", "PyPSA-Eur Documentation", [author], 1)]
texinfo_documents = [
(
master_doc,
<<<<<<< HEAD
"PyPSA-Eur",
"PyPSA-Eur Documentation",
author,
"PyPSA-Eur",
=======
"PyPSA-Eur-Sec",
"PyPSA-Eur-Sec Documentation",
author,
"PyPSA-Eur-Sec",
>>>>>>> pypsa-eur-sec/master
"One line description of project.",
"Miscellaneous",
),

29
doc/data.csv Normal file
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@ -0,0 +1,29 @@
description,file/folder,licence,source
JRC IDEES database,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees
urban/rural fraction,urban_percent.csv,unknown,unknown
JRC biomass potentials,biomass/,unknown,https://doi.org/10.2790/39014
JRC ENSPRESO biomass potentials,remote,CC BY 4.0,https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f
EEA emission statistics,eea/UNFCCC_v23.csv,EEA standard re-use policy,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
Eurostat Energy Balances,eurostat-energy_balances-*/,Eurostat,https://ec.europa.eu/eurostat/web/energy/data/energy-balances
Swiss energy statistics from Swiss Federal Office of Energy,switzerland-sfoe/,unknown,http://www.bfe.admin.ch/themen/00526/00541/00542/02167/index.html?dossier_id=02169
BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Stundenwerte.html?nn=626916
BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib
heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University
George Lavidas wind/wave costs,WindWaveWEC_GLTB.xlsx,unknown,George Lavidas
country codes,Country_codes.csv,CC BY 4.0,Marta Victoria
co2 budgets,co2_budget.csv,CC BY 4.0,https://arxiv.org/abs/2004.11009
existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
IRENA existing VRE capacities,existing_infrastructure/{solar|onwind|offwind}_capcity_IRENA.csv,unknown,https://www.irena.org/Statistics/Download-Data
USGS ammonia production,myb1-2017-nitro.xls,unknown,https://www.usgs.gov/centers/nmic/nitrogen-statistics-and-information
hydrogen salt cavern potentials,h2_salt_caverns_GWh_per_sqkm.geojson,CC BY 4.0,https://doi.org/10.1016/j.ijhydene.2019.12.161 https://doi.org/10.20944/preprints201910.0187.v1
international port trade volumes,attributed_ports.json,CC BY 4.0,https://datacatalog.worldbank.org/search/dataset/0038118/Global---International-Ports
hotmaps industrial site database,Industrial_Database.csv,CC BY 4.0,https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database
Hotmaps building stock data,data_building_stock.csv,CC BY 4.0,https://gitlab.com/hotmaps/building-stock
U-values Poland,u_values_poland.csv,unknown,https://data.europa.eu/euodp/de/data/dataset/building-stock-observatory
Floor area missing in hotmaps building stock data,floor_area_missing.csv,unknown,https://data.europa.eu/euodp/de/data/dataset/building-stock-observatory
Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment
Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unknown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
District heating missing countries,district_heat_share.csv,unknown,https://www.euroheat.org/knowledge-hub/country-profiles,,
Can't render this file because it has a wrong number of fields in line 28.

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@ -1,3 +1,4 @@
<<<<<<< HEAD
..
SPDX-FileCopyrightText: 2019-2023 The PyPSA-Eur Authors
@ -51,6 +52,100 @@ Transformation in Energy Systems <https:/www.ensys.tu-berlin.de>`_ at the
developed within the `IAI <http://www.iai.kit.edu>`_ at the `Karlsruhe Institute of
Technology (KIT) <http://www.kit.edu/english/index.php>`_ and by the `Renewable
Energy Group
=======
PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
=====================================================================================
.. image:: https://img.shields.io/github/v/release/pypsa/pypsa-eur-sec?include_prereleases
:alt: GitHub release (latest by date including pre-releases)
.. image:: https://readthedocs.org/projects/pypsa-eur-sec/badge/?version=latest
:target: https://pypsa-eur-sec.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status
.. image:: https://img.shields.io/github/license/pypsa/pypsa-eur-sec
:alt: GitHub
.. image:: https://img.shields.io/github/repo-size/pypsa/pypsa-eur-sec
:alt: GitHub repo size
PyPSA-Eur-Sec is an open model dataset of the European energy system at the
transmission network level that covers the full ENTSO-E area.
PyPSA-Eur-Sec builds on the electricity generation and transmission
model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ to add demand
and supply for the following sectors: transport, space and water
heating, biomass, energy consumption in the agriculture, industry
and industrial feedstocks, carbon management, carbon capture and usage/sequestration.
This completes the energy system and includes all greenhouse gas emitters except waste management, agriculture,
forestry and land use.
**WARNING**: PyPSA-Eur-Sec is under active development and has several
`limitations <https://pypsa-eur-sec.readthedocs.io/en/latest/limitations.html>`_ which
you should understand before using the model. The github repository
`issues <https://github.com/PyPSA/pypsa-eur-sec/issues>`_ collect known
topics we are working on (please feel free to help or make suggestions).
The `documentation <https://pypsa-eur-sec.readthedocs.io/>`_ remains somewhat
patchy.
We cannot support this model if you choose to use it.
.. note::
You can find showcases of the model's capabilities in the Supplementary Materials of the
preprint `Benefits of a Hydrogen Network in Europe
<https://arxiv.org/abs/2207.05816>`_, the Supplementary Materials of the `paper in Joule with a
description of the industry sector
<https://arxiv.org/abs/2109.09563>`_, or in `a 2021 presentation
at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_.
This diagram gives an overview of the sectors and the links between
them:
.. image:: ../graphics/multisector_figure.png
PyPSA-Eur-Sec was initially based on the model PyPSA-Eur-Sec-30 described
in the paper `Synergies of sector coupling and transmission
reinforcement in a cost-optimised, highly renewable European energy
system <https://arxiv.org/abs/1801.05290>`_ (2018) but it differs by
being based on the higher resolution electricity transmission model
`PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ rather than a
one-node-per-country model, and by including biomass, industry,
industrial feedstocks, aviation, shipping, better carbon management,
carbon capture and usage/sequestration, and gas networks.
PyPSA-Eur-Sec includes PyPSA-Eur as a
`snakemake <https://snakemake.readthedocs.io/en/stable/index.html>`_
`subworkflow <https://snakemake.readthedocs.io/en/stable/snakefiles/modularization.html#snakefiles-sub-workflows>`_. PyPSA-Eur-Sec
uses PyPSA-Eur to build the clustered transmission model along with
wind, solar PV and hydroelectricity potentials and time series. Then
PyPSA-Eur-Sec adds other conventional generators, storage units and
the additional sectors.
Currently the scripts to solve and process the resulting PyPSA models
are also included in PyPSA-Eur-Sec, although they could in future be
better integrated with the corresponding scripts in PyPSA-Eur. A
stumbling block to sharing solve_network.py between PyPSA-Eur and
PyPSA-Eur-Sec is the different extra_functionality required to build
storage and CHP constraints.
PyPSA-Eur-Sec is designed to be imported into the open toolbox `PyPSA
<https://www.pypsa.org>`_ for which `documentation <https://pypsa.org/doc>`_ is
available as well.
This project is currently maintained by the `Department of Digital
Transformation in Energy Systems <https://tub-ensys.github.io>`_ at the
`Technical University of Berlin <https://www.tu.berlin>`_. Previous versions
were developed by the `Energy System Modelling group
<https://www.iai.kit.edu/english/2338.php>`_ at the `Institute for Automation
and Applied Informatics <https://www.iai.kit.edu/english/index.php>`_ at the
`Karlsruhe Institute of Technology <http://www.kit.edu/english/index.php>`_
which was funded by the `Helmholtz Association <https://www.helmholtz.de/en/>`_,
and by the `Renewable Energy Group
>>>>>>> pypsa-eur-sec/master
<https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/>`_
at `FIAS <https://fias.uni-frankfurt.de/>`_ to carry out simulations for the
`CoNDyNet project <http://condynet.de/>`_, financed by the `German Federal
@ -58,23 +153,40 @@ Ministry for Education and Research (BMBF) <https://www.bmbf.de/en/index.html>`_
as part of the `Stromnetze Research Initiative
<http://forschung-stromnetze.info/projekte/grundlagen-und-konzepte-fuer-effiziente-dezentrale-stromnetze/>`_.
<<<<<<< HEAD
A version of the model that adds building heating, transport and industry sectors to the model,
as well as gas networks, is currently being developed in the `PyPSA-Eur-Sec repository <https://github.com/pypsa/pypsa-eur-sec>`_.
=======
Workflow Outline
================
.. image:: ../graphics/workflow.png
.. note::
The graph above was generated using
``snakemake --rulegraph -F | sed -n "/digraph/,/}/p" | dot -Tpng -o workflow.png``
>>>>>>> pypsa-eur-sec/master
Documentation
=============
**Getting Started**
<<<<<<< HEAD
* :doc:`introduction`
* :doc:`installation`
* :doc:`tutorial`
=======
* :doc:`installation`
>>>>>>> pypsa-eur-sec/master
.. toctree::
:hidden:
:maxdepth: 1
:caption: Getting Started
<<<<<<< HEAD
introduction
installation
tutorial
@ -84,10 +196,20 @@ Documentation
* :doc:`wildcards`
* :doc:`configuration`
* :doc:`costs`
=======
installation
**Implementation details**
* :doc:`spatial_resolution`
* :doc:`supply_demand`
* :doc:`technology_assumptions`
>>>>>>> pypsa-eur-sec/master
.. toctree::
:hidden:
:maxdepth: 1
<<<<<<< HEAD
:caption: Configuration
wildcards
@ -100,22 +222,46 @@ Documentation
* :doc:`simplification`
* :doc:`solving`
* :doc:`plotting`
=======
:caption: Implementation details
spatial_resolution
supply_demand
technology_assumptions
**Foresight options**
* :doc:`overnight`
* :doc:`myopic`
* :doc:`perfect`
>>>>>>> pypsa-eur-sec/master
.. toctree::
:hidden:
:maxdepth: 1
<<<<<<< HEAD
:caption: Rules Overview
preparation
simplification
solving
plotting
=======
:caption: Foresight options
overnight
myopic
perfect
>>>>>>> pypsa-eur-sec/master
**References**
* :doc:`release_notes`
* :doc:`limitations`
<<<<<<< HEAD
* :doc:`contributing`
=======
>>>>>>> pypsa-eur-sec/master
.. toctree::
:hidden:
@ -124,6 +270,7 @@ Documentation
release_notes
limitations
<<<<<<< HEAD
contributing
Warnings
@ -197,10 +344,14 @@ The included ``.nc`` files are PyPSA network files which can be imported with Py
filename = "elec_s_1024_ec.nc" # example
n = pypsa.Network(filename)
=======
>>>>>>> pypsa-eur-sec/master
Licence
=======
<<<<<<< HEAD
PyPSA-Eur work is released under multiple licenses:
* All original source code is licensed as free software under `MIT <LICENSES/MIT.txt>`_.
@ -221,3 +372,9 @@ More details are included in
* *BY: Attribute Source*
* *NC: Non-Commercial Use Only*
* *SA: Share Alike*
=======
The code in PyPSA-Eur-Sec is released as free software under the
`MIT license <https://opensource.org/licenses/MIT>`_, see
`LICENSE <https://github.com/PyPSA/pypsa-eur-sec/blob/master/LICENSE.txt>`_.
However, different licenses and terms of use may apply to the various input data.
>>>>>>> pypsa-eur-sec/master

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<<<<<<< HEAD
..
SPDX-FileCopyrightText: 2019-2023 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
=======
>>>>>>> pypsa-eur-sec/master
.. _installation:
##########################################
@ -12,6 +15,7 @@ Installation
The subsequently described installation steps are demonstrated as shell commands, where the path before the ``%`` sign denotes the
directory in which the commands following the ``%`` should be entered.
<<<<<<< HEAD
Clone the Repository
====================
@ -38,6 +42,47 @@ For instructions for your operating system follow the ``conda`` `installation gu
The python package requirements are curated in the `envs/environment.yaml <https://github.com/PyPSA/pypsa-eur/blob/master/envs/environment.yaml>`_ file.
The environment can be installed and activated using
=======
Install PyPSA-Eur and its data
==============================
First install `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ and all
its dependencies. Clone the repository:
.. code:: bash
projects % git clone https://github.com/PyPSA/pypsa-eur.git
then download and unpack all the PyPSA-Eur data files by running the following snakemake rule:
.. code:: bash
projects/pypsa-eur % snakemake -j 1 retrieve_databundle
Clone PyPSA-Eur-Sec repository
==============================
Create a parallel directory for `PyPSA-Eur-Sec <https://github.com/PyPSA/pypsa-eur-sec>`_ with:
.. code:: bash
projects % git clone https://github.com/PyPSA/pypsa-eur-sec.git
Environment/package requirements
================================
The requirements are the same as `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_. For
``solve_network.py`` in addition you need ``gurobipy``. If you have
xarray version >= 0.15.1, you will need the latest master branch of
atlite version 0.0.2.
You can create an environment using the environment.yaml file in pypsa-eur/envs:
>>>>>>> pypsa-eur-sec/master
.. code:: bash
@ -45,6 +90,7 @@ The environment can be installed and activated using
.../pypsa-eur % conda activate pypsa-eur
<<<<<<< HEAD
Note that activation is local to the currently open shell!
After opening a new terminal window, one needs to reissue the second command!
@ -127,3 +173,65 @@ Before first use, create a ``config.yaml`` by copying the example.
Users are advised to regularly check their own ``config.yaml`` against changes in the ``config.default.yaml``
when pulling a new version from the remote repository.
=======
See details in `PyPSA-Eur Installation <https://pypsa-eur.readthedocs.io/en/latest/installation.html>`_
Data requirements
=================
Small data files are included directly in the git repository, while
larger ones are archived in a data bundle on zenodo (`10.5281/zenodo.5824485 <https://doi.org/10.5281/zenodo.5824485>`_).
The data bundle's size is around 640 MB.
To download and extract the data bundle on the command line:
.. code:: bash
projects/pypsa-eur-sec/data % wget "https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz"
projects/pypsa-eur-sec/data % tar -xvzf pypsa-eur-sec-data-bundle.tar.gz
The data licences and sources are given in the following table.
.. csv-table::
:header-rows: 1
:file: data.csv
Set up the default configuration
================================
First make your own copy of the ``config.yaml`` based on
``config.default.yaml``. For example:
.. code:: bash
projects/pypsa-eur-sec % cp config.default.yaml config.yaml
Getting started
===============
In ``config.yaml`` you can control the settings for the scenarios you
want to run, such as the number of nodes, the CO2 limit, the
installable potentials for solar and wind, which technologies are
activated, etc.
To run the full optimization with your settings:
.. code:: bash
projects/pypsa-eur-sec % snakemake -j1
Warning: you may need a computer cluster for this (with e.g. 10-100 GB of RAM
and several processors).
To only prepare the networks, you can run the scripts up to the point before optimization:
.. code:: bash
projects/pypsa-eur-sec % snakemake -j1 prepare_sector_networks
>>>>>>> pypsa-eur-sec/master

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@ -1,21 +1,37 @@
<<<<<<< HEAD
..
SPDX-FileCopyrightText: 2019-2023 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
=======
>>>>>>> pypsa-eur-sec/master
##########################################
Limitations
##########################################
<<<<<<< HEAD
While the benefit of an openly available, functional and partially validated
model of the European transmission system is high, many approximations have
=======
While the benefit of an openly available, functional and partially validated
model of the European energy system is high, many approximations have
>>>>>>> pypsa-eur-sec/master
been made due to missing data.
The limitations of the dataset are listed below,
both as a warning to the user and as an encouragement to assist in
improving the approximations.
<<<<<<< HEAD
- **Network topology:**
=======
This list of limitations is incomplete and will be added to over time.
See also the `GitHub repository issues <https://github.com/PyPSA/pypsa-eur-sec/issues>`_.
- **Electricity transmission network topology:**
>>>>>>> pypsa-eur-sec/master
The grid data is based on a map of the ENTSO-E area that is known
to contain small distortions to improve readability. Since the exact impedances
of the lines are unknown, approximations based on line lengths and standard
@ -23,20 +39,44 @@ improving the approximations.
particular lines. There is no openly available data on busbar configurations, switch
locations, transformers or reactive power compensation assets.
<<<<<<< HEAD
- **Distribution networks:**
=======
- **Assignment of electricity demand to transmission nodes:**
>>>>>>> pypsa-eur-sec/master
Using Voronoi cells to aggregate load and generator data to transmission
network substations ignores the topology of the underlying distribution network,
meaning that assets may be connected to the wrong substation.
<<<<<<< HEAD
- **Power Demand:**
Assumptions
have been made about the distribution of load in each country proportional to
=======
- **Incomplete information on existing assets:** Approximations have
been made for missing data, including: existing distribution grid
capacities and costs, existing space and water heating supply,
existing industry facilities, existing transport vehicle fleets.
- **Exogenous pathways for transformation of transport and industry:**
To avoid penny-switching the transformation of transport and
industry away from fossil fuels is determined exogenously.
- **Industry materials production constant and inelastic:**
For industry, the production of different materials per country is
assumed to remain constant and no industry demand elasticity is included in the modelled.
- **Energy demand distribution within countries:**
Assumptions
have been made about the distribution of demand in each country proportional to
>>>>>>> pypsa-eur-sec/master
population and GDP that may not reflect local circumstances.
Openly available
data on load time series may not correspond to the true vertical load and is
not spatially disaggregated; assuming, as we have done, that the load time series
shape is the same at each node within each country ignores local differences.
<<<<<<< HEAD
- **Currently installed renewable capacities:**
Information on existing wind, solar and small hydro, geothermal, marine and
biomass power plants are excluded from the dataset because of a lack of data
@ -44,6 +84,8 @@ improving the approximations.
plants in each country can be generated that are proportional to the capacity
factor at each location.
=======
>>>>>>> pypsa-eur-sec/master
- **Hydro-electric power plants:**
The database of hydro-electric power plants does not include plant-specific
energy storage information, so that blanket values based on country storage
@ -56,3 +98,9 @@ improving the approximations.
Belarus, Ukraine, Turkey and Morocco have not been taken into account;
islands which are not connected to the main European system, such as Malta,
Crete and Cyprus, are also excluded from the model.
<<<<<<< HEAD
=======
- **Demand sufficiency:** Further measures of demand reduction may be
possible beyond the assumptions made here.
>>>>>>> pypsa-eur-sec/master

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@ -0,0 +1,139 @@
.. _myopic:
##########################################
Myopic transition path
##########################################
The myopic code can be used to investigate progressive changes in a network, for instance, those taking place throughout a transition path. The capacities installed in a certain time step are maintained in the network until their operational lifetime expires.
The myopic approach was initially developed and used in the paper `Early decarbonisation of the European Energy system pays off (2020) <https://www.nature.com/articles/s41467-020-20015-4>`__ and later further extended in `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__. The current implementation complies with the PyPSA-Eur-Sec standard working flow and is compatible with using the higher resolution electricity transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`__ rather than a one-node-per-country model.
The current code applies the myopic approach to generators, storage technologies and links in the power sector. It furthermore applies it to the space and water heating sector (e.g., the share of district heating and reduced space heat demand), industry processes (e.g., steel, direct reduced iron, and aluminum production via primary route), the share of fuel cell and battery electric vehicles in land transport, and the hydrogen share in shipping (see :doc:`supply_demand` for further information).
The following subjects within the land transport and biomass currently do not evolve with the myopic approach:
- The percentage of electric vehicles that allow demand-side management and vehicle-to-grid services.
- The annual biomass potential (default year and scenario for which potential is taken is 2030, defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L109>`_)
Configuration
=================
PyPSA-Eur-Sec has several configuration options which are collected in a config.yaml file located in the root directory. For myopic optimization, users should copy the provided default configuration ``config.default.yaml`` and make their own modifications and assumptions in the user-specific configuration file (``config.yaml``).
The following options included in the config.yaml file are relevant for the myopic code.
To activate the myopic option select ``foresight: 'myopic'`` in ``config.yaml``.
The {planning_horizons} wildcard indicates the year in which the network is optimized. For a myopic optimization, this is equivalent to the investment year. To set the investment years which are sequentially simulated for the myopic investment planning, select for example:
planning_horizons:
\- 2020
\- 2030
\- 2040
\- 2050
in ``config.yaml``.
**existing capacities**
Grouping years indicates the bins limits for grouping the existing capacities of different technologies. Note that separate bins are defined for the power and heating plants due to different data sources.
``grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]``
``grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]``
**threshold capacity**
If for a technology, node, and grouping bin, the capacity is lower than threshold_capacity, it is ignored.
``threshold_capacity: 10``
**conventional carriers**
Conventional carriers indicate carriers used in the existing conventional technologies.
conventional_carriers:
\- lignite
\- coal
\- oil
\- uranium
Options
=============
The total carbon budget for the entire transition path can be indicated in the `sector_opts <https://github.com/PyPSA/pypsa-eur-sec/blob/f13902510010b734c510c38c4cae99356f683058/config.default.yaml#L25>`_ in ``config.yaml``. The carbon budget can be split among the ``planning_horizons`` following an exponential or beta decay.
E.g. ``'cb40ex0'`` splits a carbon budget equal to 40 Gt :math:`_{CO_2}` following an exponential decay whose initial linear growth rate r is zero.
They can also follow some user-specified path, if defined `here <https://github.com/PyPSA/pypsa-eur-sec/blob/413254e241fb37f55b41caba7264644805ad8e97/config.default.yaml#L56>`_.
The paper `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__ defines CO_2 budgets corresponding to global temperature increases (1.5C – 2C) as response to the emissions. Here, global carbon budgets are converted to European budgets assuming equal-per capita distribution which translates into a 6.43% share for Europe. The carbon budgets are in this paper distributed throughout the transition paths assuming an exponential decay. Emissions e(t) in every year t are limited by
.. math::
e(t) = e_0 (1+ (r+m)t) e^{-mt}
where r is the initial linear growth rate, which here is assumed to be r=0, and the decay parameter m is determined by imposing the integral of the path to be equal to the budget for Europe. Following this approach, the CO_2 budget is defined. Following the same approach as in this paper, add the following to the ``scenario.sector_opts``
E.g. ``-cb25.7ex0`` (1.5C increase)
Or ``cb73.9ex0`` (2C increase).
See details in Supplemental Note S1 `Speed of technological transformations required in Europe to achieve different climate goals (2022) <https://doi.org/10.1016/j.joule.2022.04.016>`__.
General myopic code structure
===============================
The myopic code solves the network for the time steps included in ``planning_horizons`` in a recursive loop, so that:
1. The existing capacities (those installed before the base year are added as fixed capacities with p_nom=value, p_nom_extendable=False). E.g. for baseyear=2020, capacities installed before 2020 are added. In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``.
The base year is the first element in ``planning_horizons``. Step 1 is implemented with the rule add_baseyear for the base year and with the rule add_brownfield for the remaining planning_horizons.
2. The 2020 network is optimized. The solved network is saved in ``results/run_name/networks/postnetworks``
3. For the next planning horizon, e.g. 2030, the capacities from a previous time step are added if they are still in operation (i.e., if they fulfil planning horizon <= commissioned year + lifetime). In addition, the network comprises additional generator, storage, and link capacities with p_nom_extendable=True. The non-solved network is saved in ``results/run_name/networks/prenetworks-brownfield``.
Steps 2 and 3 are solved recursively for all the planning_horizons included in ``config.yaml``.
Rule overview
===============================
- rule add_existing baseyear
The rule add_existing_baseyear loads the network in ‘results/run_name/networks/prenetworks’ and performs the following operations:
1. Add the conventional, wind and solar power generators that were installed before the base year.
2. Add the heating capacities that were installed before the base year.
The existing conventional generators are retrieved from the `powerplants.csv file <https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__ generated by pypsa-eur which, in turn, is based on the `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`__ database.
Existing wind and solar capacities are retrieved from `IRENA annual statistics <https://www.irena.org/Statistics/Download-Data>`__ and distributed among the nodes in a country proportional to capacity factor. (This will be updated to include capacity distributions closer to reality.)
Existing heating capacities are retrieved from the report `Mapping and analyses of the current and future (2020 - 2030) heating/cooling fuel deployment (fossil/renewables)
<https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1>`__.
The heating capacities are assumed to have a lifetime indicated by the parameter lifetime in the configuration file, e.g 25 years. They are assumed to be decommissioned linearly starting on the base year, e.g., from 2020 to 2045.
Then, the resulting network is saved in ``results/run_name/networks/prenetworks-brownfield``.
- rule add_brownfield
The rule add_brownfield loads the network in ``results/run_name/networks/prenetworks`` and performs the following operation:
1. Read the capacities optimized in the previous time step and add them to the network if they are still in operation (i.e., if they fulfill planning horizon < commissioned year + lifetime)
Then, the resulting network is saved in ``results/run_name/networks/prenetworks_brownfield``.

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.. _overnight:
##########################################
Overnight (greenfield) scenarios
##########################################
The default is to calculate a rebuilding of the energy system to meet demand, a so-called overnight or greenfield approach.
For this, use ``foresight : 'overnight'`` in ``config.yaml``, like the example in ``config.default.yaml``.
In this case, the ``planning_horizons : [2030]`` scenario parameter can be set to use the year from which cost and other technology assumptions are set (forecasts for 2030 in this case).

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.. _perfect:
##########################################
Perfect foresight scenarios
##########################################
Perfect foresight is currently under development but it is not yet implemented.
For this, use ``foresight : 'perfect'`` in ``config.yaml``.

