merge master
This commit is contained in:
Fabian Neumann
commit
fa66c50dc1
109
.github/workflows/ci.yaml
vendored
Normal file
109
.github/workflows/ci.yaml
vendored
Normal file
@ -0,0 +1,109 @@
|
|||||||
|
# SPDX-FileCopyrightText: : 2021 The PyPSA-Eur Authors
|
||||||
|
#
|
||||||
|
# SPDX-License-Identifier: CC0-1.0
|
||||||
|
|
||||||
|
name: CI
|
||||||
|
|
||||||
|
# Caching method based on and described by:
|
||||||
|
# epassaro (2021): https://dev.to/epassaro/caching-anaconda-environments-in-github-actions-5hde
|
||||||
|
# and code in GitHub repo: https://github.com/epassaro/cache-conda-envs
|
||||||
|
|
||||||
|
on:
|
||||||
|
push:
|
||||||
|
branches:
|
||||||
|
- master
|
||||||
|
pull_request:
|
||||||
|
branches:
|
||||||
|
- master
|
||||||
|
schedule:
|
||||||
|
- cron: "0 5 * * TUE"
|
||||||
|
|
||||||
|
env:
|
||||||
|
CONDA_CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
|
||||||
|
DATA_CACHE_NUMBER: 1
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
build:
|
||||||
|
|
||||||
|
strategy:
|
||||||
|
matrix:
|
||||||
|
include:
|
||||||
|
# Matrix required to handle caching with Mambaforge
|
||||||
|
- os: ubuntu-latest
|
||||||
|
label: ubuntu-latest
|
||||||
|
prefix: /usr/share/miniconda3/envs/pypsa-eur
|
||||||
|
|
||||||
|
# - os: macos-latest
|
||||||
|
# label: macos-latest
|
||||||
|
# prefix: /Users/runner/miniconda3/envs/pypsa-eur
|
||||||
|
|
||||||
|
# - os: windows-latest
|
||||||
|
# label: windows-latest
|
||||||
|
# prefix: C:\Miniconda3\envs\pypsa-eur
|
||||||
|
|
||||||
|
name: ${{ matrix.label }}
|
||||||
|
|
||||||
|
runs-on: ${{ matrix.os }}
|
||||||
|
|
||||||
|
defaults:
|
||||||
|
run:
|
||||||
|
shell: bash -l {0}
|
||||||
|
|
||||||
|
steps:
|
||||||
|
- uses: actions/checkout@v2
|
||||||
|
|
||||||
|
- name: Clone pypsa-eur and technology-data repositories
|
||||||
|
run: |
|
||||||
|
git clone https://github.com/pypsa/pypsa-eur ../pypsa-eur
|
||||||
|
git clone https://github.com/pypsa/technology-data ../technology-data
|
||||||
|
cp ../pypsa-eur/test/config.test1.yaml ../pypsa-eur/config.yaml
|
||||||
|
|
||||||
|
- name: Setup secrets
|
||||||
|
run: |
|
||||||
|
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
|
||||||
|
|
||||||
|
- name: Add solver to environment
|
||||||
|
run: |
|
||||||
|
echo -e " - coincbc\n - ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml
|
||||||
|
|
||||||
|
- name: Setup Mambaforge
|
||||||
|
uses: conda-incubator/setup-miniconda@v2
|
||||||
|
with:
|
||||||
|
miniforge-variant: Mambaforge
|
||||||
|
miniforge-version: latest
|
||||||
|
activate-environment: pypsa-eur
|
||||||
|
use-mamba: true
|
||||||
|
|
||||||
|
- name: Set cache dates
|
||||||
|
run: |
|
||||||
|
echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
|
||||||
|
echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV
|
||||||
|
|
||||||
|
- name: Cache data and cutouts folders
|
||||||
|
uses: actions/cache@v3
|
||||||
|
with:
|
||||||
|
path: |
|
||||||
|
data
|
||||||
|
../pypsa-eur/cutouts
|
||||||
|
../pypsa-eur/data
|
||||||
|
key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }}
|
||||||
|
|
||||||
|
- name: Create environment cache
|
||||||
|
uses: actions/cache@v2
|
||||||
|
id: cache
|
||||||
|
with:
|
||||||
|
path: ${{ matrix.prefix }}
|
||||||
|
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 ../pypsa-eur/envs/environment.yaml
|
||||||
|
if: steps.cache.outputs.cache-hit != 'true'
|
||||||
|
|
||||||
|
- name: Test snakemake workflow
|
||||||
|
run: |
|
||||||
|
conda activate pypsa-eur
|
||||||
|
conda list
|
||||||
|
cp test/config.overnight.yaml config.yaml
|
||||||
|
snakemake -call solve_all_networks
|
||||||
|
cp test/config.myopic.yaml config.yaml
|
||||||
|
snakemake -call solve_all_networks
|
||||||
27
README.md
27
README.md
@ -7,19 +7,8 @@
|
|||||||
|
|
||||||
# PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
|
# PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
|
||||||
|
|
||||||
|
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.
|
||||||
**WARNING**: This model is under construction and contains serious problems that
|
|
||||||
distort the results. See the github repository
|
|
||||||
[issues](https://github.com/PyPSA/pypsa-eur-sec/issues) for some of the problems
|
|
||||||
(please feel free to help or make suggestions). There is neither a full
|
|
||||||
documentation nor a paper yet, but we hope to have a preprint out by the end of 2021.
|
|
||||||
You can find out more about the model capabilities in [a recent
|
|
||||||
presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf) or the
|
|
||||||
following [preprint with a description of the industry
|
|
||||||
sector](https://arxiv.org/abs/2109.09563). We cannot support this model if you
|
|
||||||
choose to use it.
|
|
||||||
|
|
||||||
|
|
||||||
PyPSA-Eur-Sec builds on the electricity generation and transmission
|
PyPSA-Eur-Sec builds on the electricity generation and transmission
|
||||||
model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
|
model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
|
||||||
@ -28,6 +17,18 @@ heating, biomass, industry and industrial feedstocks, agriculture,
|
|||||||
forestry and fishing. This completes the energy system and includes
|
forestry and fishing. This completes the energy system and includes
|
||||||
all greenhouse gas emitters except waste management and land use.
|
all greenhouse gas emitters except waste management 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) collects known
|
||||||
|
topics we are working on (please feel free to help or make suggestions). There is neither a full
|
||||||
|
documentation nor a paper yet, but we hope to have a preprint out by mid-2022.
|
||||||
|
You can find out more about the model capabilities in [a recent
|
||||||
|
presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf) or the
|
||||||
|
following [paper in Joule with a description of the industry
|
||||||
|
sector](https://arxiv.org/abs/2109.09563). We cannot support this model if you
|
||||||
|
choose to use it.
|
||||||
|
|
||||||
Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
|
Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
|
||||||
for installation instructions and other useful information about the snakemake workflow.
|
for installation instructions and other useful information about the snakemake workflow.
|
||||||
|
|
||||||
|
|||||||
64
Snakefile
64
Snakefile
@ -46,18 +46,22 @@ rule prepare_sector_networks:
|
|||||||
**config['scenario'])
|
**config['scenario'])
|
||||||
|
|
||||||
datafiles = [
|
datafiles = [
|
||||||
"eea/UNFCCC_v23.csv",
|
"data/eea/UNFCCC_v23.csv",
|
||||||
"switzerland-sfoe/switzerland-new_format.csv",
|
"data/switzerland-sfoe/switzerland-new_format.csv",
|
||||||
"nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
|
"data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
|
||||||
"myb1-2017-nitro.xls",
|
"data/myb1-2017-nitro.xls",
|
||||||
"Industrial_Database.csv",
|
"data/Industrial_Database.csv",
|
||||||
"emobility/KFZ__count",
|
"data/emobility/KFZ__count",
|
||||||
"emobility/Pkw__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):
|
if config.get('retrieve_sector_databundle', True):
|
||||||
rule retrieve_sector_databundle:
|
rule retrieve_sector_databundle:
|
||||||
output: expand('data/{file}', file=datafiles)
|
output: *datafiles
|
||||||
log: "logs/retrieve_sector_databundle.log"
|
log: "logs/retrieve_sector_databundle.log"
|
||||||
script: 'scripts/retrieve_sector_databundle.py'
|
script: 'scripts/retrieve_sector_databundle.py'
|
||||||
|
|
||||||
@ -253,9 +257,9 @@ rule build_biomass_potentials:
|
|||||||
enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
|
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
|
nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
|
||||||
regions_onshore=pypsaeur("resources/regions_onshore_elec{weather_year}_s{simpl}_{clusters}.geojson"),
|
regions_onshore=pypsaeur("resources/regions_onshore_elec{weather_year}_s{simpl}_{clusters}.geojson"),
|
||||||
nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz",
|
nuts3_population=pypsaeur("data/bundle/nama_10r_3popgdp.tsv.gz"),
|
||||||
swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv",
|
swiss_cantons=pypsaeur("data/bundle/ch_cantons.csv"),
|
||||||
swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls",
|
swiss_population=pypsaeur("data/bundle/je-e-21.03.02.xls"),
|
||||||
country_shapes=pypsaeur('resources/country_shapes.geojson')
|
country_shapes=pypsaeur('resources/country_shapes.geojson')
|
||||||
output:
|
output:
|
||||||
biomass_potentials_all='resources/biomass_potentials_all{weather_year}_s{simpl}_{clusters}.csv',
|
biomass_potentials_all='resources/biomass_potentials_all{weather_year}_s{simpl}_{clusters}.csv',
|
||||||
@ -428,16 +432,46 @@ else:
|
|||||||
build_retro_cost_output = {}
|
build_retro_cost_output = {}
|
||||||
|
|
||||||
|
|
||||||
|
rule build_population_weighted_energy_totals:
|
||||||
|
input:
|
||||||
|
energy_totals='resources/energy_totals.csv',
|
||||||
|
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv"
|
||||||
|
output: "resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv"
|
||||||
|
threads: 1
|
||||||
|
resources: mem_mb=2000
|
||||||
|
script: "scripts/build_population_weighted_energy_totals.py"
|
||||||
|
|
||||||
|
|
||||||
|
rule build_transport_demand:
|
||||||
|
input:
|
||||||
|
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
|
||||||
|
pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
|
||||||
|
transport_data='resources/transport_data.csv',
|
||||||
|
traffic_data_KFZ="data/emobility/KFZ__count",
|
||||||
|
traffic_data_Pkw="data/emobility/Pkw__count",
|
||||||
|
temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
|
||||||
|
output:
|
||||||
|
transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
|
||||||
|
transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
|
||||||
|
avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
|
||||||
|
dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv"
|
||||||
|
threads: 1
|
||||||
|
resources: mem_mb=2000
|
||||||
|
script: "scripts/build_transport_demand.py"
|
||||||
|
|
||||||
|
|
||||||
rule prepare_sector_network:
|
rule prepare_sector_network:
|
||||||
input:
|
input:
|
||||||
overrides="data/override_component_attrs",
|
overrides="data/override_component_attrs",
|
||||||
network=pypsaeur('networks/elec{weather_year}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
|
network=pypsaeur('networks/elec{weather_year}_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
|
||||||
energy_totals_name='resources/energy_totals.csv',
|
energy_totals_name='resources/energy_totals.csv',
|
||||||
|
pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
|
||||||
|
transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
|
||||||
|
transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
|
||||||
|
avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
|
||||||
|
dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv",
|
||||||
co2_totals_name='resources/co2_totals.csv',
|
co2_totals_name='resources/co2_totals.csv',
|
||||||
transport_name='resources/transport_data.csv',
|
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
|
||||||
traffic_data_KFZ="data/emobility/KFZ__count",
|
|
||||||
traffic_data_Pkw="data/emobility/Pkw__count",
|
|
||||||
biomass_potentials='resources/biomass_potentials{weather_year}_s{simpl}_{clusters}.csv',
|
|
||||||
heat_profile="data/heat_load_profile_BDEW.csv",
|
heat_profile="data/heat_load_profile_BDEW.csv",
|
||||||
costs=CDIR + "costs_{planning_horizons}.csv",
|
costs=CDIR + "costs_{planning_horizons}.csv",
|
||||||
profile_offwind_ac=pypsaeur("resources/profile{weather_year}_offwind-ac.nc"),
|
profile_offwind_ac=pypsaeur("resources/profile{weather_year}_offwind-ac.nc"),
|
||||||
|
|||||||
@ -136,7 +136,7 @@ solar_thermal:
|
|||||||
|
|
||||||
# only relevant for foresight = myopic or perfect
|
# only relevant for foresight = myopic or perfect
|
||||||
existing_capacities:
|
existing_capacities:
|
||||||
grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
|
grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
|
||||||
threshold_capacity: 10
|
threshold_capacity: 10
|
||||||
conventional_carriers:
|
conventional_carriers:
|
||||||
- lignite
|
- lignite
|
||||||
@ -390,6 +390,9 @@ plotting:
|
|||||||
color_geomap:
|
color_geomap:
|
||||||
ocean: white
|
ocean: white
|
||||||
land: whitesmoke
|
land: whitesmoke
|
||||||
|
eu_node_location:
|
||||||
|
x: -5.5
|
||||||
|
y: 46.
