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lisazeyen 2023-08-22 10:21:42 +02:00
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53
.github/workflows/ci.yaml vendored
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@ -19,7 +19,6 @@ on:
- cron: "0 5 * * TUE" - cron: "0 5 * * TUE"
env: env:
CONDA_CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
DATA_CACHE_NUMBER: 2 DATA_CACHE_NUMBER: 2
jobs: jobs:
@ -27,22 +26,12 @@ jobs:
strategy: strategy:
fail-fast: false fail-fast: false
max-parallel: 3
matrix: matrix:
include: os:
# Matrix required to handle caching with Mambaforge - ubuntu-latest
- os: ubuntu-latest - macos-latest
label: ubuntu-latest - windows-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 }} runs-on: ${{ matrix.os }}
@ -60,24 +49,25 @@ jobs:
- name: Add solver to environment - name: Add solver to environment
run: | run: |
echo -e "- glpk\n- ipopt<3.13.3" >> envs/environment.yaml echo -e "- glpk\n- ipopt<3.13.3" >> envs/environment.yaml
if: ${{ matrix.label }} == 'windows-latest' if: ${{ matrix.os }} == 'windows-latest'
- name: Add solver to environment - name: Add solver to environment
run: | run: |
echo -e "- glpk\n- ipopt" >> envs/environment.yaml echo -e "- glpk\n- ipopt" >> envs/environment.yaml
if: ${{ matrix.label }} != 'windows-latest' if: ${{ matrix.os }} != 'windows-latest'
- name: Setup Mambaforge - name: Setup micromamba
uses: conda-incubator/setup-miniconda@v2 uses: mamba-org/setup-micromamba@v1
with: with:
miniforge-variant: Mambaforge micromamba-version: latest
miniforge-version: latest environment-file: envs/environment.yaml
activate-environment: pypsa-eur log-level: debug
use-mamba: true init-shell: bash
cache-environment: true
cache-downloads: true
- name: Set cache dates - name: Set cache dates
run: | run: |
echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV
- name: Cache data and cutouts folders - name: Cache data and cutouts folders
@ -88,21 +78,8 @@ jobs:
cutouts cutouts
key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }} key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }}
- name: Create environment cache
uses: actions/cache@v3
id: cache
with:
path: ${{ matrix.prefix }}
key: ${{ matrix.label }}-conda-${{ env.DATE }}-${{ env.CONDA_CACHE_NUMBER }}
- name: Update environment due to outdated or unavailable cache
run: mamba env update -n pypsa-eur -f envs/environment.yaml
if: steps.cache.outputs.cache-hit != 'true'
- name: Test snakemake workflow - name: Test snakemake workflow
run: | run: |
conda activate pypsa-eur
conda list
snakemake -call solve_elec_networks --configfile config/test/config.electricity.yaml --rerun-triggers=mtime snakemake -call solve_elec_networks --configfile config/test/config.electricity.yaml --rerun-triggers=mtime
snakemake -call all --configfile config/test/config.overnight.yaml --rerun-triggers=mtime snakemake -call all --configfile config/test/config.overnight.yaml --rerun-triggers=mtime
snakemake -call all --configfile config/test/config.myopic.yaml --rerun-triggers=mtime snakemake -call all --configfile config/test/config.myopic.yaml --rerun-triggers=mtime

2
.pre-commit-config.yaml
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@ -87,6 +87,6 @@ repos:
# Check for FSFE REUSE compliance (licensing) # Check for FSFE REUSE compliance (licensing)
- repo: https://github.com/fsfe/reuse-tool - repo: https://github.com/fsfe/reuse-tool
rev: v2.0.0 rev: v2.1.0
hooks: hooks:
- id: reuse - id: reuse

11
.sync-send Normal file
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@ -0,0 +1,11 @@
# SPDX-FileCopyrightText: : 2021-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
rules
scripts
config
config/test
envs
matplotlibrc
Snakefile

21
.syncignore-receive
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@ -1,21 +0,0 @@
# SPDX-FileCopyrightText: : 2021-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
.snakemake
.git
.pytest_cache
.ipynb_checkpoints
.vscode
.DS_Store
__pycache__
*.pyc
*.pyo
*.ipynb
notebooks
doc
cutouts
data
benchmarks
*.nc
configs

23
.syncignore-send
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@ -1,23 +0,0 @@
# SPDX-FileCopyrightText: : 2021-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
.snakemake
.git
.pytest_cache
.ipynb_checkpoints
.vscode
.DS_Store
__pycache__
*.pyc
*.pyo
*.ipynb
notebooks
benchmarks
logs
resources*
results
networks*
cutouts
data/bundle
doc

2
CITATION.cff
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@ -6,7 +6,7 @@ cff-version: 1.1.0
message: "If you use this package, please cite it in the following way." message: "If you use this package, please cite it in the following way."