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@ -1,12 +1,16 @@
<<<<<<< HEAD
..
SPDX-FileCopyrightText: 2019-2023 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
=======
>>>>>>> pypsa-eur-sec/master
##########################################
Release Notes
##########################################
<<<<<<< HEAD
Upcoming Release
================
@ -601,10 +605,578 @@ This is the first release of PyPSA-Eur, a model of the European power system at
* Logfiles for all rules of the ``snakemake`` workflow are now written in the folder ``log/`` [`#102 <https://github.com/PyPSA/pypsa-eur/pull/102>`_].
* The new function ``_helpers.mock_snakemake`` creates a ``snakemake`` object which mimics the actual ``snakemake`` object produced by workflow by parsing the ``Snakefile`` and setting all paths for inputs, outputs, and logs. This allows running all scripts within a (I)python terminal (or just by calling ``python <script-name>``) and thereby facilitates developing and debugging scripts significantly [`#107 <https://github.com/PyPSA/pypsa-eur/pull/107>`_].
=======
Future release
==============
.. note::
This unreleased version currently may require the master branches of PyPSA, PyPSA-Eur, and the technology-data repository.
* new feature
PyPSA-Eur-Sec 0.7.0 (16th February 2023)
========================================
This release includes many new features. Highlights include new gas
infrastructure data with retrofitting options for hydrogen transport, improved
carbon management and infrastructure planning, regionalised potentials for
hydrogen underground storage and carbon sequestration, new applications for
biomass, and explicit modelling of methanol and ammonia as separate energy
carriers.
This release is known to work with `PyPSA-Eur
<https://github.com/PyPSA/pypsa-eur>`_ Version 0.7.0 and `Technology Data
<https://github.com/PyPSA/technology-data>`_ Version 0.5.0.
**Gas Transmission Network**
* New rule ``retrieve_gas_infrastructure_data`` that downloads and extracts the
SciGRID_gas `IGGIELGN <https://zenodo.org/record/4767098>`_ dataset from
zenodo. It includes data on the transmission routes, pipe diameters,
capacities, pressure, and whether the pipeline is bidirectional and carries
H-Gas or L-Gas.
* New rule ``build_gas_network`` processes and cleans the pipeline data from
SciGRID_gas. Missing or uncertain pipeline capacities can be inferred by
diameter.
* New rule ``build_gas_input_locations`` compiles the LNG import capacities
(from the Global Energy Monitor's `Europe Gas Tracker
<https://globalenergymonitor.org/projects/europe-gas-tracker/>`_, pipeline
entry capacities and local production capacities for each region of the model.
These are the regions where fossil gas can eventually enter the model.
* New rule ``cluster_gas_network`` that clusters the gas transmission network
data to the model resolution. Cross-regional pipeline capacities are
aggregated (while pressure and diameter compatibility is ignored),
intra-regional pipelines are dropped. Lengths are recalculated based on the
regions' centroids.
* With the option ``sector: gas_network:``, the existing gas network is added
with a lossless transport model. A length-weighted `k-edge augmentation
algorithm
<https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation>`_
can be run to add new candidate gas pipelines such that all regions of the
model can be connected to the gas network. The number of candidates can be
controlled via the setting ``sector: gas_network_connectivity_upgrade:``. When
the gas network is activated, all the gas demands are regionally disaggregated
as well.
* New constraint allows endogenous retrofitting of gas pipelines to hydrogen
pipelines. This option is activated via the setting ``sector: H2_retrofit:``.
For every unit of gas pipeline capacity dismantled, ``sector:
H2_retrofit_capacity_per_CH4`` units are made available as hydrogen pipeline
capacity in the corresponding corridor. These repurposed hydrogen pipelines
have lower costs than new hydrogen pipelines. Both new and repurposed
pipelines can be built simultaneously. The retrofitting option ``sector:
H2_retrofit:`` also works with a copperplated methane infrastructure, i.e.
when ``sector: gas_network: false``.
* New hydrogen pipelines can now be built where there are already power or gas
transmission routes. Previously, only the electricity transmission routes were
considered.
**Carbon Management and Biomass**
* Add option to spatially resolve carrier representing stored carbon dioxide
(``co2_spatial``). This allows for more detailed modelling of CCUTS, e.g.
regarding the capturing of industrial process emissions, usage as feedstock
for electrofuels, transport of carbon dioxide, and geological sequestration
sites.
* Add option for regionally-resolved geological carbon dioxide sequestration
potentials through new rule ``build_sequestration_potentials`` based on
`CO2StoP <https://setis.ec.europa.eu/european-co2-storage-database_en>`_. This
can be controlled in the section ``regional_co2_sequestration_potential`` of
the ``config.yaml``. It includes options to select the level of conservatism,
whether onshore potentials should be included, the respective upper and lower
limits per region, and an annualisation parameter for the cumulative
potential. The defaults are preliminary and will be validated the next
release.
* Add option to sweep the global CO2 sequestration potentials with keyword
``seq200`` in the ``{sector_opts}`` wildcard (for limit of 200 Mt CO2).
* Add option to include `Allam cycle gas power plants
<https://en.wikipedia.org/wiki/Allam_power_cycle>`_ (``allam_cycle``).
* Add option for planning a new carbon dioxide network (``co2network``).
* Separate option to regionally resolve biomass (``biomass_spatial``) from
option to allow biomass transport (``biomass_transport``).
* Add option for biomass boilers (wood pellets) for decentral heating.
* Add option for BioSNG (methane from biomass) with and without carbon capture.
* Add option for BtL (biomass to liquid fuel/oil) with and without carbon
capture.
**Other new features**
* Add regionalised hydrogen salt cavern storage potentials from `Technical
Potential of Salt Caverns for Hydrogen Storage in Europe
<https://doi.org/10.20944/preprints201910.0187.v1>`_. This data is compiled in
a new rule ``build_salt_cavern_potentials``.
* Add option to resolve ammonia as separate energy carrier with Haber-Bosch
synthesis, ammonia cracking, storage and industrial demand. The ammonia
carrier can be nodally resolved or copperplated across Europe (see
``ammonia``).
* Add methanol as energy carrier, methanolisation as process, and option for
methanol demand in shipping sector.
* Shipping demand now defaults to methanol rather than liquefied hydrogen
until 2050.
* Demand for liquid hydrogen in international shipping is now geographically
distributed by port trade volumes in a new rule ``build_shipping_demand``
using data from the `World Bank Data Catalogue
<https://datacatalog.worldbank.org/search/dataset/0038118/Global---International-Ports>`_.
Domestic shipping remains distributed by population.
* Add option to aggregate network temporally using representative snapshots or
segments (with `tsam <https://github.com/FZJ-IEK3-VSA/tsam>`_).
* Add option for minimum part load for Fischer-Tropsch plants (default: 90%) and
methanolisation plants (default: 50%).
* Add option to use waste heat of electrolysis in district heating networks
(``use_electrolysis_waste_heat``).
* Add option for coal CHPs with carbon capture (see ``coal_cc``).
* In overnight optimisation, it is now possible to specify a year for the
technology cost projections separate from the planning horizon.
* New config options for changing energy demands in aviation
(``aviation_demand_factor``) and HVC industry (``HVC_demand_factor``), as well
as explicit ICE shares for land transport (``land_transport_ice_share``) and
agriculture machinery (``agriculture_machinery_oil_share``).
* It is now possible to merge residential and services heat buses to reduce the
problem size (see ``cluster_heat_nodes``).
* Added option to tweak (almost) any configuration parameter through the
``{sector_opts}`` wildcard. The regional_co2_sequestration_potential is
triggered by the prefix ``CF+`` after which it is possible to pipe to any
setting that does not contain underscores (``_``). Example:
``CF+sector+v2g+false`` disables vehicle-to-grid flexibility.
* Option ``retrieve_sector_databundle`` to automatically retrieve and extract
data bundle.
* Removed the need to clone ``technology-data`` repository in a parallel
directory. The new approach automatically retrieves the technology data from
remote in the rule ``retrieve_cost_data``.
* Improved network plots including better legends, hydrogen retrofitting network
display, and change to EqualEarth projection. A new color scheme for
technologies was also introduced.
* Add two new rules ``build_transport_demand`` and
``build_population_weighted_energy_totals`` using code previously contained in
``prepare_sector_network``.
* Rules that convert weather data with ``atlite`` now largely run separately for
categories residential, rural and total.
* Units are assigned to the buses. These only provide a better understanding.
The specifications of the units are not taken into account in the
optimisation, which means that no automatic conversion of units takes place.
* Configuration file and wildcards are now stored under ``n.meta`` in every
PyPSA network.
* Updated `data bundle
<https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_
that includes the hydrogan salt cavern storage potentials.
* Updated and extended documentation in
<https://pypsa-eur-sec.readthedocs.io/en/latest/>
* Added new rule ``copy_conda_env`` that exports a list of packages with which
the workflow was executed.
* Add basic continuous integration using Github Actions.
* Add basic ``rsync`` setup.
**Bugfixes**
* The CO2 sequestration limit implemented as GlobalConstraint (introduced in the
previous version) caused a failure to read in the shadow prices of other
global constraints.
* Correct capital cost of Fischer-Tropsch according to new units in
``technology-data`` repository.
* Fix unit conversion error for thermal energy storage.
* For myopic pathway optimisation, set optimised capacities of power grid
expansion of previous iteration as minimum capacity for next iteration.
* Further rather minor bugfixes for myopic optimisation code (see `#256
<https://github.com/PyPSA/pypsa-eur-sec/pull/256>`_).
Many thanks to all who contributed to this release!
PyPSA-Eur-Sec 0.6.0 (4 October 2021)
====================================
This release includes
improvements regarding the basic chemical production,
the addition of plastics recycling,
the addition of the agriculture, forestry and fishing sector,
more regionally resolved biomass potentials,
CO2 pipeline transport and storage, and
more options in setting exogenous transition paths,
besides many performance improvements.
This release is known to work with `PyPSA-Eur
<https://github.com/PyPSA/pypsa-eur>`_ Version 0.4.0, `Technology Data
<https://github.com/PyPSA/technology-data>`_ Version 0.3.0 and
`PyPSA <https://github.com/PyPSA/PyPSA>`_ Version 0.18.0.
Please note that the data bundle has also been updated.
**General**
* With this release, we change the license from copyleft GPLv3 to the more
liberal MIT license with the consent of all contributors.
**New features and functionality**
* Distinguish costs for home battery storage and inverter from utility-scale
battery costs.
* Separate basic chemicals into HVC (high-value chemicals), chlorine, methanol and ammonia
[`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
* Add option to specify reuse, primary production, and mechanical and chemical
recycling fraction of platics
[`#166 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/166>`_].
* Include energy demands and CO2 emissions for the agriculture, forestry and fishing sector.
It is included by default through the option ``A`` in the ``sector_opts`` wildcard.
Part of the emissions (1.A.4.c) was previously assigned to "industry non-elec" in the ``co2_totals.csv``.
Hence, excluding the agriculture sector will now lead to a tighter CO2 limit.
Energy demands are taken from the JRC IDEES database (missing countries filled with eurostat data)
and are split into
electricity (lighting, ventilation, specific electricity uses, pumping devices (electric)),
heat (specific heat uses, low enthalpy heat)
machinery oil (motor drives, farming machine drives, pumping devices (diesel)).
Heat demand is assigned at "services rural heat" buses.
Electricity demands are added to low-voltage buses.
Time series for demands are constant and distributed inside countries by population
[`#147 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/147>`_].
* Include today's district heating shares in myopic optimisation and add option
to specify exogenous path for district heating share increase under ``sector:
district_heating:`` [`#149 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/149>`_].
* Added option for hydrogen liquefaction costs for hydrogen demand in shipping.
This introduces a new ``H2 liquid`` bus at each location. It is activated via
``sector: shipping_hydrogen_liquefaction: true``.
* The share of shipping transformed into hydrogen fuel cell can be now defined
for different years in the ``config.yaml`` file. The carbon emission from the
remaining share is treated as a negative load on the atmospheric carbon dioxide
bus, just like aviation and land transport emissions.
* The transformation of the Steel and Aluminium production can be now defined
for different years in the ``config.yaml`` file.
* Include the option to alter the maximum energy capacity of a store via the
``carrier+factor`` in the ``{sector_opts}`` wildcard. This can be useful for
sensitivity analyses. Example: ``co2 stored+e2`` multiplies the ``e_nom_max`` by
factor 2. In this example, ``e_nom_max`` represents the CO2 sequestration
potential in Europe.
* Use `JRC ENSPRESO database <https://data.jrc.ec.europa.eu/dataset/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ to
spatially disaggregate biomass potentials to PyPSA-Eur regions based on
overlaps with NUTS2 regions from ENSPRESO (proportional to area) (`#151
<https://github.com/PyPSA/pypsa-eur-sec/pull/151>`_).
* Add option to regionally disaggregate biomass potential to individual nodes
(previously given per country, then distributed by population density within)
and allow the transport of solid biomass. The transport costs are determined
based on the `JRC-EU-Times Bioenergy report
<http://dx.doi.org/10.2790/01017>`_ in the new optional rule
``build_biomass_transport_costs``. Biomass transport can be activated with the
setting ``sector: biomass_transport: true``.
* Add option to regionally resolve CO2 storage and add CO2 pipeline transport
because geological storage potential,
CO2 utilisation sites and CO2 capture sites may be separated. The CO2 network
is built from zero based on the topology of the electricity grid (greenfield).
Pipelines are assumed to be bidirectional and lossless. Furthermore, neither
retrofitting of natural gas pipelines (required pressures are too high, 80-160
bar vs <80 bar) nor other modes of CO2 transport (by ship, road or rail) are
considered. The regional representation of CO2 is activated with the config
setting ``sector: co2_network: true`` but is deactivated by default. The
global limit for CO2 sequestration now applies to the sum of all CO2 stores
via an ``extra_functionality`` constraint.
* The myopic option can now be used together with different clustering for the
generators and the network. The existing renewable capacities are split evenly
among the regions in every country [`#144 <https://github.com/PyPSA/PyPSA-Eur-Sec/pull/144>`_].
* Add optional function to use ``geopy`` to locate entries of the Hotmaps
database of industrial sites with missing location based on city and country,
which reduces missing entries by half. It can be activated by setting
``industry: hotmaps_locate_missing: true``, takes a few minutes longer, and
should only be used if spatial resolution is coarser than city level.
**Performance and Structure**
* Extended use of ``multiprocessing`` for much better performance
(from up to 20 minutes to less than one minute).
* Handle most input files (or base directories) via ``snakemake.input``.
* Use of ``mock_snakemake`` from PyPSA-Eur.
* Update ``solve_network`` rule to match implementation in PyPSA-Eur by using
``n.ilopf()`` and remove outdated code using ``pyomo``.
Allows the new setting to skip iterated impedance updates with ``solving:
options: skip_iterations: true``.
* The component attributes that are to be overridden are now stored in the folder
``data/override_component_attrs`` analogous to ``pypsa/component_attrs``.
This reduces verbosity and also allows circumventing the ``n.madd()`` hack
for individual components with non-default attributes.
This data is also tracked in the Snakefile.
A function ``helper.override_component_attrs`` was added that loads this data
and can pass the overridden component attributes into ``pypsa.Network()``.
* Add various parameters to ``config.default.yaml`` which were previously hardcoded inside the scripts
(e.g. energy reference years, BEV settings, solar thermal collector models, geomap colours).
* Removed stale industry demand rules ``build_industrial_energy_demand_per_country``
and ``build_industrial_demand``. These are superseded with more regionally resolved rules.
* Use simpler and shorter ``gdf.sjoin()`` function to allocate industrial sites
from the Hotmaps database to onshore regions.
This change also fixes a bug:
The previous version allocated sites to the closest bus,
but at country borders (where Voronoi cells are distorted by the borders),
this had resulted in e.g. a Spanish site close to the French border
being wrongly allocated to the French bus if the bus center was closer.
* Retrofitting rule is now only triggered if endogeneously optimised.
* Show progress in build rules with ``tqdm`` progress bars.
* Reduced verbosity of ``Snakefile`` through directory prefixes.
* Improve legibility of ``config.default.yaml`` and remove unused options.
* Use the country-specific time zone mappings from ``pytz`` rather than a manual mapping.
* A function ``add_carrier_buses()`` was added to the ``prepare_network`` rule to reduce code duplication.
* In the ``prepare_network`` rule the cost and potential adjustment was moved into an
own function ``maybe_adjust_costs_and_potentials()``.
* Use ``matplotlibrc`` to set the default plotting style and backend.
* Added benchmark files for each rule.
* Consistent use of ``__main__`` block and further unspecific code cleaning.
* Updated data bundle and moved data bundle to zenodo.org (`10.5281/zenodo.5546517 <https://doi.org/10.5281/zenodo.5546517>`_).
**Bugfixes and Compatibility**
* Compatibility with ``atlite>=0.2``. Older versions of ``atlite`` will no longer work.
* Corrected calculation of "gas for industry" carbon capture efficiency.
* Implemented changes to ``n.snapshot_weightings`` in PyPSA v0.18.0.
* Compatibility with ``xarray`` version 0.19.
* New dependencies: ``tqdm``, ``atlite>=0.2.4``, ``pytz`` and ``geopy`` (optional).
These are included in the environment specifications of PyPSA-Eur v0.4.0.
Many thanks to all who contributed to this release!
PyPSA-Eur-Sec 0.5.0 (21st May 2021)
===================================
This release includes improvements to the cost database for building retrofits, carbon budget management and wildcard settings, as well as an important bugfix for the emissions from land transport.
This release is known to work with `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ Version 0.3.0 and `Technology Data <https://github.com/PyPSA/technology-data>`_ Version 0.2.0.
Please note that the data bundle has also been updated.
New features and bugfixes:
* The cost database for retrofitting of the thermal envelope of buildings has been updated. Now, for calculating the space heat savings of a building, losses by thermal bridges and ventilation are included as well as heat gains (internal and by solar radiation). See the section :ref:`retro` for more details on the retrofitting module.
* For the myopic investment option, a carbon budget and a type of decay (exponential or beta) can be selected in the ``config.yaml`` file to distribute the budget across the ``planning_horizons``. For example, ``cb40ex0`` in the ``{sector_opts}`` wildcard will distribute a carbon budget of 40 GtCO2 following an exponential decay with initial growth rate 0.
* Added an option to alter the capital cost or maximum capacity of carriers by a factor via ``carrier+factor`` in the ``{sector_opts}`` wildcard. This can be useful for exploring uncertain cost parameters. Example: ``solar+c0.5`` reduces the ``capital_cost`` of solar to 50\% of original values. Similarly ``solar+p3`` multiplies the ``p_nom_max`` by 3.
* Rename the bus for European liquid hydrocarbons from ``Fischer-Tropsch`` to ``EU oil``, since it can be supplied not just with the Fischer-Tropsch process, but also with fossil oil.
* Bugfix: The new separation of land transport by carrier in Version 0.4.0 failed to account for the carbon dioxide emissions from internal combustion engines in land transport. This is now treated as a negative load on the atmospheric carbon dioxide bus, just like aviation emissions.
* Bugfix: Fix reading in of ``pypsa-eur/resources/powerplants.csv`` to PyPSA-Eur Version 0.3.0 (use column attribute name ``DateIn`` instead of old ``YearDecommissioned``).
* Bugfix: Make sure that ``Store`` components (battery and H2) are also removed from PyPSA-Eur, so they can be added later by PyPSA-Eur-Sec.
Thanks to Lisa Zeyen (KIT) for the retrofitting improvements and Marta Victoria (Aarhus University) for the carbon budget and wildcard management.
PyPSA-Eur-Sec 0.4.0 (11th December 2020)
=========================================
This release includes a more accurate nodal disaggregation of industry demand within each country, fixes to CHP and CCS representations, as well as changes to some configuration settings.
It has been released to coincide with `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ Version 0.3.0 and `Technology Data <https://github.com/PyPSA/technology-data>`_ Version 0.2.0, and is known to work with these releases.
New features:
* The `Hotmaps Industrial Database <https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database>`_ is used to disaggregate the industrial demand spatially to the nodes inside each country (previously it was distributed by population density).
* Electricity demand from industry is now separated from the regular electricity demand and distributed according to the industry demand. Only the remaining regular electricity demand for households and services is distributed according to GDP and population.
* A cost database for the retrofitting of the thermal envelope of residential and services buildings has been integrated, as well as endogenous optimisation of the level of retrofitting. This is described in the paper `Mitigating heat demand peaks in buildings in a highly renewable European energy system <https://arxiv.org/abs/2012.01831>`_. Retrofitting can be activated both exogenously and endogenously from the ``config.yaml``.
* The biomass and gas combined heat and power (CHP) parameters ``c_v`` and ``c_b`` were read in assuming they were extraction plants rather than back pressure plants. The data is now corrected in `Technology Data <https://github.com/PyPSA/technology-data>`_ Version 0.2.0 to the correct DEA back pressure assumptions and they are now implemented as single links with a fixed ratio of electricity to heat output (even as extraction plants, they were always sitting on the backpressure line in simulations, so there was no point in modelling the full heat-electricity feasibility polygon). The old assumptions underestimated the heat output.
* The Danish Energy Agency released `new assumptions for carbon capture <https://ens.dk/en/our-services/projections-and-models/technology-data/technology-data-industrial-process-heat-and>`_ in October 2020, which have now been incorporated in PyPSA-Eur-Sec, including direct air capture (DAC) and post-combustion capture on CHPs, cement kilns and other industrial facilities. The electricity and heat demand for DAC is modelled for each node (with heat coming from district heating), but currently the electricity and heat demand for industrial capture is not modelled very cleanly (for process heat, 10% of the energy is assumed to go to carbon capture) - a new issue will be opened on this.
* Land transport is separated by energy carrier (fossil, hydrogen fuel cell electric vehicle, and electric vehicle), but still needs to be separated into heavy and light vehicles (the data is there, just not the code yet).
* For assumptions that change with the investment year, there is a new time-dependent format in the ``config.yaml`` using a dictionary with keys for each year. Implemented examples include the CO2 budget, exogenous retrofitting share and land transport energy carrier; more parameters will be dynamised like this in future.
* Some assumptions have been moved out of the code and into the ``config.yaml``, including the carbon sequestration potential and cost, the heat pump sink temperature, reductions in demand for high value chemicals, and some BEV DSM parameters and transport efficiencies.
* Documentation on :doc:`supply_demand` options has been added.
Many thanks to Fraunhofer ISI for opening the hotmaps database and to Lisa Zeyen (KIT) for implementing the building retrofitting.
PyPSA-Eur-Sec 0.3.0 (27th September 2020)
=========================================
This releases focuses on improvements to industry demand and the generation of intermediate files for demand for basic materials. There are still inconsistencies with CCS and waste management that need to be improved.
It is known to work with PyPSA-Eur v0.1.0 (commit bb3477cd69), PyPSA v0.17.1 and technology-data v0.1.0. Please note that the data bundle has also been updated.
New features:
* In previous version of PyPSA-Eur-Sec the energy demand for industry was calculated directly for each location. Now, instead, the production of each material (steel, cement, aluminium) at each location is calculated as an intermediate data file, before the energy demand is calculated from it. This allows us in future to have competing industrial processes for supplying the same material demand.
* The script ``build_industrial_production_per_country_tomorrow.py`` determines the future industrial production of materials based on today's levels as well as assumed recycling and demand change measures.
* The energy demand for each industry sector and each location in 2015 is also calculated, so that it can be later incorporated in the pathway optimization.
* Ammonia production data is taken from the USGS and deducted from JRC-IDEES's "basic chemicals" so that it ammonia can be handled separately from the others (olefins, aromatics and chlorine).
* Solid biomass is no longer allowed to be used for process heat in cement and basic chemicals, since the wastes and residues cannot be guaranteed to reach the high temperatures required. Instead, solid biomass is used in the paper and pulp as well as food, beverages and tobacco industries, where required temperatures are lower (see `DOI:10.1002/er.3436 <https://doi.org/10.1002/er.3436>`_ and `DOI:10.1007/s12053-017-9571-y <https://doi.org/10.1007/s12053-017-9571-y>`_).
* National installable potentials for salt caverns are now applied.
* When electricity distribution grids are activated, new industry electricity demand, resistive heaters and micro-CHPs are now connected to the lower voltage levels.
* Gas distribution grid costs are included for gas boilers and micro-CHPs.
* Installable potentials for rooftop PV are included with an assumption of 1 kWp per person.
* Some intermediate files produced by scripts have been moved from the folder ``data`` to the folder ``resources``. Now ``data`` only includes input data, while ``resources`` only includes intermediate files necessary for building the network models. Please note that the data bundle has also been updated.
* Biomass potentials for different years and scenarios from the JRC are generated in an intermediate file, so that a selection can be made more explicitly by specifying the biomass types from the ``config.yaml``.
PyPSA-Eur-Sec 0.2.0 (21st August 2020)
======================================
This release introduces pathway optimization over many years (e.g. 2020, 2030, 2040, 2050) with myopic foresight, as well as outsourcing the technology assumptions to the `technology-data <https://github.com/PyPSA/technology-data>`_ repository.
It is known to work with PyPSA-Eur v0.1.0 (commit bb3477cd69), PyPSA v0.17.1 and technology-data v0.1.0.
New features:
* Option for pathway optimization with myopic foresight, based on the paper `Early decarbonisation of the European Energy system pays off (2020) <https://arxiv.org/abs/2004.11009>`_. Investments are optimized sequentially for multiple years (e.g. 2020, 2030, 2040, 2050) taking account of existing assets built in previous years and their lifetimes. The script uses data on the existing assets for electricity and building heating technologies, but there are no assumptions yet for existing transport and industry (if you include these, the model will greenfield them). There are also some `outstanding issues <https://github.com/PyPSA/pypsa-eur-sec/issues/19#issuecomment-678194802>`_ on e.g. the distribution of existing wind, solar and heating technologies within each country. To use myopic foresight, set ``foresight : 'myopic'`` in the ``config.yaml`` instead of the default ``foresight : 'overnight'``. An example configuration can be found in ``config.myopic.yaml``. More details on the implementation can be found in :doc:`myopic`.
* Technology assumptions (costs, efficiencies, etc.) are no longer stored in the repository. Instead, you have to install the `technology-data <https://github.com/PyPSA/technology-data>`_ database in a parallel directory. These assumptions are largely based on the `Danish Energy Agency Technology Data <https://ens.dk/en/our-services/projections-and-models/technology-data>`_. More details on the installation can be found in :doc:`installation`.
* Logs and benchmarks are now stored with the other model outputs in ``results/run-name/``.
* All buses now have a ``location`` attribute, e.g. bus ``DE0 3 urban central heat`` has a ``location`` of ``DE0 3``.
* All assets have a ``lifetime`` attribute (integer in years). For the myopic foresight, a ``build_year`` attribute is also stored.
* Costs for solar and onshore and offshore wind are recalculated by PyPSA-Eur-Sec based on the investment year, including the AC or DC connection costs for offshore wind.
Many thanks to Marta Victoria for implementing the myopic foresight, and Marta Victoria, Kun Zhu and Lisa Zeyen for developing the technology assumptions database.
PyPSA-Eur-Sec 0.1.0 (8th July 2020)
===================================
This is the first proper release of PyPSA-Eur-Sec, a model of the European energy system at the transmission network level that covers the full ENTSO-E area.
It is known to work with PyPSA-Eur v0.1.0 (commit bb3477cd69) and PyPSA v0.17.0.
We are making this release since in version 0.2.0 we will introduce changes to allow myopic investment planning that will require minor changes for users of the overnight investment planning.
PyPSA-Eur-Sec builds on the electricity generation and transmission
model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ to add demand
and supply for the following sectors: transport, space and water
heating, biomass, industry and industrial feedstocks. This completes
the energy system and includes all greenhouse gas emitters except
waste management, agriculture, forestry and land use.
PyPSA-Eur-Sec was initially based on the model PyPSA-Eur-Sec-30 (Version 0.0.1 below) described
in the paper `Synergies of sector coupling and transmission
reinforcement in a cost-optimised, highly renewable European energy
system <https://arxiv.org/abs/1801.05290>`_ (2018) but it differs by
being based on the higher resolution electricity transmission model
`PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_ rather than a
one-node-per-country model, and by including biomass, industry,
industrial feedstocks, aviation, shipping, better carbon management,
carbon capture and usage/sequestration, and gas networks.
PyPSA-Eur-Sec includes PyPSA-Eur as a
`snakemake <https://snakemake.readthedocs.io/en/stable/index.html>`_
`subworkflow <https://snakemake.readthedocs.io/en/stable/snakefiles/modularization.html#snakefiles-sub-workflows>`_. PyPSA-Eur-Sec
uses PyPSA-Eur to build the clustered transmission model along with
wind, solar PV and hydroelectricity potentials and time series. Then
PyPSA-Eur-Sec adds other conventional generators, storage units and
the additional sectors.
PyPSA-Eur-Sec 0.0.2 (4th September 2020)
========================================
This version, also called PyPSA-Eur-Sec-30-Path, built on
PyPSA-Eur-Sec 0.0.1 (also called PyPSA-Eur-Sec-30) to include myopic
pathway optimisation for the paper `Early decarbonisation of the
European energy system pays off <https://arxiv.org/abs/2004.11009>`_
(2020). The myopic pathway optimisation was then merged into the main
PyPSA-Eur-Sec codebase in Version 0.2.0 above.
This model has `its own github repository
<https://github.com/martavp/pypsa-eur-sec-30-path>`_ and is `archived
on Zenodo <https://zenodo.org/record/4014807>`_.
PyPSA-Eur-Sec 0.0.1 (12th January 2018)
========================================
This is the first published version of PyPSA-Eur-Sec, also called
PyPSA-Eur-Sec-30. It was first used in the research paper `Synergies of
sector coupling and transmission reinforcement in a cost-optimised,
highly renewable European energy system
<https://arxiv.org/abs/1801.05290>`_ (2018). The model covers 30
European countries with one node per country. It includes demand and
supply for electricity, space and water heating in buildings, and land
transport.
It is `archived on Zenodo <https://zenodo.org/record/1146666>`_.
>>>>>>> pypsa-eur-sec/master
Release Process
===============
<<<<<<< HEAD
* Checkout a new release branch ``git checkout -b release-v0.x.x``.
* Finalise release notes at ``doc/release_notes.rst``.
@ -627,3 +1199,22 @@ Release Process
* Upload pre-built networks to `zenodo data repository <https://doi.org/10.5281/zenodo.3601881>`_ with `CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>`_ license.
* Send announcement on the `PyPSA and PyPSA-Eur mailing list <https://groups.google.com/forum/#!forum/pypsa>`_.
=======
* Finalise release notes at ``doc/release_notes.rst``.
* Update version number in ``doc/conf.py`` and ``*config.*.yaml``.
* Make a ``git commit``.
* Tag a release by running ``git tag v0.x.x``, ``git push``, ``git push --tags``. Include release notes in the tag message.
* Make a `GitHub release <https://github.com/PyPSA/pypsa-eur-sec/releases>`_, which automatically triggers archiving by `zenodo <https://doi.org/10.5281/zenodo.3938042>`_.
* Send announcement on the `PyPSA mailing list <https://groups.google.com/forum/#!forum/pypsa>`_.
To make a new release of the data bundle, make an archive of the files in ``data`` which are not already included in the git repository:
.. code:: bash
data % tar pczf pypsa-eur-sec-data-bundle.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv retro/tabula-calculator-calcsetbuilding.csv nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson h2_salt_caverns_GWh_per_sqkm.geojson
>>>>>>> pypsa-eur-sec/master

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.. _spatial_resolution:
##########################################
Spatial resolution
##########################################
The default nodal resolution of the model follows the electricity generation and transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_, which clusters down the electricity transmission substations in each European country based on the k-means algorithm (See `cluster_network <https://pypsa-eur.readthedocs.io/en/latest/simplification/cluster_network.html#rule-cluster-network>`_ for a complete explanation). This gives nodes which correspond to major load and generation centres (typically cities).
The total number of nodes for Europe is set in the ``config.yaml`` file under ``clusters``. The number of nodes can vary between 37, the number of independent countries / synchronous areas, and several hundred. With 200-300 nodes the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
Exemplary unsolved network clustered to 512 nodes:
.. image:: ../graphics/elec_s_512.png
Exemplary unsolved network clustered to 37 nodes:
.. image:: ../graphics/elec_s_37.png
The total number of nodes for Europe is set in the config.yaml file under `clusters <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L20>`_. The number of nodes can vary between 37, the number of independent countries/synchronous areas, and several hundred. With 200-300 nodes, the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
Not all of the sectors are at the full nodal resolution, and some demand for some sectors is distributed to nodes using heuristics that need to be corrected. Some networks are copper-plated to reduce computational times.
Here are some examples of how spatial resolution is set for different sectors in PyPSA-Eur-Sec:
• Electricity network: Modeled as nodal.
• Electricity residential and commercial demand: Modeled as nodal, distributed in each country based on population and GDP.
• Electricity distribution network: Not included in the model, but a link per node can be used to represent energy transferred between distribution and transmission levels (explained more in detail below).
• Residential and commercial building heating demand: Modeled as nodal, distributed in each country based on population.
• Electricity demand in industry: Modeled as nodal, based on the location of industrial facilities from HotMaps database.
• Industry demand (heat, chemicals, etc.) : Modeled as nodal, distributed in each country based on locations of industry from HotMaps database.
• Hydrogen network: Modeled as nodal (if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L260>`_ file).
• Methane network: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_. One node can be considered reasonable since future demand is expected to be low and no bottlenecks are expected. Also, the nodally resolved methane grid is based on SciGRID_gas data.
• Solid biomass: It can be modeled as a single node for Europe or it can be nodally resolved if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. Nodal modeling includes modeling biomass potential per country (given per country, then distributed by population density within) and the transport of solid biomass between countries.
• CO2: It can be modeled as a single node for Europe or it can be nodally resolved with CO2 transport pipelines if activated in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_. It should mentioned that in single node mode a transport and storage cost is added for sequestered CO2, the cost of which can be adjusted in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
• Liquid hydrocarbons: Modeled as a single node for Europe, since transport costs for liquids are low and no bottlenecks are expected.
**Electricity distribution network**
Contrary to the transmission grid, the grid topology at the distribution level (at and below 110 kV) is not included due to the very high computational burden. However, a link per node can be used (if activated in the `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L257>`_ file) to represent energy transferred between distribution and transmission levels at every node. In essence, the total energy capacity connecting the transmission grid and the low-voltage level is optimized. The cost assumptions for this link can be adjusted in Config file `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L258>`_ , and is currently assumed to be 500 Eur/kW.
Rooftop PV, heat pumps, resistive heater, home batteries chargers for passenger EVs, as well as individual heating technologies (heat pumps and resistive heaters) are connected to low-voltage level. All the remaining generation and storage technologies are connected to the transmission grid. In practice, this means that the distribution grid capacity is only extended if it is necessary to balance the mismatch between local generation and demand.