|
||||||
costs_max: 1000
|
costs_max: 1000
|
||||||
costs_threshold: 1
|
costs_threshold: 1
|
||||||
energy_max: 20000
|
energy_max: 20000
|
||||||
|
|||||||
@ -1,3 +1,4 @@
|
|||||||
attribute,type,unit,default,description,status
|
attribute,type,unit,default,description,status
|
||||||
build_year,integer,year,n/a,build year,Input (optional)
|
carrier,string,n/a,n/a,carrier,Input (optional)
|
||||||
lifetime,float,years,n/a,lifetime,Input (optional)
|
lifetime,float,years,inf,lifetime,Input (optional)
|
||||||
|
build_year,int,year ,0,build year,Input (optional)
|
||||||
|
|||||||
|
@ -2,12 +2,12 @@ attribute,type,unit,default,description,status
|
|||||||
bus2,string,n/a,n/a,2nd bus,Input (optional)
|
bus2,string,n/a,n/a,2nd bus,Input (optional)
|
||||||
bus3,string,n/a,n/a,3rd bus,Input (optional)
|
bus3,string,n/a,n/a,3rd bus,Input (optional)
|
||||||
bus4,string,n/a,n/a,4th 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)
|
efficiency2,static or series,per unit,1,2nd bus efficiency,Input (optional)
|
||||||
efficiency3,static or series,per unit,1.,3rd 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)
|
efficiency4,static or series,per unit,1,4th bus efficiency,Input (optional)
|
||||||
p2,series,MW,0.,2nd bus output,Output
|
p2,series,MW,0,2nd bus output,Output
|
||||||
p3,series,MW,0.,3rd bus output,Output
|
p3,series,MW,0,3rd bus output,Output
|
||||||
p4,series,MW,0.,4th bus output,Output
|
p4,series,MW,0,4th bus output,Output
|
||||||
build_year,integer,year,n/a,build year,Input (optional)
|
|
||||||
lifetime,float,years,n/a,lifetime,Input (optional)
|
|
||||||
carrier,string,n/a,n/a,carrier,Input (optional)
|
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)
|
||||||
|
|||||||
|
@ -1,4 +1,4 @@
|
|||||||
attribute,type,unit,default,description,status
|
attribute,type,unit,default,description,status
|
||||||
build_year,integer,year,n/a,build year,Input (optional)
|
|
||||||
lifetime,float,years,n/a,lifetime,Input (optional)
|
|
||||||
carrier,string,n/a,n/a,carrier,Input (optional)
|
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)
|
||||||
|
|||||||
|
@ -29,10 +29,21 @@ heating, biomass, industry and industrial feedstocks. This completes
|
|||||||
the energy system and includes all greenhouse gas emitters except
|
the energy system and includes all greenhouse gas emitters except
|
||||||
waste management, agriculture, forestry and land use.
|
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>`_ collects known
|
||||||
|
topics we are working on (please feel free to help or make suggestions). There is neither a full
|
||||||
|
documentation nor a paper yet, but we hope to have a preprint out by mid-2022.
|
||||||
|
We cannot support this model if you
|
||||||
|
choose to use it.
|
||||||
|
|
||||||
|
|
||||||
.. note::
|
.. note::
|
||||||
More about the current model capabilities and preliminary results
|
More about the current model capabilities and preliminary results
|
||||||
can be found in `a recent presentation at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_
|
can be found in `a recent presentation at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_
|
||||||
and the the following `preprint with a description of the industry sector <https://arxiv.org/abs/2109.09563>`_.
|
and the following `paper in Joule with a description of the industry sector <https://arxiv.org/abs/2109.09563>`_.
|
||||||
|
|
||||||
This diagram gives an overview of the sectors and the links between
|
This diagram gives an overview of the sectors and the links between
|
||||||
them:
|
them:
|
||||||
@ -131,6 +142,7 @@ Documentation
|
|||||||
**References**
|
**References**
|
||||||
|
|
||||||
* :doc:`release_notes`
|
* :doc:`release_notes`
|
||||||
|
* :doc:`limitations`
|
||||||
|
|
||||||
.. toctree::
|
.. toctree::
|
||||||
:hidden:
|
:hidden:
|
||||||
@ -138,18 +150,7 @@ Documentation
|
|||||||
:caption: References
|
:caption: References
|
||||||
|
|
||||||
release_notes
|
release_notes
|
||||||
|
limitations
|
||||||
|
|
||||||
Warnings
|
|
||||||
========
|
|
||||||
|
|
||||||
**WARNING**: This model is under construction and contains serious
|
|
||||||
problems that distort the results. See the github repository
|
|
||||||
`issues <https://github.com/PyPSA/pypsa-eur-sec/issues>`_ for some of
|
|
||||||
the problems (please feel free to help or make suggestions). There is
|
|
||||||
neither documentation nor a paper yet, but we hope to have a preprint
|
|
||||||
out by summer 2020. We cannot support this model if you choose to use
|
|
||||||
it.
|
|
||||||
|
|
||||||
|
|
||||||
Licence
|
Licence
|
||||||
|
|||||||
61
doc/limitations.rst
Normal file
61
doc/limitations.rst
Normal file
@ -0,0 +1,61 @@
|
|||||||
|
##########################################
|
||||||
|
Limitations
|
||||||
|
##########################################
|
||||||
|
|
||||||
|
While the benefit of an openly available, functional and partially validated
|
||||||
|
model of the European energy system is high, many approximations have
|
||||||
|
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.
|
||||||
|
|
||||||
|
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:**
|
||||||
|
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
|
||||||
|
line parameters were made that ignore specific conductoring choices for
|
||||||
|
particular lines. There is no openly available data on busbar configurations, switch
|
||||||
|
locations, transformers or reactive power compensation assets.
|
||||||
|
|
||||||
|
- **Assignment of electricity demand to transmission nodes:**
|
||||||
|
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.
|
||||||
|
|
||||||
|
- **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.
|
||||||
|
|
||||||
|
- **Energy demand distribution within countries:**
|
||||||
|
Assumptions
|
||||||
|
have been made about the distribution of demand in each country proportional to
|
||||||
|
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.
|
||||||
|
|
||||||
|
- **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
|
||||||
|
totals have been used. Inflow time series are based on country-wide approximations,
|
||||||
|
ignoring local topography and basin drainage; in principle a full
|
||||||
|
hydrological model should be used.
|
||||||
|
|
||||||
|
- **International interactions:**
|
||||||
|
Border connections and power flows to Russia,
|
||||||
|
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.
|
||||||
|
|
||||||
|
- **Demand sufficiency:** Further measures of demand reduction may be
|
||||||
|
possible beyond the assumptions made here.
|
||||||
@ -8,15 +8,22 @@ idx = pd.IndexSlice
|
|||||||
|
|
||||||
import pypsa
|
import pypsa
|
||||||
import yaml
|
import yaml
|
||||||
|
import numpy as np
|
||||||
|
|
||||||
from add_existing_baseyear import add_build_year_to_new_assets
|
from add_existing_baseyear import add_build_year_to_new_assets
|
||||||
from helper import override_component_attrs
|
from helper import override_component_attrs
|
||||||
|
from solve_network import basename
|
||||||
|
|
||||||
|
|
||||||
def add_brownfield(n, n_p, year):
|
def add_brownfield(n, n_p, year):
|
||||||
|
|
||||||
print("adding brownfield")
|
print("adding brownfield")
|
||||||
|
|
||||||
|
# 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"]):
|
for c in n_p.iterate_components(["Link", "Generator", "Store"]):
|
||||||
|
|
||||||
attr = "e" if c.name == "Store" else "p"
|
attr = "e" if c.name == "Store" else "p"
|
||||||
@ -25,7 +32,7 @@ def add_brownfield(n, n_p, year):
|
|||||||
# CO2 or global EU values since these are already in n
|
# CO2 or global EU values since these are already in n
|
||||||
n_p.mremove(
|
n_p.mremove(
|
||||||
c.name,
|
c.name,
|
||||||
c.df.index[c.df.lifetime.isna()]
|
c.df.index[c.df.lifetime==np.inf]
|
||||||
)
|
)
|
||||||
|
|
||||||
# remove assets whose build_year + lifetime < year
|
# remove assets whose build_year + lifetime < year
|
||||||
@ -75,7 +82,34 @@ def add_brownfield(n, n_p, year):
|
|||||||
for tattr in n.component_attrs[c.name].index[selection]:
|
for tattr in n.component_attrs[c.name].index[selection]:
|
||||||
n.import_series_from_dataframe(c.pnl[tattr], c.name, tattr)
|
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: basename(x).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.
|
||||||
|
n.links.loc[new_pipes, "p_nom_min"] = 0.