title: "PyPSA-Eur: An open sector-coupled optimisation model of the European energy system" title: "PyPSA-Eur: An open sector-coupled optimisation model of the European energy system"
repository: https://github.com/pypsa/pypsa-eur repository: https://github.com/pypsa/pypsa-eur
version: 0.8.0 version: 0.8.1
license: MIT license: MIT
authors: authors:
- family-names: Brown - family-names: Brown

19
README.md
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@ -9,7 +9,7 @@ SPDX-License-Identifier: CC-BY-4.0
![Size](https://img.shields.io/github/repo-size/pypsa/pypsa-eur) ![Size](https://img.shields.io/github/repo-size/pypsa/pypsa-eur)
[![Zenodo PyPSA-Eur](https://zenodo.org/badge/DOI/10.5281/zenodo.3520874.svg)](https://doi.org/10.5281/zenodo.3520874) [![Zenodo PyPSA-Eur](https://zenodo.org/badge/DOI/10.5281/zenodo.3520874.svg)](https://doi.org/10.5281/zenodo.3520874)
[![Zenodo PyPSA-Eur-Sec](https://zenodo.org/badge/DOI/10.5281/zenodo.3938042.svg)](https://doi.org/10.5281/zenodo.3938042) [![Zenodo PyPSA-Eur-Sec](https://zenodo.org/badge/DOI/10.5281/zenodo.3938042.svg)](https://doi.org/10.5281/zenodo.3938042)
[![Snakemake](https://img.shields.io/badge/snakemake-≥5.0.0-brightgreen.svg?style=flat)](https://snakemake.readthedocs.io) [![Snakemake](https://img.shields.io/badge/snakemake-≥7.7.0-brightgreen.svg?style=flat)](https://snakemake.readthedocs.io)
[![REUSE status](https://api.reuse.software/badge/github.com/pypsa/pypsa-eur)](https://api.reuse.software/info/github.com/pypsa/pypsa-eur) [![REUSE status](https://api.reuse.software/badge/github.com/pypsa/pypsa-eur)](https://api.reuse.software/info/github.com/pypsa/pypsa-eur)
[![Stack Exchange questions](https://img.shields.io/stackexchange/stackoverflow/t/pypsa)](https://stackoverflow.com/questions/tagged/pypsa) [![Stack Exchange questions](https://img.shields.io/stackexchange/stackoverflow/t/pypsa)](https://stackoverflow.com/questions/tagged/pypsa)
@ -35,17 +35,18 @@ The model is designed to be imported into the open toolbox
[PyPSA](https://github.com/PyPSA/PyPSA). [PyPSA](https://github.com/PyPSA/PyPSA).
**WARNING**: PyPSA-Eur is under active development and has several **WARNING**: PyPSA-Eur is under active development and has several
[limitations](https://pypsa-eur.readthedocs.io/en/latest/limitations.html) [limitations](https://pypsa-eur.readthedocs.io/en/latest/limitations.html) which
which you should understand before using the model. The github repository you should understand before using the model. The github repository
[issues](https://github.com/PyPSA/pypsa-eur/issues) collect known topics we are [issues](https://github.com/PyPSA/pypsa-eur/issues) collect known topics we are
working on (please feel free to help or make suggestions). The working on (please feel free to help or make suggestions). The
[documentation](https://pypsa-eur.readthedocs.io/) remains somewhat patchy. You [documentation](https://pypsa-eur.readthedocs.io/) remains somewhat patchy. You
can find showcases of the model's capabilities in the preprint [Benefits of a can find showcases of the model's capabilities in the Joule paper [The potential
Hydrogen Network in Europe](https://arxiv.org/abs/2207.05816), a [paper in Joule role of a hydrogen network in
with a description of the industry sector](https://arxiv.org/abs/2109.09563), or Europe](https://doi.org/10.1016/j.joule.2023.06.016), another [paper in Joule
in [a 2021 presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf). with a description of the industry
We cannot support this model if you choose to use it. We do not recommend to use sector](https://doi.org/10.1016/j.joule.2022.04.016), or in [a 2021 presentation
the full resolution network model for simulations. At high granularity the at EMP-E](https://nworbmot.org/energy/brown-empe.pdf). We do not recommend to
use the full resolution network model for simulations. At high granularity the