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##########################################
Supply and demand
##########################################
An initial orientation to the supply and demand options in the model
PyPSA-Eur-Sec can be found in the description of the model
PyPSA-Eur-Sec-30 in the paper `Synergies of sector coupling and
transmission reinforcement in a cost-optimised, highly renewable
European energy system <https://arxiv.org/abs/1801.05290>`_ (2018).
The latest version of PyPSA-Eur-Sec differs by including biomass,
industry, industrial feedstocks, aviation, shipping, better carbon
management, carbon capture and usage/sequestration, and gas networks.
The basic supply (left column) and demand (right column) options in the model are described in this figure:
.. image:: ../graphics/multisector_figure.png
.. _Electricity supply and demand:
Electricity supply and demand
=============================
Electricity supply and demand follows the electricity generation and
transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_,
except that hydrogen storage is integrated into the hydrogen supply,
demand and network, and PyPSA-Eur-Sec includes CHPs.
Unlike PyPSA-Eur, PyPSA-Eur-Sec does not distribution electricity demand for industry according to population and GDP, but uses the
geographical data from the `Hotmaps Industrial Database
<https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database>`_.
Also unlike PyPSA-Eur, PyPSA-Eur-Sec subtracts existing electrified heating from the existing electricity demand, so that power-to-heat can be optimised separately.
The remaining electricity demand for households and services is distributed inside each country proportional to GDP and population.
.. _Heat demand:
Heat demand
===========
Building heating in residential and services sectors is resolved regionally, both for individual buildings and district heating systems, which include different supply options (see :ref:`heat-supply`.)
Annual heat demands per country are retrieved from `JRC-IDEES <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ and split into space and water heating. For space heating, the annual demands are converted to daily values based on the population-weighted Heating Degree Day (HDD) using the `atlite tool <https://github.com/PyPSA/atlite>`_, where space heat demand is proportional to the difference between the daily average ambient temperature (read from `ERA5 <https://doi.org/10.1002/qj.3803>`_) and a threshold temperature above which space heat demand is zero. A threshold temperature of 15 °C is assumed by default. The daily space heat demand is distributed to the hours of the day following heat demand profiles from `BDEW <https://github.com/oemof/demandlib>`_. These differ for weekdays and weekends/holidays and between residential and services demand.
*Space heating*
The space heating demand can be exogenously reduced by retrofitting measures that improve the buildings’ thermal envelopes.
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 205
Co-optimsing of building renovation is also possible, if it is activated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_.
Renovation of the thermal envelope reduces the space heating demand and is optimised at each node for every heat bus. Renovation measures through additional insulation material and replacement of energy inefficient windows are considered.
In a first step, costs per energy savings are estimated in `build_retro_cost.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/build_retro_cost.py>`_. They depend on the insulation condition of the building stock and costs for renovation of the building elements. In a second step, for those cost per energy savings two possible renovation strengths are determined: a moderate renovation with lower costs, a lower maximum possible space heat savings, and an ambitious renovation with associated higher costs and higher efficiency gains. They are added by step-wise linearisation in form of two additional generations in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_.
Further information are given in the publication :
`Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
*Water heating*
Hot water demand is assumed to be constant throughout the year.
*Urban and rural heating*
For every country, heat demand is split between low and high population density areas. These country-level totals are then distributed to each region in proportion to their rural and urban populations respectively. Urban areas with dense heat demand can be supplied with large-scale district heating systems. The percentage of urban heat demand that can be supplied by district heating networks as well as lump-sum losses in district heating systems is exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L153>`_.
*Cooling demand*
Cooling is electrified and is included in the electricity demand. Cooling demand is assumed to remain at current levels. An example of regional distribution of the total heat demand for network 181 regions is depicted below.
.. image:: ../graphics/demand-map-heat.png
As below figure shows, the current total heat demand in Europe is similar to the total electricity demand but features much more pronounced seasonal variations. The current total building heating demand in Europe adds up to 3084 TWh/a of which 78% occurs in urban areas.
.. image:: ../graphics/Heat_and_el_demand_timeseries.png
In practice, in PyPSA-Eur-Sec, there are heat demand buses to which the corresponding heat demands are added.
1) Urban central heat: large-scale district heating networks in urban areas with dense heat population. Residential and services demand in these areas are added as demands to this bus
2) Residential urban decentral heat: heating for residential buildings in urban areas not using district heating
3) Services urban decentral heat: heating for services buildings in urban areas not using district heating
4) Residential rural heat: heating for residential buildings in rural areas with low population density.
5) Services rural heat: heating for residential services buildings in rural areas with low population density. Heat demand from agriculture sector is also included here.
.. _heat-supply:
Heat supply
=======================
Different supply options are available depending on whether demand is met centrally through district heating systems, or decentrally through appliances in individual buildings.
**Urban central heat**
For large-scale district heating systems the following options are available: combined heat and power (CHP) plants consuming gas or biomass from waste and residues with and without carbon capture (CC), large-scale air-sourced heat pumps, gas and oil boilers, resistive heaters, and fuel cell CHPs. Additionally, waste heat from the `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_ and `Sabatier <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L240>`_ processes for the production of synthetic hydrocarbons can supply district heating systems. For more detailed explanation of these processes, see :ref:`Oil-based products supply` and :ref:`Methane supply`.
**Residential and Urban decentral heat**
Supply options in individual buildings include gas and oil boilers, air- and ground-sourced heat pumps, resistive heaters, and solar thermal collectors.
Ground-source heat pumps are only allowed in rural areas because of space constraints. Thus, only air- source heat pumps are allowed in urban areas. This is a conservative assumption, since there are many possible sources of low-temperature heat that could be tapped in cities (e.g. waste water, ground water, or natural bodies of water). Costs, lifetimes and efficiencies for these technologies are retrieved from the `technology-data repository <https://github.com/PyPSA/technology-data>`_.
Below are more detailed explanations for each heating supply component, all of which are modelled as `links <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#link>`_ in PyPSA-Eur-Sec.
.. _Large-scale CHP:
**Large-scale CHP**
Large Combined Heat and Power plants are included in the model if it is specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L235>`_.
CHPs are based on back pressure plants operating with a fixed ratio of electricity to heat output. The efficiencies of each are given on the back pressure line, where the back pressure coefficient cb is the electricity output divided by the heat output. (For a more complete explanation of the operation of CHPs refer to the study by Dahl et al. : `Cost sensitivity of optimal sector-coupled district heating production systems <https://arxiv.org/pdf/1804.07557.pdf>`_.
PyPSA-Eur-Sec includes CHP plants fueled by methane and solid biomass from waste and residues. Hydrogen fuel cells also produce both electricity and heat.
The methane CHP is modeled on the Danish Energy Agency (DEA) “Gas turbine simple cycle (large)” while the solid biomass CHP is based on the DEA’s “09b Wood Pellets Medium”. For biomass CHP, cb = `0.46 <https://ens.dk/sites/ens.dk/files/Statistik/technology_data_catalogue_for_el_and_dh_-_0009.pdf#page=156>`_ , whereas for gas CHP, cb = `1 <https://ens.dk/sites/ens.dk/files/Statistik/technology_data_catalogue_for_el_and_dh_-_0009.pdf#page=64>`_.
NB: The old PyPSA-Eur-Sec-30 model assumed an extraction plant (like the DEA coal CHP) for gas which has flexible production of heat and electricity within the feasibility diagram of Figure 4 in the study by `Brown et al. <https://arxiv.org/abs/1801.05290>`_ We have switched to the DEA back pressure plants since these are more common for smaller plants for biomass, and because the extraction plants were on the back pressure line for 99.5% of the time anyway. The plants were all changed to back pressure in PyPSA-Eur-Sec v0.4.0.
**Micro-CHP**
PyPSA-Eur-Sec allows individual buildings to make use of `micro gas CHPs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L236>`_ that are assumed to be installed at the distribution grid level.
**Heat pumps**
The coefficient of performance (COP) of air- and ground-sourced heat pumps depends on the ambient or soil temperature respectively. Hence, the COP is a time-varying parameter (refer to `Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L206>`_ file). Generally, the COP will be lower during winter when temperatures are low. Because the ambient temperature is more volatile than the soil temperature, the COP of ground-sourced heat pumps is less variable. Moreover, the COP depends on the difference between the source and sink temperatures:
.. math::
\Delta T = T_{sink} − T_{source}
For the sink water temperature Tsink we assume 55 °C [`Config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L207>`_ file]. For the time- and location-dependent source temperatures Tsource, we rely on the `ERA5 <https://doi.org/10.1002/qj.3803>`_ reanalysis weather data. The temperature differences are converted into COP time series using results from a regression analysis performed in the study by `Stafell et al. <https://pubs.rsc.org/en/content/articlelanding/2012/EE/c2ee22653g>`_. For air-sourced heat pumps (ASHP), we use the function:
.. math::
COP (\Delta T) = 6.81 + 0.121\Delta T + 0.000630\Delta T^2
for ground-sourced heat pumps (GSHP), we use the function:
.. math::
COP(\Delta T) = 8.77 + 0.150\Delta T + 0.000734\Delta T^2
**Resistive heaters**
Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ option.
Resistive heaters produce heat with a fixed conversion efficiency (refer to `Technology-data repository <https://github.com/PyPSA/technology-data>`_ ).
**Gas, oil, and biomass boilers**
Can be activated in Config from the `boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L232>`_ , `oil boilers <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L233>`_ , and `biomass boiler <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L234>`_ option.
Similar to resistive heaters, boilers have a fixed efficiency and produce heat using gas, oil or biomass.
**Solar thermal collectors**
Can be activated in the config file from the `solar_thermal <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L237>`_ option.
Solar thermal profiles are built based on weather data and also have the `options <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L134>`_ for setting the sky model and the orientation of the panel in the config file, which are then used by the atlite tool to calculate the solar resource time series.
**Waste heat from Fuel Cells, Methanation and Fischer-Tropsch plants**
Waste heat from `fuel cells <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_ in addition to processes like `Fischer-Tropsch <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_, methanation, and Direct Air Capture (DAC) is dumped into district heating networks.
**Existing heating capacities and decommissioning**
For the myopic transition paths, capacities already existing for technologies supplying heat are retrieved from `“Mapping and analyses of the current and future (2020 - 2030)” <https://ec.europa.eu/energy/en/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment>`_ . For the sake of simplicity, coal, oil and gas boiler capacities are assimilated to gas boilers. Besides that, existing capacities for heat resistors, air-sourced and ground-sourced heat pumps are included in the model. For heating capacities, 25% of existing capacities in 2015 are assumed to be decommissioned in every 5-year time step after 2020.
**Thermal Energy Storage**
Activated in Config from the `tes <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L228>`_ option.
Thermal energy can be stored in large water pits associated with district heating systems and individual thermal energy storage (TES), i.e., small water tanks. Water tanks are modelled as `stores <https://pypsa.readthedocs.io/en/latest/components.html?highlight=distribution#store, which are connected to heat demand buses through water charger/discharger links>`_.
A thermal energy density of 46.8 kWh :math:`_{th}`/m3 is assumed, corresponding to a temperature difference of 40 K. The decay of thermal energy in the stores: 1- :math:`e^{-1/24τ}` is assumed to have a time constant of  τ=180 days for central TES and  τ=3 days for individual TES, both modifiable through `tes_tau <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L229>`_ in config file. Charging and discharging efficiencies are 90% due to pipe losses.
**Retrofitting of the thermal envelope of buildings**
Co-optimising building renovation is only enabled if in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L222>`_ file. To reduce the computational burden,
default setting is set as false.
Renovation of the thermal envelope reduces the space heating demand and is
optimised at each node for every heat bus. Renovation measures through additional
insulation material and replacement of energy inefficient windows are considered.
In a first step, costs per energy savings are estimated in the `build_retro_cost.py <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_retro_cost.py>`_ script.
They depend on the insulation condition of the building stock and costs for
renovation of the building elements.
In a second step, for those cost per energy savings two possible renovation
strengths are determined: a moderate renovation with lower costs and lower
maximum possible space heat savings, and an ambitious renovation with associated
higher costs and higher efficiency gains. They are added by step-wise
linearisation in form of two additional generations in
the `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L1600>`_ script.
Settings in the config.yaml concerning the endogenously optimisation of building
renovation include `cost factor <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L223>`_, `interest rate <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L224>`_, `annualised cost <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L225>`_, `tax weighting <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L226>`_, and `construction index <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L227>`_.
Further information are given in the study by Zeyen et al. : `Mitigating heat demand peaks in buildings in a highly renewable European energy system, (2021) <https://arxiv.org/abs/2012.01831>`_.
.. _Hydrogen demand:
Hydrogen demand
=============================
Hydrogen is consumed in the industry sector (see :ref:`Industry demand`) to produce ammonia (see :ref:`Chemicals Industry`) and direct reduced iron (DRI) (see :ref:`Iron and Steel`). Hydrogen is also consumed to produce synthetic methane (see :ref:`Methane supply`) and liquid hydrocarbons (see :ref:`Oil-based products supply`) which have multiple uses in industry and other sectors.
Hydrogen is also used for transport applications (see :ref:`Transportation`), where it is exogenously fixed. It is used in `heavy-duty land transport <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_ and as liquified hydrogen in the shipping sector (see :ref:`Shipping`). Furthermore, stationary fuel cells may re-electrify hydrogen (with waste heat as a byproduct) to balance renewable fluctuations (see :ref:`Electricity supply and demand`). The waste heat from the stationary fuel cells can be used in `district-heating systems <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L256>`_.
.. _Hydrogen supply:
Hydrogen supply
=============================
Today, most of the :math:`H_2` consumed globally is produced from natural gas by steam methane reforming (SMR)
.. math::
CH_4 + H_2O \xrightarrow{} CO + 3H_2
combined with a water-gas shift reaction
.. math::
CO + H_2O \xrightarrow{} CO_2 + H_2
SMR is included `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L245>`_.
PyPSA-Eur-Sec allows this route of :math:`H_2` production with and without [carbon capture (CC)] (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`). These routes are often referred to as blue and grey hydrogen. Here, methane input can be both of fossil or synthetic origin.
Green hydrogen can be produced by electrolysis to split water into hydrogen and oxygen
.. math::
2H_2O \xrightarrow{} 2H_2 + O_2
For the electrolysis, alkaline electrolysers are chosen since they have lower cost and higher cumulative installed capacity than polymer electrolyte membrane (PEM) electrolysers. The techno-economic assumptions are taken from the technology-data repository. Waste heat from electrolysis is not leveraged in the model.
**Transport**
Hydrogen is transported by pipelines. :math:`H_2` pipelines are endogenously generated, either via a greenfield :math:`H_2` network, or by `retrofitting natural gas pipelines <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L262>`_). Retrofitting is implemented in such a way that for every unit of decommissioned gas pipeline, a share (60% is used in the study by `Neumann et al. <https://arxiv.org/abs/2207.05816>`_) of its nominal capacity (exogenously determined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L266>`_.) is available for hydrogen transport. When the gas network is not resolved, this input denotes the potential for gas pipelines repurposed into hydrogen pipelines.
New pipelines can be built additionally on all routes where there currently is a gas or electricity network connection. These new pipelines will be built where no sufficient retrofitting options are available. The capacities of new and repurposed pipelines are a result of the optimisation.
**Storage**
Hydrogen can be stored in overground steel tanks or `underground salt caverns <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L250>`_. For the latter, energy storage capacities in every country are limited to the potential estimation for onshore salt caverns within `50 km <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L251>`_ of shore to avoid environmental issues associated with brine solution disposal. Underground storage potentials for hydrogen in European salt caverns is acquired from `Caglayan et al. <https://doi.org/10.1016/j.ijhydene.2019.12.161>`_
.. _Methane demand:
Methane demand
====================================
Methane is used in individual and large-scale gas boilers, in CHP plants with and without carbon capture, in OCGT and CCGT power plants, and in some industry subsectors for the provision of high temperature heat (see :ref:`Industry demand`). Methane is not used in the transport sector because of engine slippage.
.. _Methane supply:
Methane supply
===================================
In addition to methane from fossil origins, the model also considers biogenic and synthetic sources. `The gas network can either be modelled, or it can be assumed that gas transport is not limited <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_. If gas infrastructure is regionally resolved, fossil gas can enter the system only at existing and planned LNG terminals, pipeline entry-points, and intra- European gas extraction sites, which are retrieved from the SciGRID Gas IGGIELGN dataset and the GEM Wiki.
Biogas can be upgraded to methane.
Synthetic methane can be produced by processing hydrogen and captures :math:`CO_2` in the Sabatier reaction
.. math::
CO_2 + 4H_2 \xrightarrow{} CH_4 + 2H_2O
Direct power-to-methane conversion with efficient heat integration developed in the HELMETH project is also an option. The share of synthetic, biogenic and fossil methane is an optimisation result depending on the techno-economic assumptions.
*Methane transport*
The existing European gas transmission network is represented based on the SciGRID Gas IGGIELGN dataset. This dataset is based on compiled and merged data from the ENTSOG maps and other publicly available data sources. It includes data on the capacity, diameter, pressure, length, and directionality of pipelines. Missing capacity data is conservatively inferred from the pipe diameter following conversion factors derived from an EHB report. The gas network is clustered to the selected number of model regions. Gas pipelines can be endogenously expanded or repurposed for hydrogen transport. Gas flows are represented by a lossless transport model. Methane is assumed to be transmitted without cost or capacity constraints because future demand is predicted to be low compared to available transport capacities.
The following figure shows the unclustered European gas transmission network based on the SciGRID Gas IGGIELGN dataset. Pipelines are color-coded by estimated capacities. Markers indicate entry-points, sites of fossil resource extraction, and LNG terminals.
.. image:: ../graphics/gas_pipeline_figure.png
.. _Biomass supply:
Biomass Supply
=====================
Biomass supply potentials for each European country are taken from the `JRC ENSPRESO database <http://data.europa.eu/89h/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ where data is available for various years (2010, 2020, 2030, 2040 and 2050) and scenarios (low, medium, high). No biomass import from outside Europe is assumed. More information on the data set can be found `here <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.
.. _Biomass demand:
Biomass demand
=====================
Biomass supply potentials for every NUTS2 region are taken from the `JRC ENSPRESO database <http://data.europa.eu/89h/74ed5a04-7d74-4807-9eab-b94774309d9f>`_ where data is available for various years (2010, 2020, 2030, 2040 and 2050) and different availability scenarios (low, medium, high). No biomass import from outside Europe is assumed. More information on the data set can be found `here <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_. The data for NUTS2 regions is mapped to PyPSA-Eur-Sec model regions in proportion to the area overlap.
The desired scenario can be selected in the PyPSA-Eur-Sec `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_. The script for building the biomass potentials from the JRC ENSPRESO data base is located `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_potentials.py#L43>`_. Consult the script to see the keywords that specify the scenario options.
The `configuration <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L108>`_ also allows the user to define how the various types of biomass are used in the model by using the following categories: biogas, solid biomass, and not included. Feedstocks categorized as biogas, typically manure and sludge waste, are available to the model as biogas, which can be upgraded to biomethane. Feedstocks categorized as solid biomass, e.g. secondary forest residues or municipal waste, are available for combustion in combined-heat-and power (CHP) plants and for medium temperature heat (below 500 °C) applications in industry. It can also converted to gas or liquid fuels.
Feedstocks labeled as not included are ignored by the model.
A `typical use case for biomass <https://arxiv.org/abs/2109.09563>`_ would be the medium availability scenario for 2030 where only residues from agriculture and forestry as well as biodegradable municipal waste are considered as energy feedstocks. Fuel crops are avoided because they compete with scarce land for food production, while primary wood, as well as wood chips and pellets, are avoided because of concerns about sustainability. See the supporting materials of the `paper <https://www.sciencedirect.com/science/article/pii/S1364032117302034>`_ for more details.
*Solid biomass conversion and use*
Solid biomass can be used directly to provide process heat up to 500˚C in the industry. It can also be burned in CHP plants and boilers associated with heating systems. These technologies are described elsewhere (see :ref:`Large-scale CHP` and :ref:`Industry demand`).
Solid biomass can be converted to syngas if the option is enabled in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L274>`_. In this case the model will enable the technology BioSNG both with and without the option for carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_).
Liquefaction of solid biomass `can be enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L273>`_ allowing the model to convert it into liquid hydrocarbons that can replace conventional oil products. This technology also comes with and without carbon capture (see `Technology-data repository <https://github.com/PyPSA/technology-data>`_).
*Transport of solid biomass*
The transport of solid biomass can either be assumed unlimited between countries or it can be associated with a country specific cost per MWh/km. In the config file these options are toggled `here <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L270>`_. If the option is off, use of solid biomass is transport. If it is turned on, a biomass transport network will be `created <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L1803>`_ between all nodes. This network resembles road transport of biomass and the cost of transportation is a variable cost which is proportional to distance and a country specific cost per MWh/km. The latter is `estimated <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_biomass_transport_costs.py>`_ from the country specific costs per ton/km used in the publication `“The JRC-EU-TIMES model. Bioenergy potentials for EU and neighbouring countries” <https://publications.jrc.ec.europa.eu/repository/handle/JRC98626>`_.
*Biogas transport and use*
Biogas will be aggregated into a common European resources if a gas network is not modelled explicitly, i.e., the `gas_network <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L261>`_ option is set to false. If, on the other hand, a gas network is included, the biogas potential will be associated with each node of origin.
The model can only use biogas by first upgrading it to natural gas quality [see :ref:`Methane supply`] (bio methane) which is fed into the general gas network.
.. _Oil-based products demand:
Oil-based products demand
========================
Naphtha is used as a feedstock in the chemicals industry (see :ref:`Chemicals Industry`). Furthermore, kerosene is used as transport fuel in the aviation sector (see :ref:`Aviation`). Non-electrified agriculture machinery also consumes gasoline.
Land transport [(see :ref:`Land transport`) that is not electrified or converted into using :math:`H_2`-fuel cells also consumes oil-based products. While there is regional distribution of demand, the carrier is copperplated in the model, which means that transport costs and constraints are neglected.
.. _Oil-based products supply:
Oil-based products supply
========================
Oil-based products can be either of fossil origin or synthetically produced by combining :math:`H_2` (see :ref:`Hydrogen supply`) and captured :math:`CO_2` (see :ref:`Carbon dioxide capture, usage and sequestration (CCU/S)`) in Fischer-Tropsch plants
.. math::
𝑛CO+(2𝑛+1)H_2 → C_{n}H_{2n + 2} +𝑛H_2O
with costs as included from the `technology-data repository <https://github.com/PyPSA/technology-data/blob/master/latex_tables/tables_in_latex.pdf>`_. The waste heat from the Fischer-Tropsch process is supplied to `district heating networks <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L255>`_. The share of fossil and synthetic oil is an optimisation result depending on the techno-economic assumptions.
*Oil-based transport*
Liquid hydrocarbons are assumed to be transported freely among the model region since future demand is predicted to be low, transport costs for liquids are low and no bottlenecks are expected.
.. _Industry demand:
Industry demand
================
Industry demand is split into a dozen different sectors with specific energy demands, process
emissions of carbon dioxide, as well as existing and prospective mitigation strategies.
The Subsection overview below provides a general description of the modelling approach for the industry sector. The following subsections describe the current energy demands, available mitigation strategies, and whether mitigation is exogenously fixed or co-optimised with the other components of the model for each industry subsector in more detail. See details for Iron and Steel (see :ref:`Iron and Steel`), Chemicals Industry and Ammonia (see :ref:`Chemicals Industry`), Non-metallic Mineral products , Non-ferrous Metals , and other Industry Subsectors.
.. _Overview:
**Overview**
Greenhouse gas emissions associated with industry can be classified into energy-related and process-related emissions. Today, fossil fuels are used for process heat energy in the chemicals industry, but also as a non-energy feedstock for chemicals like ammonia ( :math:`NH_3`), ethylene ( :math:`C_2H_4`) and methanol ( :math:`CH_3OH`). Energy-related emissions can be curbed by using low-emission energy sources. The only option to reduce process-related emissions is by using an alternative manufacturing process or by assuming a certain rate of recycling so that a lower amount of virgin material is needed.
The overarching modelling procedure can be described as follows. First, the energy demands and process emissions for every unit of material output are estimated based on data from the `JRC-IDEES database <https://data.europa.eu/doi/10.2760/182725>`_ and the fuel and process switching described in the subsequent sections. Second, the 2050 energy demands and process emissions are calculated using the per-unit-of-material ratios based on the industry transformations and the `country-level material production in 2015 <https://data.europa.eu/doi/10.2760/182725>`_, assuming constant material demand.
Missing or too coarsely aggregated data in the JRC-IDEES database is supplemented with additional datasets: `Eurostat energy balances <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_, `United States <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_, `Geological Survey <https://www.usgs.gov/media/files/%20nitrogen-2017-xlsx>`_ for ammonia production, `DECHEMA <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_ for methanol and chlorine, and `national statistics from Switzerland <https://www.bfe.admin.ch/bfe/de/home/versorgung/statistik-und-geodaten/energiestatistiken.html>`_.
Where there are fossil and electrified alternatives for the same process (e.g. in glass manufacture or drying), we assume that the process is completely electrified. Current electricity demands (lighting, air compressors, motor drives, fans, pumps) will remain electric. Processes that require temperatures below 500 °C are supplied with solid biomass, since we assume that residues and wastes are not suitable for high-temperature applications. We see solid biomass use primarily in the pulp and paper industry, where it is already widespread, and in food, beverages and tobacco, where it replaces natural gas. Industries which require high temperatures (above 500 °C), such as metals, chemicals and non-metallic minerals are either electrified where suitable processes already exist, or the heat is provided with synthetic methane.
Hydrogen for high-temperature process heat is not part of the model currently.
Where process heat is required, our approach depends on the necessary temperature. For example, due to the high share of high-temperature process heat demand (see `Naegler et al. <https://doi.org/10.1002/er.3436>`_ and `Rehfeldt el al. <https://link.springer.com/article/10.1007/s12053-017-9571-y>`_), we disregard geothermal and solar thermal energy as sources for process heat since they cannot attain high-temperature heat.
The following figure shows the final consumption of energy and non-energy feedstocks in industry today in comparison to the scenario in 2050 assumed in `Neumann et al <https://arxiv.org/abs/2207.05816>`_.
.. image:: ../graphics/fec_industry_today_tomorrow.png
The following figure shows the process emissions in industry today (top bar) and in 2050 without
carbon capture (bottom bar) assumed in `Neumann et al <https://arxiv.org/abs/2207.05816>`_.
.. image:: ../graphics/process-emissions.png
Inside each country the industrial demand is then distributed using the `Hotmaps Industrial Database <https://zenodo.org/record/4687147#.YvOaxhxBy5c>`_, which is illustrated in the figure below. This open database includes georeferenced industrial sites of energy-intensive industry sectors in EU28, including cement, basic chemicals, glass, iron and steel, non-ferrous metals, non-metallic minerals, paper, and refineries subsectors. The use of this spatial dataset enables the calculation of regional and process-specific energy demands. This approach assumes that there will be no significant migration of energy-intensive industries.
.. image:: ../graphics/hotmaps.png
.. _Iron and Steel:
**Iron and Steel**
Two alternative routes are used today to manufacture steel in Europe. The primary route (integrated steelworks) represents 60% of steel production, while the secondary route (electric arc furnaces, EAF), represents the other 40% `(Lechtenböhmer et. al) <https://doi.org/10.1016/j.energy.2016.07.110>`_.
The primary route uses blast furnaces in which coke is used to reduce iron ore into molten iron, which is then converted into steel:
.. math::
CO_2 + C \xrightarrow{} 2 CO
.. math::
3 Fe_2O_3 + CO \xrightarrow{} 2 Fe_3O_4 + CO
.. math::
Fe_3O_4 + CO \xrightarrow{} 3 FeO + CO_2
.. math::
FeO + CO \xrightarrow{} Fe + CO_2
The primary route of steelmaking implies large process emissions of 0.22 t :math:`_{CO_2}` /t of steel, amounting to 7% of global greenhouse gas emissions `(Vogl et. al) <https://doi.org/10.1016/j.joule.2021.09.007>`_.
In the secondary route, electric arc furnaces are used to melt scrap metal. This limits the :math:`CO_2` emissions to the burning of graphite electrodes `(Friedrichsen et. al) <https://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, and reduces process emissions to 0.03 t :math:`_{CO_2}` /t of steel.
We assume that the primary route can be replaced by a third route in 2050, using direct reduced iron (DRI) and subsequent processing in an EAF.
.. math::
3 Fe_2O_3 + H_2 \xrightarrow{} 2 Fe_3O_4 + H_2O
.. math::
Fe_3O_4 +H_2 \xrightarrow{} 3FeO+H_2O
.. math::
FeO + H_2 \xrightarrow{} Fe + H_2O
This circumvents the process emissions associated with the use of coke. For hydrogen- based DRI, we assume energy requirements of 1.7 MWh :math:`_{H_2}` /t steel `(Vogl et. al) <https://doi.org/10.1016/j.jclepro.2018.08.279>`_ and 0.322 MWh :math:`_{el}`/t steel `(HYBRIT 2016) <https://dh5k8ug1gwbyz.cloudfront.net/uploads/2021/02/Hybrit-broschure-engelska.pdf>`_.
The share of steel produced via the primary route is exogenously set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L279>`_. The share of steel obtained via hydrogen-based DRI plus EAF is also set exogenously in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L287>`_. The remaining share is manufactured through the secondary route using scrap metal in EAF. Bioenergy as alternative to coke in blast furnaces is not considered in the model (`Mandova et.al <https://doi.org/10.1016/j.biombioe.2018.04.021>`_, `Suopajärvi et.al <https://doi.org/10.1016/j.apenergy.2018.01.060>`_).
For the remaining subprocesses in this sector, the following transformations are assumed. Methane is used as energy source for the smelting process. Activities associated with furnaces, refining and rolling, and product finishing are electrified assuming the current efficiency values for these cases. These transformations result in changes in process emissions as outlined in the process emissions figure presented in the industry overview section (see :ref:`Overview`).
.. _Chemicals Industry:
**Chemicals Industry**
The chemicals industry includes a wide range of diverse industries, including the production of basic organic compounds (olefins, alcohols, aromatics), basic inorganic compounds (ammonia, chlorine), polymers (plastics), and end-user products (cosmetics, pharmaceutics).
The chemicals industry consumes large amounts of fossil-fuel based feedstocks (see `Levi et. al <https://pubs.acs.org/doi/10.1021/acs.est.7b04573>`_), which can also be produced from renewables as outlined for hydrogen (see :ref:`Hydrogen supply`), for methane (see :ref:`Methane supply`), and for oil-based products (see :ref:`Oil-based products supply`). The ratio between synthetic and fossil-based fuels used in the industry is an endogenous result of the optimisation.
The basic chemicals consumption data from the `JRC IDEES <https://op.europa.eu/en/publication-detail/-/publication/989282db-ad65-11e7-837e-01aa75ed71a1/language-en>`_ database comprises high- value chemicals (ethylene, propylene and BTX), chlorine, methanol and ammonia. However, it is necessary to separate out these chemicals because their current and future production routes are different.
Statistics for the production of ammonia, which is commonly used as a fertilizer, are taken from the `USGS <https://www.usgs.gov/media/files/nitrogen-2017-xlsx>`_ for every country. Ammonia can be made from hydrogen and nitrogen using the Haber-Bosch process.
.. math::
N_2 + 3H_2 \xrightarrow{} 2NH_3
The Haber-Bosch process is not explicitly represented in the model, such that demand for ammonia enters the model as a demand for hydrogen ( 6.5 MWh :math:`_{H_2}` / t :math:`_{NH_3}` ) and electricity ( 1.17 MWh :math:`_{el}` /t :math:`_{NH_3}` ) (see `Wang et. al <https://doi.org/10.1016/j.joule.2018.04.017>`_). Today, natural gas dominates in Europe as the source for the hydrogen used in the Haber-Bosch process, but the model can choose among the various hydrogen supply options described in the hydrogen section (see :ref:`Hydrogen supply`)
The total production and specific energy consumption of chlorine and methanol is taken from a `DECHEMA report <https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry.pdf>`_. According to this source, the production of chlorine amounts to 9.58 MtCl/a, which is assumed to require electricity at 3.6 MWh :math:`_{el}`/t of chlorine and yield hydrogen at 0.937 MWh :math:`_{H_2}`/t of chlorine in the chloralkali process. The production of methanol adds up to 1.5 MtMeOH/a, requiring electricity at 0.167 MWh :math:`_{el}`/t of methanol and methane at 10.25 MWh :math:`_{CH_4}`/t of methanol.
The production of ammonia, methanol, and chlorine production is deducted from the JRC IDEES basic chemicals, leaving the production totals of high-value chemicals. For this, we assume that the liquid hydrocarbon feedstock comes from synthetic or fossil- origin naphtha (14 MWh :math:`_{naphtha}`/t of HVC, similar to `Lechtenböhmer et al <https://doi.org/10.1016/j.energy.2016.07.110>`_), ignoring the methanol-to-olefin route. Furthermore, we assume the following transformations of the energy-consuming processes in the production of plastics: the final energy consumption in steam processing is converted to methane since requires temperature above 500 °C (4.1 MWh :math:`_{CH_4}` /t of HVC, see `Rehfeldt et al. <https://doi.org/10.1007/s12053-017-9571-y>`_); and the remaining processes are electrified using the current efficiency of microwave for high-enthalpy heat processing, electric furnaces, electric process cooling and electric generic processes (2.85 MWh :math:`_{el}`/t of HVC).
The process emissions from feedstock in the chemical industry are as high as 0.369 t :math:`_{CO_2}`/t of ethylene equivalent. We consider process emissions for all the material output, which is a conservative approach since it assumes that all plastic-embedded :math:`CO_2` will eventually be released into the atmosphere. However, plastic disposal in landfilling will avoid, or at least delay, associated :math:`CO_2` emissions.
Circular economy practices drastically reduce the amount of primary feedstock needed for the production of plastics in the model (see `Kullmann et al. <https://doi.org/10.1016/j.energy.2022.124660>`_, `Meys et al. (2021) <https://doi.org/10.1126/science.abg9853>`_, `Meys et al. (2020) <https://doi.org/10/gmxv6z>`_, `Gu et al. <https://doi.org/10/gf8n9w>`_) and consequently, also the energy demands and level of process emission. The percentage of plastics that are assumed to be mechanically recycled can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L315>`_, as well as
the percentage that is chemically recycled, see `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L316>`_ The energy consumption for those recycling processes are respectively 0.547 MWh :math:`_{el}`/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L318>`_) (`Meys et al. (2020) <https://doi.org/10/gmxv6z>`_), and 6.9 MWh :math:`_{el}`/t of HVC (as indicated in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/776596ab9ac6a6cc93422ccfd0383abeffb0baa9/config.default.yaml#L319>`_) based on pyrolysis and electric steam cracking (see `Materials Economics <https://materialeconomics.com/publications/industrial-transformation-2050>`_ report).
**Non-metallic Mineral Products**
This subsector includes the manufacturing of cement, ceramics, and glass.
*Cement*
Cement is used in construction to make concrete. The production of cement involves high energy consumption and large process emissions. The calcination of limestone to chemically reactive calcium oxide, also known as lime, involves process emissions of 0.54 t :math:`_{CO_2}` /t cement (see `Akhtar et al. <https://doi.org/10.1109/CITCON.2013.6525276>`_.
.. math::
CaCO_3 \xrightarrow{} CaO + CO_2
Additionally, :math:`CO_2` is emitted from the combustion of fossil fuels to provide process heat. Thereby, cement constitutes the biggest source of industry process emissions in Europe.
Cement process emissions can be captured assuming a capture rate of 90%. Whether emissions are captured is decided by the model taking into account the capital costs of carbon capture modules. The electricity and heat demand of process emission carbon capture is currently ignored. For net-zero emission scenarios, the remaining process emissions need to be compensated by negative emissions.
With the exception of electricity demand and biomass demand for low-temperature heat (0.06 MWh/t and 0.2 MWh/t), the final energy consumption of this subsector is assumed to be supplied by methane (0.52 MWh/t), which is capable of delivering the required high-temperature heat. This implies a switch from burning solid fuels to burning gas which will require adjustments of the `kilns <10.1109/CITCON.2013.6525276>`_. The share of fossil vs. synthetic methane consumed is a result of the optimisation
*Ceramics*
The ceramics sector is assumed to be fully electrified based on the current efficiency of already electrified processes which include microwave drying and sintering of raw materials, electric kilns for primary production processes, electric furnaces for the `product finishing <https://data.europa.eu/doi/10.2760/182725>`_. In total, the final electricity consumption is 0.44 MWh/t of ceramic. The manufacturing of ceramics includes process emissions of 0.03 t :math:`_{CO_2}`/t of ceramic. For a detailed overview of the ceramics industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
*Glass*
The production of glass is assumed to be fully electrified based on the current efficiency of electric melting tanks and electric annealing which adds up to an electricity demand of 2.07 MWh :math:`_{el}`/t of `glass <https://doi.org/10/f9df2m>`_. The manufacturing of glass incurs process emissions of 0.1 t :math:`_{CO_2}`/t of glass. Potential efficiency improvements, which according to `Lechtenböhmer et al <https://doi.org/10/f9df2m>`_ could reduce energy demands to 0.85 MW :math:`_{el}`/t of glass, have not been considered. For a detailed overview of the glass industry sector see `Furszyfer Del Rio et al <https://doi.org/10.1016/j.rser.2021.111885>`_.
**Non-ferrous Metals**
The non-ferrous metal subsector includes the manufacturing of base metals (aluminium, copper, lead, zinc), precious metals (gold, silver), and technology metals (molybdenum, cobalt, silicon).
The manufacturing of aluminium accounts for more than half of the final energy consumption of this subsector. Two alternative processing routes are used today to manufacture aluminium in Europe. The primary route represents 40% of the aluminium pro- duction, while the secondary route represents the remaining 60%.
The primary route involves two energy-intensive processes: the production of alumina from bauxite (aluminium ore) and the electrolysis to transform alumina into aluminium via the Hall-Héroult process
.. math::
2Al_2O_3 +3C \xrightarrow{} 4Al+3CO_2
The primary route requires high-enthalpy heat (2.3 MWh/t) to produce alumina which is supplied by methane and causes process emissions of 1.5 t :math:`_{CO_2}`/t aluminium. According to `Friedrichsen et al. <http://www.umweltbundesamt.de/en/publikationen/comparative-analysis-of-options-potential-for>`_, inert anodes might become commercially available by 2030 that would eliminate the process emissions, but they are not included in the model. Assuming all subprocesses are electrified, the primary route requires 15.4 MWh :math:`_{el}`/t of aluminium.
In the secondary route, scrap aluminium is remelted. The energy demand for this process is only 10% of the primary route and there are no associated process emissions. Assuming all subprocesses are electrified, the secondary route requires 1.7 MWh/t of aluminium. The share of aliminum manufactured by the primary and secondary route can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L297>`_]
For the other non-ferrous metals, we assume the electrification of the entire manufacturing process with an average electricity demand of 3.2 MWh :math:`_{el}`/t lead equivalent.
**Other Industry Subsectors**
The remaining industry subsectors include (a) pulp, paper, printing, (b) food, beverages, tobacco, (c) textiles and leather, (d) machinery equipment, (e) transport equipment, (f) wood and wood products, (g) others. Low- and mid-temperature process heat in these industries is assumed to be `supplied by biomass <https://doi.org/10.1016/j.rser.2021.110856>`_ while the remaining processes are electrified. None of the subsectors involve process emissions.
Agriculture demand
=========================
Energy demands for the agriculture, forestry and fishing sector per country are taken from the `JRC-IDEES database <http://data.europa.eu/89h/jrc-10110-10001>`_. Missing countries are filled with `Eurostat data <https://ec.europa.eu/eurostat/web/energy/data/energy-balances>`_. Agricultural energy demands are split into electricity (lighting, ventilation, specific electricity uses, electric pumping devices), heat (specific heat uses, low enthalpy heat), and machinery oil (motor drives, farming machine drives, diesel-fueled pumping devices). Heat demand is assigned at “services rural heat” buses. Time series for demands are assumed to be constant and distributed inside countries by population.
.. _Transportation:
Transportation
=========================
Annual energy demands for land transport, aviation and shipping for every country are retrieved from `JRC-IDEES data set <http://data.europa.eu/89h/jrc-10110-10001>`_. Below, the details of how each of these categories are treated is explained.
.. _Land transport:
**Land transport**
Both road and rail transport is combined as `land transport demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/build_transport_demand.py#L74>`_ although electrified rail transport is excluded because that demand is included in the current electricity demand.
The most important settings for land transport are the exogenously fixed fuel mix (an option enabling the endogeous optimization of transport electrification is planned but not yet implemented). In the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L181>`_, the share of battery electric vehicles (BEV) and hydrogen fuel cell vehicles (FCEV) can be set. The remaining percentage will be treated as internal combustion engines (ICE) that consume oil products.
*Battery Electric vehicles (BEV)*
For the electrified land transport, country-specific factors are computed by comparing the `current car final energy consumption per km in <https://www.sciencedirect.com/science/article/pii/S0360544216310295>`_ (average for Europe 0.7 kWh/km) to the 0.18 kWh/km value assumed for battery-to-wheels efficiency in EVs. The characteristic `weekly profile <https://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/zaehl_node.html>`_ provided by the German Federal Highway Research Institute (BASt) is used to obtain hourly time series for European countries taking into account the corresponding local times. Furthermore, a temperature dependence is included in the time series to account for heating/cooling demand in transport. For temperatures `below <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L166>`_/`above <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L165>`_ certain threshold values, e.g. 15 °C/20 °C, `temperature coefficients <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L169>`_ of typically 0.98%/°C and 0.63%/°C are assumed, based on the `paper <https://www.sciencedirect.com/science/article/pii/S036054421831288X>`_.
For BEVs the user can define the `storage energy capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L173>`_, `charging power capacity <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L176>`_, and `charging efficiency <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L174>`_.
For BEV, smart charging is an option. A `certain share <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L172>`_ of the BEV fleet can shift their charging time. The BEV state of charge is forced to be higher than a `set percentage <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L163>`_, e.g. 75%, every day at a `specified hour <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L164>`_, e.g., 7 am, to ensure that the batteries are sufficiently charged for peak usage in the morning and they not behave as seasonal storage. They also have the option to participate in vehicle-to-grid (V2G) services to facilitate system operation if that `is enabled <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L179>`_.
The battery cost of BEV is not included in the model since it is assumed that BEV owners buy them to primarily satisfy their mobility needs.
*Hydrogen fuel cell vehicles (FCEV)*
The share of all land transport that is specified to be be FCEV will be converted to a demand for hydrogen (see :ref:`Hydrogen supply`) using the `FCEV efficiency
<https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L191>`_.
FCEVs are typically used to simulate demand for transport that is hard to electrify directly, e.g. heavy construction machinery. But it may also be used to investigate a more widespread adoption of the technology.
*Internal combustion engine vehicles (ICE)*
All land transport that is not specified to be either BEV or FCEV will be treated as conventional ICEs. The transport demand is converted to a demand for oil products (see :ref:`Oil-based products supply`) using the `ICE efficiency
<https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L192>`_.
.. _Aviation:
**Aviation**
The `demand for aviation <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2193>`_ includes international and domestic use. It is modelled as an oil demand since aviation consumes kerosene. This can be produced synthetically or have fossil-origin (see :ref:`Oil-based products supply`).
.. _Shipping:
**Shipping**
Shipping energy demand is covered by a combination of oil and hydrogen. Other fuel options, like methanol or ammonia, are currently not included in PyPSA-Eur-Sec. The share of shipping that is assumed to be supplied by hydrogen can be selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L198>`_.
To estimate the `hydrogen demand <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2090>`_, the average fuel efficiency of the fleet is used in combination with the efficiency of the fuel cell defined in the technology-data repository. The average fuel efficiency is set in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L196>`_.
The consumed hydrogen comes from the general hydrogen bus where it can be produced by SMR, SMR+CC or electrolysers (see :ref:`Hydrogen supply`). The fraction that is not converted into hydrogen use oil products, i.e. is connected to the general oil bus.
The energy demand for liquefaction of the hydrogen used for shipping can be `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L197>`_. If this option is selected, liquifaction will happen at the `node where the shipping demand occurs <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/scripts/prepare_sector_network.py#L2064>`_.
.. _Carbon dioxide capture, usage and sequestration (CCU/S):
Carbon dioxide capture, usage and sequestration (CCU/S)
=========================================================
PyPSA-Eur-Sec includes carbon capture from air (i.e., direct air capture (DAC)), electricity generators, and industrial facilities. It furthermore includes carbon dioxide storage and transport, the usage of carbon dioxide in synthetic methane and oil products, as well as the sequestration of carbon dioxide underground.
**Carbon dioxide capture**
For the following point source emissions, carbon capture is applicable:
• Industry process emissions, e.g., from limestone in cement production
• Methane or biomass used for process heat in the industry
• Hydrogen production by SMR
• CHP plants using biomass or methane
• `Coal power plants <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L242>`_.
Point source emissions are captured assuming a capture rate, e.g. 90%, which can be specified in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L249>`_. The electricity and heat demand of process emission carbon capture
is currently ignored.
DAC (if `included <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L243>`_) includes the adsorption phase where electricity and heat consumptionsare required to assist the adsorption process and regenerate the adsorbent. It also includes the drying and compression of :math:`CO_2` prior to storage which consumes electricity and rejects heat.
*Carbon dioxide usage*
Captured :math:`CO_2` can be used to produce synthetic methane and synthetic oil products (e.g.
naphtha). If captured carbon is used, the :math:`CO_2` emissions of the synthetic fuels are net-neutral.
*Carbon dioxide sequestration*
Captured :math:`CO_2` can also be sequestered underground up to an annual sequestration limit of 200 Mt :math:`_{CO_2}`/a. This limit can be chosen in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L246>`_. As stored carbon dioxide is modelled as a single node for Europe, :math:`CO_2` transport constraints are neglected. Since :math:`CO_2` sequestration is an immature technology, the cost assumption is defined in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L247>`_.
*Carbon dioxide transport*
Carbon dioxide can be modelled as a single node for Europe (in this case, :math:`CO_2` transport constraints are neglected). A network for modelling the transport of :math:`CO_2` among the different nodes can also be created if selected in the `config file <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L248>`_.

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.. _technology_assumptions:
##########################################
Technology and cost assumptions
##########################################
*Techno-Economic Assumptions*
For the technological assumptions (cost, efficiency, lifetime, etc.), we take estimates for the investment year specified in the `config <https://github.com/PyPSA/pypsa-eur-sec/blob/3daff49c9999ba7ca7534df4e587e1d516044fc3/config.default.yaml#L43>`_. Many of those come from a database published by the Danish Energy Agency (`DEA <https://ens.dk/en/our-services/projections-and-models/technology-data>`_). Assumptions are maintained at the `technology data repository <https://github.com/PyPSA/technology-data>`_.

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# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import pandas as pd
idx = pd.IndexSlice
import numpy as np
import pypsa
import yaml
from add_existing_baseyear import add_build_year_to_new_assets
from helper import override_component_attrs, update_config_with_sector_opts
def add_brownfield(n, n_p, year):
logger.info(f"Preparing brownfield for the year {year}")
# electric transmission grid set optimised capacities of previous as minimum
n.lines.s_nom_min = n_p.lines.s_nom_opt
dc_i = n.links[n.links.carrier == "DC"].index
n.links.loc[dc_i, "p_nom_min"] = n_p.links.loc[dc_i, "p_nom_opt"]
for c in n_p.iterate_components(["Link", "Generator", "Store"]):
attr = "e" if c.name == "Store" else "p"
# first, remove generators, links and stores that track
# CO2 or global EU values since these are already in n
n_p.mremove(c.name, c.df.index[c.df.lifetime == np.inf])
# remove assets whose build_year + lifetime < year
n_p.mremove(c.name, c.df.index[c.df.build_year + c.df.lifetime < year])
# remove assets if their optimized nominal capacity is lower than a threshold
# since CHP heat Link is proportional to CHP electric Link, make sure threshold is compatible
chp_heat = c.df.index[
(
c.df[attr + "_nom_extendable"]
& c.df.index.str.contains("urban central")
& c.df.index.str.contains("CHP")
& c.df.index.str.contains("heat")
)
]
threshold = snakemake.config["existing_capacities"]["threshold_capacity"]
if not chp_heat.empty:
threshold_chp_heat = (
threshold
* c.df.efficiency[chp_heat.str.replace("heat", "electric")].values
* c.df.p_nom_ratio[chp_heat.str.replace("heat", "electric")].values
/ c.df.efficiency[chp_heat].values
)
n_p.mremove(
c.name,
chp_heat[c.df.loc[chp_heat, attr + "_nom_opt"] < threshold_chp_heat],
)
n_p.mremove(
c.name,
c.df.index[
c.df[attr + "_nom_extendable"]
& ~c.df.index.isin(chp_heat)
& (c.df[attr + "_nom_opt"] < threshold)
],
)
# copy over assets but fix their capacity
c.df[attr + "_nom"] = c.df[attr + "_nom_opt"]
c.df[attr + "_nom_extendable"] = False
n.import_components_from_dataframe(c.df, c.name)
# copy time-dependent
selection = n.component_attrs[c.name].type.str.contains(
"series"
) & n.component_attrs[c.name].status.str.contains("Input")
for tattr in n.component_attrs[c.name].index[selection]:
n.import_series_from_dataframe(c.pnl[tattr], c.name, tattr)
# deal with gas network
pipe_carrier = ["gas pipeline"]
if snakemake.config["sector"]["H2_retrofit"]:
# drop capacities of previous year to avoid duplicating
to_drop = n.links.carrier.isin(pipe_carrier) & (n.links.build_year != year)
n.mremove("Link", n.links.loc[to_drop].index)
# subtract the already retrofitted from today's gas grid capacity
h2_retrofitted_fixed_i = n.links[
(n.links.carrier == "H2 pipeline retrofitted")
& (n.links.build_year != year)
].index
gas_pipes_i = n.links[n.links.carrier.isin(pipe_carrier)].index
CH4_per_H2 = 1 / snakemake.config["sector"]["H2_retrofit_capacity_per_CH4"]
fr = "H2 pipeline retrofitted"
to = "gas pipeline"
# today's pipe capacity
pipe_capacity = n.links.loc[gas_pipes_i, "p_nom"]
# already retrofitted capacity from gas -> H2
already_retrofitted = (
n.links.loc[h2_retrofitted_fixed_i, "p_nom"]
.rename(lambda x: x.split("-2")[0].replace(fr, to))
.groupby(level=0)
.sum()
)
remaining_capacity = (
pipe_capacity
- CH4_per_H2
* already_retrofitted.reindex(index=pipe_capacity.index).fillna(0)
)
n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity
else:
new_pipes = n.links.carrier.isin(pipe_carrier) & (
n.links.build_year == year
)
n.links.loc[new_pipes, "p_nom"] = 0.0
n.links.loc[new_pipes, "p_nom_min"] = 0.0
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"add_brownfield",
simpl="",
clusters="37",
opts="",
lv=1.0,
sector_opts="168H-T-H-B-I-solar+p3-dist1",
planning_horizons=2030,
)
logging.basicConfig(level=snakemake.config["logging_level"])
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
logger.info(f"Preparing brownfield from the file {snakemake.input.network_p}")
year = int(snakemake.wildcards.planning_horizons)
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
add_build_year_to_new_assets(n, year)
n_p = pypsa.Network(snakemake.input.network_p, override_component_attrs=overrides)
add_brownfield(n, n_p, year)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