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
#%%
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
if 'snakemake' not in globals():
|
if 'snakemake' not in globals():
|
||||||
from helper import mock_snakemake
|
from helper import mock_snakemake
|
||||||
@ -83,9 +117,10 @@ if __name__ == "__main__":
|
|||||||
'add_brownfield',
|
'add_brownfield',
|
||||||
weather_year='',
|
weather_year='',
|
||||||
simpl='',
|
simpl='',
|
||||||
clusters=48,
|
clusters="37",
|
||||||
|
opts="",
|
||||||
lv=1.0,
|
lv=1.0,
|
||||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
sector_opts='168H-T-H-B-I-solar+p3-dist1',
|
||||||
planning_horizons=2030,
|
planning_horizons=2030,
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|||||||
@ -12,9 +12,11 @@ import xarray as xr
|
|||||||
import pypsa
|
import pypsa
|
||||||
import yaml
|
import yaml
|
||||||
|
|
||||||
from prepare_sector_network import prepare_costs
|
from prepare_sector_network import prepare_costs, define_spatial
|
||||||
from helper import override_component_attrs
|
from helper import override_component_attrs
|
||||||
|
|
||||||
|
from types import SimpleNamespace
|
||||||
|
spatial = SimpleNamespace()
|
||||||
|
|
||||||
def add_build_year_to_new_assets(n, baseyear):
|
def add_build_year_to_new_assets(n, baseyear):
|
||||||
"""
|
"""
|
||||||
@ -28,7 +30,7 @@ def add_build_year_to_new_assets(n, baseyear):
|
|||||||
# Give assets with lifetimes and no build year the build year baseyear
|
# Give assets with lifetimes and no build year the build year baseyear
|
||||||
for c in n.iterate_components(["Link", "Generator", "Store"]):
|
for c in n.iterate_components(["Link", "Generator", "Store"]):
|
||||||
|
|
||||||
assets = c.df.index[~c.df.lifetime.isna() & c.df.build_year==0]
|
assets = c.df.index[(c.df.lifetime!=np.inf) & (c.df.build_year==0)]
|
||||||
c.df.loc[assets, "build_year"] = baseyear
|
c.df.loc[assets, "build_year"] = baseyear
|
||||||
|
|
||||||
# add -baseyear to name
|
# add -baseyear to name
|
||||||
@ -153,8 +155,8 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
|||||||
df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
|
df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
|
||||||
|
|
||||||
# assign clustered bus
|
# assign clustered bus
|
||||||
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0, squeeze=True)
|
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
|
||||||
busmap = pd.read_csv(snakemake.input.busmap, index_col=0, squeeze=True)
|
busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
|
||||||
|
|
||||||
inv_busmap = {}
|
inv_busmap = {}
|
||||||
for k, v in busmap.iteritems():
|
for k, v in busmap.iteritems():
|
||||||
@ -201,6 +203,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
|||||||
suffix = '-ac' if generator == 'offwind' else ''
|
suffix = '-ac' if generator == 'offwind' else ''
|
||||||
name_suffix = f' {generator}{suffix}-{baseyear}'
|
name_suffix = f' {generator}{suffix}-{baseyear}'
|
||||||
|
|
||||||
|
# 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()
|
||||||
|
|
||||||
if 'm' in snakemake.wildcards.clusters:
|
if 'm' in snakemake.wildcards.clusters:
|
||||||
|
|
||||||
for ind in capacity.index:
|
for ind in capacity.index:
|
||||||
@ -220,7 +227,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
|||||||
carrier=generator,
|
carrier=generator,
|
||||||
p_nom=capacity[ind] / len(inv_ind), # split among regions in a country
|
p_nom=capacity[ind] / len(inv_ind), # split among regions in a country
|
||||||
marginal_cost=costs.at[generator,'VOM'],
|
marginal_cost=costs.at[generator,'VOM'],
|
||||||
capital_cost=costs.at[generator,'fixed'],
|
capital_cost=capital_cost,
|
||||||
efficiency=costs.at[generator, 'efficiency'],
|
efficiency=costs.at[generator, 'efficiency'],
|
||||||
p_max_pu=p_max_pu,
|
p_max_pu=p_max_pu,
|
||||||
build_year=grouping_year,
|
build_year=grouping_year,
|
||||||
@ -238,7 +245,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
|||||||
carrier=generator,
|
carrier=generator,
|
||||||
p_nom=capacity,
|
p_nom=capacity,
|
||||||
marginal_cost=costs.at[generator, 'VOM'],
|
marginal_cost=costs.at[generator, 'VOM'],
|
||||||
capital_cost=costs.at[generator, 'fixed'],
|
capital_cost=capital_cost,
|
||||||
efficiency=costs.at[generator, 'efficiency'],
|
efficiency=costs.at[generator, 'efficiency'],
|
||||||
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
|
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
|
||||||
build_year=grouping_year,
|
build_year=grouping_year,
|
||||||
@ -246,11 +253,14 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
|||||||
)
|
)
|
||||||
|
|
||||||
else:
|
else:
|
||||||
|
bus0 = vars(spatial)[carrier[generator]].nodes
|
||||||
|
if "EU" not in vars(spatial)[carrier[generator]].locations:
|
||||||
|
bus0 = bus0.intersection(capacity.index + " gas")
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
capacity.index,
|
capacity.index,
|
||||||
suffix= " " + generator +"-" + str(grouping_year),
|
suffix= " " + generator +"-" + str(grouping_year),
|
||||||
bus0="EU " + carrier[generator],
|
bus0=bus0,
|
||||||
bus1=capacity.index,
|
bus1=capacity.index,
|
||||||
bus2="co2 atmosphere",
|
bus2="co2 atmosphere",
|
||||||
carrier=generator,
|
carrier=generator,
|
||||||
@ -399,10 +409,11 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
|
|||||||
lifetime=costs.at[costs_name, 'lifetime']
|
lifetime=costs.at[costs_name, 'lifetime']
|
||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes[name],
|
nodes[name],
|
||||||
suffix= f" {name} gas boiler-{grouping_year}",
|
suffix= f" {name} gas boiler-{grouping_year}",
|
||||||
bus0="EU gas",
|
bus0=spatial.gas.nodes,
|
||||||
bus1=nodes[name] + " " + name + " heat",
|
bus1=nodes[name] + " " + name + " heat",
|
||||||
bus2="co2 atmosphere",
|
bus2="co2 atmosphere",
|
||||||
carrier=name + " gas boiler",
|
carrier=name + " gas boiler",
|
||||||
@ -417,7 +428,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
|
|||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes[name],
|
nodes[name],
|
||||||
suffix=f" {name} oil boiler-{grouping_year}",
|
suffix=f" {name} oil boiler-{grouping_year}",
|
||||||
bus0="EU oil",
|
bus0=spatial.oil.nodes,
|
||||||
bus1=nodes[name] + " " + name + " heat",
|
bus1=nodes[name] + " " + name + " heat",
|
||||||
bus2="co2 atmosphere",
|
bus2="co2 atmosphere",
|
||||||
carrier=name + " oil boiler",
|
carrier=name + " oil boiler",
|
||||||
@ -436,7 +447,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
|
|||||||
threshold = snakemake.config['existing_capacities']['threshold_capacity']
|
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])
|
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 __name__ == "__main__":
|
||||||
if 'snakemake' not in globals():
|
if 'snakemake' not in globals():
|
||||||
from helper import mock_snakemake
|
from helper import mock_snakemake
|
||||||
@ -444,10 +455,10 @@ if __name__ == "__main__":
|
|||||||
'add_existing_baseyear',
|
'add_existing_baseyear',
|
||||||
weather_year='',
|
weather_year='',
|
||||||
simpl='',
|
simpl='',
|
||||||
clusters=45,
|
clusters="37",
|
||||||
lv=1.0,
|
lv=1.0,
|
||||||
opts='',
|
opts='',
|
||||||
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
|
sector_opts='168H-T-H-B-I-solar+p3-dist1',
|
||||||
planning_horizons=2020,
|
planning_horizons=2020,
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -460,7 +471,8 @@ if __name__ == "__main__":
|
|||||||
|
|
||||||
overrides = override_component_attrs(snakemake.input.overrides)
|
overrides = override_component_attrs(snakemake.input.overrides)
|
||||||
n = pypsa.Network(snakemake.input.network, override_component_attrs=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)
|
add_build_year_to_new_assets(n, baseyear)
|
||||||
|
|
||||||
Nyears = n.snapshot_weightings.generators.sum() / 8760.
|
Nyears = n.snapshot_weightings.generators.sum() / 8760.
|
||||||
@ -472,7 +484,7 @@ if __name__ == "__main__":
|
|||||||
snakemake.config['costs']['lifetime']
|
snakemake.config['costs']['lifetime']
|
||||||
)
|
)
|
||||||
|
|
||||||
grouping_years=snakemake.config['existing_capacities']['grouping_years']
|
grouping_years = snakemake.config['existing_capacities']['grouping_years']
|
||||||
add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear)
|
add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear)
|
||||||
|
|
||||||
if "H" in opts:
|
if "H" in opts:
|
||||||
|
|||||||
@ -144,10 +144,12 @@ def build_nuts2_shapes():
|
|||||||
nuts2 = gpd.GeoDataFrame(gpd.read_file(snakemake.input.nuts2).set_index('id').geometry)
|
nuts2 = gpd.GeoDataFrame(gpd.read_file(snakemake.input.nuts2).set_index('id').geometry)
|
||||||
|
|
||||||
countries = gpd.read_file(snakemake.input.country_shapes).set_index('name')
|
countries = gpd.read_file(snakemake.input.country_shapes).set_index('name')
|
||||||
missing = countries.loc[["AL", "RS", "BA"]]
|
missing_iso2 = countries.index.intersection(["AL", "RS", "BA"])
|
||||||
|
missing = countries.loc[missing_iso2]
|
||||||
|
|
||||||
nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
|
nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
|
||||||
|
|
||||||
return nuts2.append(missing)
|
return pd.concat([nuts2, missing])
|
||||||
|
|
||||||
|
|
||||||
def area(gdf):
|
def area(gdf):
|
||||||
|
|||||||
@ -26,7 +26,7 @@ def build_gas_input_locations(lng_fn, planned_lng_fn, entry_fn, prod_fn, countri
|
|||||||
planned_lng = pd.read_csv(planned_lng_fn)
|
planned_lng = pd.read_csv(planned_lng_fn)
|
||||||
planned_lng.geometry = planned_lng.geometry.apply(wkt.loads)
|
planned_lng.geometry = planned_lng.geometry.apply(wkt.loads)
|
||||||
planned_lng = gpd.GeoDataFrame(planned_lng, crs=4326)
|
planned_lng = gpd.GeoDataFrame(planned_lng, crs=4326)
|
||||||
lng = lng.append(planned_lng, ignore_index=True)
|
lng = pd.concat([lng, planned_lng], ignore_index=True)
|
||||||
|
|
||||||
# Entry points from outside the model scope
|
# Entry points from outside the model scope
|
||||||
entry = read_scigrid_gas(entry_fn)
|
entry = read_scigrid_gas(entry_fn)
|
||||||
|
|||||||
@ -115,14 +115,14 @@ def get_energy_ratio(country):
|
|||||||
# estimate physical output, energy consumption in the sector and country
|
# estimate physical output, energy consumption in the sector and country
|
||||||
fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
|
fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
|
||||||
df = pd.read_excel(fn, sheet_name='2016', index_col=2,
|
df = pd.read_excel(fn, sheet_name='2016', index_col=2,
|
||||||
header=0, skiprows=1, squeeze=True)
|
header=0, skiprows=1).squeeze('columns')
|
||||||
e_country = df.loc[eb_sectors.keys(
|
e_country = df.loc[eb_sectors.keys(
|
||||||
), 'Total all products'].rename(eb_sectors)
|
), 'Total all products'].rename(eb_sectors)
|
||||||
|
|
||||||
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx'
|
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx'
|
||||||
|
|
||||||
df = pd.read_excel(fn, sheet_name='Ind_Summary',
|
df = pd.read_excel(fn, sheet_name='Ind_Summary',
|
||||||
index_col=0, header=0, squeeze=True)
|
index_col=0, header=0).squeeze('columns')
|
||||||
|
|
||||||
assert df.index[48] == "by sector"
|
assert df.index[48] == "by sector"
|
||||||
year_i = df.columns.get_loc(year)
|
year_i = df.columns.get_loc(year)
|
||||||
@ -142,7 +142,7 @@ def industry_production_per_country(country):
|
|||||||
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx'
|
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx'
|
||||||
sheet = sub_sheet_name_dict[sector]
|
sheet = sub_sheet_name_dict[sector]
|
||||||
df = pd.read_excel(fn, sheet_name=sheet,
|
df = pd.read_excel(fn, sheet_name=sheet,
|
||||||
index_col=0, header=0, squeeze=True)
|
index_col=0, header=0).squeeze('columns')
|
||||||
|
|
||||||
year_i = df.columns.get_loc(year)
|
year_i = df.columns.get_loc(year)
|
||||||
df = df.iloc[find_physical_output(df), year_i]
|
df = df.iloc[find_physical_output(df), year_i]
|
||||||
|
|||||||
@ -78,12 +78,11 @@ def load_idees_data(sector, country="EU28"):
|
|||||||
sheet_name=list(sheets.values()),
|
sheet_name=list(sheets.values()),
|
||||||
index_col=0,
|
index_col=0,
|
||||||
header=0,
|
header=0,
|
||||||
squeeze=True,
|
|
||||||
usecols=usecols,
|
usecols=usecols,
|
||||||
)
|
)
|
||||||
|
|
||||||
for k, v in sheets.items():
|
for k, v in sheets.items():
|
||||||
idees[k] = idees.pop(v)
|
idees[k] = idees.pop(v).squeeze()
|
||||||
|
|
||||||
return idees
|
return idees
|
||||||
|
|
||||||
|
|||||||
@ -39,7 +39,7 @@ if __name__ == '__main__':
|
|||||||
|
|
||||||
urban_fraction = pd.read_csv(snakemake.input.urban_percent,
|
urban_fraction = pd.read_csv(snakemake.input.urban_percent,
|
||||||
header=None, index_col=0,
|
header=None, index_col=0,
|
||||||
names=['fraction'], squeeze=True) / 100.