assignment of loads and generators to the nearest network node may not be a assignment of loads and generators to the nearest network node may not be a
correct assumption, depending on the topology of the underlying distribution correct assumption, depending on the topology of the underlying distribution
grid, and local grid bottlenecks may cause unrealistic load-shedding or grid, and local grid bottlenecks may cause unrealistic load-shedding or

14
Snakefile
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@ -40,7 +40,7 @@ localrules:
wildcard_constraints: wildcard_constraints:
simpl="[a-zA-Z0-9]*", simpl="[a-zA-Z0-9]*",
clusters="[0-9]+m?|all", clusters="[0-9]+(m|c)?|all",
ll="(v|c)([0-9\.]+|opt)", ll="(v|c)([0-9\.]+|opt)",
opts="[-+a-zA-Z0-9\.]*", opts="[-+a-zA-Z0-9\.]*",
sector_opts="[-+a-zA-Z0-9\.\s]*", sector_opts="[-+a-zA-Z0-9\.\s]*",
@ -53,6 +53,7 @@ include: "rules/build_electricity.smk"
include: "rules/build_sector.smk" include: "rules/build_sector.smk"
include: "rules/solve_electricity.smk" include: "rules/solve_electricity.smk"
include: "rules/postprocess.smk" include: "rules/postprocess.smk"
include: "rules/validate.smk"
if config["foresight"] == "overnight": if config["foresight"] == "overnight":
@ -103,3 +104,14 @@ rule doc:
directory("doc/_build"), directory("doc/_build"),
shell: shell:
"make -C doc html" "make -C doc html"
rule sync:
params:
cluster=f"{config['remote']['ssh']}:{config['remote']['path']}",
shell:
"""
rsync -uvarh --ignore-missing-args --files-from=.sync-send . {params.cluster}
rsync -uvarh --no-g {params.cluster}/results . || echo "No results directory, skipping rsync"
rsync -uvarh --no-g {params.cluster}/logs . || echo "No logs directory, skipping rsync"
"""

55
config/config.default.yaml
View File

@ -3,13 +3,21 @@
# SPDX-License-Identifier: CC0-1.0 # SPDX-License-Identifier: CC0-1.0
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#top-level-configuration # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#top-level-configuration
version: 0.8.0 version: 0.8.1
tutorial: false tutorial: false
logging: logging:
level: INFO level: INFO
format: '%(levelname)s:%(name)s:%(message)s' format: '%(levelname)s:%(name)s:%(message)s'
private:
keys:
entsoe_api:
remote:
ssh: ""
path: ""
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#run # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#run
run: run:
name: "" name: ""
@ -18,7 +26,7 @@ run:
shared_cutouts: true shared_cutouts: true
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#foresight # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#foresight
foresight: overnight foresight: perfect
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#scenario # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#scenario
# Wildcard docs in https://pypsa-eur.readthedocs.io/en/latest/wildcards.html # Wildcard docs in https://pypsa-eur.readthedocs.io/en/latest/wildcards.html
@ -26,21 +34,22 @@ scenario:
simpl: simpl:
- '' - ''
ll: ll:
- v1.0
- v1.5 - v1.5
clusters: clusters:
- 37 - 37
- 128 - 128
- 256 # - 256
- 512 # - 512
- 1024 #- 1024
opts: opts:
- '' - ''
sector_opts: sector_opts:
- Co2L0-3H-T-H-B-I-A-solar+p3-dist1 - Co2L0-3H-T-H-B-I-A-solar+p3-dist1
planning_horizons: planning_horizons:
# - 2020 - 2020
# - 2030 - 2030
# - 2040 - 2040
- 2050 - 2050
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#countries # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#countries
@ -209,10 +218,14 @@ renewable:
carriers: [ror, PHS, hydro] carriers: [ror, PHS, hydro]
PHS_max_hours: 6 PHS_max_hours: 6
hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float
flatten_dispatch: false
flatten_dispatch_buffer: 0.2
clip_min_inflow: 1.0 clip_min_inflow: 1.0
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#conventional # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#conventional
conventional: conventional:
unit_commitment: false