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# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import pandas as pd
idx = pd.IndexSlice
from types import SimpleNamespace
import numpy as np
import pypsa
import xarray as xr
import yaml
from helper import override_component_attrs, update_config_with_sector_opts
from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs
spatial = SimpleNamespace()
def add_build_year_to_new_assets(n, baseyear):
"""
Parameters
----------
n : pypsa.Network
baseyear : int
year in which optimized assets are built
"""
# Give assets with lifetimes and no build year the build year baseyear
for c in n.iterate_components(["Link", "Generator", "Store"]):
assets = c.df.index[(c.df.lifetime != np.inf) & (c.df.build_year == 0)]
c.df.loc[assets, "build_year"] = baseyear
# add -baseyear to name
rename = pd.Series(c.df.index, c.df.index)
rename[assets] += "-" + str(baseyear)
c.df.rename(index=rename, inplace=True)
# rename time-dependent
selection = n.component_attrs[c.name].type.str.contains(
"series"
) & n.component_attrs[c.name].status.str.contains("Input")
for attr in n.component_attrs[c.name].index[selection]:
c.pnl[attr].rename(columns=rename, inplace=True)
def add_existing_renewables(df_agg):
"""
Append existing renewables to the df_agg pd.DataFrame with the conventional
power plants.
"""
cc = pd.read_csv(snakemake.input.country_codes, index_col=0)
carriers = {"solar": "solar", "onwind": "onwind", "offwind": "offwind-ac"}
for tech in ["solar", "onwind", "offwind"]:
carrier = carriers[tech]
df = pd.read_csv(snakemake.input[f"existing_{tech}"], index_col=0).fillna(0.0)
df.columns = df.columns.astype(int)
rename_countries = {
"Czechia": "Czech Republic",
"UK": "United Kingdom",
"Bosnia Herzg": "Bosnia Herzegovina",
"North Macedonia": "Macedonia",
}
df.rename(index=rename_countries, inplace=True)
df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
# calculate yearly differences
df.insert(loc=0, value=0.0, column="1999")
df = df.diff(axis=1).drop("1999", axis=1).clip(lower=0)
# distribute capacities among nodes according to capacity factor
# weighting with nodal_fraction
elec_buses = n.buses.index[n.buses.carrier == "AC"].union(
n.buses.index[n.buses.carrier == "DC"]
)
nodal_fraction = pd.Series(0.0, elec_buses)
for country in n.buses.loc[elec_buses, "country"].unique():
gens = n.generators.index[
(n.generators.index.str[:2] == country)
& (n.generators.carrier == carrier)
]
cfs = n.generators_t.p_max_pu[gens].mean()
cfs_key = cfs / cfs.sum()
nodal_fraction.loc[n.generators.loc[gens, "bus"]] = cfs_key.values
nodal_df = df.loc[n.buses.loc[elec_buses, "country"]]
nodal_df.index = elec_buses
nodal_df = nodal_df.multiply(nodal_fraction, axis=0)
for year in nodal_df.columns:
for node in nodal_df.index:
name = f"{node}-{tech}-{year}"
capacity = nodal_df.loc[node, year]
if capacity > 0.0:
df_agg.at[name, "Fueltype"] = tech
df_agg.at[name, "Capacity"] = capacity
df_agg.at[name, "DateIn"] = year
df_agg.at[name, "cluster_bus"] = node
def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear):
"""
Parameters
----------
n : pypsa.Network
grouping_years :
intervals to group existing capacities
costs :
to read lifetime to estimate YearDecomissioning
baseyear : int
"""
logger.debug(
f"Adding power capacities installed before {baseyear} from powerplants.csv"
)
df_agg = pd.read_csv(snakemake.input.powerplants, index_col=0)
rename_fuel = {
"Hard Coal": "coal",
"Lignite": "lignite",
"Nuclear": "nuclear",
"Oil": "oil",
"OCGT": "OCGT",
"CCGT": "CCGT",
"Natural Gas": "gas",
"Bioenergy": "urban central solid biomass CHP",
}
fueltype_to_drop = [
"Hydro",
"Wind",
"Solar",
"Geothermal",
"Waste",
"Other",
"CCGT, Thermal",
]
technology_to_drop = ["Pv", "Storage Technologies"]
# drop unused fueltyps and technologies
df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)], inplace=True)
df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)], inplace=True)
df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
# Intermediate fix for DateIn & DateOut
# Fill missing DateIn
biomass_i = df_agg.loc[df_agg.Fueltype == "urban central solid biomass CHP"].index
mean = df_agg.loc[biomass_i, "DateIn"].mean()
df_agg.loc[biomass_i, "DateIn"] = df_agg.loc[biomass_i, "DateIn"].fillna(int(mean))
# Fill missing DateOut
dateout = df_agg.loc[biomass_i, "DateIn"] + snakemake.config["costs"]["lifetime"]
df_agg.loc[biomass_i, "DateOut"] = df_agg.loc[biomass_i, "DateOut"].fillna(dateout)
# drop assets which are already phased out / decommissioned
phased_out = df_agg[df_agg["DateOut"] < baseyear].index
df_agg.drop(phased_out, inplace=True)
# calculate remaining lifetime before phase-out (+1 because assuming
# phase out date at the end of the year)
df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
# assign clustered bus
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
inv_busmap = {}
for k, v in busmap.items():
inv_busmap[v] = inv_busmap.get(v, []) + [k]
clustermaps = busmap_s.map(busmap)
clustermaps.index = clustermaps.index.astype(int)
df_agg["cluster_bus"] = df_agg.bus.map(clustermaps)
# include renewables in df_agg
add_existing_renewables(df_agg)
df_agg["grouping_year"] = np.take(
grouping_years, np.digitize(df_agg.DateIn, grouping_years, right=True)
)
df = df_agg.pivot_table(
index=["grouping_year", "Fueltype"],
columns="cluster_bus",
values="Capacity",
aggfunc="sum",
)
lifetime = df_agg.pivot_table(
index=["grouping_year", "Fueltype"],
columns="cluster_bus",
values="lifetime",
aggfunc="mean", # currently taken mean for clustering lifetimes
)
carrier = {
"OCGT": "gas",
"CCGT": "gas",
"coal": "coal",
"oil": "oil",
"lignite": "lignite",
"nuclear": "uranium",
"urban central solid biomass CHP": "biomass",
}
for grouping_year, generator in df.index:
# capacity is the capacity in MW at each node for this
capacity = df.loc[grouping_year, generator]
capacity = capacity[~capacity.isna()]
capacity = capacity[
capacity > snakemake.config["existing_capacities"]["threshold_capacity"]
]
suffix = "-ac" if generator == "offwind" else ""
name_suffix = f" {generator}{suffix}-{grouping_year}"
asset_i = capacity.index + name_suffix
if generator in ["solar", "onwind", "offwind"]:
# to consider electricity grid connection costs or a split between
# solar utility and rooftop as well, rather take cost assumptions
# from existing network than from the cost database
capital_cost = n.generators.loc[
n.generators.carrier == generator + suffix, "capital_cost"
].mean()
marginal_cost = n.generators.loc[
n.generators.carrier == generator + suffix, "marginal_cost"
].mean()
# check if assets are already in network (e.g. for 2020)
already_build = n.generators.index.intersection(asset_i)
new_build = asset_i.difference(n.generators.index)
# this is for the year 2020
if not already_build.empty:
n.generators.loc[already_build, "p_nom_min"] = capacity.loc[
already_build.str.replace(name_suffix, "")
].values
new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
if "m" in snakemake.wildcards.clusters:
for ind in new_capacity.index:
# existing capacities are split evenly among regions in every country
inv_ind = [i for i in inv_busmap[ind]]
# for offshore the splitting only includes coastal regions
inv_ind = [
i for i in inv_ind if (i + name_suffix) in n.generators.index
]
p_max_pu = n.generators_t.p_max_pu[
[i + name_suffix for i in inv_ind]
]
p_max_pu.columns = [i + name_suffix for i in inv_ind]
n.madd(
"Generator",
[i + name_suffix for i in inv_ind],
bus=ind,
carrier=generator,
p_nom=new_capacity[ind]
/ len(inv_ind), # split among regions in a country
marginal_cost=marginal_cost,
capital_cost=capital_cost,
efficiency=costs.at[generator, "efficiency"],
p_max_pu=p_max_pu,
build_year=grouping_year,
lifetime=costs.at[generator, "lifetime"],
)
else:
p_max_pu = n.generators_t.p_max_pu[
capacity.index + f" {generator}{suffix}-{baseyear}"
]
if not new_build.empty:
n.madd(
"Generator",
new_capacity.index,
suffix=" " + name_suffix,
bus=new_capacity.index,
carrier=generator,
p_nom=new_capacity,
marginal_cost=marginal_cost,
capital_cost=capital_cost,
efficiency=costs.at[generator, "efficiency"],
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
build_year=grouping_year,
lifetime=costs.at[generator, "lifetime"],
)
else:
bus0 = vars(spatial)[carrier[generator]].nodes
if "EU" not in vars(spatial)[carrier[generator]].locations:
bus0 = bus0.intersection(capacity.index + " gas")
already_build = n.links.index.intersection(asset_i)
new_build = asset_i.difference(n.links.index)
lifetime_assets = lifetime.loc[grouping_year, generator].dropna()
# this is for the year 2020
if not already_build.empty:
n.links.loc[already_build, "p_nom_min"] = capacity.loc[
already_build.str.replace(name_suffix, "")
].values
if not new_build.empty:
new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
if generator != "urban central solid biomass CHP":
n.madd(
"Link",
new_capacity.index,
suffix=name_suffix,
bus0=bus0,
bus1=new_capacity.index,
bus2="co2 atmosphere",
carrier=generator,
marginal_cost=costs.at[generator, "efficiency"]
* costs.at[generator, "VOM"], # NB: VOM is per MWel
capital_cost=costs.at[generator, "efficiency"]
* costs.at[generator, "fixed"], # NB: fixed cost is per MWel
p_nom=new_capacity / costs.at[generator, "efficiency"],
efficiency=costs.at[generator, "efficiency"],
efficiency2=costs.at[carrier[generator], "CO2 intensity"],
build_year=grouping_year,
lifetime=lifetime_assets.loc[new_capacity.index],
)
else:
key = "central solid biomass CHP"
n.madd(
"Link",
new_capacity.index,
suffix=name_suffix,
bus0=spatial.biomass.df.loc[new_capacity.index]["nodes"].values,
bus1=new_capacity.index,
bus2=new_capacity.index + " urban central heat",
carrier=generator,
p_nom=new_capacity / costs.at[key, "efficiency"],
capital_cost=costs.at[key, "fixed"]
* costs.at[key, "efficiency"],
marginal_cost=costs.at[key, "VOM"],
efficiency=costs.at[key, "efficiency"],
build_year=grouping_year,
efficiency2=costs.at[key, "efficiency-heat"],
lifetime=lifetime_assets.loc[new_capacity.index],
)
# check if existing capacities are larger than technical potential
existing_large = n.generators[
n.generators["p_nom_min"] > n.generators["p_nom_max"]
].index
if len(existing_large):
logger.warning(
f"Existing capacities larger than technical potential for {existing_large},\
adjust technical potential to existing capacities"
)
n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
existing_large, "p_nom_min"
]
def add_heating_capacities_installed_before_baseyear(
n,
baseyear,
grouping_years,
ashp_cop,
gshp_cop,
time_dep_hp_cop,
costs,
default_lifetime,
):
"""
Parameters
----------
n : pypsa.Network
baseyear : last year covered in the existing capacities database
grouping_years : intervals to group existing capacities
linear decommissioning of heating capacities from 2020 to 2045 is
currently assumed heating capacities split between residential and
services proportional to heating load in both 50% capacities
in rural busess 50% in urban buses
"""
logger.debug(f"Adding heating capacities installed before {baseyear}")
# Add existing heating capacities, data comes from the study
# "Mapping and analyses of the current and future (2020 - 2030)
# heating/cooling fuel deployment (fossil/renewables) "
# https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
# TODO start from original file
# retrieve existing heating capacities
techs = [
"gas boiler",
"oil boiler",
"resistive heater",
"air heat pump",
"ground heat pump",
]
df = pd.read_csv(snakemake.input.existing_heating, index_col=0, header=0)
# data for Albania, Montenegro and Macedonia not included in database
df.loc["Albania"] = np.nan
df.loc["Montenegro"] = np.nan
df.loc["Macedonia"] = np.nan
df.fillna(0.0, inplace=True)
# convert GW to MW
df *= 1e3
cc = pd.read_csv(snakemake.input.country_codes, index_col=0)
df.rename(index=cc["2 letter code (ISO-3166-2)"], inplace=True)
# coal and oil boilers are assimilated to oil boilers
df["oil boiler"] = df["oil boiler"] + df["coal boiler"]
df.drop(["coal boiler"], axis=1, inplace=True)
# distribute technologies to nodes by population
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
nodal_df = df.loc[pop_layout.ct]
nodal_df.index = pop_layout.index
nodal_df = nodal_df.multiply(pop_layout.fraction, axis=0)
# split existing capacities between residential and services
# proportional to energy demand
ratio_residential = pd.Series(
[
(
n.loads_t.p_set.sum()["{} residential rural heat".format(node)]
/ (
n.loads_t.p_set.sum()["{} residential rural heat".format(node)]
+ n.loads_t.p_set.sum()["{} services rural heat".format(node)]
)
)
for node in nodal_df.index
],
index=nodal_df.index,
)
for tech in techs:
nodal_df["residential " + tech] = nodal_df[tech] * ratio_residential
nodal_df["services " + tech] = nodal_df[tech] * (1 - ratio_residential)
names = [
"residential rural",
"services rural",
"residential urban decentral",
"services urban decentral",
"urban central",
]
nodes = {}
p_nom = {}
for name in names:
name_type = "central" if name == "urban central" else "decentral"
nodes[name] = pd.Index(
[
n.buses.at[index, "location"]
for index in n.buses.index[
n.buses.index.str.contains(name)
& n.buses.index.str.contains("heat")
]
]
)
heat_pump_type = "air" if "urban" in name else "ground"
heat_type = "residential" if "residential" in name else "services"
if name == "urban central":
p_nom[name] = nodal_df["air heat pump"][nodes[name]]
else:
p_nom[name] = nodal_df[f"{heat_type} {heat_pump_type} heat pump"][
nodes[name]
]
# Add heat pumps
costs_name = f"decentral {heat_pump_type}-sourced heat pump"
cop = {"air": ashp_cop, "ground": gshp_cop}
if time_dep_hp_cop:
efficiency = cop[heat_pump_type][nodes[name]]
else:
efficiency = costs.at[costs_name, "efficiency"]
for i, grouping_year in enumerate(grouping_years):
if int(grouping_year) + default_lifetime <= int(baseyear):
continue
# installation is assumed to be linear for the past 25 years (default lifetime)
ratio = (int(grouping_year) - int(grouping_years[i - 1])) / default_lifetime
n.madd(
"Link",
nodes[name],
suffix=f" {name} {heat_pump_type} heat pump-{grouping_year}",
bus0=nodes[name],
bus1=nodes[name] + " " + name + " heat",
carrier=f"{name} {heat_pump_type} heat pump",
efficiency=efficiency,
capital_cost=costs.at[costs_name, "efficiency"]
* costs.at[costs_name, "fixed"],
p_nom=p_nom[name] * ratio / costs.at[costs_name, "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[costs_name, "lifetime"],
)
# add resistive heater, gas boilers and oil boilers
# (50% capacities to rural buses, 50% to urban buses)
n.madd(
"Link",
nodes[name],
suffix=f" {name} resistive heater-{grouping_year}",
bus0=nodes[name],
bus1=nodes[name] + " " + name + " heat",
carrier=name + " resistive heater",
efficiency=costs.at[name_type + " resistive heater", "efficiency"],
capital_cost=costs.at[name_type + " resistive heater", "efficiency"]
* costs.at[name_type + " resistive heater", "fixed"],
p_nom=0.5
* nodal_df[f"{heat_type} resistive heater"][nodes[name]]
* ratio
/ costs.at[name_type + " resistive heater", "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[costs_name, "lifetime"],
)
n.madd(
"Link",
nodes[name],
suffix=f" {name} gas boiler-{grouping_year}",
bus0=spatial.gas.nodes,
bus1=nodes[name] + " " + name + " heat",
bus2="co2 atmosphere",
carrier=name + " gas boiler",
efficiency=costs.at[name_type + " gas boiler", "efficiency"],
efficiency2=costs.at["gas", "CO2 intensity"],
capital_cost=costs.at[name_type + " gas boiler", "efficiency"]
* costs.at[name_type + " gas boiler", "fixed"],
p_nom=0.5
* nodal_df[f"{heat_type} gas boiler"][nodes[name]]
* ratio
/ costs.at[name_type + " gas boiler", "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[name_type + " gas boiler", "lifetime"],
)
n.madd(
"Link",
nodes[name],
suffix=f" {name} oil boiler-{grouping_year}",
bus0=spatial.oil.nodes,
bus1=nodes[name] + " " + name + " heat",
bus2="co2 atmosphere",
carrier=name + " oil boiler",
efficiency=costs.at["decentral oil boiler", "efficiency"],
efficiency2=costs.at["oil", "CO2 intensity"],
capital_cost=costs.at["decentral oil boiler", "efficiency"]
* costs.at["decentral oil boiler", "fixed"],
p_nom=0.5
* nodal_df[f"{heat_type} oil boiler"][nodes[name]]
* ratio
/ costs.at["decentral oil boiler", "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[name_type + " gas boiler", "lifetime"],
)
# delete links with p_nom=nan corresponding to extra nodes in country
n.mremove(
"Link",
[
index
for index in n.links.index.to_list()
if str(grouping_year) in index and np.isnan(n.links.p_nom[index])
],
)
# delete links with capacities below threshold
threshold = snakemake.config["existing_capacities"]["threshold_capacity"]
n.mremove(
"Link",
[
index
for index in n.links.index.to_list()
if str(grouping_year) in index and n.links.p_nom[index] < threshold
],
)
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"add_existing_baseyear",
simpl="",
clusters="45",
lv=1.0,
opts="",
sector_opts="8760H-T-H-B-I-A-solar+p3-dist1",
planning_horizons=2020,
)
logging.basicConfig(level=snakemake.config["logging_level"])
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
options = snakemake.config["sector"]
opts = snakemake.wildcards.sector_opts.split("-")
baseyear = snakemake.config["scenario"]["planning_horizons"][0]
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
# define spatial resolution of carriers
spatial = define_spatial(n.buses[n.buses.carrier == "AC"].index, options)
add_build_year_to_new_assets(n, baseyear)
Nyears = n.snapshot_weightings.generators.sum() / 8760.0
costs = prepare_costs(
snakemake.input.costs,
snakemake.config["costs"]["USD2013_to_EUR2013"],
snakemake.config["costs"]["discountrate"],
Nyears,
snakemake.config["costs"]["lifetime"],
)
grouping_years_power = snakemake.config["existing_capacities"][
"grouping_years_power"
]
grouping_years_heat = snakemake.config["existing_capacities"]["grouping_years_heat"]
add_power_capacities_installed_before_baseyear(
n, grouping_years_power, costs, baseyear
)
if "H" in opts:
time_dep_hp_cop = options["time_dep_hp_cop"]
ashp_cop = (
xr.open_dataarray(snakemake.input.cop_air_total)
.to_pandas()
.reindex(index=n.snapshots)
)
gshp_cop = (
xr.open_dataarray(snakemake.input.cop_soil_total)
.to_pandas()
.reindex(index=n.snapshots)
)
default_lifetime = snakemake.config["costs"]["lifetime"]
add_heating_capacities_installed_before_baseyear(
n,
baseyear,
grouping_years_heat,
ashp_cop,
gshp_cop,
time_dep_hp_cop,
costs,
default_lifetime,
)
if options.get("cluster_heat_buses", False):
cluster_heat_buses(n)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

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# -*- coding: utf-8 -*-
"""
Build ammonia production.
"""
import pandas as pd
country_to_alpha2 = {
"Austriae": "AT",
"Bulgaria": "BG",
"Belgiume": "BE",
"Croatia": "HR",
"Czechia": "CZ",
"Estonia": "EE",
"Finland": "FI",
"France": "FR",
"Germany": "DE",
"Greece": "GR",
"Hungarye": "HU",
"Italye": "IT",
"Lithuania": "LT",
"Netherlands": "NL",
"Norwaye": "NO",
"Poland": "PL",
"Romania": "RO",
"Serbia": "RS",
"Slovakia": "SK",
"Spain": "ES",
"Switzerland": "CH",
"United Kingdom": "GB",
}
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_ammonia_production")
ammonia = pd.read_excel(
snakemake.input.usgs,
sheet_name="T12",
skiprows=5,
header=0,
index_col=0,
skipfooter=19,
)
ammonia.rename(country_to_alpha2, inplace=True)
years = [str(i) for i in range(2013, 2018)]
countries = country_to_alpha2.values()
ammonia = ammonia.loc[countries, years].astype(float)
# convert from ktonN to ktonNH3
ammonia *= 17 / 14
ammonia.index.name = "ktonNH3/a"
ammonia.to_csv(snakemake.output.ammonia_production)

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# -*- coding: utf-8 -*-
import geopandas as gpd
import pandas as pd
def build_nuts_population_data(year=2013):
pop = pd.read_csv(
snakemake.input.nuts3_population,
sep=r"\,| \t|\t",
engine="python",
na_values=[":"],
index_col=1,
)[str(year)]
# only countries
pop.drop("EU28", inplace=True)
# mapping from Cantons to NUTS3
cantons = pd.read_csv(snakemake.input.swiss_cantons)
cantons = cantons.set_index(cantons.HASC.str[3:]).NUTS
cantons = cantons.str.pad(5, side="right", fillchar="0")
# get population by NUTS3
swiss = pd.read_excel(
snakemake.input.swiss_population, skiprows=3, index_col=0
).loc["Residents in 1000"]
swiss = swiss.rename(cantons).filter(like="CH")
# aggregate also to higher order NUTS levels
swiss = [swiss.groupby(swiss.index.str[:i]).sum() for i in range(2, 6)]
# merge Europe + Switzerland
pop = pd.concat([pop, pd.concat(swiss)]).to_frame("total")
# add missing manually
pop["AL"] = 2893
pop["BA"] = 3871
pop["RS"] = 7210
pop["ct"] = pop.index.str[:2]
return pop
def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
"""
Loads the JRC ENSPRESO biomass potentials.
Parameters
----------
year : int
The year for which potentials are to be taken.
Can be {2010, 2020, 2030, 2040, 2050}.
scenario : str
The scenario. Can be {"ENS_Low", "ENS_Med", "ENS_High"}.
Returns
-------
pd.DataFrame
Biomass potentials for given year and scenario
in TWh/a by commodity and NUTS2 region.
"""
glossary = pd.read_excel(
str(snakemake.input.enspreso_biomass),
sheet_name="Glossary",
usecols="B:D",
skiprows=1,
index_col=0,
)
df = pd.read_excel(
str(snakemake.input.enspreso_biomass),
sheet_name="ENER - NUTS2 BioCom E",
usecols="A:H",
)
df["group"] = df["E-Comm"].map(glossary.group)
df["commodity"] = df["E-Comm"].map(glossary.description)
to_rename = {
"NUTS2 Potential available by Bio Commodity": "potential",
"NUST2": "NUTS2",
}
df.rename(columns=to_rename, inplace=True)
# fill up with NUTS0 if NUTS2 is not given
df.NUTS2 = df.apply(lambda x: x.NUTS0 if x.NUTS2 == "-" else x.NUTS2, axis=1)
# convert PJ to TWh
df.potential /= 3.6
df.Unit = "TWh/a"
dff = df.query("Year == @year and Scenario == @scenario")
bio = dff.groupby(["NUTS2", "commodity"]).potential.sum().unstack()
# currently Serbia and Kosovo not split, so aggregate
bio.loc["RS"] += bio.loc["XK"]
bio.drop("XK", inplace=True)
return bio
def disaggregate_nuts0(bio):
"""
Some commodities are only given on NUTS0 level. These are disaggregated
here using the NUTS2 population as distribution key.
Parameters
----------
bio : pd.DataFrame
from enspreso_biomass_potentials()
Returns
-------
pd.DataFrame
"""
pop = build_nuts_population_data()
# get population in nuts2
pop_nuts2 = pop.loc[pop.index.str.len() == 4]
by_country = pop_nuts2.total.groupby(pop_nuts2.ct).sum()
pop_nuts2["fraction"] = pop_nuts2.total / pop_nuts2.ct.map(by_country)
# distribute nuts0 data to nuts2 by population
bio_nodal = bio.loc[pop_nuts2.ct]
bio_nodal.index = pop_nuts2.index
bio_nodal = bio_nodal.mul(pop_nuts2.fraction, axis=0)
# update inplace
bio.update(bio_nodal)
return bio
def build_nuts2_shapes():
"""
- load NUTS2 geometries
- add RS, AL, BA country shapes (not covered in NUTS 2013)
- consistently name ME, MK
"""
nuts2 = gpd.GeoDataFrame(
gpd.read_file(snakemake.input.nuts2).set_index("id").geometry
)
countries = gpd.read_file(snakemake.input.country_shapes).set_index("name")
missing_iso2 = countries.index.intersection(["AL", "RS", "BA"])
missing = countries.loc[missing_iso2]
nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
return pd.concat([nuts2, missing])
def area(gdf):
"""
Returns area of GeoDataFrame geometries in square kilometers.
"""
return gdf.to_crs(epsg=3035).area.div(1e6)
def convert_nuts2_to_regions(bio_nuts2, regions):
"""
Converts biomass potentials given in NUTS2 to PyPSA-Eur regions based on
the overlay of both GeoDataFrames in proportion to the area.
Parameters
----------
bio_nuts2 : gpd.GeoDataFrame
JRC ENSPRESO biomass potentials indexed by NUTS2 shapes.
regions : gpd.GeoDataFrame
PyPSA-Eur clustered onshore regions
Returns
-------
gpd.GeoDataFrame
"""
# calculate area of nuts2 regions
bio_nuts2["area_nuts2"] = area(bio_nuts2)
overlay = gpd.overlay(regions, bio_nuts2, keep_geom_type=True)
# calculate share of nuts2 area inside region
overlay["share"] = area(overlay) / overlay["area_nuts2"]
# multiply all nuts2-level values with share of nuts2 inside region
adjust_cols = overlay.columns.difference(
{"name", "area_nuts2", "geometry", "share"}
)
overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0)
bio_regions = overlay.groupby("name").sum()
bio_regions.drop(["area_nuts2", "share"], axis=1, inplace=True)
return bio_regions
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_biomass_potentials", simpl="", clusters="5")
config = snakemake.config["biomass"]
year = config["year"]
scenario = config["scenario"]
enspreso = enspreso_biomass_potentials(year, scenario)
enspreso = disaggregate_nuts0(enspreso)
nuts2 = build_nuts2_shapes()
df_nuts2 = gpd.GeoDataFrame(nuts2.geometry).join(enspreso)
regions = gpd.read_file(snakemake.input.regions_onshore)
df = convert_nuts2_to_regions(df_nuts2, regions)
df.to_csv(snakemake.output.biomass_potentials_all)
grouper = {v: k for k, vv in config["classes"].items() for v in vv}
df = df.groupby(grouper, axis=1).sum()
df *= 1e6 # TWh/a to MWh/a
df.index.name = "MWh/a"
df.to_csv(snakemake.output.biomass_potentials)

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# -*- coding: utf-8 -*-
"""
Reads biomass transport costs for different countries of the JRC report.
"The JRC-EU-TIMES model.
Bioenergy potentials
for EU and neighbouring countries."
(2015)
converts them from units 'EUR per km/ton' -> 'EUR/ (km MWh)'
assuming as an approximation energy content of wood pellets
@author: bw0928
"""
import pandas as pd
import tabula as tbl
ENERGY_CONTENT = 4.8 # unit MWh/t (wood pellets)
def get_countries():
pandas_options = dict(skiprows=range(6), header=None, index_col=0)
return tbl.read_pdf(
str(snakemake.input.transport_cost_data),
pages="145",
multiple_tables=False,
pandas_options=pandas_options,
)[0].index
def get_cost_per_tkm(page, countries):
pandas_options = dict(
skiprows=range(6),
header=0,
sep=" |,",
engine="python",
index_col=False,
)
sc = tbl.read_pdf(
str(snakemake.input.transport_cost_data),
pages=page,
multiple_tables=False,
pandas_options=pandas_options,
)[0]
sc.index = countries
sc.columns = sc.columns.str.replace("€", "EUR")
return sc
def build_biomass_transport_costs():
countries = get_countries()
sc1 = get_cost_per_tkm(146, countries)
sc2 = get_cost_per_tkm(147, countries)
# take mean of both supply chains
to_concat = [sc1["EUR/km/ton"], sc2["EUR/km/ton"]]
transport_costs = pd.concat(to_concat, axis=1).mean(axis=1)
# convert tonnes to MWh
transport_costs /= ENERGY_CONTENT
transport_costs.name = "EUR/km/MWh"
# rename country names
to_rename = {"UK": "GB", "XK": "KO", "EL": "GR"}
transport_costs.rename(to_rename, inplace=True)
# add missing Norway with data from Sweden
transport_costs["NO"] = transport_costs["SE"]
transport_costs.to_csv(snakemake.output[0])
if __name__ == "__main__":
build_biomass_transport_costs()

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# -*- coding: utf-8 -*-
"""
Build clustered population layouts.
"""
import atlite
import geopandas as gpd
import pandas as pd
import xarray as xr
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_clustered_population_layouts",
simpl="",
clusters=48,
)
cutout = atlite.Cutout(snakemake.config["atlite"]["cutout"])
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop = {}
for item in ["total", "urban", "rural"]:
pop_layout = xr.open_dataarray(snakemake.input[f"pop_layout_{item}"])
pop[item] = I.dot(pop_layout.stack(spatial=("y", "x")))
pop = pd.DataFrame(pop, index=clustered_regions.index)
pop["ct"] = pop.index.str[:2]
country_population = pop.total.groupby(pop.ct).sum()
pop["fraction"] = pop.total / pop.ct.map(country_population)
pop.to_csv(snakemake.output.clustered_pop_layout)

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# -*- coding: utf-8 -*-
"""
Build COP time series for air- or ground-sourced heat pumps.
"""
import xarray as xr
def coefficient_of_performance(delta_T, source="air"):
"""
COP is function of temp difference source to sink.
The quadratic regression is based on Staffell et al. (2012)
https://doi.org/10.1039/C2EE22653G.
"""
if source == "air":
return 6.81 - 0.121 * delta_T + 0.000630 * delta_T**2
elif source == "soil":
return 8.77 - 0.150 * delta_T + 0.000734 * delta_T**2
else:
raise NotImplementedError("'source' must be one of ['air', 'soil']")
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_cop_profiles",
simpl="",
clusters=48,
)
for area in ["total", "urban", "rural"]:
for source in ["air", "soil"]:
source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_{area}"])
delta_T = snakemake.config["sector"]["heat_pump_sink_T"] - source_T
cop = coefficient_of_performance(delta_T, source)
cop.to_netcdf(snakemake.output[f"cop_{source}_{area}"])