|
names=['fraction']).squeeze() / 100.
|
||||||
|
|
||||||
# fill missing Balkans values
|
# fill missing Balkans values
|
||||||
missing = ["AL", "ME", "MK"]
|
missing = ["AL", "ME", "MK"]
|
||||||
|
|||||||
22
scripts/build_population_weighted_energy_totals.py
Normal file
22
scripts/build_population_weighted_energy_totals.py
Normal file
@ -0,0 +1,22 @@
|
|||||||
|
"""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.)
|
||||||
|
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])
|
||||||
201
scripts/build_transport_demand.py
Normal file
201
scripts/build_transport_demand.py
Normal file
@ -0,0 +1,201 @@
|
|||||||
|
"""Build transport demand."""
|
||||||
|
|
||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
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=True
|
||||||
|
)
|
||||||
|
|
||||||
|
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=True)
|
||||||
|
|
||||||
|
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)
|
||||||
@ -1,4 +1,5 @@
|
|||||||
import os
|
import os
|
||||||
|
import pytz
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
from pathlib import Path
|
from pathlib import Path
|
||||||
from pypsa.descriptors import Dict
|
from pypsa.descriptors import Dict
|
||||||
@ -101,3 +102,24 @@ def progress_retrieve(url, file):
|
|||||||
pbar.update( int(count * blockSize * 100 / totalSize) )
|
pbar.update( int(count * blockSize * 100 / totalSize) )
|
||||||
|
|
||||||
urllib.request.urlretrieve(url, file, reporthook=dlProgress)
|
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
|
||||||
@ -115,7 +115,9 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
|
|||||||
costs = costs.stack() # .sort_index()
|
costs = costs.stack() # .sort_index()
|
||||||
|
|
||||||
# hack because impossible to drop buses...
|
# hack because impossible to drop buses...
|
||||||
n.buses.loc["EU gas", ["x", "y"]] = n.buses.loc["DE0 0", ["x", "y"]]
|
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.drop(n.links.index[(n.links.carrier != "DC") & (
|
||||||
n.links.carrier != "B2B")], inplace=True)
|
n.links.carrier != "B2B")], inplace=True)
|
||||||
@ -223,6 +225,26 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
|
|||||||
bbox_inches="tight"
|
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["p_nom_opt"].groupby(level=0).sum()
|
||||||
|
|
||||||
|
return pipe_capacity
|
||||||
|
|
||||||
|
|
||||||
def plot_h2_map(network):
|
def plot_h2_map(network):
|
||||||
|
|
||||||
@ -235,7 +257,7 @@ def plot_h2_map(network):
|
|||||||
bus_size_factor = 1e5
|
bus_size_factor = 1e5
|
||||||
linewidth_factor = 1e4
|
linewidth_factor = 1e4
|
||||||
# MW below which not drawn
|
# MW below which not drawn
|
||||||
line_lower_threshold = 1e3
|
line_lower_threshold = 1e2
|
||||||
|
|
||||||
# Drop non-electric buses so they don't clutter the plot
|
# Drop non-electric buses so they don't clutter the plot
|
||||||
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
|
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
|
||||||
@ -246,28 +268,20 @@ def plot_h2_map(network):
|
|||||||
|
|
||||||
# make a fake MultiIndex so that area is correct for legend
|
# make a fake MultiIndex so that area is correct for legend
|
||||||
bus_sizes.rename(index=lambda x: x.replace(" H2", ""), level=0, inplace=True)
|
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)
|
n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
|
||||||
|
|
||||||
h2_new = n.links.loc[n.links.carrier=="H2 pipeline", "p_nom_opt"]
|
h2_new = n.links.loc[n.links.carrier=="H2 pipeline"]
|
||||||
|
|
||||||
h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
|
h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
|
||||||
|
# sum capacitiy for pipelines from different investment periods
|
||||||
|
h2_new = group_pipes(h2_new)
|
||||||
|
h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
|
||||||
|
|
||||||
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 = h2_retro.apply(
|
|
||||||
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
|
|
||||||
axis=1
|
|
||||||
)
|
|
||||||
|
|
||||||
h2_retro = h2_retro["p_nom_opt"]
|
|
||||||
|
|
||||||
|
n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
|
||||||
|
n.links = n.links.groupby(level=0).first()
|
||||||
link_widths_total = (h2_new + h2_retro) / linewidth_factor
|
link_widths_total = (h2_new + h2_retro) / linewidth_factor
|
||||||
link_widths_total = link_widths_total.groupby(level=0).sum().reindex(n.links.index).fillna(0.)
|
link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.)
|
||||||
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
|
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
|
||||||
|
|
||||||
retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)
|
retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)
|
||||||
@ -512,7 +526,9 @@ def plot_map_without(network):
|
|||||||
|
|
||||||
# hack because impossible to drop buses...
|
# hack because impossible to drop buses...
|
||||||
if "EU gas" in n.buses.index:
|
if "EU gas" in n.buses.index:
|
||||||
n.buses.loc["EU gas", ["x", "y"]] = n.buses.loc["DE0 0", ["x", "y"]]
|
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")]
|
to_drop = n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")]
|
||||||
n.links.drop(to_drop, inplace=True)
|
n.links.drop(to_drop, inplace=True)
|
||||||
@ -696,11 +712,11 @@ if __name__ == "__main__":
|
|||||||
'plot_network',
|
'plot_network',
|
||||||
weather_year='',
|
weather_year='',
|
||||||
simpl='',
|
simpl='',
|
||||||
clusters=45,
|
clusters="45",
|
||||||
lv=1.5,
|
lv=1.0,
|
||||||
opts='',
|
opts='',
|
||||||
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
|
sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
|
||||||
planning_horizons=2030,
|
planning_horizons="2050",
|
||||||
)
|
)
|
||||||
|
|
||||||
overrides = override_component_attrs(snakemake.input.overrides)
|
overrides = override_component_attrs(snakemake.input.overrides)
|
||||||
|
|||||||
@ -3,7 +3,6 @@
|
|||||||
import pypsa
|
import pypsa
|
||||||
import re
|
import re
|
||||||
import os
|
import os
|
||||||
import pytz
|
|
||||||
|
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
import numpy as np
|
import numpy as np
|
||||||
@ -15,7 +14,7 @@ from scipy.stats import beta
|
|||||||
from vresutils.costdata import annuity
|
from vresutils.costdata import annuity
|
||||||
|
|
||||||
from build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
|
from build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
|
||||||
from helper import override_component_attrs
|
from helper import override_component_attrs, generate_periodic_profiles
|
||||||
|
|
||||||
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
|
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
|
||||||
from networkx.algorithms import complement
|
from networkx.algorithms import complement
|
||||||
@ -28,7 +27,7 @@ from types import SimpleNamespace
|
|||||||
spatial = SimpleNamespace()
|
spatial = SimpleNamespace()
|
||||||
|
|
||||||
|
|
||||||
def define_spatial(nodes):
|
def define_spatial(nodes, options):
|
||||||
"""
|
"""
|
||||||
Namespace for spatial
|
Namespace for spatial
|
||||||
|
|
||||||
@ -38,7 +37,6 @@ def define_spatial(nodes):
|
|||||||
"""
|
"""
|
||||||
|
|
||||||
global spatial
|
global spatial
|
||||||
global options
|
|
||||||
|
|
||||||
spatial.nodes = nodes
|
spatial.nodes = nodes
|
||||||
|
|
||||||
@ -95,6 +93,28 @@ def define_spatial(nodes):
|
|||||||
|
|
||||||
spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
|
spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
|
||||||
|
|
||||||
|
# oil
|
||||||
|
spatial.oil = SimpleNamespace()
|
||||||
|
spatial.oil.nodes = ["EU oil"]
|
||||||
|
spatial.oil.locations = ["EU"]
|
||||||
|
|
||||||
|
# uranium
|
||||||
|
spatial.uranium = SimpleNamespace()
|
||||||
|
spatial.uranium.nodes = ["EU uranium"]
|
||||||
|
spatial.uranium.locations = ["EU"]
|
||||||
|
|
||||||
|
# coal
|
||||||
|
spatial.coal = SimpleNamespace()
|
||||||
|
spatial.coal.nodes = ["EU coal"]
|
||||||
|
spatial.coal.locations = ["EU"]
|
||||||
|
|
||||||
|
# lignite
|
||||||
|
spatial.lignite = SimpleNamespace()
|
||||||
|
spatial.lignite.nodes = ["EU lignite"]
|
||||||
|
spatial.lignite.locations = ["EU"]
|
||||||
|
|
||||||
|
return spatial
|
||||||
|
|
||||||
|
|
||||||
from types import SimpleNamespace
|
from types import SimpleNamespace
|
||||||
spatial = SimpleNamespace()
|
spatial = SimpleNamespace()
|
||||||
@ -252,6 +272,7 @@ def create_network_topology(n, prefix, carriers=["DC"], connector=" -> ", bidire
|
|||||||
|
|
||||||
ln_attrs = ["bus0", "bus1", "length"]
|
ln_attrs = ["bus0", "bus1", "length"]
|
||||||
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
|
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
|
||||||
|
lk_attrs = n.links.columns.intersection(lk_attrs)
|
||||||
|
|
||||||
candidates = pd.concat([
|
candidates = pd.concat([
|
||||||
n.lines[ln_attrs],
|
n.lines[ln_attrs],
|
||||||
@ -278,7 +299,7 @@ def create_network_topology(n, prefix, carriers=["DC"], connector=" -> ", bidire
|
|||||||
topo_reverse = topo.copy()
|
topo_reverse = topo.copy()
|
||||||
topo_reverse.rename(columns=swap_buses, inplace=True)
|
topo_reverse.rename(columns=swap_buses, inplace=True)
|
||||||
topo_reverse.index = topo_reverse.apply(make_index, axis=1)
|
topo_reverse.index = topo_reverse.apply(make_index, axis=1)
|
||||||
topo = topo.append(topo_reverse)
|
topo = pd.concat([topo, topo_reverse])
|
||||||
|
|
||||||
return topo
|
return topo
|
||||||
|
|
||||||
@ -352,7 +373,8 @@ def add_carrier_buses(n, carrier, nodes=None):
|
|||||||
"""
|
"""
|
||||||
|
|
||||||
if nodes is None:
|
if nodes is None:
|
||||||
nodes = ["EU " + carrier]
|
nodes = vars(spatial)[carrier].nodes
|
||||||
|
location = vars(spatial)[carrier].locations
|
||||||
|
|
||||||
# skip if carrier already exists
|
# skip if carrier already exists
|
||||||
if carrier in n.carriers.index:
|
if carrier in n.carriers.index:
|
||||||
@ -365,7 +387,7 @@ def add_carrier_buses(n, carrier, nodes=None):
|
|||||||
|
|
||||||
n.madd("Bus",
|
n.madd("Bus",
|
||||||
nodes,
|
nodes,
|
||||||
location=nodes.str.replace(" " + carrier, ""),
|
location=location,
|
||||||
carrier=carrier
|
carrier=carrier
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -565,27 +587,6 @@ def average_every_nhours(n, offset):
|
|||||||
return m
|
return m
|
||||||
|
|
||||||
|
|
||||||
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 cycling_shift(df, steps=1):
|
def cycling_shift(df, steps=1):
|
||||||
"""Cyclic shift on index of pd.Series|pd.DataFrame by number of steps"""
|
"""Cyclic shift on index of pd.Series|pd.DataFrame by number of steps"""
|
||||||
df = df.copy()
|
df = df.copy()
|
||||||
@ -594,179 +595,6 @@ def cycling_shift(df, steps=1):
|
|||||||
return df
|
return df
|
||||||
|
|
||||||
|
|
||||||
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.