dynamic_fuel_price: false
nuclear: nuclear:
p_max_pu: "data/nuclear_p_max_pu.csv" # float of file name p_max_pu: "data/nuclear_p_max_pu.csv" # float of file name
@ -227,6 +240,12 @@ lines:
max_extension: .inf max_extension: .inf
length_factor: 1.25 length_factor: 1.25
under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
dynamic_line_rating:
activate: false
cutout: europe-2013-era5
correction_factor: 0.95
max_voltage_difference: false
max_line_rating: false
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#links # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#links
links: links:
@ -528,7 +547,7 @@ industry:
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#costs # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#costs
costs: costs:
year: 2030 year: 2030
version: v0.5.0 version: v0.6.0
rooftop_share: 0.14 # based on the potentials, assuming (0.1 kW/m2 and 10 m2/person) rooftop_share: 0.14 # based on the potentials, assuming (0.1 kW/m2 and 10 m2/person)
fill_values: fill_values:
FOM: 0 FOM: 0
@ -568,16 +587,12 @@ clustering:
algorithm: kmeans algorithm: kmeans
feature: solar+onwind-time feature: solar+onwind-time
exclude_carriers: [] exclude_carriers: []
consider_efficiency_classes: false
aggregation_strategies: aggregation_strategies:
generators: generators:
p_nom_max: sum
p_nom_min: sum
p_min_pu: mean
marginal_cost: mean
committable: any committable: any
ramp_limit_up: max ramp_limit_up: max
ramp_limit_down: max ramp_limit_down: max
efficiency: mean
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#solving # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#solving
solving: solving:
@ -585,13 +600,17 @@ solving:
options: options:
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
load_shedding: false load_shedding: false
transmission_losses: 0
noisy_costs: true noisy_costs: true
skip_iterations: true skip_iterations: true
rolling_horizon: false
seed: 123
# options that go into the optimize function
track_iterations: false track_iterations: false
min_iterations: 4 min_iterations: 4
max_iterations: 6 max_iterations: 6
seed: 123 transmission_losses: 0
linearized_unit_commitment: true
horizon: 365
solver: solver:
name: gurobi name: gurobi
@ -619,7 +638,6 @@ solving:
AggFill: 0 AggFill: 0
PreDual: 0 PreDual: 0
GURO_PAR_BARDENSETHRESH: 200 GURO_PAR_BARDENSETHRESH: 200
seed: 10 # Consistent seed for all plattforms
gurobi-numeric-focus: gurobi-numeric-focus:
name: gurobi name: gurobi
NumericFocus: 3 # Favour numeric stability over speed NumericFocus: 3 # Favour numeric stability over speed
@ -652,6 +670,7 @@ solving:
glpk-default: {} # Used in CI glpk-default: {} # Used in CI
mem: 30000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2 mem: 30000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
walltime: "12:00:00"
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#plotting # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#plotting
plotting: plotting:
@ -682,6 +701,8 @@ plotting:
H2: "Hydrogen Storage" H2: "Hydrogen Storage"
lines: "Transmission Lines" lines: "Transmission Lines"
ror: "Run of River" ror: "Run of River"
ac: "AC"
dc: "DC"
tech_colors: tech_colors:
# wind # wind

98
config/config.validation.yaml Normal file
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@ -0,0 +1,98 @@
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
run:
name: "validation"
scenario:
ll:
- v1.0
clusters:
- 37
opts:
- 'Ept'
snapshots:
start: "2019-01-01"
end: "2020-01-01"
inclusive: 'left'
enable:
retrieve_cutout: false
electricity:
co2limit: 1e9
extendable_carriers:
Generator: []
StorageUnit: []
Store: []
Link: []
powerplants_filter: not (DateOut < 2019)
conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
renewable_carriers: [solar, onwind, offwind-ac, offwind-dc, hydro]
estimate_renewable_capacities:
year: 2019
atlite:
default_cutout: europe-2019-era5
cutouts:
europe-2019-era5:
module: era5
x: [-12., 35.]