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# -*- coding: utf-8 -*-
import logging
logger = logging.getLogger(__name__)
import multiprocessing as mp
from functools import partial
import geopandas as gpd
import numpy as np
import pandas as pd
from helper import mute_print
from tqdm import tqdm
idx = pd.IndexSlice
def cartesian(s1, s2):
"""
Cartesian product of two pd.Series.
"""
return pd.DataFrame(np.outer(s1, s2), index=s1.index, columns=s2.index)
def reverse(dictionary):
"""
Reverses a keys and values of a dictionary.
"""
return {v: k for k, v in dictionary.items()}
# translations for Eurostat
eurostat_country_to_alpha2 = {
"EU28": "EU",
"EA19": "EA",
"Belgium": "BE",
"Bulgaria": "BG",
"Czech Republic": "CZ",
"Denmark": "DK",
"Germany": "DE",
"Estonia": "EE",
"Ireland": "IE",
"Greece": "GR",
"Spain": "ES",
"France": "FR",
"Croatia": "HR",
"Italy": "IT",
"Cyprus": "CY",
"Latvia": "LV",
"Lithuania": "LT",
"Luxembourg": "LU",
"Hungary": "HU",
"Malta": "MA",
"Netherlands": "NL",
"Austria": "AT",
"Poland": "PL",
"Portugal": "PT",
"Romania": "RO",
"Slovenia": "SI",
"Slovakia": "SK",
"Finland": "FI",
"Sweden": "SE",
"United Kingdom": "GB",
"Iceland": "IS",
"Norway": "NO",
"Montenegro": "ME",
"FYR of Macedonia": "MK",
"Albania": "AL",
"Serbia": "RS",
"Turkey": "TU",
"Bosnia and Herzegovina": "BA",
"Kosovo\n(UNSCR 1244/99)": "KO", # 2017 version
# 2016 version
"Kosovo\n(under United Nations Security Council Resolution 1244/99)": "KO",
"Moldova": "MO",
"Ukraine": "UK",
"Switzerland": "CH",
}
non_EU = ["NO", "CH", "ME", "MK", "RS", "BA", "AL"]
idees_rename = {"GR": "EL", "GB": "UK"}
eu28 = [
"FR",
"DE",
"GB",
"IT",
"ES",
"PL",
"SE",
"NL",
"BE",
"FI",
"CZ",
"DK",
"PT",
"RO",
"AT",
"BG",
"EE",
"GR",
"LV",
"HU",
"IE",
"SK",
"LT",
"HR",
"LU",
"SI",
] + ["CY", "MT"]
eu28_eea = eu28.copy()
eu28_eea.remove("GB")
eu28_eea.append("UK")
to_ipcc = {
"electricity": "1.A.1.a - Public Electricity and Heat Production",
"residential non-elec": "1.A.4.b - Residential",
"services non-elec": "1.A.4.a - Commercial/Institutional",
"rail non-elec": "1.A.3.c - Railways",
"road non-elec": "1.A.3.b - Road Transportation",
"domestic navigation": "1.A.3.d - Domestic Navigation",
"international navigation": "1.D.1.b - International Navigation",
"domestic aviation": "1.A.3.a - Domestic Aviation",
"international aviation": "1.D.1.a - International Aviation",
"total energy": "1 - Energy",
"industrial processes": "2 - Industrial Processes and Product Use",
"agriculture": "3 - Agriculture",
"agriculture, forestry and fishing": "1.A.4.c - Agriculture/Forestry/Fishing",
"LULUCF": "4 - Land Use, Land-Use Change and Forestry",
"waste management": "5 - Waste management",
"other": "6 - Other Sector",
"indirect": "ind_CO2 - Indirect CO2",
"total wL": "Total (with LULUCF)",
"total woL": "Total (without LULUCF)",
}
def build_eurostat(input_eurostat, countries, report_year, year):
"""
Return multi-index for all countries' energy data in TWh/a.
"""
filenames = {
2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx",
}
with mute_print():
dfs = pd.read_excel(
input_eurostat + filenames[report_year],
sheet_name=None,
skiprows=1,
index_col=list(range(4)),
)
# sorted_index necessary for slicing
lookup = eurostat_country_to_alpha2
labelled_dfs = {
lookup[df.columns[0]]: df
for df in dfs.values()
if lookup[df.columns[0]] in countries
}
df = pd.concat(labelled_dfs, sort=True).sort_index()
# drop non-numeric and country columns
non_numeric_cols = df.columns[df.dtypes != float]
country_cols = df.columns.intersection(lookup.keys())
to_drop = non_numeric_cols.union(country_cols)
df.drop(to_drop, axis=1, inplace=True)
# convert ktoe/a to TWh/a
df *= 11.63 / 1e3
return df
def build_swiss(year):
"""
Return a pd.Series of Swiss energy data in TWh/a.
"""
fn = snakemake.input.swiss
df = pd.read_csv(fn, index_col=[0, 1]).loc["CH", str(year)]
# convert PJ/a to TWh/a
df /= 3.6
return df
def idees_per_country(ct, year):
base_dir = snakemake.input.idees
ct_totals = {}
ct_idees = idees_rename.get(ct, ct)
fn_residential = f"{base_dir}/JRC-IDEES-2015_Residential_{ct_idees}.xlsx"
fn_tertiary = f"{base_dir}/JRC-IDEES-2015_Tertiary_{ct_idees}.xlsx"
fn_transport = f"{base_dir}/JRC-IDEES-2015_Transport_{ct_idees}.xlsx"
# residential
df = pd.read_excel(fn_residential, "RES_hh_fec", index_col=0)[year]
ct_totals["total residential space"] = df["Space heating"]
rows = ["Advanced electric heating", "Conventional electric heating"]
ct_totals["electricity residential space"] = df[rows].sum()
ct_totals["total residential water"] = df.at["Water heating"]
assert df.index[23] == "Electricity"
ct_totals["electricity residential water"] = df[23]
ct_totals["total residential cooking"] = df["Cooking"]
assert df.index[30] == "Electricity"
ct_totals["electricity residential cooking"] = df[30]
df = pd.read_excel(fn_residential, "RES_summary", index_col=0)[year]
row = "Energy consumption by fuel - Eurostat structure (ktoe)"
ct_totals["total residential"] = df[row]
assert df.index[47] == "Electricity"
ct_totals["electricity residential"] = df[47]
assert df.index[46] == "Derived heat"
ct_totals["derived heat residential"] = df[46]
assert df.index[50] == "Thermal uses"
ct_totals["thermal uses residential"] = df[50]
# services
df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)[year]
ct_totals["total services space"] = df["Space heating"]
rows = ["Advanced electric heating", "Conventional electric heating"]
ct_totals["electricity services space"] = df[rows].sum()
ct_totals["total services water"] = df["Hot water"]
assert df.index[24] == "Electricity"
ct_totals["electricity services water"] = df[24]
ct_totals["total services cooking"] = df["Catering"]
assert df.index[31] == "Electricity"
ct_totals["electricity services cooking"] = df[31]
df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]
row = "Energy consumption by fuel - Eurostat structure (ktoe)"
ct_totals["total services"] = df[row]
assert df.index[50] == "Electricity"
ct_totals["electricity services"] = df[50]
assert df.index[49] == "Derived heat"
ct_totals["derived heat services"] = df[49]
assert df.index[53] == "Thermal uses"
ct_totals["thermal uses services"] = df[53]
# agriculture, forestry and fishing
start = "Detailed split of energy consumption (ktoe)"
end = "Market shares of energy uses (%)"
df = pd.read_excel(fn_tertiary, "AGR_fec", index_col=0).loc[start:end, year]
rows = [
"Lighting",
"Ventilation",
"Specific electricity uses",
"Pumping devices (electric)",
]
ct_totals["total agriculture electricity"] = df[rows].sum()
rows = ["Specific heat uses", "Low enthalpy heat"]
ct_totals["total agriculture heat"] = df[rows].sum()
rows = [
"Motor drives",
"Farming machine drives (diesel oil incl. biofuels)",
"Pumping devices (diesel oil incl. biofuels)",
]
ct_totals["total agriculture machinery"] = df[rows].sum()
row = "Agriculture, forestry and fishing"
ct_totals["total agriculture"] = df[row]
# transport
df = pd.read_excel(fn_transport, "TrRoad_ene", index_col=0)[year]
ct_totals["total road"] = df["by fuel (EUROSTAT DATA)"]
ct_totals["electricity road"] = df["Electricity"]
ct_totals["total two-wheel"] = df["Powered 2-wheelers (Gasoline)"]
assert df.index[19] == "Passenger cars"
ct_totals["total passenger cars"] = df[19]
assert df.index[30] == "Battery electric vehicles"
ct_totals["electricity passenger cars"] = df[30]
assert df.index[31] == "Motor coaches, buses and trolley buses"
ct_totals["total other road passenger"] = df[31]
assert df.index[39] == "Battery electric vehicles"
ct_totals["electricity other road passenger"] = df[39]
assert df.index[41] == "Light duty vehicles"
ct_totals["total light duty road freight"] = df[41]
assert df.index[49] == "Battery electric vehicles"
ct_totals["electricity light duty road freight"] = df[49]
row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
ct_totals["total heavy duty road freight"] = df[row]
assert df.index[61] == "Passenger cars"
ct_totals["passenger car efficiency"] = df[61]
df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)[year]
ct_totals["total rail"] = df["by fuel (EUROSTAT DATA)"]
ct_totals["electricity rail"] = df["Electricity"]
assert df.index[15] == "Passenger transport"
ct_totals["total rail passenger"] = df[15]
assert df.index[16] == "Metro and tram, urban light rail"
assert df.index[19] == "Electric"
assert df.index[20] == "High speed passenger trains"
ct_totals["electricity rail passenger"] = df[[16, 19, 20]].sum()
assert df.index[21] == "Freight transport"
ct_totals["total rail freight"] = df[21]
assert df.index[23] == "Electric"
ct_totals["electricity rail freight"] = df[23]
df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)[year]
assert df.index[6] == "Passenger transport"
ct_totals["total aviation passenger"] = df[6]
assert df.index[10] == "Freight transport"
ct_totals["total aviation freight"] = df[10]
assert df.index[7] == "Domestic"
ct_totals["total domestic aviation passenger"] = df[7]
assert df.index[8] == "International - Intra-EU"
assert df.index[9] == "International - Extra-EU"
ct_totals["total international aviation passenger"] = df[[8, 9]].sum()
assert df.index[11] == "Domestic and International - Intra-EU"
ct_totals["total domestic aviation freight"] = df[11]
assert df.index[12] == "International - Extra-EU"
ct_totals["total international aviation freight"] = df[12]
ct_totals["total domestic aviation"] = (
ct_totals["total domestic aviation freight"]
+ ct_totals["total domestic aviation passenger"]
)
ct_totals["total international aviation"] = (
ct_totals["total international aviation freight"]
+ ct_totals["total international aviation passenger"]
)
df = pd.read_excel(fn_transport, "TrNavi_ene", index_col=0)[year]
# coastal and inland
ct_totals["total domestic navigation"] = df["by fuel (EUROSTAT DATA)"]
df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)[year]
assert df.index[85] == "Passenger cars"
ct_totals["passenger cars"] = df[85]
return pd.Series(ct_totals, name=ct)
def build_idees(countries, year):
nprocesses = snakemake.threads
func = partial(idees_per_country, year=year)
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build from IDEES database",
)
with mute_print():
with mp.Pool(processes=nprocesses) as pool:
totals_list = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
totals = pd.concat(totals_list, axis=1)
# convert ktoe to TWh
exclude = totals.index.str.fullmatch("passenger cars")
totals.loc[~exclude] *= 11.63 / 1e3
# convert TWh/100km to kWh/km
totals.loc["passenger car efficiency"] *= 10
# district heating share
district_heat = totals.loc[
["derived heat residential", "derived heat services"]
].sum()
total_heat = totals.loc[["thermal uses residential", "thermal uses services"]].sum()
totals.loc["district heat share"] = district_heat.div(total_heat)
return totals.T
def build_energy_totals(countries, eurostat, swiss, idees):
eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"}
to_drop = ["passenger cars", "passenger car efficiency"]
df = idees.reindex(countries).drop(to_drop, axis=1)
eurostat_countries = eurostat.index.levels[0]
in_eurostat = df.index.intersection(eurostat_countries)
# add international navigation
slicer = idx[in_eurostat, :, "Bunkers", :]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
df.loc[in_eurostat, "total international navigation"] = fill_values
# add swiss energy data
df.loc["CH"] = swiss
# get values for missing countries based on Eurostat EnergyBalances
# divide cooking/space/water according to averages in EU28
missing = df.index[df["total residential"].isna()]
to_fill = missing.intersection(eurostat_countries)
uses = ["space", "cooking", "water"]
for sector in ["residential", "services", "road", "rail"]:
eurostat_sector = sector.capitalize()
# fuel use
for fuel in ["electricity", "total"]:
slicer = idx[to_fill, :, :, eurostat_sector]
fill_values = (
eurostat.loc[slicer, eurostat_fuels[fuel]].groupby(level=0).sum()
)
df.loc[to_fill, f"{fuel} {sector}"] = fill_values
for sector in ["residential", "services"]:
# electric use
for use in uses:
fuel_use = df[f"electricity {sector} {use}"]
fuel = df[f"electricity {sector}"]
avg = fuel_use.div(fuel).mean()
logger.debug(
f"{sector}: average fraction of electricity for {use} is {avg:.3f}"
)
df.loc[to_fill, f"electricity {sector} {use}"] = (
avg * df.loc[to_fill, f"electricity {sector}"]
)
# non-electric use
for use in uses:
nonelectric_use = (
df[f"total {sector} {use}"] - df[f"electricity {sector} {use}"]
)
nonelectric = df[f"total {sector}"] - df[f"electricity {sector}"]
avg = nonelectric_use.div(nonelectric).mean()
logger.debug(
f"{sector}: average fraction of non-electric for {use} is {avg:.3f}"
)
electric_use = df.loc[to_fill, f"electricity {sector} {use}"]
nonelectric = (
df.loc[to_fill, f"total {sector}"]
- df.loc[to_fill, f"electricity {sector}"]
)
df.loc[to_fill, f"total {sector} {use}"] = electric_use + avg * nonelectric
# Fix Norway space and water heating fractions
# http://www.ssb.no/en/energi-og-industri/statistikker/husenergi/hvert-3-aar/2014-07-14
# The main heating source for about 73 per cent of the households is based on electricity
# => 26% is non-electric
elec_fraction = 0.73
no_norway = df.drop("NO")
for sector in ["residential", "services"]:
# assume non-electric is heating
nonelectric = (
df.loc["NO", f"total {sector}"] - df.loc["NO", f"electricity {sector}"]
)
total_heating = nonelectric / (1 - elec_fraction)
for use in uses:
nonelectric_use = (
no_norway[f"total {sector} {use}"]
- no_norway[f"electricity {sector} {use}"]
)
nonelectric = (
no_norway[f"total {sector}"] - no_norway[f"electricity {sector}"]
)
fraction = nonelectric_use.div(nonelectric).mean()
df.loc["NO", f"total {sector} {use}"] = total_heating * fraction
df.loc["NO", f"electricity {sector} {use}"] = (
total_heating * fraction * elec_fraction
)
# Missing aviation
slicer = idx[to_fill, :, :, "Domestic aviation"]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
df.loc[to_fill, "total domestic aviation"] = fill_values
slicer = idx[to_fill, :, :, "International aviation"]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
df.loc[to_fill, "total international aviation"] = fill_values
# missing domestic navigation
slicer = idx[to_fill, :, :, "Domestic Navigation"]
fill_values = eurostat.loc[slicer, "Total all products"].groupby(level=0).sum()
df.loc[to_fill, "total domestic navigation"] = fill_values
# split road traffic for non-IDEES
missing = df.index[df["total passenger cars"].isna()]
for fuel in ["total", "electricity"]:
selection = [
f"{fuel} passenger cars",
f"{fuel} other road passenger",
f"{fuel} light duty road freight",
]
if fuel == "total":
selection.extend([f"{fuel} two-wheel", f"{fuel} heavy duty road freight"])
road = df[selection].sum()
road_fraction = road / road.sum()
fill_values = cartesian(df.loc[missing, f"{fuel} road"], road_fraction)
df.loc[missing, road_fraction.index] = fill_values
# split rail traffic for non-IDEES
missing = df.index[df["total rail passenger"].isna()]
for fuel in ["total", "electricity"]:
selection = [f"{fuel} rail passenger", f"{fuel} rail freight"]
rail = df[selection].sum()
rail_fraction = rail / rail.sum()
fill_values = cartesian(df.loc[missing, f"{fuel} rail"], rail_fraction)
df.loc[missing, rail_fraction.index] = fill_values
# split aviation traffic for non-IDEES
missing = df.index[df["total domestic aviation passenger"].isna()]
for destination in ["domestic", "international"]:
selection = [
f"total {destination} aviation passenger",
f"total {destination} aviation freight",
]
aviation = df[selection].sum()
aviation_fraction = aviation / aviation.sum()
fill_values = cartesian(
df.loc[missing, f"total {destination} aviation"], aviation_fraction
)
df.loc[missing, aviation_fraction.index] = fill_values
for purpose in ["passenger", "freight"]:
attrs = [
f"total domestic aviation {purpose}",
f"total international aviation {purpose}",
]
df.loc[missing, f"total aviation {purpose}"] = df.loc[missing, attrs].sum(
axis=1
)
if "BA" in df.index:
# fill missing data for BA (services and road energy data)
# proportional to RS with ratio of total residential demand
missing = df.loc["BA"] == 0.0
ratio = df.at["BA", "total residential"] / df.at["RS", "total residential"]
df.loc["BA", missing] = ratio * df.loc["RS", missing]
# Missing district heating share
dh_share = pd.read_csv(
snakemake.input.district_heat_share, index_col=0, usecols=[0, 1]
)
# make conservative assumption and take minimum from both data sets
df["district heat share"] = pd.concat(
[df["district heat share"], dh_share.reindex(index=df.index) / 100], axis=1
).min(axis=1)
return df
def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
# https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
# downloaded 201228 (modified by EEA last on 201221)
df = pd.read_csv(input_co2, encoding="latin-1", low_memory=False)
df.replace(dict(Year="1985-1987"), 1986, inplace=True)
df.Year = df.Year.astype(int)
index_col = ["Country_code", "Pollutant_name", "Year", "Sector_name"]
df = df.set_index(index_col).sort_index()
emissions_scope = emissions_scope
cts = ["CH", "EUA", "NO"] + eu28_eea
slicer = idx[cts, emissions_scope, year, to_ipcc.values()]
emissions = (
df.loc[slicer, "emissions"]
.unstack("Sector_name")
.rename(columns=reverse(to_ipcc))
.droplevel([1, 2])
)
emissions.rename(index={"EUA": "EU28", "UK": "GB"}, inplace=True)
to_subtract = [
"electricity",
"services non-elec",
"residential non-elec",
"road non-elec",
"rail non-elec",
"domestic aviation",
"international aviation",
"domestic navigation",
"international navigation",
"agriculture, forestry and fishing",
]
emissions["industrial non-elec"] = emissions["total energy"] - emissions[
to_subtract
].sum(axis=1)
emissions["agriculture"] += emissions["agriculture, forestry and fishing"]
to_drop = [
"total energy",
"total wL",
"total woL",
"agriculture, forestry and fishing",
]
emissions.drop(columns=to_drop, inplace=True)
# convert from Gg to Mt
return emissions / 1e3
def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
eurostat = build_eurostat(input_eurostat, countries, report_year, year)
specific_emissions = pd.Series(index=eurostat.columns, dtype=float)
# emissions in tCO2_equiv per MWh_th
specific_emissions["Solid fuels"] = 0.36 # Approximates coal
specific_emissions["Oil (total)"] = 0.285 # Average of distillate and residue
specific_emissions["Gas"] = 0.2 # For natural gas
# oil values from https://www.eia.gov/tools/faqs/faq.cfm?id=74&t=11
# Distillate oil (No. 2) 0.276
# Residual oil (No. 6) 0.298
# https://www.eia.gov/electricity/annual/html/epa_a_03.html
return eurostat.multiply(specific_emissions).sum(axis=1)
def build_co2_totals(countries, eea_co2, eurostat_co2):
co2 = eea_co2.reindex(countries)
for ct in countries.intersection(["BA", "RS", "AL", "ME", "MK"]):
mappings = {
"electricity": (
ct,
"+",
"Conventional Thermal Power Stations",
"of which From Coal",
),
"residential non-elec": (ct, "+", "+", "Residential"),
"services non-elec": (ct, "+", "+", "Services"),
"road non-elec": (ct, "+", "+", "Road"),
"rail non-elec": (ct, "+", "+", "Rail"),
"domestic navigation": (ct, "+", "+", "Domestic Navigation"),
"international navigation": (ct, "-", "Bunkers"),
"domestic aviation": (ct, "+", "+", "Domestic aviation"),
"international aviation": (ct, "+", "+", "International aviation"),
# does not include industrial process emissions or fuel processing/refining
"industrial non-elec": (ct, "+", "Industry"),
# does not include non-energy emissions
"agriculture": (eurostat_co2.index.get_level_values(0) == ct)
& eurostat_co2.index.isin(["Agriculture / Forestry", "Fishing"], level=3),
}
for i, mi in mappings.items():
co2.at[ct, i] = eurostat_co2.loc[mi].sum()
return co2
def build_transport_data(countries, population, idees):
transport_data = pd.DataFrame(index=countries)
# collect number of cars
transport_data["number cars"] = idees["passenger cars"]
# CH from http://ec.europa.eu/eurostat/statistics-explained/index.php/Passenger_cars_in_the_EU#Luxembourg_has_the_highest_number_of_passenger_cars_per_inhabitant
transport_data.at["CH", "number cars"] = 4.136e6
missing = transport_data.index[transport_data["number cars"].isna()]
logger.info(
f"Missing data on cars from:\n{list(missing)}\nFilling gaps with averaged data."
)
cars_pp = transport_data["number cars"] / population
transport_data.loc[missing, "number cars"] = cars_pp.mean() * population
# collect average fuel efficiency in kWh/km
transport_data["average fuel efficiency"] = idees["passenger car efficiency"]
missing = transport_data.index[transport_data["average fuel efficiency"].isna()]
logger.info(
f"Missing data on fuel efficiency from:\n{list(missing)}\nFilling gapswith averaged data."
)
fill_values = transport_data["average fuel efficiency"].mean()
transport_data.loc[missing, "average fuel efficiency"] = fill_values
return transport_data
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_energy_totals")
logging.basicConfig(level=snakemake.config["logging_level"])
config = snakemake.config["energy"]
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")
population = nuts3["pop"].groupby(nuts3.country).sum()
countries = population.index
idees_countries = countries.intersection(eu28)
data_year = config["energy_totals_year"]
report_year = snakemake.config["energy"]["eurostat_report_year"]
input_eurostat = snakemake.input.eurostat
eurostat = build_eurostat(input_eurostat, countries, report_year, data_year)
swiss = build_swiss(data_year)
idees = build_idees(idees_countries, data_year)
energy = build_energy_totals(countries, eurostat, swiss, idees)
energy.to_csv(snakemake.output.energy_name)
base_year_emissions = config["base_emissions_year"]
emissions_scope = snakemake.config["energy"]["emissions"]
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
eurostat_co2 = build_eurostat_co2(
input_eurostat, countries, report_year, base_year_emissions
)
co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
co2.to_csv(snakemake.output.co2_name)
transport = build_transport_data(countries, population, idees)
transport.to_csv(snakemake.output.transport_name)

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# -*- coding: utf-8 -*-
"""
Build import locations for fossil gas from entry-points, LNG terminals and
production sites.
"""
import logging
logger = logging.getLogger(__name__)
import geopandas as gpd
import pandas as pd
from cluster_gas_network import load_bus_regions
from shapely import wkt
def read_scigrid_gas(fn):
df = gpd.read_file(fn)
df = pd.concat([df, df.param.apply(pd.Series)], axis=1)
df.drop(["param", "uncertainty", "method"], axis=1, inplace=True)
return df
def build_gem_lng_data(lng_fn):
df = pd.read_excel(lng_fn[0], sheet_name="LNG terminals - data")
df = df.set_index("ComboID")
remove_status = ["Cancelled"]
remove_country = ["Cyprus", "Turkey"]
remove_terminal = ["Puerto de la Luz LNG Terminal", "Gran Canaria LNG Terminal"]
df = df.query(
"Status != 'Cancelled' \
& Country != @remove_country \
& TerminalName != @remove_terminal \
& CapacityInMtpa != '--'"
)
geometry = gpd.points_from_xy(df["Longitude"], df["Latitude"])
return gpd.GeoDataFrame(df, geometry=geometry, crs="EPSG:4326")
def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
# LNG terminals
lng = build_gem_lng_data(lng_fn)
# Entry points from outside the model scope
entry = read_scigrid_gas(entry_fn)
entry["from_country"] = entry.from_country.str.rstrip()
entry = entry.loc[
~(entry.from_country.isin(countries) & entry.to_country.isin(countries))
& ~entry.name.str.contains("Tegelen") # only take non-EU entries
| (entry.from_country == "NO") # malformed datapoint # entries from NO to GB
]
# production sites inside the model scope
prod = read_scigrid_gas(prod_fn)
prod = prod.loc[
(prod.geometry.y > 35) & (prod.geometry.x < 30) & (prod.country_code != "DE")
]
mcm_per_day_to_mw = 437.5 # MCM/day to MWh/h
mtpa_to_mw = 1649.224 # mtpa to MWh/h
lng["p_nom"] = lng["CapacityInMtpa"] * mtpa_to_mw
entry["p_nom"] = entry["max_cap_from_to_M_m3_per_d"] * mcm_per_day_to_mw
prod["p_nom"] = prod["max_supply_M_m3_per_d"] * mcm_per_day_to_mw
lng["type"] = "lng"
entry["type"] = "pipeline"
prod["type"] = "production"
sel = ["geometry", "p_nom", "type"]
return pd.concat([prod[sel], entry[sel], lng[sel]], ignore_index=True)
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_gas_input_locations",
simpl="",
clusters="37",
)
logging.basicConfig(level=snakemake.config["logging_level"])
regions = load_bus_regions(
snakemake.input.regions_onshore, snakemake.input.regions_offshore
)
# add a buffer to eastern countries because some
# entry points are still in Russian or Ukrainian territory.
buffer = 9000 # meters
eastern_countries = ["FI", "EE", "LT", "LV", "PL", "SK", "HU", "RO"]
add_buffer_b = regions.index.str[:2].isin(eastern_countries)
regions.loc[add_buffer_b] = (
regions[add_buffer_b].to_crs(3035).buffer(buffer).to_crs(4326)
)
countries = regions.index.str[:2].unique().str.replace("GB", "UK")
gas_input_locations = build_gas_input_locations(
snakemake.input.lng,
snakemake.input.entry,
snakemake.input.production,
countries,
)
gas_input_nodes = gpd.sjoin(gas_input_locations, regions, how="left")
gas_input_nodes.rename(columns={"index_right": "bus"}, inplace=True)
gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver="GeoJSON")
gas_input_nodes_s = (
gas_input_nodes.groupby(["bus", "type"])["p_nom"].sum().unstack()
)
gas_input_nodes_s.columns.name = "p_nom"
gas_input_nodes_s.to_csv(snakemake.output.gas_input_nodes_simplified)

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# -*- coding: utf-8 -*-
"""
Preprocess gas network based on data from bthe SciGRID Gas project
(https://www.gas.scigrid.de/).
"""
import logging
logger = logging.getLogger(__name__)
import geopandas as gpd
import pandas as pd
from pypsa.geo import haversine_pts
from shapely.geometry import Point
def diameter_to_capacity(pipe_diameter_mm):
"""
Calculate pipe capacity in MW based on diameter in mm.
20 inch (500 mm) 50 bar -> 1.5 GW CH4 pipe capacity (LHV)
24 inch (600 mm) 50 bar -> 5 GW CH4 pipe capacity (LHV)
36 inch (900 mm) 50 bar -> 11.25 GW CH4 pipe capacity (LHV)
48 inch (1200 mm) 80 bar -> 21.7 GW CH4 pipe capacity (LHV)
Based on p.15 of https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
"""
# slopes definitions
m0 = (1500 - 0) / (500 - 0)
m1 = (5000 - 1500) / (600 - 500)
m2 = (11250 - 5000) / (900 - 600)
m3 = (21700 - 11250) / (1200 - 900)
# intercept
a0 = 0
a1 = -16000
a2 = -7500
a3 = -20100
if pipe_diameter_mm < 500:
return a0 + m0 * pipe_diameter_mm
elif pipe_diameter_mm < 600:
return a1 + m1 * pipe_diameter_mm
elif pipe_diameter_mm < 900:
return a2 + m2 * pipe_diameter_mm
else:
return a3 + m3 * pipe_diameter_mm
def load_dataset(fn):
df = gpd.read_file(fn)
param = df.param.apply(pd.Series)
method = df.method.apply(pd.Series)[["diameter_mm", "max_cap_M_m3_per_d"]]
method.columns = method.columns + "_method"
df = pd.concat([df, param, method], axis=1)
to_drop = ["param", "uncertainty", "method", "tags"]
to_drop = df.columns.intersection(to_drop)
df.drop(to_drop, axis=1, inplace=True)
return df
def prepare_dataset(
df,
length_factor=1.5,
correction_threshold_length=4,
correction_threshold_p_nom=8,
bidirectional_below=10,
):
# extract start and end from LineString
df["point0"] = df.geometry.apply(lambda x: Point(x.coords[0]))
df["point1"] = df.geometry.apply(lambda x: Point(x.coords[-1]))
conversion_factor = 437.5 # MCM/day to MWh/h
df["p_nom"] = df.max_cap_M_m3_per_d * conversion_factor
# for inferred diameters, assume 500 mm rather than 900 mm (more conservative)
df.loc[df.diameter_mm_method != "raw", "diameter_mm"] = 500.0
keep = [
"name",
"diameter_mm",
"is_H_gas",
"is_bothDirection",
"length_km",
"p_nom",
"max_pressure_bar",
"start_year",
"point0",
"point1",
"geometry",
]
to_rename = {
"is_bothDirection": "bidirectional",
"is_H_gas": "H_gas",
"start_year": "build_year",
"length_km": "length",
}
df = df[keep].rename(columns=to_rename)
df.bidirectional = df.bidirectional.astype(bool)
df.H_gas = df.H_gas.astype(bool)
# short lines below 10 km are assumed to be bidirectional
short_lines = df["length"] < bidirectional_below
df.loc[short_lines, "bidirectional"] = True
# correct all capacities that deviate correction_threshold factor
# to diameter-based capacities, unless they are NordStream pipelines
# also all capacities below 0.5 GW are now diameter-based capacities
df["p_nom_diameter"] = df.diameter_mm.apply(diameter_to_capacity)
ratio = df.p_nom / df.p_nom_diameter
not_nordstream = df.max_pressure_bar < 220
df.p_nom.update(
df.p_nom_diameter.where(
(df.p_nom <= 500)
| ((ratio > correction_threshold_p_nom) & not_nordstream)
| ((ratio < 1 / correction_threshold_p_nom) & not_nordstream)
)
)
# lines which have way too discrepant line lengths
# get assigned haversine length * length factor
df["length_haversine"] = df.apply(
lambda p: length_factor
* haversine_pts([p.point0.x, p.point0.y], [p.point1.x, p.point1.y]),
axis=1,
)
ratio = df.eval("length / length_haversine")
df["length"].update(
df.length_haversine.where(
(df["length"] < 20)
| (ratio > correction_threshold_length)
| (ratio < 1 / correction_threshold_length)
)
)
return df
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_gas_network")
logging.basicConfig(level=snakemake.config["logging_level"])
gas_network = load_dataset(snakemake.input.gas_network)
gas_network = prepare_dataset(gas_network)
gas_network.to_csv(snakemake.output.cleaned_gas_network)

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# -*- coding: utf-8 -*-
"""
Build heat demand time series.
"""
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
from dask.distributed import Client, LocalCluster
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_heat_demands",
simpl="",
clusters=48,
)
nprocesses = int(snakemake.threads)
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
time = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout_config = snakemake.config["atlite"]["cutout"]
cutout = atlite.Cutout(cutout_config).sel(time=time)
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
stacked_pop = pop_layout.stack(spatial=("y", "x"))
M = I.T.dot(np.diag(I.dot(stacked_pop)))
heat_demand = cutout.heat_demand(
matrix=M.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False,
)
heat_demand.to_netcdf(snakemake.output.heat_demand)

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# -*- coding: utf-8 -*-
"""
Build industrial distribution keys from hotmaps database.
"""
import logging
logger = logging.getLogger(__name__)
import uuid
from itertools import product
import geopandas as gpd
import pandas as pd
from packaging.version import Version, parse
def locate_missing_industrial_sites(df):
"""
Locate industrial sites without valid locations based on city and
countries.
Should only be used if the model's spatial resolution is coarser
than individual cities.
"""
try:
from geopy.extra.rate_limiter import RateLimiter
from geopy.geocoders import Nominatim
except:
raise ModuleNotFoundError(
"Optional dependency 'geopy' not found."
"Install via 'conda install -c conda-forge geopy'"
"or set 'industry: hotmaps_locate_missing: false'."
)
locator = Nominatim(user_agent=str(uuid.uuid4()))
geocode = RateLimiter(locator.geocode, min_delay_seconds=2)
def locate_missing(s):
if pd.isna(s.City) or s.City == "CONFIDENTIAL":
return None
loc = geocode([s.City, s.Country], geometry="wkt")
if loc is not None:
logger.debug(f"Found:\t{loc}\nFor:\t{s['City']}, {s['Country']}\n")
return f"POINT({loc.longitude} {loc.latitude})"
else:
return None
missing = df.index[df.geom.isna()]
df.loc[missing, "coordinates"] = df.loc[missing].apply(locate_missing, axis=1)
# report stats
num_still_missing = df.coordinates.isna().sum()
num_found = len(missing) - num_still_missing
share_missing = len(missing) / len(df) * 100
share_still_missing = num_still_missing / len(df) * 100
logger.warning(
f"Found {num_found} missing locations. \nShare of missing locations reduced from {share_missing:.2f}% to {share_still_missing:.2f}%."
)
return df
def prepare_hotmaps_database(regions):
"""
Load hotmaps database of industrial sites and map onto bus regions.
"""
df = pd.read_csv(snakemake.input.hotmaps_industrial_database, sep=";", index_col=0)
df[["srid", "coordinates"]] = df.geom.str.split(";", expand=True)
if snakemake.config["industry"].get("hotmaps_locate_missing", False):
df = locate_missing_industrial_sites(df)
# remove those sites without valid locations
df.drop(df.index[df.coordinates.isna()], inplace=True)
df["coordinates"] = gpd.GeoSeries.from_wkt(df["coordinates"])
gdf = gpd.GeoDataFrame(df, geometry="coordinates", crs="EPSG:4326")
kws = (
dict(op="within")
if parse(gpd.__version__) < Version("0.10")
else dict(predicate="within")
)
gdf = gpd.sjoin(gdf, regions, how="inner", **kws)
gdf.rename(columns={"index_right": "bus"}, inplace=True)
gdf["country"] = gdf.bus.str[:2]
return gdf
def build_nodal_distribution_key(hotmaps, regions):
"""
Build nodal distribution keys for each sector.
"""
sectors = hotmaps.Subsector.unique()
countries = regions.index.str[:2].unique()
keys = pd.DataFrame(index=regions.index, columns=sectors, dtype=float)
pop = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
pop["country"] = pop.index.str[:2]
ct_total = pop.total.groupby(pop["country"]).sum()
keys["population"] = pop.total / pop.country.map(ct_total)
for sector, country in product(sectors, countries):
regions_ct = regions.index[regions.index.str.contains(country)]
facilities = hotmaps.query("country == @country and Subsector == @sector")
if not facilities.empty:
emissions = facilities["Emissions_ETS_2014"]
if emissions.sum() == 0:
key = pd.Series(1 / len(facilities), facilities.index)
else:
# BEWARE: this is a strong assumption
emissions = emissions.fillna(emissions.mean())
key = emissions / emissions.sum()
key = key.groupby(facilities.bus).sum().reindex(regions_ct, fill_value=0.0)
else:
key = keys.loc[regions_ct, "population"]
keys.loc[regions_ct, sector] = key
return keys
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_industrial_distribution_key",
simpl="",
clusters=48,
)
logging.basicConfig(level=snakemake.config["logging_level"])
regions = gpd.read_file(snakemake.input.regions_onshore).set_index("name")
hotmaps = prepare_hotmaps_database(regions)
keys = build_nodal_distribution_key(hotmaps, regions)
keys.to_csv(snakemake.output.industrial_distribution_key)

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# -*- coding: utf-8 -*-
"""
Build industrial energy demand per country.
"""
import multiprocessing as mp
import pandas as pd
from tqdm import tqdm
ktoe_to_twh = 0.011630
# name in JRC-IDEES Energy Balances
sector_sheets = {
"Integrated steelworks": "cisb",
"Electric arc": "cise",
"Alumina production": "cnfa",
"Aluminium - primary production": "cnfp",
"Aluminium - secondary production": "cnfs",
"Other non-ferrous metals": "cnfo",
"Basic chemicals": "cbch",
"Other chemicals": "coch",
"Pharmaceutical products etc.": "cpha",
"Basic chemicals feedstock": "cpch",
"Cement": "ccem",
"Ceramics & other NMM": "ccer",
"Glass production": "cgla",
"Pulp production": "cpul",
"Paper production": "cpap",
"Printing and media reproduction": "cprp",
"Food, beverages and tobacco": "cfbt",
"Transport Equipment": "ctre",
"Machinery Equipment": "cmae",
"Textiles and leather": "ctel",
"Wood and wood products": "cwwp",
"Mining and quarrying": "cmiq",
"Construction": "ccon",
"Non-specified": "cnsi",
}
fuels = {
"All Products": "all",
"Solid Fuels": "solid",
"Total petroleum products (without biofuels)": "liquid",
"Gases": "gas",
"Nuclear heat": "heat",
"Derived heat": "heat",
"Biomass and Renewable wastes": "biomass",
"Wastes (non-renewable)": "waste",
"Electricity": "electricity",
}
eu28 = [
"FR",
"DE",
"GB",
"IT",
"ES",
"PL",
"SE",
"NL",
"BE",
"FI",
"DK",
"PT",
"RO",
"AT",
"BG",
"EE",
"GR",
"LV",
"CZ",
"HU",
"IE",
"SK",
"LT",
"HR",
"LU",
"SI",
"CY",
"MT",
]
jrc_names = {"GR": "EL", "GB": "UK"}
def industrial_energy_demand_per_country(country):
jrc_dir = snakemake.input.jrc
jrc_country = jrc_names.get(country, country)
fn = f"{jrc_dir}/JRC-IDEES-2015_EnergyBalance_{jrc_country}.xlsx"
sheets = list(sector_sheets.values())
df_dict = pd.read_excel(fn, sheet_name=sheets, index_col=0)
def get_subsector_data(sheet):
df = df_dict[sheet][year].groupby(fuels).sum()
df["ammonia"] = 0.0
df["other"] = df["all"] - df.loc[df.index != "all"].sum()
return df
df = pd.concat(
{sub: get_subsector_data(sheet) for sub, sheet in sector_sheets.items()}, axis=1
)
sel = ["Mining and quarrying", "Construction", "Non-specified"]
df["Other Industrial Sectors"] = df[sel].sum(axis=1)
df["Basic chemicals"] += df["Basic chemicals feedstock"]
df.drop(columns=sel + ["Basic chemicals feedstock"], index="all", inplace=True)
df *= ktoe_to_twh
return df
def add_ammonia_energy_demand(demand):
# MtNH3/a
fn = snakemake.input.ammonia_production
ammonia = pd.read_csv(fn, index_col=0)[str(year)] / 1e3
def get_ammonia_by_fuel(x):
fuels = {
"gas": config["MWh_CH4_per_tNH3_SMR"],
"electricity": config["MWh_elec_per_tNH3_SMR"],
}
return pd.Series({k: x * v for k, v in fuels.items()})
ammonia_by_fuel = ammonia.apply(get_ammonia_by_fuel).T
ammonia_by_fuel = ammonia_by_fuel.unstack().reindex(
index=demand.index, fill_value=0.0
)
ammonia = pd.DataFrame({"ammonia": ammonia * config["MWh_NH3_per_tNH3"]}).T
demand["Ammonia"] = ammonia.unstack().reindex(index=demand.index, fill_value=0.0)
demand["Basic chemicals (without ammonia)"] = (
demand["Basic chemicals"] - ammonia_by_fuel
)
demand["Basic chemicals (without ammonia)"].clip(lower=0, inplace=True)
demand.drop(columns="Basic chemicals", inplace=True)
return demand
def add_non_eu28_industrial_energy_demand(demand):
# output in MtMaterial/a
fn = snakemake.input.industrial_production_per_country
production = pd.read_csv(fn, index_col=0) / 1e3
# recombine HVC, Chlorine and Methanol to Basic chemicals (without ammonia)
chemicals = ["HVC", "Chlorine", "Methanol"]
production["Basic chemicals (without ammonia)"] = production[chemicals].sum(axis=1)
production.drop(columns=chemicals, inplace=True)
eu28_production = production.loc[eu28].sum()
eu28_energy = demand.groupby(level=1).sum()
eu28_averages = eu28_energy / eu28_production
non_eu28 = production.index.symmetric_difference(eu28)
demand_non_eu28 = pd.concat(
{k: v * eu28_averages for k, v in production.loc[non_eu28].iterrows()}
)
return pd.concat([demand, demand_non_eu28])
def industrial_energy_demand(countries):
nprocesses = snakemake.threads
func = industrial_energy_demand_per_country
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build industrial energy demand",
)
with mp.Pool(processes=nprocesses) as pool:
demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
demand = pd.concat(demand_l, keys=countries)
return demand
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_industrial_energy_demand_per_country_today")
config = snakemake.config["industry"]
year = config.get("reference_year", 2015)
demand = industrial_energy_demand(eu28)
demand = add_ammonia_energy_demand(demand)
demand = add_non_eu28_industrial_energy_demand(demand)
# for format compatibility
demand = demand.stack(dropna=False).unstack(level=[0, 2])
# style and annotation
demand.index.name = "TWh/a"
demand.sort_index(axis=1, inplace=True)
fn = snakemake.output.industrial_energy_demand_per_country_today
demand.to_csv(fn)

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# -*- coding: utf-8 -*-
"""
Build industrial energy demand per node.
"""
import pandas as pd
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_industrial_energy_demand_per_node",
simpl="",
clusters=48,
planning_horizons=2030,
)
# import EU ratios df as csv
fn = snakemake.input.industry_sector_ratios
industry_sector_ratios = pd.read_csv(fn, index_col=0)
# material demand per node and industry (kton/a)
fn = snakemake.input.industrial_production_per_node
nodal_production = pd.read_csv(fn, index_col=0)
# energy demand today to get current electricity
fn = snakemake.input.industrial_energy_demand_per_node_today
nodal_today = pd.read_csv(fn, index_col=0)
# final energy consumption per node and industry (TWh/a)
nodal_df = nodal_production.dot(industry_sector_ratios.T)
# convert GWh to TWh and ktCO2 to MtCO2
nodal_df *= 0.001
rename_sectors = {
"elec": "electricity",
"biomass": "solid biomass",
"heat": "low-temperature heat",
}
nodal_df.rename(columns=rename_sectors, inplace=True)
nodal_df["current electricity"] = nodal_today["electricity"]
nodal_df.index.name = "TWh/a (MtCO2/a)"
fn = snakemake.output.industrial_energy_demand_per_node
nodal_df.to_csv(fn, float_format="%.2f")

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# -*- coding: utf-8 -*-
"""
Build industrial energy demand per node.
"""
from itertools import product
import numpy as np
import pandas as pd
# map JRC/our sectors to hotmaps sector, where mapping exist
sector_mapping = {
"Electric arc": "Iron and steel",
"Integrated steelworks": "Iron and steel",
"DRI + Electric arc": "Iron and steel",
"Ammonia": "Chemical industry",
"Basic chemicals (without ammonia)": "Chemical industry",
"Other chemicals": "Chemical industry",
"Pharmaceutical products etc.": "Chemical industry",
"Cement": "Cement",
"Ceramics & other NMM": "Non-metallic mineral products",
"Glass production": "Glass",
"Pulp production": "Paper and printing",
"Paper production": "Paper and printing",
"Printing and media reproduction": "Paper and printing",
"Alumina production": "Non-ferrous metals",
"Aluminium - primary production": "Non-ferrous metals",
"Aluminium - secondary production": "Non-ferrous metals",
"Other non-ferrous metals": "Non-ferrous metals",
}
def build_nodal_industrial_energy_demand():
fn = snakemake.input.industrial_energy_demand_per_country_today
industrial_demand = pd.read_csv(fn, header=[0, 1], index_col=0)
fn = snakemake.input.industrial_distribution_key
keys = pd.read_csv(fn, index_col=0)
keys["country"] = keys.index.str[:2]
nodal_demand = pd.DataFrame(
0.0, dtype=float, index=keys.index, columns=industrial_demand.index
)
countries = keys.country.unique()
sectors = industrial_demand.columns.levels[1]
for country, sector in product(countries, sectors):
buses = keys.index[keys.country == country]
mapping = sector_mapping.get(sector, "population")
key = keys.loc[buses, mapping]
demand = industrial_demand[country, sector]
outer = pd.DataFrame(
np.outer(key, demand), index=key.index, columns=demand.index
)
nodal_demand.loc[buses] += outer
nodal_demand.index.name = "TWh/a"
nodal_demand.to_csv(snakemake.output.industrial_energy_demand_per_node_today)
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_industrial_energy_demand_per_node_today",
simpl="",
clusters=48,
)
build_nodal_industrial_energy_demand()