|
|
||||||
|
|
||||||
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
|
|
||||||
|
|
||||||
|
|
||||||
# TODO separate sectors and move into own rules
|
|
||||||
def prepare_data(n):
|
|
||||||
|
|
||||||
|
|
||||||
##############
|
|
||||||
#Heating
|
|
||||||
##############
|
|
||||||
|
|
||||||
|
|
||||||
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)
|
|
||||||
|
|
||||||
solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
|
|
||||||
# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
|
|
||||||
solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
|
|
||||||
|
|
||||||
energy_totals = pd.read_csv(snakemake.input.energy_totals_name, index_col=0)
|
|
||||||
|
|
||||||
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
|
|
||||||
nodal_energy_totals.index = pop_layout.index
|
|
||||||
# district heat share not weighted by population
|
|
||||||
district_heat_share = nodal_energy_totals["district heat share"].round(2)
|
|
||||||
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
|
|
||||||
|
|
||||||
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
|
|
||||||
daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
|
|
||||||
|
|
||||||
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
|
|
||||||
|
|
||||||
sectors = ["residential", "services"]
|
|
||||||
uses = ["water", "space"]
|
|
||||||
|
|
||||||
heat_demand = {}
|
|
||||||
electric_heat_supply = {}
|
|
||||||
for sector, use in product(sectors, uses):
|
|
||||||
weekday = list(intraday_profiles[f"{sector} {use} weekday"])
|
|
||||||
weekend = list(intraday_profiles[f"{sector} {use} weekend"])
|
|
||||||
weekly_profile = weekday * 5 + weekend * 2
|
|
||||||
intraday_year_profile = generate_periodic_profiles(
|
|
||||||
daily_space_heat_demand.index.tz_localize("UTC"),
|
|
||||||
nodes=daily_space_heat_demand.columns,
|
|
||||||
weekly_profile=weekly_profile
|
|
||||||
)
|
|
||||||
|
|
||||||
if use == "space":
|
|
||||||
heat_demand_shape = daily_space_heat_demand * intraday_year_profile
|
|
||||||
else:
|
|
||||||
heat_demand_shape = intraday_year_profile
|
|
||||||
|
|
||||||
heat_demand[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"total {sector} {use}"]) * 1e6
|
|
||||||
electric_heat_supply[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"electricity {sector} {use}"]) * 1e6
|
|
||||||
|
|
||||||
heat_demand = pd.concat(heat_demand, axis=1)
|
|
||||||
electric_heat_supply = pd.concat(electric_heat_supply, axis=1)
|
|
||||||
|
|
||||||
# subtract from electricity load since heat demand already in heat_demand
|
|
||||||
electric_nodes = n.loads.index[n.loads.carrier == "electricity"]
|
|
||||||
n.loads_t.p_set[electric_nodes] = n.loads_t.p_set[electric_nodes] - electric_heat_supply.groupby(level=1, axis=1).sum()[electric_nodes]
|
|
||||||
|
|
||||||
##############
|
|
||||||
#Transport
|
|
||||||
##############
|
|
||||||
|
|
||||||
## Get overall demand curve for all vehicles
|
|
||||||
|
|
||||||
traffic = pd.read_csv(snakemake.input.traffic_data_KFZ, skiprows=2, usecols=["count"], squeeze=True)
|
|
||||||
|
|
||||||
#Generate profiles
|
|
||||||
transport_shape = generate_periodic_profiles(
|
|
||||||
dt_index=n.snapshots.tz_localize("UTC"),
|
|
||||||
nodes=pop_layout.index,
|
|
||||||
weekly_profile=traffic.values
|
|
||||||
)
|
|
||||||
transport_shape = transport_shape / transport_shape.sum()
|
|
||||||
|
|
||||||
transport_data = pd.read_csv(snakemake.input.transport_name, index_col=0)
|
|
||||||
|
|
||||||
nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(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., "average fuel efficiency"] = transport_data["average fuel efficiency"].mean()
|
|
||||||
|
|
||||||
|
|
||||||
# electric motors are more efficient, so alter transport demand
|
|
||||||
|
|
||||||
plug_to_wheels_eta = options.get("bev_plug_to_wheel_efficiency", 0.2)
|
|
||||||
battery_to_wheels_eta = plug_to_wheels_eta * options.get("bev_charge_efficiency", 0.9)
|
|
||||||
|
|
||||||
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(snakemake.input.temp_air_total).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 = nodal_energy_totals["total road"] + nodal_energy_totals["total rail"] - nodal_energy_totals["electricity rail"]
|
|
||||||
|
|
||||||
transport = (transport_shape.multiply(energy_totals_transport) * 1e6 * Nyears).divide(efficiency_gain * ice_correction).multiply(1 + dd_EV)
|
|
||||||
|
|
||||||
## derive plugged-in availability for PKW's (cars)
|
|
||||||
|
|
||||||
traffic = pd.read_csv(snakemake.input.traffic_data_Pkw, skiprows=2, usecols=["count"], squeeze=True)
|
|
||||||
|
|
||||||
avail_max = options.get("bev_avail_max", 0.95)
|
|
||||||
avail_mean = options.get("bev_avail_mean", 0.8)
|
|
||||||
|
|
||||||
avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (traffic.mean() - traffic.min())
|
|
||||||
|
|
||||||
avail_profile = generate_periodic_profiles(
|
|
||||||
dt_index=n.snapshots.tz_localize("UTC"),
|
|
||||||
nodes=pop_layout.index,
|
|
||||||
weekly_profile=avail.values
|
|
||||||
)
|
|
||||||
|
|
||||||
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=n.snapshots.tz_localize("UTC"),
|
|
||||||
nodes=pop_layout.index,
|
|
||||||
weekly_profile=dsm_week
|
|
||||||
)
|
|
||||||
|
|
||||||
|
|
||||||
return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share
|
|
||||||
|
|
||||||
|
|
||||||
# TODO checkout PyPSA-Eur script
|
# TODO checkout PyPSA-Eur script
|
||||||
def prepare_costs(cost_file, USD_to_EUR, discount_rate, Nyears, lifetime):
|
def prepare_costs(cost_file, USD_to_EUR, discount_rate, Nyears, lifetime):
|
||||||
|
|
||||||
@ -806,10 +634,8 @@ def add_generation(n, costs):
|
|||||||
|
|
||||||
for generator, carrier in conventionals.items():
|
for generator, carrier in conventionals.items():
|
||||||
|
|
||||||
if carrier == 'gas':
|
|
||||||
carrier_nodes = spatial.gas.nodes
|
carrier_nodes = vars(spatial)[carrier].nodes
|
||||||
else:
|
|
||||||
carrier_nodes = ["EU " + carrier]
|
|
||||||
|
|
||||||
add_carrier_buses(n, carrier, carrier_nodes)
|
add_carrier_buses(n, carrier, carrier_nodes)
|
||||||
|
|
||||||
@ -1045,7 +871,11 @@ def add_storage_and_grids(n, costs):
|
|||||||
)
|
)
|
||||||
|
|
||||||
cavern_types = snakemake.config["sector"]["hydrogen_underground_storage_locations"]
|
cavern_types = snakemake.config["sector"]["hydrogen_underground_storage_locations"]
|
||||||
h2_caverns = pd.read_csv(snakemake.input.h2_cavern, index_col=0)[cavern_types].sum(axis=1)
|
h2_caverns = pd.read_csv(snakemake.input.h2_cavern, index_col=0)
|
||||||
|
|
||||||
|
if not h2_caverns.empty and options['hydrogen_underground_storage']:
|
||||||
|
|
||||||
|
h2_caverns = h2_caverns[cavern_types].sum(axis=1)
|
||||||
|
|
||||||
# only use sites with at least 2 TWh potential
|
# only use sites with at least 2 TWh potential
|
||||||
h2_caverns = h2_caverns[h2_caverns > 2]
|
h2_caverns = h2_caverns[h2_caverns > 2]
|
||||||
@ -1056,8 +886,6 @@ def add_storage_and_grids(n, costs):
|
|||||||
# clip at 1000 TWh for one location
|
# clip at 1000 TWh for one location
|
||||||
h2_caverns.clip(upper=1e9, inplace=True)
|
h2_caverns.clip(upper=1e9, inplace=True)
|
||||||
|
|
||||||
if options['hydrogen_underground_storage']:
|
|
||||||
|
|
||||||
logger.info("Add hydrogen underground storage")
|
logger.info("Add hydrogen underground storage")
|
||||||
|
|
||||||
h2_capital_cost = costs.at["hydrogen storage underground", "fixed"]
|
h2_capital_cost = costs.at["hydrogen storage underground", "fixed"]
|
||||||
@ -1069,7 +897,8 @@ def add_storage_and_grids(n, costs):
|
|||||||
e_nom_max=h2_caverns.values,
|
e_nom_max=h2_caverns.values,
|
||||||
e_cyclic=True,
|
e_cyclic=True,
|
||||||
carrier="H2 Store",
|
carrier="H2 Store",
|
||||||
capital_cost=h2_capital_cost
|
capital_cost=h2_capital_cost,
|
||||||
|
lifetime=costs.at["hydrogen storage underground", "lifetime"]
|
||||||
)
|
)
|
||||||
|
|
||||||
# hydrogen stored overground (where not already underground)
|
# hydrogen stored overground (where not already underground)
|
||||||
@ -1154,7 +983,10 @@ def add_storage_and_grids(n, costs):
|
|||||||
|
|
||||||
# apply k_edge_augmentation weighted by length of complement edges
|
# apply k_edge_augmentation weighted by length of complement edges
|
||||||
k_edge = options.get("gas_network_connectivity_upgrade", 3)
|
k_edge = options.get("gas_network_connectivity_upgrade", 3)
|
||||||
augmentation = k_edge_augmentation(G, k_edge, avail=complement_edges.values)
|
augmentation = list(k_edge_augmentation(G, k_edge, avail=complement_edges.values))
|
||||||
|
|
||||||
|
if augmentation:
|
||||||
|
|
||||||
new_gas_pipes = pd.DataFrame(augmentation, columns=["bus0", "bus1"])
|
new_gas_pipes = pd.DataFrame(augmentation, columns=["bus0", "bus1"])
|
||||||
new_gas_pipes["length"] = new_gas_pipes.apply(haversine, axis=1)
|
new_gas_pipes["length"] = new_gas_pipes.apply(haversine, axis=1)
|
||||||
|
|
||||||
@ -1324,6 +1156,11 @@ def add_land_transport(n, costs):
|
|||||||
|
|
||||||
logger.info("Add land transport")
|
logger.info("Add land transport")
|
||||||
|
|
||||||
|
transport = pd.read_csv(snakemake.input.transport_demand, index_col=0, parse_dates=True)
|
||||||
|
number_cars = pd.read_csv(snakemake.input.transport_data, index_col=0)["number cars"]
|
||||||
|
avail_profile = pd.read_csv(snakemake.input.avail_profile, index_col=0, parse_dates=True)
|
||||||
|
dsm_profile = pd.read_csv(snakemake.input.dsm_profile, index_col=0, parse_dates=True)
|
||||||
|
|
||||||
fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
|
fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
|
||||||
electric_share = get(options["land_transport_electric_share"], investment_year)
|
electric_share = get(options["land_transport_electric_share"], investment_year)
|
||||||
ice_share = 1 - fuel_cell_share - electric_share
|
ice_share = 1 - fuel_cell_share - electric_share
|
||||||
@ -1357,8 +1194,7 @@ def add_land_transport(n, costs):
|
|||||||
p_set=p_set
|
p_set=p_set
|
||||||
)
|
)
|
||||||
|
|
||||||
|
p_nom = number_cars * options.get("bev_charge_rate", 0.011) * electric_share
|
||||||
p_nom = nodal_transport_data["number cars"] * options.get("bev_charge_rate", 0.011) * electric_share
|
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes,
|
nodes,
|
||||||
@ -1390,7 +1226,7 @@ def add_land_transport(n, costs):
|
|||||||
|
|
||||||
if electric_share > 0 and options["bev_dsm"]:
|
if electric_share > 0 and options["bev_dsm"]:
|
||||||
|
|
||||||
e_nom = nodal_transport_data["number cars"] * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
|
e_nom = number_cars * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
|
||||||
|
|
||||||
n.madd("Store",
|
n.madd("Store",
|
||||||
nodes,
|
nodes,
|
||||||
@ -1415,10 +1251,10 @@ def add_land_transport(n, costs):
|
|||||||
|
|
||||||
if ice_share > 0:
|
if ice_share > 0:
|
||||||
|
|
||||||
if "EU oil" not in n.buses.index:
|
if "oil" not in n.buses.carrier.unique():
|
||||||
n.add("Bus",
|
n.madd("Bus",
|
||||||
"EU oil",
|
spatial.oil.nodes,
|
||||||
location="EU",
|
location=spatial.oil.locations,
|
||||||
carrier="oil"
|
carrier="oil"
|
||||||
)
|
)
|
||||||
|
|
||||||
@ -1427,7 +1263,7 @@ def add_land_transport(n, costs):
|
|||||||
n.madd("Load",
|
n.madd("Load",
|
||||||
nodes,
|
nodes,
|
||||||
suffix=" land transport oil",
|
suffix=" land transport oil",
|
||||||
bus="EU oil",
|
bus=spatial.oil.nodes,
|
||||||
carrier="land transport oil",