y: [33., 72]
dx: 0.3
dy: 0.3
time: ['2019', '2019']
renewable:
onwind:
cutout: europe-2019-era5
offwind-ac:
cutout: europe-2019-era5
offwind-dc:
cutout: europe-2019-era5
solar:
cutout: europe-2019-era5
hydro:
cutout: europe-2019-era5
flatten_dispatch: 0.01
conventional:
unit_commitment: false
dynamic_fuel_price: true
nuclear:
p_max_pu: "data/nuclear_p_max_pu.csv"
biomass:
p_max_pu: 0.65
load:
power_statistics: false
lines:
s_max_pu: 0.23
under_construction: 'remove'
links:
include_tyndp: false
costs:
year: 2020
emission_prices:
co2: 25
clustering:
simplify_network:
exclude_carriers: [oil, coal, lignite, OCGT, CCGT]
cluster_network:
consider_efficiency_classes: true
solving:
options:
load_shedding: true
rolling_horizon: false
horizon: 1000
overlap: 48

6
config/test/config.electricity.yaml
View File

@ -60,6 +60,12 @@ renewable:
clustering: clustering:
exclude_carriers: ["OCGT", "offwind-ac", "coal"] exclude_carriers: ["OCGT", "offwind-ac", "coal"]
lines:
dynamic_line_rating:
activate: true
cutout: be-03-2013-era5
max_line_rating: 1.3
solving: solving:
solver: solver:

195
data/costs.csv
View File

@ -1,195 +0,0 @@
technology,year,parameter,value,unit,source
solar-rooftop,2030,discount rate,0.04,per unit,standard for decentral
onwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind,2030,lifetime,30,years,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
solar,2030,lifetime,25,years,IEA2010
solar-rooftop,2030,lifetime,25,years,IEA2010
solar-utility,2030,lifetime,25,years,IEA2010
PHS,2030,lifetime,80,years,IEA2010
hydro,2030,lifetime,80,years,IEA2010
ror,2030,lifetime,80,years,IEA2010
OCGT,2030,lifetime,30,years,IEA2010
nuclear,2030,lifetime,45,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
CCGT,2030,lifetime,30,years,IEA2010
coal,2030,lifetime,40,years,IEA2010
lignite,2030,lifetime,40,years,IEA2010
geothermal,2030,lifetime,40,years,IEA2010
biomass,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
oil,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
onwind,2030,investment,1040,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind-ac-station,2030,investment,250,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind-ac-connection-submarine,2030,investment,2685,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind-ac-connection-underground,2030,investment,1342,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind-dc-station,2030,investment,400,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node + 13% learning reduction
offwind-dc-connection-submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
offwind-dc-connection-underground,2030,investment,1000,EUR/MW/km,Haertel 2017; average + 13% learning reduction
solar,2030,investment,600,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
biomass,2030,investment,2209,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
geothermal,2030,investment,3392,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
coal,2030,investment,1300,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
lignite,2030,investment,1500,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
solar-rooftop,2030,investment,725,EUR/kWel,ETIP PV
solar-utility,2030,investment,425,EUR/kWel,ETIP PV
PHS,2030,investment,2000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
hydro,2030,investment,2000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
ror,2030,investment,3000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
OCGT,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
nuclear,2030,investment,6000,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
CCGT,2030,investment,800,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
oil,2030,investment,400,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
onwind,2030,FOM,2.450549,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind,2030,FOM,2.304878,%/year,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
solar,2030,FOM,4.166667,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
solar-rooftop,2030,FOM,2,%/year,ETIP PV
solar-utility,2030,FOM,3,%/year,ETIP PV
biomass,2030,FOM,4.526935,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
geothermal,2030,FOM,2.358491,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
coal,2030,FOM,1.923076,%/year,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
lignite,2030,FOM,2.0,%/year,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
oil,2030,FOM,1.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
PHS,2030,FOM,1,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
hydro,2030,FOM,1,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
ror,2030,FOM,2,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
CCGT,2030,FOM,2.5,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