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# -*- coding: utf-8 -*-
"""
Build industrial production per country.
"""
import logging
logger = logging.getLogger(__name__)
import multiprocessing as mp
import numpy as np
import pandas as pd
from helper import mute_print
from tqdm import tqdm
tj_to_ktoe = 0.0238845
ktoe_to_twh = 0.01163
sub_sheet_name_dict = {
"Iron and steel": "ISI",
"Chemicals Industry": "CHI",
"Non-metallic mineral products": "NMM",
"Pulp, paper and printing": "PPA",
"Food, beverages and tobacco": "FBT",
"Non Ferrous Metals": "NFM",
"Transport Equipment": "TRE",
"Machinery Equipment": "MAE",
"Textiles and leather": "TEL",
"Wood and wood products": "WWP",
"Other Industrial Sectors": "OIS",
}
non_EU = ["NO", "CH", "ME", "MK", "RS", "BA", "AL"]
jrc_names = {"GR": "EL", "GB": "UK"}
eu28 = [
"FR",
"DE",
"GB",
"IT",
"ES",
"PL",
"SE",
"NL",
"BE",
"FI",
"DK",
"PT",
"RO",
"AT",
"BG",
"EE",
"GR",
"LV",
"CZ",
"HU",
"IE",
"SK",
"LT",
"HR",
"LU",
"SI",
"CY",
"MT",
]
sect2sub = {
"Iron and steel": ["Electric arc", "Integrated steelworks"],
"Chemicals Industry": [
"Basic chemicals",
"Other chemicals",
"Pharmaceutical products etc.",
],
"Non-metallic mineral products": [
"Cement",
"Ceramics & other NMM",
"Glass production",
],
"Pulp, paper and printing": [
"Pulp production",
"Paper production",
"Printing and media reproduction",
],
"Food, beverages and tobacco": ["Food, beverages and tobacco"],
"Non Ferrous Metals": [
"Alumina production",
"Aluminium - primary production",
"Aluminium - secondary production",
"Other non-ferrous metals",
],
"Transport Equipment": ["Transport Equipment"],
"Machinery Equipment": ["Machinery Equipment"],
"Textiles and leather": ["Textiles and leather"],
"Wood and wood products": ["Wood and wood products"],
"Other Industrial Sectors": ["Other Industrial Sectors"],
}
sub2sect = {v: k for k, vv in sect2sub.items() for v in vv}
fields = {
"Electric arc": "Electric arc",
"Integrated steelworks": "Integrated steelworks",
"Basic chemicals": "Basic chemicals (kt ethylene eq.)",
"Other chemicals": "Other chemicals (kt ethylene eq.)",
"Pharmaceutical products etc.": "Pharmaceutical products etc. (kt ethylene eq.)",
"Cement": "Cement (kt)",
"Ceramics & other NMM": "Ceramics & other NMM (kt bricks eq.)",
"Glass production": "Glass production (kt)",
"Pulp production": "Pulp production (kt)",
"Paper production": "Paper production (kt)",
"Printing and media reproduction": "Printing and media reproduction (kt paper eq.)",
"Food, beverages and tobacco": "Physical output (index)",
"Alumina production": "Alumina production (kt)",
"Aluminium - primary production": "Aluminium - primary production",
"Aluminium - secondary production": "Aluminium - secondary production",
"Other non-ferrous metals": "Other non-ferrous metals (kt lead eq.)",
"Transport Equipment": "Physical output (index)",
"Machinery Equipment": "Physical output (index)",
"Textiles and leather": "Physical output (index)",
"Wood and wood products": "Physical output (index)",
"Other Industrial Sectors": "Physical output (index)",
}
eb_names = {
"NO": "Norway",
"AL": "Albania",
"BA": "Bosnia and Herzegovina",
"MK": "FYR of Macedonia",
"GE": "Georgia",
"IS": "Iceland",
"KO": "Kosovo",
"MD": "Moldova",
"ME": "Montenegro",
"RS": "Serbia",
"UA": "Ukraine",
"TR": "Turkey",
}
eb_sectors = {
"Iron & steel industry": "Iron and steel",
"Chemical and Petrochemical industry": "Chemicals Industry",
"Non-ferrous metal industry": "Non-metallic mineral products",
"Paper, Pulp and Print": "Pulp, paper and printing",
"Food and Tabacco": "Food, beverages and tobacco",
"Non-metallic Minerals (Glass, pottery & building mat. Industry)": "Non Ferrous Metals",
"Transport Equipment": "Transport Equipment",
"Machinery": "Machinery Equipment",
"Textile and Leather": "Textiles and leather",
"Wood and Wood Products": "Wood and wood products",
"Non-specified (Industry)": "Other Industrial Sectors",
}
# TODO: this should go in a csv in `data`
# Annual energy consumption in Switzerland by sector in 2015 (in TJ)
# From: Energieverbrauch in der Industrie und im Dienstleistungssektor, Der Bundesrat
# http://www.bfe.admin.ch/themen/00526/00541/00543/index.html?lang=de&dossier_id=00775
e_switzerland = pd.Series(
{
"Iron and steel": 7889.0,
"Chemicals Industry": 26871.0,
"Non-metallic mineral products": 15513.0 + 3820.0,
"Pulp, paper and printing": 12004.0,
"Food, beverages and tobacco": 17728.0,
"Non Ferrous Metals": 3037.0,
"Transport Equipment": 14993.0,
"Machinery Equipment": 4724.0,
"Textiles and leather": 1742.0,
"Wood and wood products": 0.0,
"Other Industrial Sectors": 10825.0,
"current electricity": 53760.0,
}
)
def find_physical_output(df):
start = np.where(df.index.str.contains("Physical output", na=""))[0][0]
empty_row = np.where(df.index.isnull())[0]
end = empty_row[np.argmax(empty_row > start)]
return slice(start, end)
def get_energy_ratio(country):
if country == "CH":
e_country = e_switzerland * tj_to_ktoe
else:
# estimate physical output, energy consumption in the sector and country
fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
with mute_print():
df = pd.read_excel(
fn, sheet_name="2016", index_col=2, header=0, skiprows=1
).squeeze("columns")
e_country = df.loc[eb_sectors.keys(), "Total all products"].rename(eb_sectors)
fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx"
with mute_print():
df = pd.read_excel(fn, sheet_name="Ind_Summary", index_col=0, header=0).squeeze(
"columns"
)
assert df.index[48] == "by sector"
year_i = df.columns.get_loc(year)
e_eu28 = df.iloc[49:76, year_i]
e_eu28.index = e_eu28.index.str.lstrip()
e_ratio = e_country / e_eu28
return pd.Series({k: e_ratio[v] for k, v in sub2sect.items()})
def industry_production_per_country(country):
def get_sector_data(sector, country):
jrc_country = jrc_names.get(country, country)
fn = f"{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx"
sheet = sub_sheet_name_dict[sector]
with mute_print():
df = pd.read_excel(fn, sheet_name=sheet, index_col=0, header=0).squeeze(
"columns"
)
year_i = df.columns.get_loc(year)
df = df.iloc[find_physical_output(df), year_i]
df = df.loc[map(fields.get, sect2sub[sector])]
df.index = sect2sub[sector]
return df
ct = "EU28" if country in non_EU else country
demand = pd.concat([get_sector_data(s, ct) for s in sect2sub.keys()])
if country in non_EU:
demand *= get_energy_ratio(country)
demand.name = country
return demand
def industry_production(countries):
nprocesses = snakemake.threads
func = industry_production_per_country
tqdm_kwargs = dict(
ascii=False,
unit=" country",
total=len(countries),
desc="Build industry production",
)
with mp.Pool(processes=nprocesses) as pool:
demand_l = list(tqdm(pool.imap(func, countries), **tqdm_kwargs))
demand = pd.concat(demand_l, axis=1).T
demand.index.name = "kton/a"
return demand
def separate_basic_chemicals(demand):
"""
Separate basic chemicals into ammonia, chlorine, methanol and HVC.
"""
ammonia = pd.read_csv(snakemake.input.ammonia_production, index_col=0)
there = ammonia.index.intersection(demand.index)
missing = demand.index.symmetric_difference(there)
logger.info(f"Following countries have no ammonia demand: {missing.tolist()}")
demand["Ammonia"] = 0.0
demand.loc[there, "Ammonia"] = ammonia.loc[there, str(year)]
demand["Basic chemicals"] -= demand["Ammonia"]
# EE, HR and LT got negative demand through subtraction - poor data
demand["Basic chemicals"].clip(lower=0.0, inplace=True)
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
distribution_key = demand["Basic chemicals"] / demand["Basic chemicals"].sum()
demand["HVC"] = config["HVC_production_today"] * 1e3 * distribution_key
demand["Chlorine"] = config["chlorine_production_today"] * 1e3 * distribution_key
demand["Methanol"] = config["methanol_production_today"] * 1e3 * distribution_key
demand.drop(columns=["Basic chemicals"], inplace=True)
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_industrial_production_per_country")
logging.basicConfig(level=snakemake.config["logging_level"])
countries = non_EU + eu28
year = snakemake.config["industry"]["reference_year"]
config = snakemake.config["industry"]
jrc_dir = snakemake.input.jrc
eurostat_dir = snakemake.input.eurostat
demand = industry_production(countries)
separate_basic_chemicals(demand)
fn = snakemake.output.industrial_production_per_country
demand.to_csv(fn, float_format="%.2f")

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# -*- coding: utf-8 -*-
"""
Build future industrial production per country.
"""
import pandas as pd
from prepare_sector_network import get
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_industrial_production_per_country_tomorrow")
config = snakemake.config["industry"]
investment_year = int(snakemake.wildcards.planning_horizons)
fn = snakemake.input.industrial_production_per_country
production = pd.read_csv(fn, index_col=0)
keys = ["Integrated steelworks", "Electric arc"]
total_steel = production[keys].sum(axis=1)
st_primary_fraction = get(config["St_primary_fraction"], investment_year)
dri_fraction = get(config["DRI_fraction"], investment_year)
int_steel = production["Integrated steelworks"].sum()
fraction_persistent_primary = st_primary_fraction * total_steel.sum() / int_steel
dri = (
dri_fraction * fraction_persistent_primary * production["Integrated steelworks"]
)
production.insert(2, "DRI + Electric arc", dri)
not_dri = 1 - dri_fraction
production["Integrated steelworks"] = (
not_dri * fraction_persistent_primary * production["Integrated steelworks"]
)
production["Electric arc"] = (
total_steel
- production["DRI + Electric arc"]
- production["Integrated steelworks"]
)
keys = ["Aluminium - primary production", "Aluminium - secondary production"]
total_aluminium = production[keys].sum(axis=1)
key_pri = "Aluminium - primary production"
key_sec = "Aluminium - secondary production"
al_primary_fraction = get(config["Al_primary_fraction"], investment_year)
fraction_persistent_primary = (
al_primary_fraction * total_aluminium.sum() / production[key_pri].sum()
)
production[key_pri] = fraction_persistent_primary * production[key_pri]
production[key_sec] = total_aluminium - production[key_pri]
production["HVC (mechanical recycling)"] = (
get(config["HVC_mechanical_recycling_fraction"], investment_year)
* production["HVC"]
)
production["HVC (chemical recycling)"] = (
get(config["HVC_chemical_recycling_fraction"], investment_year)
* production["HVC"]
)
production["HVC"] *= get(config["HVC_primary_fraction"], investment_year)
fn = snakemake.output.industrial_production_per_country_tomorrow
production.to_csv(fn, float_format="%.2f")

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# -*- coding: utf-8 -*-
"""
Build industrial production per node.
"""
from itertools import product
import pandas as pd
# map JRC/our sectors to hotmaps sector, where mapping exist
sector_mapping = {
"Electric arc": "Iron and steel",
"Integrated steelworks": "Iron and steel",
"DRI + Electric arc": "Iron and steel",
"Ammonia": "Chemical industry",
"HVC": "Chemical industry",
"HVC (mechanical recycling)": "Chemical industry",
"HVC (chemical recycling)": "Chemical industry",
"Methanol": "Chemical industry",
"Chlorine": "Chemical industry",
"Other chemicals": "Chemical industry",
"Pharmaceutical products etc.": "Chemical industry",
"Cement": "Cement",
"Ceramics & other NMM": "Non-metallic mineral products",
"Glass production": "Glass",
"Pulp production": "Paper and printing",
"Paper production": "Paper and printing",
"Printing and media reproduction": "Paper and printing",
"Alumina production": "Non-ferrous metals",
"Aluminium - primary production": "Non-ferrous metals",
"Aluminium - secondary production": "Non-ferrous metals",
"Other non-ferrous metals": "Non-ferrous metals",
}
def build_nodal_industrial_production():
fn = snakemake.input.industrial_production_per_country_tomorrow
industrial_production = pd.read_csv(fn, index_col=0)
fn = snakemake.input.industrial_distribution_key
keys = pd.read_csv(fn, index_col=0)
keys["country"] = keys.index.str[:2]
nodal_production = pd.DataFrame(
index=keys.index, columns=industrial_production.columns, dtype=float
)
countries = keys.country.unique()
sectors = industrial_production.columns
for country, sector in product(countries, sectors):
buses = keys.index[keys.country == country]
mapping = sector_mapping.get(sector, "population")
key = keys.loc[buses, mapping]
nodal_production.loc[buses, sector] = (
industrial_production.at[country, sector] * key
)
nodal_production.to_csv(snakemake.output.industrial_production_per_node)
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_industrial_production_per_node",
simpl="",
clusters=48,
)
build_nodal_industrial_production()

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# -*- coding: utf-8 -*-
"""
Build mapping between grid cells and population (total, urban, rural)
"""
import logging
logger = logging.getLogger(__name__)
import multiprocessing as mp
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("build_population_layouts")
logging.basicConfig(level=snakemake.config["logging_level"])
cutout = atlite.Cutout(snakemake.config["atlite"]["cutout"])
grid_cells = cutout.grid.geometry
# nuts3 has columns country, gdp, pop, geometry
# population is given in dimensions of 1e3=k
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index("index")
# Indicator matrix NUTS3 -> grid cells
I = atlite.cutout.compute_indicatormatrix(nuts3.geometry, grid_cells)
# Indicator matrix grid_cells -> NUTS3; inprinciple Iinv*I is identity
# but imprecisions mean not perfect
Iinv = cutout.indicatormatrix(nuts3.geometry)
countries = np.sort(nuts3.country.unique())
urban_fraction = (
pd.read_csv(
snakemake.input.urban_percent, header=None, index_col=0, names=["fraction"]
).squeeze()
/ 100.0
)
# fill missing Balkans values
missing = ["AL", "ME", "MK"]
reference = ["RS", "BA"]
average = urban_fraction[reference].mean()
fill_values = pd.Series({ct: average for ct in missing})
urban_fraction = pd.concat([urban_fraction, fill_values])
# population in each grid cell
pop_cells = pd.Series(I.dot(nuts3["pop"]))
# in km^2
cell_areas = grid_cells.to_crs(3035).area / 1e6
# pop per km^2
density_cells = pop_cells / cell_areas
# rural or urban population in grid cell
pop_rural = pd.Series(0.0, density_cells.index)
pop_urban = pd.Series(0.0, density_cells.index)
for ct in countries:
logger.debug(
f"The urbanization rate for {ct} is {round(urban_fraction[ct]*100)}%"
)
indicator_nuts3_ct = nuts3.country.apply(lambda x: 1.0 if x == ct else 0.0)
indicator_cells_ct = pd.Series(Iinv.T.dot(indicator_nuts3_ct))
density_cells_ct = indicator_cells_ct * density_cells
pop_cells_ct = indicator_cells_ct * pop_cells
# correct for imprecision of Iinv*I
pop_ct = nuts3.loc[nuts3.country == ct, "pop"].sum()
pop_cells_ct *= pop_ct / pop_cells_ct.sum()
# The first low density grid cells to reach rural fraction are rural
asc_density_i = density_cells_ct.sort_values().index
asc_density_cumsum = pop_cells_ct[asc_density_i].cumsum() / pop_cells_ct.sum()
rural_fraction_ct = 1 - urban_fraction[ct]
pop_ct_rural_b = asc_density_cumsum < rural_fraction_ct
pop_ct_urban_b = ~pop_ct_rural_b
pop_ct_rural_b[indicator_cells_ct == 0.0] = False
pop_ct_urban_b[indicator_cells_ct == 0.0] = False
pop_rural += pop_cells_ct.where(pop_ct_rural_b, 0.0)
pop_urban += pop_cells_ct.where(pop_ct_urban_b, 0.0)
pop_cells = {"total": pop_cells}
pop_cells["rural"] = pop_rural
pop_cells["urban"] = pop_urban
for key, pop in pop_cells.items():
ycoords = ("y", cutout.coords["y"].data)
xcoords = ("x", cutout.coords["x"].data)
values = pop.values.reshape(cutout.shape)
layout = xr.DataArray(values, [ycoords, xcoords])
layout.to_netcdf(snakemake.output[f"pop_layout_{key}"])

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# -*- coding: utf-8 -*-
"""
Build population-weighted energy totals.
"""
import pandas as pd
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_population_weighted_energy_totals",
simpl="",
clusters=48,
)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
energy_totals = pd.read_csv(snakemake.input.energy_totals, index_col=0)
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.0)
nodal_energy_totals.index = pop_layout.index
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
nodal_energy_totals.to_csv(snakemake.output[0])

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# -*- coding: utf-8 -*-
"""
Build salt cavern potentials for hydrogen storage.
Technical Potential of Salt Caverns for Hydrogen Storage in Europe
CC-BY 4.0
https://doi.org/10.20944/preprints201910.0187.v1
https://doi.org/10.1016/j.ijhydene.2019.12.161
Figure 6. Distribution of potential salt cavern sites across Europe with their corresponding
energy densities (cavern storage potential divided by the volume).
Figure 7. Total cavern storage potential in European countries
classified as onshore, offshore and within 50 km of shore.
The regional distribution is taken from the map (Figure 6) and scaled to the
capacities from the bar chart split by nearshore (<50km from sea),
onshore (>50km from sea), offshore (Figure 7).
"""
import geopandas as gpd
import pandas as pd
def concat_gdf(gdf_list, crs="EPSG:4326"):
"""
Concatenate multiple geopandas dataframes with common coordinate reference
system (crs).
"""
return gpd.GeoDataFrame(pd.concat(gdf_list), crs=crs)
def load_bus_regions(onshore_path, offshore_path):
"""
Load pypsa-eur on- and offshore regions and concat.
"""
bus_regions_offshore = gpd.read_file(offshore_path)
bus_regions_onshore = gpd.read_file(onshore_path)
bus_regions = concat_gdf([bus_regions_offshore, bus_regions_onshore])
bus_regions = bus_regions.dissolve(by="name", aggfunc="sum")
return bus_regions
def area(gdf):
"""
Returns area of GeoDataFrame geometries in square kilometers.
"""
return gdf.to_crs(epsg=3035).area.div(1e6)
def salt_cavern_potential_by_region(caverns, regions):
# calculate area of caverns shapes
caverns["area_caverns"] = area(caverns)
overlay = gpd.overlay(regions.reset_index(), caverns, keep_geom_type=True)
# calculate share of cavern area inside region
overlay["share"] = area(overlay) / overlay["area_caverns"]
overlay["e_nom"] = overlay.eval(
"capacity_per_area * share * area_caverns / 1000"
) # TWh
caverns_regions = (
overlay.groupby(["name", "storage_type"]).e_nom.sum().unstack("storage_type")
)
return caverns_regions
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_salt_cavern_potentials", simpl="", clusters="37"
)
fn_onshore = snakemake.input.regions_onshore
fn_offshore = snakemake.input.regions_offshore
regions = load_bus_regions(fn_onshore, fn_offshore)
caverns = gpd.read_file(snakemake.input.salt_caverns) # GWh/sqkm
caverns_regions = salt_cavern_potential_by_region(caverns, regions)
caverns_regions.to_csv(snakemake.output.h2_cavern_potential)

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# -*- coding: utf-8 -*-
import geopandas as gpd
import pandas as pd
def area(gdf):
"""
Returns area of GeoDataFrame geometries in square kilometers.
"""
return gdf.to_crs(epsg=3035).area.div(1e6)
def allocate_sequestration_potential(
gdf, regions, attr="conservative estimate Mt", threshold=3
):
gdf = gdf.loc[gdf[attr] > threshold, [attr, "geometry"]]
gdf["area_sqkm"] = area(gdf)
overlay = gpd.overlay(regions, gdf, keep_geom_type=True)
overlay["share"] = area(overlay) / overlay["area_sqkm"]
adjust_cols = overlay.columns.difference({"name", "area_sqkm", "geometry", "share"})
overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0)
gdf_regions = overlay.groupby("name").sum()
gdf_regions.drop(["area_sqkm", "share"], axis=1, inplace=True)
return gdf_regions.squeeze()
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_sequestration_potentials", simpl="", clusters="181"
)
cf = snakemake.config["sector"]["regional_co2_sequestration_potential"]
gdf = gpd.read_file(snakemake.input.sequestration_potential[0])
regions = gpd.read_file(snakemake.input.regions_offshore)
if cf["include_onshore"]:
onregions = gpd.read_file(snakemake.input.regions_onshore)
regions = pd.concat([regions, onregions]).dissolve(by="name").reset_index()
s = allocate_sequestration_potential(
gdf, regions, attr=cf["attribute"], threshold=cf["min_size"]
)
s = s.where(s > cf["min_size"]).dropna()
s.to_csv(snakemake.output.sequestration_potential)

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# -*- coding: utf-8 -*-
"""
Build regional demand for international navigation based on outflow volume of
ports.
"""
import json
import geopandas as gpd
import pandas as pd
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_shipping_demand_per_node",
simpl="",
clusters=48,
)
scope = gpd.read_file(snakemake.input.scope).geometry[0]
regions = gpd.read_file(snakemake.input.regions).set_index("name")
demand = pd.read_csv(snakemake.input.demand, index_col=0)[
"total international navigation"
]
# read port data into GeoDataFrame
with open(snakemake.input.ports, "r", encoding="latin_1") as f:
ports = json.load(f)
ports = pd.json_normalize(ports, "features", sep="_")
coordinates = ports.geometry_coordinates
geometry = gpd.points_from_xy(coordinates.str[0], coordinates.str[1])
ports = gpd.GeoDataFrame(ports, geometry=geometry, crs=4326)
# filter global port data by European ports
european_ports = ports[ports.within(scope)]
# assign ports to nearest region
p = european_ports.to_crs(3857)
r = regions.to_crs(3857)
outflows = (
p.sjoin_nearest(r).groupby("index_right").properties_outflows.sum().div(1e3)
)
# calculate fraction of each country's port outflows
countries = outflows.index.str[:2]
outflows_per_country = outflows.groupby(countries).sum()
fraction = outflows / countries.map(outflows_per_country)
# distribute per-country demands to nodes based on these fractions
nodal_demand = demand.loc[countries].fillna(0.0)
nodal_demand.index = fraction.index
nodal_demand = nodal_demand.multiply(fraction, axis=0)
nodal_demand = nodal_demand.reindex(regions.index, fill_value=0)
# export nodal international navigation demands
nodal_demand.to_csv(snakemake.output[0])

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# -*- coding: utf-8 -*-
"""
Build solar thermal collector time series.
"""
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
from dask.distributed import Client, LocalCluster
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_solar_thermal_profiles",
simpl="",
clusters=48,
)
nprocesses = int(snakemake.threads)
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
config = snakemake.config["solar_thermal"]
time = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout_config = snakemake.config["atlite"]["cutout"]
cutout = atlite.Cutout(cutout_config).sel(time=time)
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
stacked_pop = pop_layout.stack(spatial=("y", "x"))
M = I.T.dot(np.diag(I.dot(stacked_pop)))
nonzero_sum = M.sum(axis=0, keepdims=True)
nonzero_sum[nonzero_sum == 0.0] = 1.0
M_tilde = M / nonzero_sum
solar_thermal = cutout.solar_thermal(
**config,
matrix=M_tilde.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False
)
solar_thermal.to_netcdf(snakemake.output.solar_thermal)

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# -*- coding: utf-8 -*-
"""
Build temperature profiles.
"""
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import xarray as xr
from dask.distributed import Client, LocalCluster
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_temperature_profiles",
simpl="",
clusters=48,
)
nprocesses = int(snakemake.threads)
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
time = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout_config = snakemake.config["atlite"]["cutout"]
cutout = atlite.Cutout(cutout_config).sel(time=time)
clustered_regions = (
gpd.read_file(snakemake.input.regions_onshore)
.set_index("name")
.buffer(0)
.squeeze()
)
I = cutout.indicatormatrix(clustered_regions)
pop_layout = xr.open_dataarray(snakemake.input.pop_layout)
stacked_pop = pop_layout.stack(spatial=("y", "x"))
M = I.T.dot(np.diag(I.dot(stacked_pop)))
nonzero_sum = M.sum(axis=0, keepdims=True)
nonzero_sum[nonzero_sum == 0.0] = 1.0
M_tilde = M / nonzero_sum
temp_air = cutout.temperature(
matrix=M_tilde.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False,
)
temp_air.to_netcdf(snakemake.output.temp_air)
temp_soil = cutout.soil_temperature(
matrix=M_tilde.T,
index=clustered_regions.index,
dask_kwargs=dict(scheduler=client),
show_progress=False,
)
temp_soil.to_netcdf(snakemake.output.temp_soil)

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# -*- coding: utf-8 -*-
"""
Build transport demand.
"""
import numpy as np
import pandas as pd
import xarray as xr
from helper import generate_periodic_profiles
def build_nodal_transport_data(fn, pop_layout):
transport_data = pd.read_csv(fn, index_col=0)
nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.0)
nodal_transport_data.index = pop_layout.index
nodal_transport_data["number cars"] = (
pop_layout["fraction"] * nodal_transport_data["number cars"]
)
nodal_transport_data.loc[
nodal_transport_data["average fuel efficiency"] == 0.0,
"average fuel efficiency",
] = transport_data["average fuel efficiency"].mean()
return nodal_transport_data
def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
## Get overall demand curve for all vehicles
traffic = pd.read_csv(traffic_fn, skiprows=2, usecols=["count"]).squeeze("columns")
transport_shape = generate_periodic_profiles(
dt_index=snapshots,
nodes=nodes,
weekly_profile=traffic.values,
)
transport_shape = transport_shape / transport_shape.sum()
# electric motors are more efficient, so alter transport demand
plug_to_wheels_eta = options["bev_plug_to_wheel_efficiency"]
battery_to_wheels_eta = plug_to_wheels_eta * options["bev_charge_efficiency"]
efficiency_gain = (
nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta
)
# get heating demand for correction to demand time series
temperature = xr.open_dataarray(airtemp_fn).to_pandas()
# correction factors for vehicle heating
dd_ICE = transport_degree_factor(
temperature,
options["transport_heating_deadband_lower"],
options["transport_heating_deadband_upper"],
options["ICE_lower_degree_factor"],
options["ICE_upper_degree_factor"],
)
dd_EV = transport_degree_factor(
temperature,
options["transport_heating_deadband_lower"],
options["transport_heating_deadband_upper"],
options["EV_lower_degree_factor"],
options["EV_upper_degree_factor"],
)
# divide out the heating/cooling demand from ICE totals
# and multiply back in the heating/cooling demand for EVs
ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()
energy_totals_transport = (
pop_weighted_energy_totals["total road"]
+ pop_weighted_energy_totals["total rail"]
- pop_weighted_energy_totals["electricity rail"]
)
transport = (
(transport_shape.multiply(energy_totals_transport) * 1e6 * Nyears)
.divide(efficiency_gain * ice_correction)
.multiply(1 + dd_EV)
)
return transport
def transport_degree_factor(
temperature,
deadband_lower=15,
deadband_upper=20,
lower_degree_factor=0.5,
upper_degree_factor=1.6,
):
"""
Work out how much energy demand in vehicles increases due to heating and
cooling.
There is a deadband where there is no increase. Degree factors are %
increase in demand compared to no heating/cooling fuel consumption.
Returns per unit increase in demand for each place and time
"""
dd = temperature.copy()
dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.0
dT_lower = deadband_lower - temperature[temperature < deadband_lower]
dd[temperature < deadband_lower] = lower_degree_factor / 100 * dT_lower
dT_upper = temperature[temperature > deadband_upper] - deadband_upper
dd[temperature > deadband_upper] = upper_degree_factor / 100 * dT_upper
return dd
def bev_availability_profile(fn, snapshots, nodes, options):
"""
Derive plugged-in availability for passenger electric vehicles.
"""
traffic = pd.read_csv(fn, skiprows=2, usecols=["count"]).squeeze("columns")
avail_max = options["bev_avail_max"]
avail_mean = options["bev_avail_mean"]
avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (
traffic.mean() - traffic.min()
)
avail_profile = generate_periodic_profiles(
dt_index=snapshots,
nodes=nodes,
weekly_profile=avail.values,
)
return avail_profile
def bev_dsm_profile(snapshots, nodes, options):
dsm_week = np.zeros((24 * 7,))
dsm_week[(np.arange(0, 7, 1) * 24 + options["bev_dsm_restriction_time"])] = options[
"bev_dsm_restriction_value"
]
dsm_profile = generate_periodic_profiles(
dt_index=snapshots,
nodes=nodes,
weekly_profile=dsm_week,
)
return dsm_profile
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_transport_demand",
simpl="",
clusters=48,
)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
nodes = pop_layout.index
pop_weighted_energy_totals = pd.read_csv(
snakemake.input.pop_weighted_energy_totals, index_col=0
)
options = snakemake.config["sector"]
snapshots = pd.date_range(freq="h", **snakemake.config["snapshots"], tz="UTC")
Nyears = 1
nodal_transport_data = build_nodal_transport_data(
snakemake.input.transport_data, pop_layout
)
transport_demand = build_transport_demand(
snakemake.input.traffic_data_KFZ,
snakemake.input.temp_air_total,
nodes,
nodal_transport_data,
)
avail_profile = bev_availability_profile(
snakemake.input.traffic_data_Pkw, snapshots, nodes, options
)
dsm_profile = bev_dsm_profile(snapshots, nodes, options)
nodal_transport_data.to_csv(snakemake.output.transport_data)
transport_demand.to_csv(snakemake.output.transport_demand)
avail_profile.to_csv(snakemake.output.avail_profile)
dsm_profile.to_csv(snakemake.output.dsm_profile)

127
scripts/cluster_gas_network.py Executable file
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@ -0,0 +1,127 @@
# -*- coding: utf-8 -*-
"""
Cluster gas network.
"""
import logging
logger = logging.getLogger(__name__)
import geopandas as gpd
import pandas as pd
from packaging.version import Version, parse
from pypsa.geo import haversine_pts
from shapely import wkt
def concat_gdf(gdf_list, crs="EPSG:4326"):
"""
Concatenate multiple geopandas dataframes with common coordinate reference
system (crs).
"""
return gpd.GeoDataFrame(pd.concat(gdf_list), crs=crs)
def load_bus_regions(onshore_path, offshore_path):
"""
Load pypsa-eur on- and offshore regions and concat.
"""
bus_regions_offshore = gpd.read_file(offshore_path)
bus_regions_onshore = gpd.read_file(onshore_path)
bus_regions = concat_gdf([bus_regions_offshore, bus_regions_onshore])
bus_regions = bus_regions.dissolve(by="name", aggfunc="sum")
return bus_regions
def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
for i in [0, 1]:
gdf = gpd.GeoDataFrame(geometry=df[f"point{i}"], crs="EPSG:4326")
kws = (
dict(op="within")
if parse(gpd.__version__) < Version("0.10")
else dict(predicate="within")
)
bus_mapping = gpd.sjoin(gdf, bus_regions, how="left", **kws).index_right
bus_mapping = bus_mapping.groupby(bus_mapping.index).first()
df[f"bus{i}"] = bus_mapping
df[f"point{i}"] = df[f"bus{i}"].map(
bus_regions.to_crs(3035).centroid.to_crs(4326)
)
# drop pipes where not both buses are inside regions
df = df.loc[~df.bus0.isna() & ~df.bus1.isna()]
# drop pipes within the same region
df = df.loc[df.bus1 != df.bus0]
# recalculate lengths as center to center * length factor
df["length"] = df.apply(
lambda p: length_factor
* haversine_pts([p.point0.x, p.point0.y], [p.point1.x, p.point1.y]),
axis=1,
)
# tidy and create new numbered index
df.drop(["point0", "point1"], axis=1, inplace=True)
df.reset_index(drop=True, inplace=True)
return df
def reindex_pipes(df):
def make_index(x):
connector = " <-> " if x.bidirectional else " -> "
return "gas pipeline " + x.bus0 + connector + x.bus1
df.index = df.apply(make_index, axis=1)
df["p_min_pu"] = df.bidirectional.apply(lambda bi: -1 if bi else 0)
df.drop("bidirectional", axis=1, inplace=True)
df.sort_index(axis=1, inplace=True)
def aggregate_parallel_pipes(df):
strategies = {
"bus0": "first",
"bus1": "first",
"p_nom": "sum",
"p_nom_diameter": "sum",
"max_pressure_bar": "mean",
"build_year": "mean",
"diameter_mm": "mean",
"length": "mean",
"name": " ".join,
"p_min_pu": "min",
}
return df.groupby(df.index).agg(strategies)
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("cluster_gas_network", simpl="", clusters="37")
logging.basicConfig(level=snakemake.config["logging_level"])
fn = snakemake.input.cleaned_gas_network
df = pd.read_csv(fn, index_col=0)
for col in ["point0", "point1"]:
df[col] = df[col].apply(wkt.loads)
bus_regions = load_bus_regions(
snakemake.input.regions_onshore, snakemake.input.regions_offshore
)
gas_network = build_clustered_gas_network(df, bus_regions)
reindex_pipes(gas_network)
gas_network = aggregate_parallel_pipes(gas_network)
gas_network.to_csv(snakemake.output.clustered_gas_network)

35
scripts/copy_config.py Normal file
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@ -0,0 +1,35 @@
# -*- coding: utf-8 -*-
from shutil import copy
import yaml
files = {
"config.yaml": "config.yaml",
"Snakefile": "Snakefile",
"scripts/solve_network.py": "solve_network.py",
"scripts/prepare_sector_network.py": "prepare_sector_network.py",
"../pypsa-eur/config.yaml": "config.pypsaeur.yaml",
}
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("copy_config")
basepath = (
snakemake.config["summary_dir"] + "/" + snakemake.config["run"] + "/configs/"
)
for f, name in files.items():
copy(f, basepath + name)
with open(basepath + "config.snakemake.yaml", "w") as yaml_file:
yaml.dump(
snakemake.config,
yaml_file,
default_flow_style=False,
allow_unicode=True,
sort_keys=False,
)

169
scripts/helper.py Normal file
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@ -0,0 +1,169 @@
# -*- coding: utf-8 -*-
import contextlib
import logging
import os
import sys
from pathlib import Path
import pandas as pd
import pytz
import yaml
from pypsa.components import component_attrs, components
from pypsa.descriptors import Dict
from snakemake.utils import update_config
logger = logging.getLogger(__name__)
# Define a context manager to temporarily mute print statements
@contextlib.contextmanager
def mute_print():
with open(os.devnull, "w") as devnull:
with contextlib.redirect_stdout(devnull):
yield
def override_component_attrs(directory):
"""Tell PyPSA that links can have multiple outputs by
overriding the component_attrs. This can be done for
as many buses as you need with format busi for i = 2,3,4,5,....
See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
Parameters
----------
directory : string
Folder where component attributes to override are stored
analogous to ``pypsa/component_attrs``, e.g. `links.csv`.
Returns
-------
Dictionary of overridden component attributes.
"""
attrs = Dict({k: v.copy() for k, v in component_attrs.items()})
for component, list_name in components.list_name.items():
fn = f"{directory}/{list_name}.csv"
if os.path.isfile(fn):
overrides = pd.read_csv(fn, index_col=0, na_values="n/a")
attrs[component] = overrides.combine_first(attrs[component])
return attrs
# from pypsa-eur/_helpers.py
def mock_snakemake(rulename, **wildcards):
"""
This function is expected to be executed from the 'scripts'-directory of '
the snakemake project. It returns a snakemake.script.Snakemake object,
based on the Snakefile.
If a rule has wildcards, you have to specify them in **wildcards.
Parameters
----------
rulename: str
name of the rule for which the snakemake object should be generated
**wildcards:
keyword arguments fixing the wildcards. Only necessary if wildcards are
needed.
"""
import os
import snakemake as sm
from packaging.version import Version, parse
from pypsa.descriptors import Dict
from snakemake.script import Snakemake
script_dir = Path(__file__).parent.resolve()
assert (
Path.cwd().resolve() == script_dir
), f"mock_snakemake has to be run from the repository scripts directory {script_dir}"
os.chdir(script_dir.parent)
for p in sm.SNAKEFILE_CHOICES:
if os.path.exists(p):
snakefile = p
break
kwargs = dict(rerun_triggers=[]) if parse(sm.__version__) > Version("7.7.0") else {}
workflow = sm.Workflow(snakefile, overwrite_configfiles=[], **kwargs)
workflow.include(snakefile)
workflow.global_resources = {}
rule = workflow.get_rule(rulename)
dag = sm.dag.DAG(workflow, rules=[rule])
wc = Dict(wildcards)
job = sm.jobs.Job(rule, dag, wc)
def make_accessable(*ios):
for io in ios:
for i in range(len(io)):
io[i] = os.path.abspath(io[i])
make_accessable(job.input, job.output, job.log)
snakemake = Snakemake(
job.input,
job.output,
job.params,
job.wildcards,
job.threads,
job.resources,
job.log,
job.dag.workflow.config,
job.rule.name,
None,
)
# create log and output dir if not existent
for path in list(snakemake.log) + list(snakemake.output):
Path(path).parent.mkdir(parents=True, exist_ok=True)
os.chdir(script_dir)
return snakemake
# from pypsa-eur/_helpers.py
def progress_retrieve(url, file):
import urllib
from progressbar import ProgressBar
pbar = ProgressBar(0, 100)
def dlProgress(count, blockSize, totalSize):
pbar.update(int(count * blockSize * 100 / totalSize))
urllib.request.urlretrieve(url, file, reporthook=dlProgress)
def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
"""
Give a 24*7 long list of weekly hourly profiles, generate this for each
country for the period dt_index, taking account of time zones and summer
time.
"""
weekly_profile = pd.Series(weekly_profile, range(24 * 7))
week_df = pd.DataFrame(index=dt_index, columns=nodes)
for node in nodes:
timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
tz_dt_index = dt_index.tz_convert(timezone)
week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
week_df[node] = week_df[node].map(weekly_profile)
week_df = week_df.tz_localize(localize)
return week_df
def parse(l):
if len(l) == 1:
return yaml.safe_load(l[0])
else:
return {l.pop(0): parse(l)}
def update_config_with_sector_opts(config, sector_opts):
for o in sector_opts.split("-"):
if o.startswith("CF+"):
l = o.split("+")[1:]
update_config(config, parse(l))