|
carrier="land transport oil",
|
||||||
p_set=ice_share / ice_efficiency * transport[nodes]
|
p_set=ice_share / ice_efficiency * transport[nodes]
|
||||||
)
|
)
|
||||||
@ -1442,12 +1278,53 @@ def add_land_transport(n, costs):
|
|||||||
)
|
)
|
||||||
|
|
||||||
|
|
||||||
|
def build_heat_demand(n):
|
||||||
|
|
||||||
|
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
|
||||||
|
daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
|
||||||
|
|
||||||
|
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
|
||||||
|
|
||||||
|
sectors = ["residential", "services"]
|
||||||
|
uses = ["water", "space"]
|
||||||
|
|
||||||
|
heat_demand = {}
|
||||||
|
electric_heat_supply = {}
|
||||||
|
for sector, use in product(sectors, uses):
|
||||||
|
weekday = list(intraday_profiles[f"{sector} {use} weekday"])
|
||||||
|
weekend = list(intraday_profiles[f"{sector} {use} weekend"])
|
||||||
|
weekly_profile = weekday * 5 + weekend * 2
|
||||||
|
intraday_year_profile = generate_periodic_profiles(
|
||||||
|
daily_space_heat_demand.index.tz_localize("UTC"),
|
||||||
|
nodes=daily_space_heat_demand.columns,
|
||||||
|
weekly_profile=weekly_profile
|
||||||
|
)
|
||||||
|
|
||||||
|
if use == "space":
|
||||||
|
heat_demand_shape = daily_space_heat_demand * intraday_year_profile
|
||||||
|
else:
|
||||||
|
heat_demand_shape = intraday_year_profile
|
||||||
|
|
||||||
|
heat_demand[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
|
||||||
|
electric_heat_supply[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
|
||||||
|
|
||||||
|
heat_demand = pd.concat(heat_demand, axis=1)
|
||||||
|
electric_heat_supply = pd.concat(electric_heat_supply, axis=1)
|
||||||
|
|
||||||
|
# subtract from electricity load since heat demand already in heat_demand
|
||||||
|
electric_nodes = n.loads.index[n.loads.carrier == "electricity"]
|
||||||
|
n.loads_t.p_set[electric_nodes] = n.loads_t.p_set[electric_nodes] - electric_heat_supply.groupby(level=1, axis=1).sum()[electric_nodes]
|
||||||
|
|
||||||
|
return heat_demand
|
||||||
|
|
||||||
|
|
||||||
def add_heat(n, costs):
|
def add_heat(n, costs):
|
||||||
|
|
||||||
logger.info("Add heat sector")
|
logger.info("Add heat sector")
|
||||||
|
|
||||||
sectors = ["residential", "services"]
|
sectors = ["residential", "services"]
|
||||||
|
|
||||||
|
heat_demand = build_heat_demand(n)
|
||||||
|
|
||||||
nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()
|
nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()
|
||||||
|
|
||||||
@ -1468,6 +1345,15 @@ def add_heat(n, costs):
|
|||||||
"urban central"
|
"urban central"
|
||||||
]
|
]
|
||||||
|
|
||||||
|
cop = {
|
||||||
|
"air": xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots),
|
||||||
|
"ground": xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
|
||||||
|
}
|
||||||
|
|
||||||
|
solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
|
||||||
|
# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
|
||||||
|
solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
|
||||||
|
|
||||||
for name in heat_systems:
|
for name in heat_systems:
|
||||||
|
|
||||||
name_type = "central" if name == "urban central" else "decentral"
|
name_type = "central" if name == "urban central" else "decentral"
|
||||||
@ -1513,7 +1399,6 @@ def add_heat(n, costs):
|
|||||||
heat_pump_type = "air" if "urban" in name else "ground"
|
heat_pump_type = "air" if "urban" in name else "ground"
|
||||||
|
|
||||||
costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
|
costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
|
||||||
cop = {"air" : ashp_cop, "ground" : gshp_cop}
|
|
||||||
efficiency = cop[heat_pump_type][nodes[name]] if options["time_dep_hp_cop"] else costs.at[costs_name, 'efficiency']
|
efficiency = cop[heat_pump_type][nodes[name]] if options["time_dep_hp_cop"] else costs.at[costs_name, 'efficiency']
|
||||||
|
|
||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
@ -1792,6 +1677,8 @@ def create_nodes_for_heat_sector():
|
|||||||
nodes[sector + " rural"] = pop_layout.index
|
nodes[sector + " rural"] = pop_layout.index
|
||||||
nodes[sector + " urban decentral"] = pop_layout.index
|
nodes[sector + " urban decentral"] = pop_layout.index
|
||||||
|
|
||||||
|
district_heat_share = pop_weighted_energy_totals["district heat share"]
|
||||||
|
|
||||||
# maximum potential of urban demand covered by district heating
|
# maximum potential of urban demand covered by district heating
|
||||||
central_fraction = options['district_heating']["potential"]
|
central_fraction = options['district_heating']["potential"]
|
||||||
# district heating share at each node
|
# district heating share at each node
|
||||||
@ -1882,8 +1769,7 @@ def add_biomass(n, costs):
|
|||||||
transport_costs = pd.read_csv(
|
transport_costs = pd.read_csv(
|
||||||
snakemake.input.biomass_transport_costs,
|
snakemake.input.biomass_transport_costs,
|
||||||
index_col=0,
|
index_col=0,
|
||||||
squeeze=True
|
).squeeze()
|
||||||
)
|
|
||||||
|
|
||||||
# add biomass transport
|
# add biomass transport
|
||||||
biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
|
biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
|
||||||
@ -2075,7 +1961,7 @@ def add_industry(n, costs):
|
|||||||
all_navigation = ["total international navigation", "total domestic navigation"]
|
all_navigation = ["total international navigation", "total domestic navigation"]
|
||||||
efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
|
efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
|
||||||
shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
|
shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
|
||||||
p_set = shipping_hydrogen_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
|
p_set = shipping_hydrogen_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
|
||||||
|
|
||||||
n.madd("Load",
|
n.madd("Load",
|
||||||
nodes,
|
nodes,
|
||||||
@ -2089,17 +1975,17 @@ def add_industry(n, costs):
|
|||||||
|
|
||||||
shipping_oil_share = 1 - shipping_hydrogen_share
|
shipping_oil_share = 1 - shipping_hydrogen_share
|
||||||
|
|
||||||
p_set = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
|
p_set = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
|
||||||
|
|
||||||
n.madd("Load",
|
n.madd("Load",
|
||||||
nodes,
|
nodes,
|
||||||
suffix=" shipping oil",
|
suffix=" shipping oil",
|
||||||
bus="EU oil",
|
bus=spatial.oil.nodes,
|
||||||
carrier="shipping oil",
|
carrier="shipping oil",
|
||||||
p_set=p_set
|
p_set=p_set
|
||||||
)
|
)
|
||||||
|
|
||||||
co2 = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
|
co2 = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
|
||||||
|
|
||||||
n.add("Load",
|
n.add("Load",
|
||||||
"shipping oil emissions",
|
"shipping oil emissions",
|
||||||
@ -2108,30 +1994,29 @@ def add_industry(n, costs):
|
|||||||
p_set=-co2
|
p_set=-co2
|
||||||
)
|
)
|
||||||
|
|
||||||
if "EU oil" not in n.buses.index:
|
if "oil" not in n.buses.carrier.unique():
|
||||||
|
n.madd("Bus",
|
||||||
n.add("Bus",
|
spatial.oil.nodes,
|
||||||
"EU oil",
|
location=spatial.oil.locations,
|
||||||
location="EU",
|
|
||||||
carrier="oil"
|
carrier="oil"
|
||||||
)
|
)
|
||||||
|
|
||||||
if "EU oil Store" not in n.stores.index:
|
if "oil" not in n.stores.carrier.unique():
|
||||||
|
|
||||||
#could correct to e.g. 0.001 EUR/kWh * annuity and O&M
|
#could correct to e.g. 0.001 EUR/kWh * annuity and O&M
|
||||||
n.add("Store",
|
n.madd("Store",
|
||||||
"EU oil Store",
|
[oil_bus + " Store" for oil_bus in spatial.oil.nodes],
|
||||||
bus="EU oil",
|
bus=spatial.oil.nodes,
|
||||||
e_nom_extendable=True,
|
e_nom_extendable=True,
|
||||||
e_cyclic=True,
|
e_cyclic=True,
|
||||||
carrier="oil",
|
carrier="oil",
|
||||||
)
|
)
|
||||||
|
|
||||||
if "EU oil" not in n.generators.index:
|
if "oil" not in n.generators.carrier.unique():
|
||||||
|
|
||||||
n.add("Generator",
|
n.madd("Generator",
|
||||||
"EU oil",
|
spatial.oil.nodes,
|
||||||
bus="EU oil",
|
bus=spatial.oil.nodes,
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
carrier="oil",
|
carrier="oil",
|
||||||
marginal_cost=costs.at["oil", 'fuel']
|
marginal_cost=costs.at["oil", 'fuel']
|
||||||
@ -2146,7 +2031,7 @@ def add_industry(n, costs):
|
|||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes_heat[name] + f" {name} oil boiler",
|
nodes_heat[name] + f" {name} oil boiler",
|
||||||
p_nom_extendable=True,
|
p_nom_extendable=True,
|
||||||
bus0="EU oil",
|
bus0=spatial.oil.nodes,
|
||||||
bus1=nodes_heat[name] + f" {name} heat",
|
bus1=nodes_heat[name] + f" {name} heat",
|
||||||
bus2="co2 atmosphere",
|
bus2="co2 atmosphere",
|
||||||
carrier=f"{name} oil boiler",
|
carrier=f"{name} oil boiler",
|
||||||
@ -2159,7 +2044,7 @@ def add_industry(n, costs):
|
|||||||
n.madd("Link",
|
n.madd("Link",
|
||||||
nodes + " Fischer-Tropsch",
|
nodes + " Fischer-Tropsch",
|
||||||
bus0=nodes + " H2",
|
bus0=nodes + " H2",
|
||||||
bus1="EU oil",
|
bus1=spatial.oil.nodes,
|
||||||
bus2=spatial.co2.nodes,
|
bus2=spatial.co2.nodes,
|
||||||
carrier="Fischer-Tropsch",
|
carrier="Fischer-Tropsch",
|
||||||
efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
|
efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
|
||||||
@ -2169,19 +2054,19 @@ def add_industry(n, costs):
|
|||||||
lifetime=costs.at['Fischer-Tropsch', 'lifetime']
|
lifetime=costs.at['Fischer-Tropsch', 'lifetime']
|
||||||
)
|
)
|
||||||
|
|
||||||
n.add("Load",
|
n.madd("Load",
|
||||||
"naphtha for industry",
|
["naphtha for industry"],
|
||||||
bus="EU oil",
|
bus=spatial.oil.nodes,
|
||||||
carrier="naphtha for industry",
|
carrier="naphtha for industry",
|
||||||
p_set=industrial_demand.loc[nodes, "naphtha"].sum() / 8760
|
p_set=industrial_demand.loc[nodes, "naphtha"].sum() / 8760
|
||||||
)
|
)
|
||||||
|
|
||||||
all_aviation = ["total international aviation", "total domestic aviation"]
|
all_aviation = ["total international aviation", "total domestic aviation"]
|
||||||
p_set = nodal_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
|
p_set = pop_weighted_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
|
||||||
|
|
||||||
n.add("Load",
|
n.madd("Load",
|
||||||
"kerosene for aviation",
|
["kerosene for aviation"],
|
||||||
bus="EU oil",
|
bus=spatial.oil.nodes,
|
||||||
carrier="kerosene for aviation",
|
carrier="kerosene for aviation",
|
||||||
p_set=p_set
|
p_set=p_set
|
||||||
)
|
)
|
||||||
@ -2297,7 +2182,7 @@ def add_agriculture(n, costs):
|
|||||||
suffix=" agriculture electricity",
|
suffix=" agriculture electricity",
|
||||||
bus=nodes,
|
bus=nodes,
|
||||||
carrier='agriculture electricity',
|
carrier='agriculture electricity',
|
||||||
p_set=nodal_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
|
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
|
||||||
)
|
)
|
||||||
|
|
||||||
# heat
|
# heat
|
||||||
@ -2307,7 +2192,7 @@ def add_agriculture(n, costs):
|
|||||||
suffix=" agriculture heat",
|
suffix=" agriculture heat",
|
||||||
bus=nodes + " services rural heat",
|
bus=nodes + " services rural heat",
|
||||||
carrier="agriculture heat",
|
carrier="agriculture heat",
|
||||||
p_set=nodal_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
|
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
|
||||||
)
|
)
|
||||||
|
|
||||||
# machinery
|
# machinery
|
||||||
@ -2316,7 +2201,7 @@ def add_agriculture(n, costs):
|
|||||||
assert electric_share <= 1.