OCGT,2030,FOM,3.75,%/year,DIW DataDoc http://hdl.handle.net/10419/80348
onwind,2030,VOM,2.3,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
offwind,2030,VOM,2.7,EUR/MWhel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
solar,2030,VOM,0.01,EUR/MWhel,RES costs made up to fix curtailment order
coal,2030,VOM,6,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
lignite,2030,VOM,7,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
CCGT,2030,VOM,4,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
OCGT,2030,VOM,3,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
nuclear,2030,VOM,8,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
gas,2030,fuel,21.6,EUR/MWhth,IEA2011b
uranium,2030,fuel,3,EUR/MWhth,DIW DataDoc http://hdl.handle.net/10419/80348
oil,2030,VOM,3,EUR/MWhel,DIW DataDoc http://hdl.handle.net/10419/80348
nuclear,2030,fuel,3,EUR/MWhth,IEA2011b
biomass,2030,fuel,7,EUR/MWhth,IEA2011b
coal,2030,fuel,8.4,EUR/MWhth,IEA2011b
lignite,2030,fuel,2.9,EUR/MWhth,IEA2011b
oil,2030,fuel,50,EUR/MWhth,IEA WEM2017 97USD/boe = http://www.iea.org/media/weowebsite/2017/WEM_Documentation_WEO2017.pdf
PHS,2030,efficiency,0.75,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
hydro,2030,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
ror,2030,efficiency,0.9,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
OCGT,2030,efficiency,0.39,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
CCGT,2030,efficiency,0.5,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
biomass,2030,efficiency,0.468,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
geothermal,2030,efficiency,0.239,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
nuclear,2030,efficiency,0.337,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
gas,2030,CO2 intensity,0.187,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
coal,2030,efficiency,0.464,per unit,DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
lignite,2030,efficiency,0.447,per unit,DIW DataDoc http://hdl.handle.net/10419/80348
oil,2030,efficiency,0.393,per unit,DIW DataDoc http://hdl.handle.net/10419/80348 CT
coal,2030,CO2 intensity,0.354,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
lignite,2030,CO2 intensity,0.334,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
oil,2030,CO2 intensity,0.248,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
geothermal,2030,CO2 intensity,0.026,tCO2/MWth,https://www.eia.gov/environment/emissions/co2_vol_mass.php
electrolysis,2030,investment,350,EUR/kWel,Palzer Thesis
electrolysis,2030,FOM,4,%/year,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
electrolysis,2030,lifetime,18,years,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
electrolysis,2030,efficiency,0.8,per unit,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
fuel cell,2030,investment,339,EUR/kWel,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
fuel cell,2030,FOM,3,%/year,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
fuel cell,2030,lifetime,20,years,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
fuel cell,2030,efficiency,0.58,per unit,NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013 conservative 2020
hydrogen storage,2030,investment,11.2,USD/kWh,budischak2013
hydrogen storage,2030,lifetime,20,years,budischak2013
hydrogen underground storage,2030,investment,0.5,EUR/kWh,maximum from https://www.nrel.gov/docs/fy10osti/46719.pdf
hydrogen underground storage,2030,lifetime,40,years,http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Materialien/ESYS_Technologiesteckbrief_Energiespeicher.pdf
H2 pipeline,2030,investment,267,EUR/MW/km,Welder et al https://doi.org/10.1016/j.ijhydene.2018.12.156
H2 pipeline,2030,lifetime,40,years,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
H2 pipeline,2030,FOM,5,%/year,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
H2 pipeline,2030,efficiency,0.98,per unit,Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
methanation,2030,investment,1000,EUR/kWH2,Schaber thesis
methanation,2030,lifetime,25,years,Schaber thesis
methanation,2030,FOM,3,%/year,Schaber thesis
methanation,2030,efficiency,0.6,per unit,Palzer; Breyer for DAC
helmeth,2030,investment,1000,EUR/kW,no source
helmeth,2030,lifetime,25,years,no source
helmeth,2030,FOM,3,%/year,no source
helmeth,2030,efficiency,0.8,per unit,HELMETH press release
DAC,2030,investment,250,EUR/(tCO2/a),Fasihi/Climeworks
DAC,2030,lifetime,30,years,Fasihi
DAC,2030,FOM,4,%/year,Fasihi
battery inverter,2030,investment,411,USD/kWel,budischak2013
battery inverter,2030,lifetime,20,years,budischak2013
battery inverter,2030,efficiency,0.9,per unit charge/discharge,budischak2013; Lund and Kempton (2008) http://dx.doi.org/10.1016/j.enpol.2008.06.007