595
scripts/make_summary.py
View File

@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
<<<<<<< HEAD
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -117,6 +118,118 @@ def calculate_costs(n, label, costs):
costs = _add_indexed_rows(costs, raw_index)
costs.loc[idx[raw_index], label] = capital_costs_grouped.values
=======
import logging
logger = logging.getLogger(__name__)
import sys
import numpy as np
import pandas as pd
import pypsa
import yaml
from helper import override_component_attrs
from prepare_sector_network import prepare_costs
idx = pd.IndexSlice
opt_name = {"Store": "e", "Line": "s", "Transformer": "s"}
def assign_carriers(n):
if "carrier" not in n.lines:
n.lines["carrier"] = "AC"
def assign_locations(n):
for c in n.iterate_components(n.one_port_components | n.branch_components):
ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
for i in ifind.unique():
names = ifind.index[ifind == i]
if i == -1:
c.df.loc[names, "location"] = ""
else:
c.df.loc[names, "location"] = names.str[:i]
def calculate_nodal_cfs(n, label, nodal_cfs):
# Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(
(n.branch_components ^ {"Line", "Transformer"})
| n.controllable_one_port_components ^ {"Load", "StorageUnit"}
):
capacities_c = c.df.groupby(["location", "carrier"])[
opt_name.get(c.name, "p") + "_nom_opt"
].sum()
if c.name == "Link":
p = c.pnl.p0.abs().mean()
elif c.name == "Generator":
p = c.pnl.p.abs().mean()
elif c.name == "Store":
p = c.pnl.e.abs().mean()
else:
sys.exit()
c.df["p"] = p
p_c = c.df.groupby(["location", "carrier"])["p"].sum()
cf_c = p_c / capacities_c
index = pd.MultiIndex.from_tuples(
[(c.list_name,) + t for t in cf_c.index.to_list()]
)
nodal_cfs = nodal_cfs.reindex(index.union(nodal_cfs.index))
nodal_cfs.loc[index, label] = cf_c.values
return nodal_cfs
def calculate_cfs(n, label, cfs):
for c in n.iterate_components(
n.branch_components
| n.controllable_one_port_components ^ {"Load", "StorageUnit"}
):
capacities_c = (
c.df[opt_name.get(c.name, "p") + "_nom_opt"].groupby(c.df.carrier).sum()
)
if c.name in ["Link", "Line", "Transformer"]:
p = c.pnl.p0.abs().mean()
elif c.name == "Store":
p = c.pnl.e.abs().mean()
else:
p = c.pnl.p.abs().mean()
p_c = p.groupby(c.df.carrier).sum()
cf_c = p_c / capacities_c
cf_c = pd.concat([cf_c], keys=[c.list_name])
cfs = cfs.reindex(cf_c.index.union(cfs.index))
cfs.loc[cf_c.index, label] = cf_c
return cfs
def calculate_nodal_costs(n, label, nodal_costs):
# Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
c.df["capital_costs"] = (
c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
)
capital_costs = c.df.groupby(["location", "carrier"])["capital_costs"].sum()
index = pd.MultiIndex.from_tuples(
[(c.list_name, "capital") + t for t in capital_costs.index.to_list()]
)
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
nodal_costs.loc[index, label] = capital_costs.values
>>>>>>> pypsa-eur-sec/master
if c.name == "Link":
p = c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
@ -129,10 +242,64 @@ def calculate_costs(n, label, costs):
else:
p = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum()
<<<<<<< HEAD
=======
# correct sequestration cost
if c.name == "Store":
items = c.df.index[
(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.0)
]
c.df.loc[items, "marginal_cost"] = -20.0
c.df["marginal_costs"] = p * c.df.marginal_cost
marginal_costs = c.df.groupby(["location", "carrier"])["marginal_costs"].sum()
index = pd.MultiIndex.from_tuples(
[(c.list_name, "marginal") + t for t in marginal_costs.index.to_list()]
)
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
nodal_costs.loc[index, label] = marginal_costs.values
return nodal_costs
def calculate_costs(n, label, costs):
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
capital_costs = c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum()
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
costs = costs.reindex(capital_costs_grouped.index.union(costs.index))
costs.loc[capital_costs_grouped.index, label] = capital_costs_grouped
if c.name == "Link":
p = c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
elif c.name == "Line":
continue
elif c.name == "StorageUnit":
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
p_all[p_all < 0.0] = 0.0
p = p_all.sum()
else:
p = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum()
# correct sequestration cost
if c.name == "Store":
items = c.df.index[
(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.0)
]
c.df.loc[items, "marginal_cost"] = -20.0
>>>>>>> pypsa-eur-sec/master
marginal_costs = p * c.df.marginal_cost
marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
<<<<<<< HEAD
costs = costs.reindex(
costs.index.union(
pd.MultiIndex.from_product(
@ -144,10 +311,95 @@ def calculate_costs(n, label, costs):
costs.loc[
idx[c.list_name, "marginal", list(marginal_costs_grouped.index)], label
] = marginal_costs_grouped.values
=======
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=["marginal"])
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=[c.list_name])
costs = costs.reindex(marginal_costs_grouped.index.union(costs.index))
costs.loc[marginal_costs_grouped.index, label] = marginal_costs_grouped
# add back in all hydro
# costs.loc[("storage_units", "capital", "hydro"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="hydro", "p_nom"].sum()
# costs.loc[("storage_units", "capital", "PHS"),label] = (0.01)*2e6*n.storage_units.loc[n.storage_units.group=="PHS", "p_nom"].sum()
# costs.loc[("generators", "capital", "ror"),label] = (0.02)*3e6*n.generators.loc[n.generators.group=="ror", "p_nom"].sum()
>>>>>>> pypsa-eur-sec/master
return costs
<<<<<<< HEAD
=======
def calculate_cumulative_cost():
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
cumulative_cost = pd.DataFrame(
index=df["costs"].sum().index,
columns=pd.Series(data=np.arange(0, 0.1, 0.01), name="social discount rate"),
)
# discount cost and express them in money value of planning_horizons[0]
for r in cumulative_cost.columns:
cumulative_cost[r] = [
df["costs"].sum()[index] / ((1 + r) ** (index[-1] - planning_horizons[0]))
for index in cumulative_cost.index
]
# integrate cost throughout the transition path
for r in cumulative_cost.columns:
for cluster in cumulative_cost.index.get_level_values(level=0).unique():
for lv in cumulative_cost.index.get_level_values(level=1).unique():
for sector_opts in cumulative_cost.index.get_level_values(
level=2
).unique():
cumulative_cost.loc[
(cluster, lv, sector_opts, "cumulative cost"), r
] = np.trapz(
cumulative_cost.loc[
idx[cluster, lv, sector_opts, planning_horizons], r
].values,
x=planning_horizons,
)
return cumulative_cost
def calculate_nodal_capacities(n, label, nodal_capacities):
# Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
nodal_capacities_c = c.df.groupby(["location", "carrier"])[
opt_name.get(c.name, "p") + "_nom_opt"
].sum()
index = pd.MultiIndex.from_tuples(
[(c.list_name,) + t for t in nodal_capacities_c.index.to_list()]
)
nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
nodal_capacities.loc[index, label] = nodal_capacities_c.values
return nodal_capacities
def calculate_capacities(n, label, capacities):
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
capacities_grouped = (
c.df[opt_name.get(c.name, "p") + "_nom_opt"].groupby(c.df.carrier).sum()
)
capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
capacities = capacities.reindex(
capacities_grouped.index.union(capacities.index)
)
capacities.loc[capacities_grouped.index, label] = capacities_grouped
return capacities
>>>>>>> pypsa-eur-sec/master
def calculate_curtailment(n, label, curtailment):
avail = (
n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt)
@ -164,7 +416,11 @@ def calculate_curtailment(n, label, curtailment):
def calculate_energy(n, label, energy):
for c in n.iterate_components(n.one_port_components | n.branch_components):
<<<<<<< HEAD
if c.name in {"Generator", "Load", "ShuntImpedance"}:
=======
if c.name in n.one_port_components:
>>>>>>> pypsa-eur-sec/master
c_energies = (
c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
.sum()
@ -172,6 +428,7 @@ def calculate_energy(n, label, energy):
.groupby(c.df.carrier)
.sum()
)
<<<<<<< HEAD
elif c.name in {"StorageUnit", "Store"}:
c_energies = (
c.pnl.p.multiply(n.snapshot_weightings.stores, axis=0)
@ -191,10 +448,33 @@ def calculate_energy(n, label, energy):
)
energy = include_in_summary(energy, [c.list_name], label, c_energies)
=======
else:
c_energies = pd.Series(0.0, c.df.carrier.unique())
for port in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
totals = (
c.pnl["p" + port]
.multiply(n.snapshot_weightings.generators, axis=0)
.sum()
)
# remove values where bus is missing (bug in nomopyomo)
no_bus = c.df.index[c.df["bus" + port] == ""]
totals.loc[no_bus] = n.component_attrs[c.name].loc[
"p" + port, "default"
]
c_energies -= totals.groupby(c.df.carrier).sum()
c_energies = pd.concat([c_energies], keys=[c.list_name])
energy = energy.reindex(c_energies.index.union(energy.index))
energy.loc[c_energies.index, label] = c_energies
>>>>>>> pypsa-eur-sec/master
return energy
<<<<<<< HEAD
def include_in_summary(summary, multiindexprefix, label, item):
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([multiindexprefix, list(item.index)])
@ -247,6 +527,24 @@ def calculate_supply(n, label, supply):
items = c.df.index[c.df.bus.map(bus_map)]
if len(items) == 0 or c.pnl.p.empty:
=======
def calculate_supply(n, label, supply):
"""
Calculate the max dispatch of each component at the buses aggregated by
carrier.
"""
bus_carriers = n.buses.carrier.unique()
for i in bus_carriers:
bus_map = n.buses.carrier == i
bus_map.at[""] = False
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0:
>>>>>>> pypsa-eur-sec/master
continue
s = (
@ -256,6 +554,7 @@ def calculate_supply(n, label, supply):
.groupby(c.df.loc[items, "carrier"])
.sum()
)
<<<<<<< HEAD
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[i], [c.list_name], list(s.index)])
@ -268,12 +567,26 @@ def calculate_supply(n, label, supply):
items = c.df.index[c.df["bus" + end].map(bus_map)]
if len(items) == 0 or c.pnl["p" + end].empty:
=======
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply = supply.reindex(s.index.union(supply.index))
supply.loc[s.index, label] = s
for c in n.iterate_components(n.branch_components):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + end].map(bus_map).fillna(False)]
if len(items) == 0:
>>>>>>> pypsa-eur-sec/master
continue
# lots of sign compensation for direction and to do maximums
s = (-1) ** (1 - int(end)) * (
(-1) ** int(end) * c.pnl["p" + end][items]
).max().groupby(c.df.loc[items, "carrier"]).sum()
<<<<<<< HEAD
supply = supply.reindex(
supply.index.union(
@ -281,12 +594,21 @@ def calculate_supply(n, label, supply):
)
)
supply.loc[idx[i, c.list_name, list(s.index)], label] = s.values
=======
s.index = s.index + end
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply = supply.reindex(s.index.union(supply.index))
supply.loc[s.index, label] = s
>>>>>>> pypsa-eur-sec/master
return supply
def calculate_supply_energy(n, label, supply_energy):
"""
<<<<<<< HEAD
calculate the total dispatch of each component at the buses where the loads
are attached.
"""
@ -303,15 +625,36 @@ def calculate_supply_energy(n, label, supply_energy):
items = c.df.index[c.df.bus.map(bus_map)]
if len(items) == 0 or c.pnl.p.empty:
=======
Calculate the total energy supply/consuption of each component at the buses
aggregated by carrier.
"""
bus_carriers = n.buses.carrier.unique()
for i in bus_carriers:
bus_map = n.buses.carrier == i
bus_map.at[""] = False
for c in n.iterate_components(n.one_port_components):
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
if len(items) == 0:
>>>>>>> pypsa-eur-sec/master
continue
s = (
c.pnl.p[items]
<<<<<<< HEAD
=======
.multiply(n.snapshot_weightings.generators, axis=0)
>>>>>>> pypsa-eur-sec/master
.sum()
.multiply(c.df.loc[items, "sign"])
.groupby(c.df.loc[items, "carrier"])
.sum()
)
<<<<<<< HEAD
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[i], [c.list_name], list(s.index)])
@ -336,11 +679,39 @@ def calculate_supply_energy(n, label, supply_energy):
)
)
supply_energy.loc[idx[i, c.list_name, list(s.index)], label] = s.values
=======
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
supply_energy.loc[s.index, label] = s
for c in n.iterate_components(n.branch_components):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + str(end)].map(bus_map).fillna(False)]
if len(items) == 0:
continue
s = (-1) * c.pnl["p" + end][items].multiply(
n.snapshot_weightings.generators, axis=0
).sum().groupby(c.df.loc[items, "carrier"]).sum()
s.index = s.index + end
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply_energy = supply_energy.reindex(
s.index.union(supply_energy.index)
)
supply_energy.loc[s.index, label] = s
>>>>>>> pypsa-eur-sec/master
return supply_energy
def calculate_metrics(n, label, metrics):
<<<<<<< HEAD
metrics = metrics.reindex(
metrics.index.union(
pd.Index(
@ -355,6 +726,18 @@ def calculate_metrics(n, label, metrics):
)
)
)
=======
metrics_list = [
"line_volume",
"line_volume_limit",
"line_volume_AC",
"line_volume_DC",
"line_volume_shadow",
"co2_shadow",
]
metrics = metrics.reindex(pd.Index(metrics_list).union(metrics.index))
>>>>>>> pypsa-eur-sec/master
metrics.at["line_volume_DC", label] = (n.links.length * n.links.p_nom_opt)[
n.links.carrier == "DC"
@ -366,8 +749,11 @@ def calculate_metrics(n, label, metrics):
if hasattr(n, "line_volume_limit"):
metrics.at["line_volume_limit", label] = n.line_volume_limit
<<<<<<< HEAD
if hasattr(n, "line_volume_limit_dual"):
=======
>>>>>>> pypsa-eur-sec/master
metrics.at["line_volume_shadow", label] = n.line_volume_limit_dual
if "CO2Limit" in n.global_constraints.index:
@ -377,6 +763,7 @@ def calculate_metrics(n, label, metrics):
def calculate_prices(n, label, prices):
<<<<<<< HEAD
bus_type = pd.Series(n.buses.index.str[3:], n.buses.index).replace(
"", "electricity"
)
@ -385,12 +772,22 @@ def calculate_prices(n, label, prices):
logger.warning("Prices are time-averaged, not load-weighted")
prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).mean()
=======
prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
# WARNING: this is time-averaged, see weighted_prices for load-weighted average
prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
>>>>>>> pypsa-eur-sec/master
return prices
def calculate_weighted_prices(n, label, weighted_prices):
<<<<<<< HEAD
logger.warning("Weighted prices don't include storage units as loads")
=======
# Warning: doesn't include storage units as loads
>>>>>>> pypsa-eur-sec/master
weighted_prices = weighted_prices.reindex(
pd.Index(
@ -450,6 +847,7 @@ def calculate_weighted_prices(n, label, weighted_prices):
continue
load += (
<<<<<<< HEAD
n.links_t.p0[names]
.groupby(n.links.loc[names, "bus0"], axis=1)
.sum(axis=1)
@ -464,17 +862,34 @@ def calculate_weighted_prices(n, label, weighted_prices):
)
stores[stores > 0.0] = 0.0
load += -stores
=======
n.links_t.p0[names].groupby(n.links.loc[names, "bus0"], axis=1).sum()
)
# Add H2 Store when charging
# if carrier == "H2":
# stores = n.stores_t.p[buses+ " Store"].groupby(n.stores.loc[buses+ " Store", "bus"],axis=1).sum(axis=1)
# stores[stores > 0.] = 0.
# load += -stores
>>>>>>> pypsa-eur-sec/master
weighted_prices.loc[carrier, label] = (
load * n.buses_t.marginal_price[buses]
).sum().sum() / load.sum().sum()
<<<<<<< HEAD
if carrier[:5] == "space":
print(load * n.buses_t.marginal_price[buses])
=======
# still have no idea what this is for, only for debug reasons.
if carrier[:5] == "space":
logger.debug(load * n.buses_t.marginal_price[buses])
>>>>>>> pypsa-eur-sec/master
return weighted_prices
<<<<<<< HEAD
outputs = [
"costs",
"curtailment",
@ -529,10 +944,144 @@ def to_csv(dfs, dir):
os.makedirs(dir, exist_ok=True)
for key, df in dfs.items():
df.to_csv(os.path.join(dir, f"{key}.csv"))
=======
def calculate_market_values(n, label, market_values):
# Warning: doesn't include storage units
carrier = "AC"
buses = n.buses.index[n.buses.carrier == carrier]
## First do market value of generators ##
generators = n.generators.index[n.buses.loc[n.generators.bus, "carrier"] == carrier]
techs = n.generators.loc[generators, "carrier"].value_counts().index
market_values = market_values.reindex(market_values.index.union(techs))
for tech in techs:
gens = generators[n.generators.loc[generators, "carrier"] == tech]
dispatch = (
n.generators_t.p[gens]
.groupby(n.generators.loc[gens, "bus"], axis=1)
.sum()
.reindex(columns=buses, fill_value=0.0)
)
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
## Now do market value of links ##
for i in ["0", "1"]:
all_links = n.links.index[n.buses.loc[n.links["bus" + i], "carrier"] == carrier]
techs = n.links.loc[all_links, "carrier"].value_counts().index
market_values = market_values.reindex(market_values.index.union(techs))
for tech in techs:
links = all_links[n.links.loc[all_links, "carrier"] == tech]
dispatch = (
n.links_t["p" + i][links]
.groupby(n.links.loc[links, "bus" + i], axis=1)
.sum()
.reindex(columns=buses, fill_value=0.0)
)
revenue = dispatch * n.buses_t.marginal_price[buses]
market_values.at[tech, label] = revenue.sum().sum() / dispatch.sum().sum()
return market_values
def calculate_price_statistics(n, label, price_statistics):
price_statistics = price_statistics.reindex(
price_statistics.index.union(
pd.Index(["zero_hours", "mean", "standard_deviation"])
)
)
buses = n.buses.index[n.buses.carrier == "AC"]
threshold = 0.1 # higher than phoney marginal_cost of wind/solar
df = pd.DataFrame(data=0.0, columns=buses, index=n.snapshots)
df[n.buses_t.marginal_price[buses] < threshold] = 1.0
price_statistics.at["zero_hours", label] = df.sum().sum() / (
df.shape[0] * df.shape[1]
)
price_statistics.at["mean", label] = (
n.buses_t.marginal_price[buses].unstack().mean()
)
price_statistics.at["standard_deviation", label] = (
n.buses_t.marginal_price[buses].unstack().std()
)
return price_statistics
def make_summaries(networks_dict):
outputs = [
"nodal_costs",
"nodal_capacities",
"nodal_cfs",
"cfs",
"costs",
"capacities",
"curtailment",
"energy",
"supply",
"supply_energy",
"prices",
"weighted_prices",
"price_statistics",
"market_values",
"metrics",
]
columns = pd.MultiIndex.from_tuples(
networks_dict.keys(), names=["cluster", "lv", "opt", "planning_horizon"]
)
df = {}
for output in outputs:
df[output] = pd.DataFrame(columns=columns, dtype=float)
for label, filename in networks_dict.items():
logger.info(f"Make summary for scenario {label}, using {filename}")
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(filename, override_component_attrs=overrides)
assign_carriers(n)
assign_locations(n)
for output in outputs:
df[output] = globals()["calculate_" + output](n, label, df[output])
return df
def to_csv(df):
for key in df:
df[key].to_csv(snakemake.output[key])
>>>>>>> pypsa-eur-sec/master
if __name__ == "__main__":
if "snakemake" not in globals():
<<<<<<< HEAD
from _helpers import mock_snakemake
snakemake = mock_snakemake(
@ -581,3 +1130,49 @@ if __name__ == "__main__":
)
to_csv(dfs, snakemake.output[0])
=======
from helper import mock_snakemake
snakemake = mock_snakemake("make_summary")
logging.basicConfig(level=snakemake.config["logging_level"])
networks_dict = {
(cluster, lv, opt + sector_opt, planning_horizon): snakemake.config[
"results_dir"
]
+ snakemake.config["run"]
+ f"/postnetworks/elec_s{simpl}_{cluster}_lv{lv}_{opt}_{sector_opt}_{planning_horizon}.nc"
for simpl in snakemake.config["scenario"]["simpl"]
for cluster in snakemake.config["scenario"]["clusters"]
for opt in snakemake.config["scenario"]["opts"]
for sector_opt in snakemake.config["scenario"]["sector_opts"]
for lv in snakemake.config["scenario"]["lv"]
for planning_horizon in snakemake.config["scenario"]["planning_horizons"]
}
Nyears = 1
costs_db = prepare_costs(
snakemake.input.costs,
snakemake.config["costs"]["USD2013_to_EUR2013"],
snakemake.config["costs"]["discountrate"],
Nyears,
snakemake.config["costs"]["lifetime"],
)
df = make_summaries(networks_dict)
df["metrics"].loc["total costs"] = df["costs"].sum()
to_csv(df)
if snakemake.config["foresight"] == "myopic":
cumulative_cost = calculate_cumulative_cost()
cumulative_cost.to_csv(
snakemake.config["summary_dir"]
+ "/"
+ snakemake.config["run"]
+ "/csvs/cumulative_cost.csv"
)
>>>>>>> pypsa-eur-sec/master

942
scripts/plot_network.py
View File

@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
<<<<<<< HEAD
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -181,10 +182,743 @@ def plot_map(n, opts, ax=None, attribute="p_nom"):
)
)
labels.append("{} GW".format(s))
=======
import logging
logger = logging.getLogger(__name__)
import cartopy.crs as ccrs
import geopandas as gpd
import matplotlib.pyplot as plt
import pandas as pd
import pypsa
from helper import override_component_attrs
from make_summary import assign_carriers
from plot_summary import preferred_order, rename_techs
from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
plt.style.use(["ggplot", "matplotlibrc"])
def rename_techs_tyndp(tech):
tech = rename_techs(tech)
if "heat pump" in tech or "resistive heater" in tech:
return "power-to-heat"
elif tech in ["H2 Electrolysis", "methanation", "helmeth", "H2 liquefaction"]:
return "power-to-gas"
elif tech == "H2":
return "H2 storage"
elif tech in ["NH3", "Haber-Bosch", "ammonia cracker", "ammonia store"]:
return "ammonia"
elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
return "gas-to-power/heat"
# elif "solar" in tech:
# return "solar"
elif tech in ["Fischer-Tropsch", "methanolisation"]:
return "power-to-liquid"
elif "offshore wind" in tech:
return "offshore wind"
elif "CC" in tech or "sequestration" in tech:
return "CCS"
else:
return tech
def assign_location(n):
for c in n.iterate_components(n.one_port_components | n.branch_components):
ifind = pd.Series(c.df.index.str.find(" ", start=4), c.df.index)
for i in ifind.value_counts().index:
# these have already been assigned defaults
if i == -1:
continue
names = ifind.index[ifind == i]
c.df.loc[names, "location"] = names.str[:i]
def plot_map(
network,
components=["links", "stores", "storage_units", "generators"],
bus_size_factor=1.7e10,
transmission=False,
with_legend=True,
):
tech_colors = snakemake.config["plotting"]["tech_colors"]
n = network.copy()
assign_location(n)
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
costs = pd.DataFrame(index=n.buses.index)
for comp in components:
df_c = getattr(n, comp)
if df_c.empty:
continue
df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
costs_c = (
(df_c.capital_cost * df_c[attr])
.groupby([df_c.location, df_c.nice_group])
.sum()
.unstack()
.fillna(0.0)
)
costs = pd.concat([costs, costs_c], axis=1)
logger.debug(f"{comp}, {costs}")
costs = costs.groupby(costs.columns, axis=1).sum()
costs.drop(list(costs.columns[(costs == 0.0).all()]), axis=1, inplace=True)
new_columns = preferred_order.intersection(costs.columns).append(
costs.columns.difference(preferred_order)
)
costs = costs[new_columns]
for item in new_columns:
if item not in tech_colors:
logger.warning(f"{item} not in config/plotting/tech_colors")
costs = costs.stack() # .sort_index()
# hack because impossible to drop buses...
eu_location = snakemake.config["plotting"].get(
"eu_node_location", dict(x=-5.5, y=46)
)
n.buses.loc["EU gas", "x"] = eu_location["x"]
n.buses.loc["EU gas", "y"] = eu_location["y"]
n.links.drop(
n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")],
inplace=True,
)
# drop non-bus
to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
if len(to_drop) != 0:
logger.info(f"Dropping non-buses {to_drop.tolist()}")
costs.drop(to_drop, level=0, inplace=True, axis=0, errors="ignore")
# make sure they are removed from index
costs.index = pd.MultiIndex.from_tuples(costs.index.values)
threshold = 100e6 # 100 mEUR/a
carriers = costs.groupby(level=1).sum()
carriers = carriers.where(carriers > threshold).dropna()
carriers = list(carriers.index)
# PDF has minimum width, so set these to zero
line_lower_threshold = 500.0
line_upper_threshold = 1e4
linewidth_factor = 4e3
ac_color = "rosybrown"
dc_color = "darkseagreen"
if snakemake.wildcards["lv"] == "1.0":
# should be zero
line_widths = n.lines.s_nom_opt - n.lines.s_nom
link_widths = n.links.p_nom_opt - n.links.p_nom
title = "added grid"
if transmission:
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
linewidth_factor = 2e3
line_lower_threshold = 0.0
title = "current grid"
else:
line_widths = n.lines.s_nom_opt - n.lines.s_nom_min
link_widths = n.links.p_nom_opt - n.links.p_nom_min
title = "added grid"
if transmission:
line_widths = n.lines.s_nom_opt
link_widths = n.links.p_nom_opt
title = "total grid"
line_widths = line_widths.clip(line_lower_threshold, line_upper_threshold)
link_widths = link_widths.clip(line_lower_threshold, line_upper_threshold)
line_widths = line_widths.replace(line_lower_threshold, 0)
link_widths = link_widths.replace(line_lower_threshold, 0)
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.EqualEarth()})
fig.set_size_inches(7, 6)
n.plot(
bus_sizes=costs / bus_size_factor,
bus_colors=tech_colors,
line_colors=ac_color,
link_colors=dc_color,
line_widths=line_widths / linewidth_factor,
link_widths=link_widths / linewidth_factor,
ax=ax,
**map_opts,
)
sizes = [20, 10, 5]
labels = [f"{s} bEUR/a" for s in sizes]
sizes = [s / bus_size_factor * 1e9 for s in sizes]
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0.01, 1.06),
labelspacing=0.8,
frameon=False,
handletextpad=0,
title="system cost",
)
add_legend_circles(
ax,
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
sizes = [10, 5]
labels = [f"{s} GW" for s in sizes]
scale = 1e3 / linewidth_factor
sizes = [s * scale for s in sizes]
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0.27, 1.06),
frameon=False,
labelspacing=0.8,
handletextpad=1,
title=title,
)
add_legend_lines(
ax, sizes, labels, patch_kw=dict(color="lightgrey"), legend_kw=legend_kw
)
legend_kw = dict(
bbox_to_anchor=(1.52, 1.04),
frameon=False,
)
if with_legend:
colors = [tech_colors[c] for c in carriers] + [ac_color, dc_color]
labels = carriers + ["HVAC line", "HVDC link"]
add_legend_patches(
ax,
colors,
labels,
legend_kw=legend_kw,
)
fig.savefig(snakemake.output.map, transparent=True, bbox_inches="tight")
def group_pipes(df, drop_direction=False):
"""
Group pipes which connect same buses and return overall capacity.
"""
if drop_direction:
positive_order = df.bus0 < df.bus1
df_p = df[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
df_n = df[~positive_order].rename(columns=swap_buses)
df = pd.concat([df_p, df_n])
# there are pipes for each investment period rename to AC buses name for plotting
df.index = df.apply(
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
axis=1,
)
# group pipe lines connecting the same buses and rename them for plotting
pipe_capacity = df.groupby(level=0).agg(
{"p_nom_opt": sum, "bus0": "first", "bus1": "first"}
)
return pipe_capacity
def plot_h2_map(network, regions):
n = network.copy()
if "H2 pipeline" not in n.links.carrier.unique():
return
assign_location(n)
h2_storage = n.stores.query("carrier == 'H2'")
regions["H2"] = h2_storage.rename(
index=h2_storage.bus.map(n.buses.location)
).e_nom_opt.div(
1e6
) # TWh
regions["H2"] = regions["H2"].where(regions["H2"] > 0.1)
bus_size_factor = 1e5
linewidth_factor = 7e3
# MW below which not drawn
line_lower_threshold = 750
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
carriers = ["H2 Electrolysis", "H2 Fuel Cell"]
elec = n.links[n.links.carrier.isin(carriers)].index
bus_sizes = (
n.links.loc[elec, "p_nom_opt"].groupby([n.links["bus0"], n.links.carrier]).sum()
/ bus_size_factor
)
# make a fake MultiIndex so that area is correct for legend
bus_sizes.rename(index=lambda x: x.replace(" H2", ""), level=0, inplace=True)
# drop all links which are not H2 pipelines
n.links.drop(
n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True
)
h2_new = n.links[n.links.carrier == "H2 pipeline"]
h2_retro = n.links[n.links.carrier == "H2 pipeline retrofitted"]
if snakemake.config["foresight"] == "myopic":
# sum capacitiy for pipelines from different investment periods
h2_new = group_pipes(h2_new)
if not h2_retro.empty:
h2_retro = (
group_pipes(h2_retro, drop_direction=True)
.reindex(h2_new.index)
.fillna(0)
)
if not h2_retro.empty:
positive_order = h2_retro.bus0 < h2_retro.bus1
h2_retro_p = h2_retro[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
h2_retro_n = h2_retro[~positive_order].rename(columns=swap_buses)
h2_retro = pd.concat([h2_retro_p, h2_retro_n])
h2_retro["index_orig"] = h2_retro.index
h2_retro.index = h2_retro.apply(
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
axis=1,
)
retro_w_new_i = h2_retro.index.intersection(h2_new.index)
h2_retro_w_new = h2_retro.loc[retro_w_new_i]
retro_wo_new_i = h2_retro.index.difference(h2_new.index)
h2_retro_wo_new = h2_retro.loc[retro_wo_new_i]
h2_retro_wo_new.index = h2_retro_wo_new.index_orig
to_concat = [h2_new, h2_retro_w_new, h2_retro_wo_new]
h2_total = pd.concat(to_concat).p_nom_opt.groupby(level=0).sum()
else:
h2_total = h2_new.p_nom_opt
link_widths_total = h2_total / linewidth_factor
n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
n.links = n.links.groupby(level=0).first()
link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.0)
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.0
retro = n.links.p_nom_opt.where(
n.links.carrier == "H2 pipeline retrofitted", other=0.0
)
link_widths_retro = retro / linewidth_factor
link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.0
n.links.bus0 = n.links.bus0.str.replace(" H2", "")
n.links.bus1 = n.links.bus1.str.replace(" H2", "")
proj = ccrs.EqualEarth()
regions = regions.to_crs(proj.proj4_init)
fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={"projection": proj})
color_h2_pipe = "#b3f3f4"
color_retrofit = "#499a9c"
bus_colors = {"H2 Electrolysis": "#ff29d9", "H2 Fuel Cell": "#805394"}
n.plot(
geomap=True,
bus_sizes=bus_sizes,
bus_colors=bus_colors,
link_colors=color_h2_pipe,
link_widths=link_widths_total,
branch_components=["Link"],
ax=ax,
**map_opts,
)
n.plot(
geomap=True,
bus_sizes=0,
link_colors=color_retrofit,
link_widths=link_widths_retro,
branch_components=["Link"],
ax=ax,
color_geomap=False,
boundaries=map_opts["boundaries"],
)
regions.plot(
ax=ax,
column="H2",
cmap="Blues",
linewidths=0,
legend=True,
vmax=6,
vmin=0,
legend_kwds={
"label": "Hydrogen Storage [TWh]",
"shrink": 0.7,
"extend": "max",
},
)
sizes = [50, 10]
labels = [f"{s} GW" for s in sizes]
sizes = [s / bus_size_factor * 1e3 for s in sizes]
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0, 1),
labelspacing=0.8,
handletextpad=0,
frameon=False,
)
add_legend_circles(
ax,
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
sizes = [30, 10]
labels = [f"{s} GW" for s in sizes]
scale = 1e3 / linewidth_factor
sizes = [s * scale for s in sizes]
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0.23, 1),
frameon=False,
labelspacing=0.8,
handletextpad=1,
)
add_legend_lines(
ax,
sizes,
labels,
patch_kw=dict(color="lightgrey"),
legend_kw=legend_kw,
)
colors = [bus_colors[c] for c in carriers] + [color_h2_pipe, color_retrofit]
labels = carriers + ["H2 pipeline (total)", "H2 pipeline (repurposed)"]
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0, 1.13),
ncol=2,
frameon=False,
)
add_legend_patches(ax, colors, labels, legend_kw=legend_kw)
ax.set_facecolor("white")
fig.savefig(
snakemake.output.map.replace("-costs-all", "-h2_network"), bbox_inches="tight"
)
def plot_ch4_map(network):
n = network.copy()
if "gas pipeline" not in n.links.carrier.unique():
return
assign_location(n)
bus_size_factor = 8e7
linewidth_factor = 1e4
# MW below which not drawn
line_lower_threshold = 1e3
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
fossil_gas_i = n.generators[n.generators.carrier == "gas"].index
fossil_gas = (
n.generators_t.p.loc[:, fossil_gas_i]
.mul(n.snapshot_weightings.generators, axis=0)
.sum()
.groupby(n.generators.loc[fossil_gas_i, "bus"])
.sum()
/ bus_size_factor
)
fossil_gas.rename(index=lambda x: x.replace(" gas", ""), inplace=True)
fossil_gas = fossil_gas.reindex(n.buses.index).fillna(0)
# make a fake MultiIndex so that area is correct for legend
fossil_gas.index = pd.MultiIndex.from_product([fossil_gas.index, ["fossil gas"]])
methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
methanation = (
abs(
n.links_t.p1.loc[:, methanation_i].mul(
n.snapshot_weightings.generators, axis=0
)
)
.sum()
.groupby(n.links.loc[methanation_i, "bus1"])
.sum()
/ bus_size_factor
)
methanation = (
methanation.groupby(methanation.index)
.sum()
.rename(index=lambda x: x.replace(" gas", ""))
)
# make a fake MultiIndex so that area is correct for legend
methanation.index = pd.MultiIndex.from_product([methanation.index, ["methanation"]])
biogas_i = n.stores[n.stores.carrier == "biogas"].index
biogas = (
n.stores_t.p.loc[:, biogas_i]
.mul(n.snapshot_weightings.generators, axis=0)
.sum()
.groupby(n.stores.loc[biogas_i, "bus"])
.sum()
/ bus_size_factor
)
biogas = (
biogas.groupby(biogas.index)
.sum()
.rename(index=lambda x: x.replace(" biogas", ""))
)
# make a fake MultiIndex so that area is correct for legend
biogas.index = pd.MultiIndex.from_product([biogas.index, ["biogas"]])
bus_sizes = pd.concat([fossil_gas, methanation, biogas])
bus_sizes.sort_index(inplace=True)
to_remove = n.links.index[~n.links.carrier.str.contains("gas pipeline")]
n.links.drop(to_remove, inplace=True)
link_widths_rem = n.links.p_nom_opt / linewidth_factor
link_widths_rem[n.links.p_nom_opt < line_lower_threshold] = 0.0
link_widths_orig = n.links.p_nom / linewidth_factor
link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.0
max_usage = n.links_t.p0.abs().max(axis=0)
link_widths_used = max_usage / linewidth_factor
link_widths_used[max_usage < line_lower_threshold] = 0.0
tech_colors = snakemake.config["plotting"]["tech_colors"]
pipe_colors = {
"gas pipeline": "#f08080",
"gas pipeline new": "#c46868",
"gas pipeline (in 2020)": "lightgrey",
"gas pipeline (available)": "#e8d1d1",
}
link_color_used = n.links.carrier.map(pipe_colors)
n.links.bus0 = n.links.bus0.str.replace(" gas", "")
n.links.bus1 = n.links.bus1.str.replace(" gas", "")
bus_colors = {
"fossil gas": tech_colors["fossil gas"],
"methanation": tech_colors["methanation"],
"biogas": "seagreen",
}
fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={"projection": ccrs.EqualEarth()})
n.plot(
bus_sizes=bus_sizes,
bus_colors=bus_colors,
link_colors=pipe_colors["gas pipeline (in 2020)"],
link_widths=link_widths_orig,
branch_components=["Link"],
ax=ax,
**map_opts,
)
n.plot(
ax=ax,
bus_sizes=0.0,
link_colors=pipe_colors["gas pipeline (available)"],
link_widths=link_widths_rem,
branch_components=["Link"],
color_geomap=False,
boundaries=map_opts["boundaries"],
)
n.plot(
ax=ax,
bus_sizes=0.0,
link_colors=link_color_used,
link_widths=link_widths_used,
branch_components=["Link"],
color_geomap=False,
boundaries=map_opts["boundaries"],
)
sizes = [100, 10]
labels = [f"{s} TWh" for s in sizes]
sizes = [s / bus_size_factor * 1e6 for s in sizes]
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0, 1.03),
labelspacing=0.8,
frameon=False,
handletextpad=1,
title="gas sources",
)
add_legend_circles(
ax,
sizes,
labels,
srid=n.srid,
patch_kw=dict(facecolor="lightgrey"),
legend_kw=legend_kw,
)
sizes = [50, 10]
labels = [f"{s} GW" for s in sizes]
scale = 1e3 / linewidth_factor
sizes = [s * scale for s in sizes]
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0.25, 1.03),
frameon=False,
labelspacing=0.8,
handletextpad=1,
title="gas pipeline",
)
add_legend_lines(
ax,
sizes,
labels,
patch_kw=dict(color="lightgrey"),
legend_kw=legend_kw,
)
colors = list(pipe_colors.values()) + list(bus_colors.values())
labels = list(pipe_colors.keys()) + list(bus_colors.keys())
# legend on the side
# legend_kw = dict(
# bbox_to_anchor=(1.47, 1.04),
# frameon=False,
# )
legend_kw = dict(
loc="upper left",
bbox_to_anchor=(0, 1.24),
ncol=2,
frameon=False,
)
add_legend_patches(
ax,
colors,
labels,
legend_kw=legend_kw,
)
fig.savefig(
snakemake.output.map.replace("-costs-all", "-ch4_network"), bbox_inches="tight"
)
def plot_map_without(network):
n = network.copy()
assign_location(n)
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
fig, ax = plt.subplots(figsize=(7, 6), subplot_kw={"projection": ccrs.EqualEarth()})
# PDF has minimum width, so set these to zero
line_lower_threshold = 200.0
line_upper_threshold = 1e4
linewidth_factor = 3e3
ac_color = "rosybrown"
dc_color = "darkseagreen"
# hack because impossible to drop buses...
if "EU gas" in n.buses.index:
eu_location = snakemake.config["plotting"].get(
"eu_node_location", dict(x=-5.5, y=46)
)
n.buses.loc["EU gas", "x"] = eu_location["x"]
n.buses.loc["EU gas", "y"] = eu_location["y"]
to_drop = n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")]
n.links.drop(to_drop, inplace=True)
if snakemake.wildcards["lv"] == "1.0":
line_widths = n.lines.s_nom
link_widths = n.links.p_nom
else:
line_widths = n.lines.s_nom_min
link_widths = n.links.p_nom_min
line_widths = line_widths.clip(line_lower_threshold, line_upper_threshold)
link_widths = link_widths.clip(line_lower_threshold, line_upper_threshold)
line_widths = line_widths.replace(line_lower_threshold, 0)
link_widths = link_widths.replace(line_lower_threshold, 0)
n.plot(
bus_colors="k",
line_colors=ac_color,
link_colors=dc_color,
line_widths=line_widths / linewidth_factor,
link_widths=link_widths / linewidth_factor,
ax=ax,
**map_opts,
)
handles = []
labels = []
for s in (10, 5):
handles.append(
plt.Line2D([0], [0], color=ac_color, linewidth=s * 1e3 / linewidth_factor)
)
labels.append(f"{s} GW")
>>>>>>> pypsa-eur-sec/master
l1_1 = ax.legend(
handles,
labels,
loc="upper left",
<<<<<<< HEAD
bbox_to_anchor=(0.24, 1.01),
frameon=False,
labelspacing=0.8,
@ -352,15 +1086,192 @@ def plot_total_cost_bar(n, opts, ax=None):
ax.set_xlim([0, 1])
ax.set_xticklabels([])
ax.grid(True, axis="y", color="k", linestyle="dotted")
=======
bbox_to_anchor=(0.05, 1.01),
frameon=False,
labelspacing=0.8,
handletextpad=1.5,
title="Today's transmission",
)
ax.add_artist(l1_1)
fig.savefig(snakemake.output.today, transparent=True, bbox_inches="tight")
def plot_series(network, carrier="AC", name="test"):
n = network.copy()
assign_location(n)
assign_carriers(n)
buses = n.buses.index[n.buses.carrier.str.contains(carrier)]
supply = pd.DataFrame(index=n.snapshots)
for c in n.iterate_components(n.branch_components):
n_port = 4 if c.name == "Link" else 2
for i in range(n_port):
supply = pd.concat(
(
supply,
(-1)
* c.pnl["p" + str(i)]
.loc[:, c.df.index[c.df["bus" + str(i)].isin(buses)]]
.groupby(c.df.carrier, axis=1)
.sum(),
),
axis=1,
)
for c in n.iterate_components(n.one_port_components):
comps = c.df.index[c.df.bus.isin(buses)]
supply = pd.concat(
(
supply,
((c.pnl["p"].loc[:, comps]).multiply(c.df.loc[comps, "sign"]))
.groupby(c.df.carrier, axis=1)
.sum(),
),
axis=1,
)
supply = supply.groupby(rename_techs_tyndp, axis=1).sum()
both = supply.columns[(supply < 0.0).any() & (supply > 0.0).any()]
positive_supply = supply[both]
negative_supply = supply[both]
positive_supply[positive_supply < 0.0] = 0.0
negative_supply[negative_supply > 0.0] = 0.0
supply[both] = positive_supply
suffix = " charging"
negative_supply.columns = negative_supply.columns + suffix
supply = pd.concat((supply, negative_supply), axis=1)
# 14-21.2 for flaute
# 19-26.1 for flaute
start = "2013-02-19"
stop = "2013-02-26"
threshold = 10e3
to_drop = supply.columns[(abs(supply) < threshold).all()]
if len(to_drop) != 0:
logger.info(f"Dropping {to_drop.tolist()} from supply")
supply.drop(columns=to_drop, inplace=True)
supply.index.name = None
supply = supply / 1e3
supply.rename(
columns={"electricity": "electric demand", "heat": "heat demand"}, inplace=True
)
supply.columns = supply.columns.str.replace("residential ", "")
supply.columns = supply.columns.str.replace("services ", "")
supply.columns = supply.columns.str.replace("urban decentral ", "decentral ")
preferred_order = pd.Index(
[
"electric demand",
"transmission lines",
"hydroelectricity",
"hydro reservoir",
"run of river",
"pumped hydro storage",
"CHP",
"onshore wind",
"offshore wind",
"solar PV",
"solar thermal",
"building retrofitting",
"ground heat pump",
"air heat pump",
"resistive heater",
"OCGT",
"gas boiler",
"gas",
"natural gas",
"methanation",
"hydrogen storage",
"battery storage",
"hot water storage",
]
)
new_columns = preferred_order.intersection(supply.columns).append(
supply.columns.difference(preferred_order)
)
supply = supply.groupby(supply.columns, axis=1).sum()
fig, ax = plt.subplots()
fig.set_size_inches((8, 5))
(
supply.loc[start:stop, new_columns].plot(
ax=ax,
kind="area",
stacked=True,
linewidth=0.0,
color=[
snakemake.config["plotting"]["tech_colors"][i.replace(suffix, "")]
for i in new_columns
],
)
)
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
new_handles = []
new_labels = []
for i, item in enumerate(labels):
if "charging" not in item:
new_handles.append(handles[i])
new_labels.append(labels[i])
ax.legend(new_handles, new_labels, ncol=3, loc="upper left", frameon=False)
ax.set_xlim([start, stop])
ax.set_ylim([-1300, 1900])
ax.grid(True)
ax.set_ylabel("Power [GW]")
fig.tight_layout()
fig.savefig(
"{}{}/maps/series-{}-{}-{}-{}-{}.pdf".format(
snakemake.config["results_dir"],
snakemake.config["run"],
snakemake.wildcards["lv"],
carrier,
start,
stop,
name,
),
transparent=True,
)
>>>>>>> pypsa-eur-sec/master
if __name__ == "__main__":
if "snakemake" not in globals():
<<<<<<< HEAD
from _helpers import mock_snakemake
=======
from helper import mock_snakemake
>>>>>>> pypsa-eur-sec/master
snakemake = mock_snakemake(
"plot_network",
simpl="",
<<<<<<< HEAD
clusters="5",
ll="copt",
opts="Co2L-24H",
@ -407,3 +1318,34 @@ if __name__ == "__main__":
)
fig.savefig(snakemake.output.ext, transparent=True, bbox_inches="tight")
=======
clusters="181",
lv="opt",
opts="",
sector_opts="Co2L0-730H-T-H-B-I-A-solar+p3-linemaxext10",
planning_horizons="2050",
)
logging.basicConfig(level=snakemake.config["logging_level"])
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
regions = gpd.read_file(snakemake.input.regions).set_index("name")
map_opts = snakemake.config["plotting"]["map"]
plot_map(
n,
components=["generators", "links", "stores", "storage_units"],
bus_size_factor=2e10,
transmission=False,
)
plot_h2_map(n, regions)
plot_ch4_map(n)
plot_map_without(n)
# plot_series(n, carrier="AC", name=suffix)
# plot_series(n, carrier="heat", name=suffix)
>>>>>>> pypsa-eur-sec/master