|
assert electric_share <= 1.
|
||||||
ice_share = 1 - electric_share
|
ice_share = 1 - electric_share
|
||||||
|
|
||||||
machinery_nodal_energy = nodal_energy_totals.loc[nodes, "total agriculture machinery"]
|
machinery_nodal_energy = pop_weighted_energy_totals.loc[nodes, "total agriculture machinery"]
|
||||||
|
|
||||||
if electric_share > 0:
|
if electric_share > 0:
|
||||||
|
|
||||||
@ -2332,9 +2217,9 @@ def add_agriculture(n, costs):
|
|||||||
|
|
||||||
if ice_share > 0:
|
if ice_share > 0:
|
||||||
|
|
||||||
n.add("Load",
|
n.madd("Load",
|
||||||
"agriculture machinery oil",
|
["agriculture machinery oil"],
|
||||||
bus="EU oil",
|
bus=spatial.oil.nodes,
|
||||||
carrier="agriculture machinery oil",
|
carrier="agriculture machinery oil",
|
||||||
p_set=ice_share * machinery_nodal_energy.sum() * 1e6 / 8760
|
p_set=ice_share * machinery_nodal_energy.sum() * 1e6 / 8760
|
||||||
)
|
)
|
||||||
@ -2437,9 +2322,11 @@ if __name__ == "__main__":
|
|||||||
Nyears,
|
Nyears,
|
||||||
snakemake.config['costs']['lifetime'])
|
snakemake.config['costs']['lifetime'])
|
||||||
|
|
||||||
|
pop_weighted_energy_totals = pd.read_csv(snakemake.input.pop_weighted_energy_totals, index_col=0)
|
||||||
|
|
||||||
patch_electricity_network(n)
|
patch_electricity_network(n)
|
||||||
|
|
||||||
define_spatial(pop_layout.index)
|
spatial = define_spatial(pop_layout.index, options)
|
||||||
|
|
||||||
if snakemake.config["foresight"] == 'myopic':
|
if snakemake.config["foresight"] == 'myopic':
|
||||||
|
|
||||||
@ -2467,8 +2354,6 @@ if __name__ == "__main__":
|
|||||||
if o == "biomasstransport":
|
if o == "biomasstransport":
|
||||||
options["biomass_transport"] = True
|
options["biomass_transport"] = True
|
||||||
|
|
||||||
nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share = prepare_data(n)
|
|
||||||
|
|
||||||
if "nodistrict" in opts:
|
if "nodistrict" in opts:
|
||||||
options["district_heating"]["progress"] = 0.0
|
options["district_heating"]["progress"] = 0.0
|
||||||
|
|
||||||
@ -2516,7 +2401,7 @@ if __name__ == "__main__":
|
|||||||
fn = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/carbon_budget_distribution.csv'
|
fn = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/carbon_budget_distribution.csv'
|
||||||
if not os.path.exists(fn):
|
if not os.path.exists(fn):
|
||||||
build_carbon_budget(o, fn)
|
build_carbon_budget(o, fn)
|
||||||
co2_cap = pd.read_csv(fn, index_col=0, squeeze=True)
|
co2_cap = pd.read_csv(fn, index_col=0).squeeze()
|
||||||
limit = co2_cap[investment_year]
|
limit = co2_cap[investment_year]
|
||||||
break
|
break
|
||||||
for o in opts:
|
for o in opts:
|
||||||
|
|||||||
@ -185,28 +185,31 @@ def add_chp_constraints(n):
|
|||||||
|
|
||||||
define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
|
define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
|
||||||
|
|
||||||
|
def basename(x):
|
||||||
|
return x.split("-2")[0]
|
||||||
|
|
||||||
def add_pipe_retrofit_constraint(n):
|
def add_pipe_retrofit_constraint(n):
|
||||||
"""Add constraint for retrofitting existing CH4 pipelines to H2 pipelines."""
|
"""Add constraint for retrofitting existing CH4 pipelines to H2 pipelines."""
|
||||||
|
|
||||||
gas_pipes_i = n.links[n.links.carrier=="gas pipeline"].index
|
gas_pipes_i = n.links.query("carrier == 'gas pipeline' and p_nom_extendable").index
|
||||||
h2_retrofitted_i = n.links[n.links.carrier=='H2 pipeline retrofitted'].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
|
if h2_retrofitted_i.empty or gas_pipes_i.empty: return
|
||||||
|
|
||||||
link_p_nom = get_var(n, "Link", "p_nom")
|
link_p_nom = get_var(n, "Link", "p_nom")
|
||||||
|
|
||||||
pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom']
|
|
||||||
|
|
||||||
CH4_per_H2 = 1 / n.config["sector"]["H2_retrofit_capacity_per_CH4"]
|
CH4_per_H2 = 1 / n.config["sector"]["H2_retrofit_capacity_per_CH4"]
|
||||||
|
|
||||||
fr = "H2 pipeline retrofitted"
|
fr = "H2 pipeline retrofitted"
|
||||||
to = "gas pipeline"
|
to = "gas pipeline"
|
||||||
|
|
||||||
|
pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom'].rename(basename)
|
||||||
|
|
||||||
lhs = linexpr(
|
lhs = linexpr(
|
||||||
(CH4_per_H2, link_p_nom.loc[h2_retrofitted_i].rename(index=lambda x: x.replace(fr, to))),
|
(CH4_per_H2, link_p_nom.loc[h2_retrofitted_i].rename(index=lambda x: x.replace(fr, to))),
|
||||||
(1, link_p_nom.loc[gas_pipes_i])
|
(1, link_p_nom.loc[gas_pipes_i])
|
||||||
)
|
)
|
||||||
|
|
||||||
|
lhs.rename(basename, inplace=True)
|
||||||
define_constraints(n, lhs, "=", pipe_capacity, 'Link', 'pipe_retrofit')
|
define_constraints(n, lhs, "=", pipe_capacity, 'Link', 'pipe_retrofit')
|
||||||
|
|
||||||
|
|
||||||
@ -278,10 +281,11 @@ if __name__ == "__main__":
|
|||||||
'solve_network',
|
'solve_network',
|
||||||
weather_year='',
|
weather_year='',
|
||||||
simpl='',
|
simpl='',
|
||||||
clusters=48,
|
opts="",
|
||||||
|
clusters="37",
|
||||||
lv=1.0,
|
lv=1.0,
|
||||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
|
||||||
planning_horizons=2050,
|
planning_horizons="2030",
|
||||||
)
|
)
|
||||||
|
|
||||||
logging.basicConfig(filename=snakemake.log.python,
|
logging.basicConfig(filename=snakemake.log.python,
|
||||||
|
|||||||
607
test/config.myopic.yaml
Normal file
607
test/config.myopic.yaml
Normal file
@ -0,0 +1,607 @@
|
|||||||
|
version: 0.6.0
|
||||||
|
|
||||||
|
logging_level: INFO
|
||||||
|
|
||||||
|
retrieve_sector_databundle: true
|
||||||
|
|
||||||
|
results_dir: results/
|
||||||
|
summary_dir: results
|
||||||
|
costs_dir: ../technology-data/outputs/
|
||||||
|
run: test-myopic # use this to keep track of runs with different settings
|
||||||
|
foresight: myopic # 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: # only relevant for PyPSA-Eur
|
||||||
|
- ''
|
||||||
|
lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
|
||||||
|
- 1.5
|
||||||
|
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
|
||||||
|
- 5
|
||||||
|
opts: # only relevant for PyPSA-Eur
|
||||||
|
- ''
|
||||||
|
sector_opts: # this is where the main scenario settings are
|
||||||
|
- 191H-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
|
||||||
|
# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
|
||||||
|
# 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; or costs year for overnight
|
||||||
|
- 2030
|
||||||
|
- 2040
|
||||||
|
- 2050
|
||||||
|
# for example, set to [2020, 2030, 2040, 2050] for myopic foresight
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
|
||||||
|
snapshots:
|
||||||
|
# arguments to pd.date_range
|
||||||
|
start: "2013-03-01"
|
||||||
|
end: "2013-04-01"
|
||||||
|
closed: left # end is not inclusive
|
||||||
|
|
||||||
|
atlite:
|
||||||
|
cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
|
||||||
|
|
||||||
|
# this information is NOT used but needed as an argument for
|
||||||
|
# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
|
||||||
|
electricity:
|
||||||
|
max_hours:
|
||||||
|
battery: 6
|
||||||
|
H2: 168
|
||||||
|
|
||||||
|
# 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: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
|
||||||
|
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
|
||||||
|
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
|
||||||
|
transport_fuel_cell_efficiency: 0.5
|
||||||
|
transport_internal_combustion_efficiency: 0.3
|
||||||
|
agriculture_machinery_electric_share: 0
|
||||||
|
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
|
||||||
|
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
|
||||||
|
shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
|
||||||
|
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
|
||||||
|
shipping_hydrogen_share:
|
||||||
|
2020: 0
|
||||||
|
2025: 0
|
||||||
|
2030: 0.05
|
||||||
|
2035: 0.15
|
||||||
|
2040: 0.3
|
||||||
|
2045: 0.6
|
||||||
|
2050: 1
|
||||||
|
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: # 0.29 # 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
|
||||||
|
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
|
||||||
|
dac: true
|
||||||
|
co2_vent: true
|
||||||
|
SMR: true
|
||||||
|
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
|
||||||
|
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
|
||||||
|
co2_network: 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
|
||||||
|
use_fischer_tropsch_waste_heat: true
|
||||||
|
use_fuel_cell_waste_heat: true
|
||||||
|
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_transport: false # biomass transport between nodes
|
||||||
|
conventional_generation: # generator : carrier
|
||||||
|
OCGT: gas
|
||||||
|
|
||||||
|
|
||||||
|
industry:
|
||||||
|
St_primary_fraction: # 0.3 # 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: # 1 # 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: # 0.2 # 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_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)
|
||||||
|
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:
|
||||||
|
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
|
||||||
|
offwind: 0.015
|
||||||
|
hydro: 0.
|
||||||
|
H2: 0.
|
||||||
|
battery: 0.
|
||||||
|
|
||||||
|
emission_prices: # only used with the option Ep (emission prices)
|
||||||
|
co2: 0.