battery inverter,2030,FOM,3,%/year,budischak2013
battery storage,2030,investment,192,USD/kWh,budischak2013
battery storage,2030,lifetime,15,years,budischak2013
decentral air-sourced heat pump,2030,investment,1050,EUR/kWth,HP; Palzer thesis
decentral air-sourced heat pump,2030,lifetime,20,years,HP; Palzer thesis
decentral air-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
decentral air-sourced heat pump,2030,efficiency,3,per unit,default for costs
decentral air-sourced heat pump,2030,discount rate,0.04,per unit,Palzer thesis
decentral ground-sourced heat pump,2030,investment,1400,EUR/kWth,Palzer thesis
decentral ground-sourced heat pump,2030,lifetime,20,years,Palzer thesis
decentral ground-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
decentral ground-sourced heat pump,2030,efficiency,4,per unit,default for costs
decentral ground-sourced heat pump,2030,discount rate,0.04,per unit,Palzer thesis
central air-sourced heat pump,2030,investment,700,EUR/kWth,Palzer thesis
central air-sourced heat pump,2030,lifetime,20,years,Palzer thesis
central air-sourced heat pump,2030,FOM,3.5,%/year,Palzer thesis
central air-sourced heat pump,2030,efficiency,3,per unit,default for costs
retrofitting I,2030,discount rate,0.04,per unit,Palzer thesis
retrofitting I,2030,lifetime,50,years,Palzer thesis
retrofitting I,2030,FOM,1,%/year,Palzer thesis
retrofitting I,2030,investment,50,EUR/m2/fraction reduction,Palzer thesis
retrofitting II,2030,discount rate,0.04,per unit,Palzer thesis
retrofitting II,2030,lifetime,50,years,Palzer thesis
retrofitting II,2030,FOM,1,%/year,Palzer thesis
retrofitting II,2030,investment,250,EUR/m2/fraction reduction,Palzer thesis
water tank charger,2030,efficiency,0.9,per unit,HP
water tank discharger,2030,efficiency,0.9,per unit,HP
decentral water tank storage,2030,investment,860,EUR/m3,IWES Interaktion
decentral water tank storage,2030,FOM,1,%/year,HP
decentral water tank storage,2030,lifetime,20,years,HP
decentral water tank storage,2030,discount rate,0.04,per unit,Palzer thesis
central water tank storage,2030,investment,30,EUR/m3,IWES Interaktion
central water tank storage,2030,FOM,1,%/year,HP
central water tank storage,2030,lifetime,40,years,HP
decentral resistive heater,2030,investment,100,EUR/kWhth,Schaber thesis
decentral resistive heater,2030,lifetime,20,years,Schaber thesis
decentral resistive heater,2030,FOM,2,%/year,Schaber thesis
decentral resistive heater,2030,efficiency,0.9,per unit,Schaber thesis
decentral resistive heater,2030,discount rate,0.04,per unit,Palzer thesis
central resistive heater,2030,investment,100,EUR/kWhth,Schaber thesis
central resistive heater,2030,lifetime,20,years,Schaber thesis
central resistive heater,2030,FOM,2,%/year,Schaber thesis
central resistive heater,2030,efficiency,0.9,per unit,Schaber thesis
decentral gas boiler,2030,investment,175,EUR/kWhth,Palzer thesis
decentral gas boiler,2030,lifetime,20,years,Palzer thesis
decentral gas boiler,2030,FOM,2,%/year,Palzer thesis
decentral gas boiler,2030,efficiency,0.9,per unit,Palzer thesis
decentral gas boiler,2030,discount rate,0.04,per unit,Palzer thesis
central gas boiler,2030,investment,63,EUR/kWhth,Palzer thesis
central gas boiler,2030,lifetime,22,years,Palzer thesis
central gas boiler,2030,FOM,1,%/year,Palzer thesis
central gas boiler,2030,efficiency,0.9,per unit,Palzer thesis
decentral CHP,2030,lifetime,25,years,HP
decentral CHP,2030,investment,1400,EUR/kWel,HP
decentral CHP,2030,FOM,3,%/year,HP
decentral CHP,2030,discount rate,0.04,per unit,Palzer thesis
central CHP,2030,lifetime,25,years,HP
central CHP,2030,investment,650,EUR/kWel,HP
central CHP,2030,FOM,3,%/year,HP
decentral solar thermal,2030,discount rate,0.04,per unit,Palzer thesis
decentral solar thermal,2030,FOM,1.3,%/year,HP
decentral solar thermal,2030,investment,270000,EUR/1000m2,HP
decentral solar thermal,2030,lifetime,20,years,HP
central solar thermal,2030,FOM,1.4,%/year,HP
central solar thermal,2030,investment,140000,EUR/1000m2,HP
central solar thermal,2030,lifetime,20,years,HP
HVAC overhead,2030,investment,400,EUR/MW/km,Hagspiel
HVAC overhead,2030,lifetime,40,years,Hagspiel
HVAC overhead,2030,FOM,2,%/year,Hagspiel
HVDC overhead,2030,investment,400,EUR/MW/km,Hagspiel
HVDC overhead,2030,lifetime,40,years,Hagspiel
HVDC overhead,2030,FOM,2,%/year,Hagspiel
HVDC submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
HVDC submarine,2030,lifetime,40,years,Hagspiel
HVDC submarine,2030,FOM,2,%/year,Hagspiel
HVDC inverter pair,2030,investment,150000,EUR/MW,Hagspiel
HVDC inverter pair,2030,lifetime,40,years,Hagspiel
HVDC inverter pair,2030,FOM,2,%/year,Hagspiel
1 technology year parameter value unit source
2 solar-rooftop 2030 discount rate 0.04 per unit standard for decentral