523
scripts/plot_summary.py
View File

@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
<<<<<<< HEAD
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -49,6 +50,87 @@ def rename_techs(label):
elif "battery" in label:
label = "battery storage"
=======
import logging
logger = logging.getLogger(__name__)
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
plt.style.use("ggplot")
from prepare_sector_network import co2_emissions_year
# consolidate and rename
def rename_techs(label):
prefix_to_remove = [
"residential ",
"services ",
"urban ",
"rural ",
"central ",
"decentral ",
]
rename_if_contains = [
"CHP",
"gas boiler",
"biogas",
"solar thermal",
"air heat pump",
"ground heat pump",
"resistive heater",
"Fischer-Tropsch",
]
rename_if_contains_dict = {
"water tanks": "hot water storage",
"retrofitting": "building retrofitting",
# "H2 Electrolysis": "hydrogen storage",
# "H2 Fuel Cell": "hydrogen storage",
# "H2 pipeline": "hydrogen storage",
"battery": "battery storage",
# "CC": "CC"
}
rename = {
"solar": "solar PV",
"Sabatier": "methanation",
"offwind": "offshore wind",
"offwind-ac": "offshore wind (AC)",
"offwind-dc": "offshore wind (DC)",
"onwind": "onshore wind",
"ror": "hydroelectricity",
"hydro": "hydroelectricity",
"PHS": "hydroelectricity",
"NH3": "ammonia",
"co2 Store": "DAC",
"co2 stored": "CO2 sequestration",
"AC": "transmission lines",
"DC": "transmission lines",
"B2B": "transmission lines",
}
for ptr in prefix_to_remove:
if label[: len(ptr)] == ptr:
label = label[len(ptr) :]
for rif in rename_if_contains:
if rif in label:
label = rif
for old, new in rename_if_contains_dict.items():
if old in label:
label = new
for old, new in rename.items():
if old == label:
label = new
>>>>>>> pypsa-eur-sec/master
return label
@ -59,6 +141,7 @@ preferred_order = pd.Index(
"hydro reservoir",
"run of river",
"pumped hydro storage",
<<<<<<< HEAD
"onshore wind",
"offshore wind ac",
"offshore wind dc",
@ -67,13 +150,53 @@ preferred_order = pd.Index(
"OCGT",
"hydrogen storage",
"battery storage",
=======
"solid biomass",
"biogas",
"onshore wind",
"offshore wind",
"offshore wind (AC)",
"offshore wind (DC)",
"solar PV",
"solar thermal",
"solar rooftop",
"solar",
"building retrofitting",
"ground heat pump",
"air heat pump",
"heat pump",
"resistive heater",
"power-to-heat",
"gas-to-power/heat",
"CHP",
"OCGT",
"gas boiler",
"gas",
"natural gas",
"helmeth",
"methanation",
"ammonia",
"hydrogen storage",
"power-to-gas",
"power-to-liquid",
"battery storage",
"hot water storage",
"CO2 sequestration",
>>>>>>> pypsa-eur-sec/master
]
)
<<<<<<< HEAD
def plot_costs(infn, config, fn=None):
## For now ignore the simpl header
cost_df = pd.read_csv(infn, index_col=list(range(3)), header=[1, 2, 3])
=======
def plot_costs():
cost_df = pd.read_csv(
snakemake.input.costs, index_col=list(range(3)), header=list(range(n_header))
)
>>>>>>> pypsa-eur-sec/master
df = cost_df.groupby(cost_df.index.get_level_values(2)).sum()
@ -82,6 +205,7 @@ def plot_costs(infn, config, fn=None):
df = df.groupby(df.index.map(rename_techs)).sum()
<<<<<<< HEAD
to_drop = df.index[df.max(axis=1) < config["plotting"]["costs_threshold"]]
print("dropping")
@ -93,19 +217,41 @@ def plot_costs(infn, config, fn=None):
print(df.sum())
new_index = (preferred_order.intersection(df.index)).append(
=======
to_drop = df.index[df.max(axis=1) < snakemake.config["plotting"]["costs_threshold"]]
logger.info(
f"Dropping technology with costs below {snakemake.config['plotting']['costs_threshold']} EUR billion per year"
)
logger.debug(df.loc[to_drop])
df = df.drop(to_drop)
logger.info(f"Total system cost of {round(df.sum()[0])} EUR billion per year")
new_index = preferred_order.intersection(df.index).append(
>>>>>>> pypsa-eur-sec/master
df.index.difference(preferred_order)
)
new_columns = df.sum().sort_values().index
<<<<<<< HEAD
fig, ax = plt.subplots()
fig.set_size_inches((12, 8))
=======
fig, ax = plt.subplots(figsize=(12, 8))
>>>>>>> pypsa-eur-sec/master
df.loc[new_index, new_columns].T.plot(
kind="bar",
ax=ax,
stacked=True,
<<<<<<< HEAD
color=[config["plotting"]["tech_colors"][i] for i in new_index],
=======
color=[snakemake.config["plotting"]["tech_colors"][i] for i in new_index],
>>>>>>> pypsa-eur-sec/master
)
handles, labels = ax.get_legend_handles_labels()
@ -113,12 +259,17 @@ def plot_costs(infn, config, fn=None):
handles.reverse()
labels.reverse()
<<<<<<< HEAD
ax.set_ylim([0, config["plotting"]["costs_max"]])
=======
ax.set_ylim([0, snakemake.config["plotting"]["costs_max"]])
>>>>>>> pypsa-eur-sec/master
ax.set_ylabel("System Cost [EUR billion per year]")
ax.set_xlabel("")
<<<<<<< HEAD
ax.grid(axis="y")
ax.legend(handles, labels, ncol=4, loc="upper left")
@ -131,6 +282,21 @@ def plot_costs(infn, config, fn=None):
def plot_energy(infn, config, fn=None):
energy_df = pd.read_csv(infn, index_col=list(range(2)), header=[1, 2, 3])
=======
ax.grid(axis="x")
ax.legend(
handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False
)
fig.savefig(snakemake.output.costs, bbox_inches="tight")
def plot_energy():
energy_df = pd.read_csv(
snakemake.input.energy, index_col=list(range(2)), header=list(range(n_header))
)
>>>>>>> pypsa-eur-sec/master
df = energy_df.groupby(energy_df.index.get_level_values(1)).sum()
@ -139,6 +305,7 @@ def plot_energy(infn, config, fn=None):
df = df.groupby(df.index.map(rename_techs)).sum()
<<<<<<< HEAD
to_drop = df.index[df.abs().max(axis=1) < config["plotting"]["energy_threshold"]]
print("dropping")
@ -150,19 +317,45 @@ def plot_energy(infn, config, fn=None):
print(df.sum())
new_index = (preferred_order.intersection(df.index)).append(
=======
to_drop = df.index[
df.abs().max(axis=1) < snakemake.config["plotting"]["energy_threshold"]
]
logger.info(
f"Dropping all technology with energy consumption or production below {snakemake.config['plotting']['energy_threshold']} TWh/a"
)
logger.debug(df.loc[to_drop])
df = df.drop(to_drop)
logger.info(f"Total energy of {round(df.sum()[0])} TWh/a")
new_index = preferred_order.intersection(df.index).append(
>>>>>>> pypsa-eur-sec/master
df.index.difference(preferred_order)
)
new_columns = df.columns.sort_values()
<<<<<<< HEAD
fig, ax = plt.subplots()
fig.set_size_inches((12, 8))
=======
fig, ax = plt.subplots(figsize=(12, 8))
logger.debug(df.loc[new_index, new_columns])
>>>>>>> pypsa-eur-sec/master
df.loc[new_index, new_columns].T.plot(
kind="bar",
ax=ax,
stacked=True,
<<<<<<< HEAD
color=[config["plotting"]["tech_colors"][i] for i in new_index],
=======
color=[snakemake.config["plotting"]["tech_colors"][i] for i in new_index],
>>>>>>> pypsa-eur-sec/master
)
handles, labels = ax.get_legend_handles_labels()
@ -170,12 +363,22 @@ def plot_energy(infn, config, fn=None):
handles.reverse()
labels.reverse()
<<<<<<< HEAD
ax.set_ylim([config["plotting"]["energy_min"], config["plotting"]["energy_max"]])
=======
ax.set_ylim(
[
snakemake.config["plotting"]["energy_min"],
snakemake.config["plotting"]["energy_max"],
]
)
>>>>>>> pypsa-eur-sec/master
ax.set_ylabel("Energy [TWh/a]")
ax.set_xlabel("")
<<<<<<< HEAD
ax.grid(axis="y")
ax.legend(handles, labels, ncol=4, loc="upper left")
@ -184,10 +387,309 @@ def plot_energy(infn, config, fn=None):
if fn is not None:
fig.savefig(fn, transparent=True)
=======
ax.grid(axis="x")
ax.legend(
handles, labels, ncol=1, loc="upper left", bbox_to_anchor=[1, 1], frameon=False
)
fig.savefig(snakemake.output.energy, bbox_inches="tight")
def plot_balances():
co2_carriers = ["co2", "co2 stored", "process emissions"]
balances_df = pd.read_csv(
snakemake.input.balances, index_col=list(range(3)), header=list(range(n_header))
)
balances = {i.replace(" ", "_"): [i] for i in balances_df.index.levels[0]}
balances["energy"] = [
i for i in balances_df.index.levels[0] if i not in co2_carriers
]
fig, ax = plt.subplots(figsize=(12, 8))
for k, v in balances.items():
df = balances_df.loc[v]
df = df.groupby(df.index.get_level_values(2)).sum()
# convert MWh to TWh
df = df / 1e6
# remove trailing link ports
df.index = [
i[:-1]
if ((i not in ["co2", "NH3"]) and (i[-1:] in ["0", "1", "2", "3"]))
else i
for i in df.index
]
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[
df.abs().max(axis=1) < snakemake.config["plotting"]["energy_threshold"] / 10
]
if v[0] in co2_carriers:
units = "MtCO2/a"
else:
units = "TWh/a"
logger.debug(
f"Dropping technology energy balance smaller than {snakemake.config['plotting']['energy_threshold']/10} {units}"
)
logger.debug(df.loc[to_drop])
df = df.drop(to_drop)
logger.debug(f"Total energy balance for {v} of {round(df.sum()[0],2)} {units}")
if df.empty:
continue
new_index = preferred_order.intersection(df.index).append(
df.index.difference(preferred_order)
)
new_columns = df.columns.sort_values()
df.loc[new_index, new_columns].T.plot(
kind="bar",
ax=ax,
stacked=True,
color=[snakemake.config["plotting"]["tech_colors"][i] for i in new_index],
)
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
if v[0] in co2_carriers:
ax.set_ylabel("CO2 [MtCO2/a]")
else:
ax.set_ylabel("Energy [TWh/a]")
ax.set_xlabel("")
ax.grid(axis="x")
ax.legend(
handles,
labels,
ncol=1,
loc="upper left",
bbox_to_anchor=[1, 1],
frameon=False,
)
fig.savefig(snakemake.output.balances[:-10] + k + ".pdf", bbox_inches="tight")
plt.cla()
def historical_emissions(cts):
"""
Read historical emissions to add them to the carbon budget plot.
"""
# https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
# downloaded 201228 (modified by EEA last on 201221)
fn = "data/eea/UNFCCC_v23.csv"
df = pd.read_csv(fn, encoding="latin-1")
df.loc[df["Year"] == "1985-1987", "Year"] = 1986
df["Year"] = df["Year"].astype(int)
df = df.set_index(
["Year", "Sector_name", "Country_code", "Pollutant_name"]
).sort_index()
e = pd.Series()
e["electricity"] = "1.A.1.a - Public Electricity and Heat Production"
e["residential non-elec"] = "1.A.4.b - Residential"
e["services non-elec"] = "1.A.4.a - Commercial/Institutional"
e["rail non-elec"] = "1.A.3.c - Railways"
e["road non-elec"] = "1.A.3.b - Road Transportation"
e["domestic navigation"] = "1.A.3.d - Domestic Navigation"
e["international navigation"] = "1.D.1.b - International Navigation"
e["domestic aviation"] = "1.A.3.a - Domestic Aviation"
e["international aviation"] = "1.D.1.a - International Aviation"
e["total energy"] = "1 - Energy"
e["industrial processes"] = "2 - Industrial Processes and Product Use"
e["agriculture"] = "3 - Agriculture"
e["LULUCF"] = "4 - Land Use, Land-Use Change and Forestry"
e["waste management"] = "5 - Waste management"
e["other"] = "6 - Other Sector"
e["indirect"] = "ind_CO2 - Indirect CO2"
e["total wL"] = "Total (with LULUCF)"
e["total woL"] = "Total (without LULUCF)"
pol = ["CO2"] # ["All greenhouse gases - (CO2 equivalent)"]
cts
if "GB" in cts:
cts.remove("GB")
cts.append("UK")
year = np.arange(1990, 2018).tolist()
idx = pd.IndexSlice
co2_totals = (
df.loc[idx[year, e.values, cts, pol], "emissions"]
.unstack("Year")
.rename(index=pd.Series(e.index, e.values))
)
co2_totals = (1 / 1e6) * co2_totals.groupby(level=0, axis=0).sum() # Gton CO2
co2_totals.loc["industrial non-elec"] = (
co2_totals.loc["total energy"]
- co2_totals.loc[
[
"electricity",
"services non-elec",
"residential non-elec",
"road non-elec",
"rail non-elec",
"domestic aviation",
"international aviation",
"domestic navigation",
"international navigation",
]
].sum()
)
emissions = co2_totals.loc["electricity"]
if "T" in opts:
emissions += co2_totals.loc[[i + " non-elec" for i in ["rail", "road"]]].sum()
if "H" in opts:
emissions += co2_totals.loc[
[i + " non-elec" for i in ["residential", "services"]]
].sum()
if "I" in opts:
emissions += co2_totals.loc[
[
"industrial non-elec",
"industrial processes",
"domestic aviation",
"international aviation",
"domestic navigation",
"international navigation",
]
].sum()
return emissions
def plot_carbon_budget_distribution(input_eurostat):
"""
Plot historical carbon emissions in the EU and decarbonization path.
"""
import seaborn as sns
sns.set()
sns.set_style("ticks")
plt.style.use("seaborn-ticks")
plt.rcParams["xtick.direction"] = "in"
plt.rcParams["ytick.direction"] = "in"
plt.rcParams["xtick.labelsize"] = 20
plt.rcParams["ytick.labelsize"] = 20
plt.figure(figsize=(10, 7))
gs1 = gridspec.GridSpec(1, 1)
ax1 = plt.subplot(gs1[0, 0])
ax1.set_ylabel("CO$_2$ emissions (Gt per year)", fontsize=22)
ax1.set_ylim([0, 5])
ax1.set_xlim([1990, snakemake.config["scenario"]["planning_horizons"][-1] + 1])
path_cb = snakemake.config["results_dir"] + snakemake.config["run"] + "/csvs/"
countries = pd.read_csv(snakemake.input.country_codes, index_col=1)
cts = countries.index.to_list()
e_1990 = co2_emissions_year(cts, input_eurostat, opts, year=1990)
CO2_CAP = pd.read_csv(path_cb + "carbon_budget_distribution.csv", index_col=0)
ax1.plot(e_1990 * CO2_CAP[o], linewidth=3, color="dodgerblue", label=None)
emissions = historical_emissions(cts)
ax1.plot(emissions, color="black", linewidth=3, label=None)
# plot committed and uder-discussion targets
# (notice that historical emissions include all countries in the
# network, but targets refer to EU)
ax1.plot(
[2020],
[0.8 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="black",
markeredgecolor="black",
)
ax1.plot(
[2030],
[0.45 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="white",
markeredgecolor="black",
)
ax1.plot(
[2030],
[0.6 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="black",
markeredgecolor="black",
)
ax1.plot(
[2050, 2050],
[x * emissions[1990] for x in [0.2, 0.05]],
color="gray",
linewidth=2,
marker="_",
alpha=0.5,
)
ax1.plot(
[2050],
[0.01 * emissions[1990]],
marker="*",
markersize=12,
markerfacecolor="white",
linewidth=0,
markeredgecolor="black",
label="EU under-discussion target",
zorder=10,
clip_on=False,
)
ax1.plot(
[2050],
[0.125 * emissions[1990]],
"ro",
marker="*",
markersize=12,
markerfacecolor="black",
markeredgecolor="black",
label="EU committed target",
)
ax1.legend(
fancybox=True, fontsize=18, loc=(0.01, 0.01), facecolor="white", frameon=True
)
path_cb_plot = (
snakemake.config["results_dir"] + snakemake.config["run"] + "/graphs/"
)
plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
>>>>>>> pypsa-eur-sec/master
if __name__ == "__main__":
if "snakemake" not in globals():
<<<<<<< HEAD
from _helpers import mock_snakemake
snakemake = mock_snakemake(
@ -214,3 +716,24 @@ if __name__ == "__main__":
func(
os.path.join(snakemake.input[0], f"{summary}.csv"), config, snakemake.output[0]
)
=======
from helper import mock_snakemake
snakemake = mock_snakemake("plot_summary")
logging.basicConfig(level=snakemake.config["logging_level"])
n_header = 4
plot_costs()
plot_energy()
plot_balances()
for sector_opts in snakemake.config["scenario"]["sector_opts"]:
opts = sector_opts.split("-")
for o in opts:
if "cb" in o:
plot_carbon_budget_distribution(snakemake.input.eurostat)
>>>>>>> pypsa-eur-sec/master

3411
scripts/prepare_sector_network.py Normal file
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File diff suppressed because it is too large Load Diff

39
scripts/retrieve_gas_infrastructure_data.py Normal file
View File

@ -0,0 +1,39 @@
# -*- coding: utf-8 -*-
"""
Retrieve gas infrastructure data from
https://zenodo.org/record/4767098/files/IGGIELGN.zip.
"""
import logging
import zipfile
from pathlib import Path
from helper import progress_retrieve
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake("retrieve_gas_network_data")
rootpath = ".."
else:
rootpath = "."
url = "https://zenodo.org/record/4767098/files/IGGIELGN.zip"
# Save locations
zip_fn = Path(f"{rootpath}/IGGIELGN.zip")
to_fn = Path(f"{rootpath}/data/gas_network/scigrid-gas")
logger.info(f"Downloading databundle from '{url}'.")
progress_retrieve(url, zip_fn)
logger.info(f"Extracting databundle.")
zipfile.ZipFile(zip_fn).extractall(to_fn)
zip_fn.unlink()
logger.info(f"Gas infrastructure data available in '{to_fn}'.")

36
scripts/retrieve_sector_databundle.py Normal file
View File

@ -0,0 +1,36 @@
# -*- coding: utf-8 -*-
"""
Retrieve and extract sector data bundle.
"""
import logging
logger = logging.getLogger(__name__)
import os
import sys
import tarfile
from pathlib import Path
# Add pypsa-eur scripts to path for import of _helpers
sys.path.insert(0, os.getcwd() + "/../pypsa-eur/scripts")
from _helpers import configure_logging, progress_retrieve
if __name__ == "__main__":
configure_logging(snakemake)
url = "https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz"
tarball_fn = Path("sector-bundle.tar.gz")
to_fn = Path("data")
logger.info(f"Downloading databundle from '{url}'.")
progress_retrieve(url, tarball_fn)
logger.info(f"Extracting databundle.")
tarfile.open(tarball_fn).extractall(to_fn)
tarball_fn.unlink()
logger.info(f"Databundle available in '{to_fn}'.")

330
scripts/solve_network.py
View File

@ -1,4 +1,5 @@
# -*- coding: utf-8 -*-
<<<<<<< HEAD
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -122,19 +123,166 @@ def prepare_network(n, solve_opts):
carrier="load",
sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
marginal_cost=load_shedding,
=======
"""
Solve network.
"""
import logging
import numpy as np
import pypsa
from helper import override_component_attrs, update_config_with_sector_opts
from vresutils.benchmark import memory_logger
logger = logging.getLogger(__name__)
pypsa.pf.logger.setLevel(logging.WARNING)
def add_land_use_constraint(n):
if "m" in snakemake.wildcards.clusters:
_add_land_use_constraint_m(n)
else:
_add_land_use_constraint(n)
def _add_land_use_constraint(n):
# warning: this will miss existing offwind which is not classed AC-DC and has carrier 'offwind'
for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
ext_i = (n.generators.carrier == carrier) & ~n.generators.p_nom_extendable
existing = (
n.generators.loc[ext_i, "p_nom"]
.groupby(n.generators.bus.map(n.buses.location))
.sum()
)
existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
n.generators.loc[existing.index, "p_nom_max"] -= existing
# check if existing capacities are larger than technical potential
existing_large = n.generators[
n.generators["p_nom_min"] > n.generators["p_nom_max"]
].index
if len(existing_large):
logger.warning(
f"Existing capacities larger than technical potential for {existing_large},\
adjust technical potential to existing capacities"
)
n.generators.loc[existing_large, "p_nom_max"] = n.generators.loc[
existing_large, "p_nom_min"
]
n.generators.p_nom_max.clip(lower=0, inplace=True)
def _add_land_use_constraint_m(n):
# if generators clustering is lower than network clustering, land_use accounting is at generators clusters
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
grouping_years = snakemake.config["existing_capacities"]["grouping_years"]
current_horizon = snakemake.wildcards.planning_horizons
for carrier in ["solar", "onwind", "offwind-ac", "offwind-dc"]:
existing = n.generators.loc[n.generators.carrier == carrier, "p_nom"]
ind = list(
set(
[
i.split(sep=" ")[0] + " " + i.split(sep=" ")[1]
for i in existing.index
]
)
)
previous_years = [
str(y)
for y in planning_horizons + grouping_years
if y < int(snakemake.wildcards.planning_horizons)
]
for p_year in previous_years:
ind2 = [
i for i in ind if i + " " + carrier + "-" + p_year in existing.index
]
sel_current = [i + " " + carrier + "-" + current_horizon for i in ind2]
sel_p_year = [i + " " + carrier + "-" + p_year for i in ind2]
n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[
sel_p_year
].rename(lambda x: x[:-4] + current_horizon)
n.generators.p_nom_max.clip(lower=0, inplace=True)
def add_co2_sequestration_limit(n, limit=200):
"""
Add a global constraint on the amount of Mt CO2 that can be sequestered.
"""
n.carriers.loc["co2 stored", "co2_absorptions"] = -1
n.carriers.co2_absorptions = n.carriers.co2_absorptions.fillna(0)
limit = limit * 1e6
for o in opts:
if not "seq" in o:
continue
limit = float(o[o.find("seq") + 3 :]) * 1e6
break
n.add(
"GlobalConstraint",
"co2_sequestration_limit",
sense="<=",
constant=limit,
type="primary_energy",
carrier_attribute="co2_absorptions",
)
def prepare_network(n, solve_opts=None, config=None):
if "clip_p_max_pu" in solve_opts:
for df in (
n.generators_t.p_max_pu,
n.generators_t.p_min_pu,
n.storage_units_t.inflow,
):
df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)
if solve_opts.get("load_shedding"):
# intersect between macroeconomic and surveybased willingness to pay
# http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
n.add("Carrier", "Load")
n.madd(
"Generator",
n.buses.index,
" load",
bus=n.buses.index,
carrier="load",
sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
marginal_cost=1e2, # Eur/kWh
>>>>>>> pypsa-eur-sec/master
p_nom=1e9, # kW
)
if solve_opts.get("noisy_costs"):
<<<<<<< HEAD
for t in n.iterate_components(n.one_port_components):
# if 'capital_cost' in t.df:
# t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
if "marginal_cost" in t.df:
=======
for t in n.iterate_components():
# if 'capital_cost' in t.df:
# t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
if "marginal_cost" in t.df:
np.random.seed(174)
>>>>>>> pypsa-eur-sec/master
t.df["marginal_cost"] += 1e-2 + 2e-3 * (
np.random.random(len(t.df)) - 0.5
)
for t in n.iterate_components(["Line", "Link"]):
<<<<<<< HEAD
=======
np.random.seed(123)
>>>>>>> pypsa-eur-sec/master
t.df["capital_cost"] += (
1e-1 + 2e-2 * (np.random.random(len(t.df)) - 0.5)
) * t.df["length"]
@ -144,6 +292,7 @@ def prepare_network(n, solve_opts):
n.set_snapshots(n.snapshots[:nhours])
n.snapshot_weightings[:] = 8760.0 / nhours
<<<<<<< HEAD
return n
@ -370,6 +519,116 @@ def extra_functionality(n, snapshots):
if "EQ" in o:
add_EQ_constraints(n, o)
add_battery_constraints(n)
=======
if snakemake.config["foresight"] == "myopic":
add_land_use_constraint(n)
if n.stores.carrier.eq("co2 stored").any():
limit = config["sector"].get("co2_sequestration_potential", 200)
add_co2_sequestration_limit(n, limit=limit)
return n
def add_battery_constraints(n):
"""
Add constraint ensuring that charger = discharger:
1 * charger_size - efficiency * discharger_size = 0
"""
discharger_bool = n.links.index.str.contains("battery discharger")
charger_bool = n.links.index.str.contains("battery charger")
dischargers_ext = n.links[discharger_bool].query("p_nom_extendable").index
chargers_ext = n.links[charger_bool].query("p_nom_extendable").index
eff = n.links.efficiency[dischargers_ext].values
lhs = (
n.model["Link-p_nom"].loc[chargers_ext]
- n.model["Link-p_nom"].loc[dischargers_ext] * eff
)
n.model.add_constraints(lhs == 0, name="Link-charger_ratio")
def add_chp_constraints(n):
electric = (
n.links.index.str.contains("urban central")
& n.links.index.str.contains("CHP")
& n.links.index.str.contains("electric")
)
heat = (
n.links.index.str.contains("urban central")
& n.links.index.str.contains("CHP")
& n.links.index.str.contains("heat")
)
electric_ext = n.links[electric].query("p_nom_extendable").index
heat_ext = n.links[heat].query("p_nom_extendable").index
electric_fix = n.links[electric].query("~p_nom_extendable").index
heat_fix = n.links[heat].query("~p_nom_extendable").index
p = n.model["Link-p"] # dimension: [time, link]
# output ratio between heat and electricity and top_iso_fuel_line for extendable
if not electric_ext.empty:
p_nom = n.model["Link-p_nom"]
lhs = (
p_nom.loc[electric_ext]
* (n.links.p_nom_ratio * n.links.efficiency)[electric_ext].values
- p_nom.loc[heat_ext] * n.links.efficiency[heat_ext].values
)
n.model.add_constraints(lhs == 0, name="chplink-fix_p_nom_ratio")
rename = {"Link-ext": "Link"}
lhs = (
p.loc[:, electric_ext]
+ p.loc[:, heat_ext]
- p_nom.rename(rename).loc[electric_ext]
)
n.model.add_constraints(lhs <= 0, name="chplink-top_iso_fuel_line_ext")
# top_iso_fuel_line for fixed
if not electric_fix.empty:
lhs = p.loc[:, electric_fix] + p.loc[:, heat_fix]
rhs = n.links.p_nom[electric_fix]
n.model.add_constraints(lhs <= rhs, name="chplink-top_iso_fuel_line_fix")
# back-pressure
if not electric.empty:
lhs = (
p.loc[:, heat] * (n.links.efficiency[heat] * n.links.c_b[electric].values)
- p.loc[:, electric] * n.links.efficiency[electric]
)
n.model.add_constraints(lhs <= rhs, name="chplink-backpressure")
def add_pipe_retrofit_constraint(n):
"""
Add constraint for retrofitting existing CH4 pipelines to H2 pipelines.
"""
gas_pipes_i = n.links.query("carrier == 'gas pipeline' and p_nom_extendable").index
h2_retrofitted_i = n.links.query(
"carrier == 'H2 pipeline retrofitted' and p_nom_extendable"
).index
if h2_retrofitted_i.empty or gas_pipes_i.empty:
return
p_nom = n.model["Link-p_nom"]
CH4_per_H2 = 1 / n.config["sector"]["H2_retrofit_capacity_per_CH4"]
lhs = p_nom.loc[gas_pipes_i] + CH4_per_H2 * p_nom.loc[h2_retrofitted_i]
rhs = n.links.p_nom[gas_pipes_i].rename_axis("Link-ext")
n.model.add_constraints(lhs == rhs, name="Link-pipe_retrofit")
def extra_functionality(n, snapshots):
add_battery_constraints(n)
add_pipe_retrofit_constraint(n)
>>>>>>> pypsa-eur-sec/master
def solve_network(n, config, opts="", **kwargs):
@ -393,6 +652,7 @@ def solve_network(n, config, opts="", **kwargs):
logger.info("No expandable lines found. Skipping iterative solving.")
if skip_iterations:
<<<<<<< HEAD
network_lopf(
n, solver_name=solver_name, solver_options=solver_options, **kwargs
)
@ -422,10 +682,65 @@ if __name__ == "__main__":
if tmpdir is not None:
Path(tmpdir).mkdir(parents=True, exist_ok=True)
opts = snakemake.wildcards.opts.split("-")
=======
status, condition = n.optimize(
solver_name=solver_name,
extra_functionality=extra_functionality,
**solver_options,
**kwargs,
)
else:
status, condition = n.optimize.optimize_transmission_expansion_iteratively(
solver_name=solver_name,
track_iterations=track_iterations,
min_iterations=min_iterations,
max_iterations=max_iterations,
extra_functionality=extra_functionality,
**solver_options,
**kwargs,
)
if status != "ok":
logger.warning(
f"Solving status '{status}' with termination condition '{condition}'"
)
return n
# %%
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"solve_network_myopic",
simpl="",
opts="",
clusters="45",
lv=1.0,
sector_opts="8760H-T-H-B-I-A-solar+p3-dist1",
planning_horizons="2020",
)
logging.basicConfig(
filename=snakemake.log.python, level=snakemake.config["logging_level"]
)
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
tmpdir = snakemake.config["solving"].get("tmpdir")
if tmpdir is not None:
from pathlib import Path
Path(tmpdir).mkdir(parents=True, exist_ok=True)
opts = snakemake.wildcards.sector_opts.split("-")
>>>>>>> pypsa-eur-sec/master
solve_opts = snakemake.config["solving"]["options"]
fn = getattr(snakemake.log, "memory", None)
with memory_logger(filename=fn, interval=30.0) as mem:
<<<<<<< HEAD
n = pypsa.Network(snakemake.input[0])
n = prepare_network(n, solve_opts)
n = solve_network(
@ -436,6 +751,21 @@ if __name__ == "__main__":
solver_dir=tmpdir,
solver_logfile=snakemake.log.solver,
)
=======
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
n = prepare_network(n, solve_opts, config=snakemake.config)
n = solve_network(
n, config=snakemake.config, opts=opts, log_fn=snakemake.log.solver
)
if "lv_limit" in n.global_constraints.index:
n.line_volume_limit = n.global_constraints.at["lv_limit", "constant"]
n.line_volume_limit_dual = n.global_constraints.at["lv_limit", "mu"]
>>>>>>> pypsa-eur-sec/master
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

27
test/config.myopic.yaml Normal file
View File

@ -0,0 +1,27 @@
run: test-myopic
foresight: myopic
scenario:
lv:
- 1.5
clusters:
- 5
sector_opts:
- 191H-T-H-B-I-A-solar+p3-dist1
planning_horizons:
- 2030
- 2040
- 2050
snapshots:
start: "2013-03-01"
end: "2013-04-01"
atlite:
cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
solving:
solver:
name: cbc
options: cbc-default
mem: 4000

29
test/config.overnight.yaml Normal file
View File

@ -0,0 +1,29 @@
run: test-overnight
foresight: overnight
scenario:
lv:
- 1.5
clusters:
- 5
sector_opts:
- CO2L0-191H-T-H-B-I-A-solar+p3-dist1
planning_horizons:
- 2030
snapshots:
start: "2013-03-01"
end: "2013-04-01"
atlite:
cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
sector:
gas_network: true
H2_retrofit: true
solving:
solver:
name: cbc
options: cbc-default
mem: 4000