|
||||||
|
|
||||||
|
lines:
|
||||||
|
length_factor: 1.25 #to estimate offwind connection costs
|
||||||
|
|
||||||
|
|
||||||
|
solving:
|
||||||
|
#tmpdir: "path/to/tmp"
|
||||||
|
options:
|
||||||
|
formulation: kirchhoff
|
||||||
|
clip_p_max_pu: 1.e-2
|
||||||
|
load_shedding: false
|
||||||
|
noisy_costs: true
|
||||||
|
skip_iterations: true
|
||||||
|
track_iterations: false
|
||||||
|
min_iterations: 4
|
||||||
|
max_iterations: 6
|
||||||
|
keep_shadowprices:
|
||||||
|
- Bus
|
||||||
|
- Line
|
||||||
|
- Link
|
||||||
|
- Transformer
|
||||||
|
- GlobalConstraint
|
||||||
|
- Generator
|
||||||
|
- Store
|
||||||
|
- StorageUnit
|
||||||
|
|
||||||
|
solver:
|
||||||
|
name: cbc
|
||||||
|
# threads: 4
|
||||||
|
# method: 2 # barrier
|
||||||
|
# crossover: 0
|
||||||
|
# BarConvTol: 1.e-6
|
||||||
|
# Seed: 123
|
||||||
|
# AggFill: 0
|
||||||
|
# PreDual: 0
|
||||||
|
# GURO_PAR_BARDENSETHRESH: 200
|
||||||
|
#FeasibilityTol: 1.e-6
|
||||||
|
|
||||||
|
#name: cplex
|
||||||
|
#threads: 4
|
||||||
|
#lpmethod: 4 # barrier
|
||||||
|
#solutiontype: 2 # non basic solution, ie no crossover
|
||||||
|
#barrier_convergetol: 1.e-5
|
||||||
|
#feasopt_tolerance: 1.e-6
|
||||||
|
mem: 4000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
|
||||||
|
|
||||||
|
|
||||||
|
plotting:
|
||||||
|
map:
|
||||||
|
boundaries: [-11, 30, 34, 71]
|
||||||
|
color_geomap:
|
||||||
|
ocean: white
|
||||||
|
land: whitesmoke
|
||||||
|
costs_max: 1000
|
||||||
|
costs_threshold: 1
|
||||||
|
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
|
||||||
|
tech_colors:
|
||||||
|
# wind
|
||||||
|
onwind: "#235ebc"
|
||||||
|
onshore wind: "#235ebc"
|
||||||
|
offwind: "#6895dd"
|
||||||
|
offshore wind: "#6895dd"
|
||||||
|
offwind-ac: "#6895dd"
|
||||||
|
offshore wind (AC): "#6895dd"
|
||||||
|
offwind-dc: "#74c6f2"
|
||||||
|
offshore wind (DC): "#74c6f2"
|
||||||
|
# water
|
||||||
|
hydro: '#298c81'
|
||||||
|
hydro reservoir: '#298c81'
|
||||||
|
ror: '#3dbfb0'
|
||||||
|
run of river: '#3dbfb0'
|
||||||
|
hydroelectricity: '#298c81'
|
||||||
|
PHS: '#51dbcc'
|
||||||
|
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'
|
||||||
|
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'
|
||||||
|
# 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'
|
||||||
|
# syngas
|
||||||
|
Sabatier: '#9850ad'
|
||||||
|
methanation: '#c44ce6'
|
||||||
|
methane: '#c44ce6'
|
||||||
|
helmeth: '#e899ff'
|
||||||
|
# synfuels
|
||||||
|
Fischer-Tropsch: '#25c49a'
|
||||||
|
liquid: '#25c49a'
|
||||||
|
kerosene for aviation: '#a1ffe6'
|
||||||
|
naphtha for industry: '#57ebc4'
|
||||||
|
# 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"
|
||||||
|
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'
|
||||||
605
test/config.overnight.yaml
Normal file
605
test/config.overnight.yaml
Normal file
@ -0,0 +1,605 @@
|
|||||||
|
version: 0.6.0
|
||||||
|
|
||||||
|
logging_level: INFO
|
||||||
|
|
||||||
|
retrieve_sector_databundle: true
|
||||||
|
|
||||||
|
results_dir: results/
|
||||||
|
summary_dir: results
|
||||||
|
costs_dir: ../technology-data/outputs/
|
||||||
|
run: test-overnight # use this to keep track of runs with different settings
|
||||||
|
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: # only relevant for PyPSA-Eur
|
||||||
|
- ''
|
||||||
|
lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
|
||||||
|
- 1.5
|
||||||
|
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
|
||||||
|
- 5
|
||||||
|
opts: # only relevant for PyPSA-Eur
|
||||||
|
- ''
|
||||||
|
sector_opts: # this is where the main scenario settings are
|
||||||
|
- CO2L0-191H-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
|
||||||
|
# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
|
||||||
|
# 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; or costs year for overnight
|
||||||
|
- 2030
|
||||||
|
# for example, set to [2020, 2030, 2040, 2050] for myopic foresight
|
||||||
|
|
||||||
|
# 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
|
||||||
|
|
||||||
|
# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
|
||||||
|
snapshots:
|
||||||
|
# arguments to pd.date_range
|
||||||
|
start: "2013-03-01"
|
||||||
|
end: "2013-04-01"
|
||||||
|
closed: left # end is not inclusive
|
||||||
|
|
||||||
|
atlite:
|
||||||
|
cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
|
||||||
|
|
||||||
|
# this information is NOT used but needed as an argument for
|
||||||
|
# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
|
||||||
|
electricity:
|
||||||
|
max_hours:
|
||||||
|
battery: 6
|
||||||
|
H2: 168
|
||||||
|
|
||||||
|
# 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: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
|
||||||
|
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: 1
|
||||||
|
# 2020: 0.0
|
||||||
|
# 2030: 0.3
|
||||||
|
# 2040: 0.6
|
||||||
|
# 2050: 1.0
|
||||||
|
district_heating_loss: 0.15
|
||||||
|
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: 0.15 # 1 means all FCEVs
|
||||||
|
# 2020: 0
|
||||||
|
# 2030: 0.05
|
||||||
|
# 2040: 0.1
|
||||||
|
# 2050: 0.15
|
||||||
|
land_transport_electric_share: 0.85 # 1 means all EVs
|
||||||
|
# 2020: 0
|
||||||
|
# 2030: 0.25
|
||||||
|
# 2040: 0.6
|
||||||
|
# 2050: 0.85
|
||||||
|
transport_fuel_cell_efficiency: 0.5
|
||||||
|
transport_internal_combustion_efficiency: 0.3
|
||||||
|
agriculture_machinery_electric_share: 0
|
||||||
|
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
|
||||||
|
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
|
||||||
|
shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
|
||||||
|
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
|
||||||
|
shipping_hydrogen_share: 1 # 1 means all hydrogen FC
|
||||||
|
# 2020: 0
|
||||||
|
# 2025: 0
|
||||||
|
# 2030: 0.05
|
||||||
|
# 2035: 0.15
|
||||||
|
# 2040: 0.3
|
||||||
|
# 2045: 0.6
|
||||||
|
# 2050: 1
|
||||||
|
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: 0.29 # 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
|
||||||
|
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
|
||||||
|
dac: true
|
||||||
|
co2_vent: true
|
||||||
|
SMR: true
|
||||||
|
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
|
||||||
|
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
|
||||||
|
co2_network: 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
|
||||||
|
use_fischer_tropsch_waste_heat: true
|
||||||
|
use_fuel_cell_waste_heat: true
|
||||||
|
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: true
|
||||||
|
H2_retrofit: true # 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_transport: false # biomass transport between nodes
|
||||||
|
conventional_generation: # generator : carrier
|
||||||
|
OCGT: gas
|
||||||
|
|
||||||
|
|
||||||
|
industry:
|
||||||
|
St_primary_fraction: 0.3 # 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: 1 # 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: 0.2 # 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_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)
|
||||||
|
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:
|
||||||
|
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
|
||||||
|
offwind: 0.015
|
||||||
|
hydro: 0.
|
||||||
|
H2: 0.
|
||||||
|
battery: 0.
|
||||||
|
|
||||||
|
emission_prices: # only used with the option Ep (emission prices)
|
||||||
|
co2: 0.
|
||||||
|
|
||||||
|
lines:
|
||||||
|
length_factor: 1.25 #to estimate offwind connection costs
|
||||||
|
|
||||||
|
|
||||||
|
solving:
|
||||||
|
#tmpdir: "path/to/tmp"
|
||||||
|
options:
|
||||||
|
formulation: kirchhoff
|
||||||
|
clip_p_max_pu: 1.e-2
|
||||||
|
load_shedding: false
|
||||||
|
noisy_costs: true
|
||||||
|
skip_iterations: true
|
||||||
|
track_iterations: false
|
||||||
|
min_iterations: 4
|
||||||
|
max_iterations: 6
|
||||||
|
keep_shadowprices:
|
||||||
|
- Bus
|
||||||
|
- Line
|
||||||
|
- Link
|
||||||
|
- Transformer
|
||||||
|
- GlobalConstraint
|
||||||
|
- Generator
|
||||||
|
- Store
|
||||||
|
- StorageUnit
|
||||||
|
|
||||||
|
solver:
|
||||||
|
name: cbc
|
||||||
|
# threads: 4
|
||||||
|
# method: 2 # barrier
|
||||||
|
# crossover: 0
|
||||||
|
# BarConvTol: 1.e-6
|
||||||
|
# Seed: 123
|
||||||
|
# AggFill: 0
|
||||||
|
# PreDual: 0
|
||||||
|
# GURO_PAR_BARDENSETHRESH: 200
|
||||||
|
#FeasibilityTol: 1.e-6
|
||||||
|
|
||||||
|
#name: cplex
|
||||||
|
#threads: 4
|
||||||
|
#lpmethod: 4 # barrier
|
||||||
|
#solutiontype: 2 # non basic solution, ie no crossover
|
||||||
|
#barrier_convergetol: 1.e-5
|
||||||
|
#feasopt_tolerance: 1.e-6
|
||||||
|
mem: 4000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
|
||||||
|
|
||||||
|
|
||||||
|
plotting:
|
||||||
|
map:
|
||||||
|
boundaries: [-11, 30, 34, 71]
|
||||||
|
color_geomap:
|
||||||
|
ocean: white
|
||||||
|
land: whitesmoke
|
||||||
|
costs_max: 1000
|
||||||
|
costs_threshold: 1
|
||||||
|
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
|
||||||
|
tech_colors:
|
||||||
|
# wind
|
||||||
|
onwind: "#235ebc"
|
||||||
|
onshore wind: "#235ebc"
|
||||||
|
offwind: "#6895dd"
|
||||||
|
offshore wind: "#6895dd"
|
||||||
|
offwind-ac: "#6895dd"
|
||||||
|
offshore wind (AC): "#6895dd"
|
||||||
|
offwind-dc: "#74c6f2"
|
||||||
|
offshore wind (DC): "#74c6f2"
|
||||||
|
# water
|
||||||
|
hydro: '#298c81'
|
||||||
|
hydro reservoir: '#298c81'
|
||||||
|
ror: '#3dbfb0'
|
||||||
|
run of river: '#3dbfb0'
|
||||||
|
hydroelectricity: '#298c81'
|
||||||
|
PHS: '#51dbcc'
|
||||||
|
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'
|
||||||
|
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'
|
||||||
|
# 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'
|
||||||
|
# syngas
|
||||||
|
Sabatier: '#9850ad'
|
||||||
|
methanation: '#c44ce6'
|
||||||
|
methane: '#c44ce6'
|
||||||
|
helmeth: '#e899ff'
|
||||||
|
# synfuels
|
||||||
|
Fischer-Tropsch: '#25c49a'
|
||||||
|
liquid: '#25c49a'
|
||||||
|
kerosene for aviation: '#a1ffe6'
|
||||||
|
naphtha for industry: '#57ebc4'
|
||||||
|
# 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"
|
||||||
|
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'
|
||||||
Loading…
Reference in New Issue
Block a user