3 onwind 2030 lifetime 30 years DEA https://ens.dk/en/our-services/projections-and-models/technology-data
4 offwind 2030 lifetime 30 years DEA https://ens.dk/en/our-services/projections-and-models/technology-data
5 solar 2030 lifetime 25 years IEA2010
6 solar-rooftop 2030 lifetime 25 years IEA2010
7 solar-utility 2030 lifetime 25 years IEA2010
8 PHS 2030 lifetime 80 years IEA2010
9 hydro 2030 lifetime 80 years IEA2010
10 ror 2030 lifetime 80 years IEA2010
11 OCGT 2030 lifetime 30 years IEA2010
12 nuclear 2030 lifetime 45 years ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
13 CCGT 2030 lifetime 30 years IEA2010
14 coal 2030 lifetime 40 years IEA2010
15 lignite 2030 lifetime 40 years IEA2010
16 geothermal 2030 lifetime 40 years IEA2010
17 biomass 2030 lifetime 30 years ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
18 oil 2030 lifetime 30 years ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
19 onwind 2030 investment 1040 EUR/kWel DEA https://ens.dk/en/our-services/projections-and-models/technology-data
20 offwind 2030 investment 1640 EUR/kWel DEA https://ens.dk/en/our-services/projections-and-models/technology-data
21 offwind-ac-station 2030 investment 250 EUR/kWel DEA https://ens.dk/en/our-services/projections-and-models/technology-data
22 offwind-ac-connection-submarine 2030 investment 2685 EUR/MW/km DEA https://ens.dk/en/our-services/projections-and-models/technology-data
23 offwind-ac-connection-underground 2030 investment 1342 EUR/MW/km DEA https://ens.dk/en/our-services/projections-and-models/technology-data
24 offwind-dc-station 2030 investment 400 EUR/kWel Haertel 2017; assuming one onshore and one offshore node + 13% learning reduction
25 offwind-dc-connection-submarine 2030 investment 2000 EUR/MW/km DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
26 offwind-dc-connection-underground 2030 investment 1000 EUR/MW/km Haertel 2017; average + 13% learning reduction
27 solar 2030 investment 600 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
28 biomass 2030 investment 2209 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
29 geothermal 2030 investment 3392 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
30 coal 2030 investment 1300 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
31 lignite 2030 investment 1500 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
32 solar-rooftop 2030 investment 725 EUR/kWel ETIP PV
33 solar-utility 2030 investment 425 EUR/kWel ETIP PV
34 PHS 2030 investment 2000 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
35 hydro 2030 investment 2000 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
36 ror 2030 investment 3000 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
37 OCGT 2030 investment 400 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
38 nuclear 2030 investment 6000 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
39 CCGT 2030 investment 800 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
40 oil 2030 investment 400 EUR/kWel DIW DataDoc http://hdl.handle.net/10419/80348
41 onwind 2030 FOM 2.450549 %/year DEA https://ens.dk/en/our-services/projections-and-models/technology-data
42 offwind 2030 FOM 2.304878 %/year DEA https://ens.dk/en/our-services/projections-and-models/technology-data
43 solar 2030 FOM 4.166667 %/year DIW DataDoc http://hdl.handle.net/10419/80348
44 solar-rooftop 2030 FOM 2 %/year ETIP PV
45 solar-utility 2030 FOM 3 %/year ETIP PV
46 biomass 2030 FOM 4.526935 %/year DIW DataDoc http://hdl.handle.net/10419/80348
47 geothermal 2030 FOM 2.358491 %/year DIW DataDoc http://hdl.handle.net/10419/80348
48 coal 2030 FOM 1.923076 %/year DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
49 lignite 2030 FOM 2.0 %/year DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
50 oil 2030 FOM 1.5 %/year DIW DataDoc http://hdl.handle.net/10419/80348
51 PHS 2030 FOM 1 %/year DIW DataDoc http://hdl.handle.net/10419/80348
52 hydro 2030 FOM 1 %/year DIW DataDoc http://hdl.handle.net/10419/80348
53 ror 2030 FOM 2 %/year DIW DataDoc http://hdl.handle.net/10419/80348
54 CCGT 2030 FOM 2.5 %/year DIW DataDoc http://hdl.handle.net/10419/80348
55 OCGT 2030 FOM 3.75 %/year DIW DataDoc http://hdl.handle.net/10419/80348
56 onwind 2030 VOM 2.3 EUR/MWhel DEA https://ens.dk/en/our-services/projections-and-models/technology-data
57 offwind 2030 VOM 2.7 EUR/MWhel DEA https://ens.dk/en/our-services/projections-and-models/technology-data
58 solar 2030 VOM 0.01 EUR/MWhel RES costs made up to fix curtailment order
59 coal 2030 VOM 6 EUR/MWhel DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
60 lignite 2030 VOM 7 EUR/MWhel DIW DataDoc http://hdl.handle.net/10419/80348
61 CCGT 2030 VOM 4 EUR/MWhel DIW DataDoc http://hdl.handle.net/10419/80348
62 OCGT 2030 VOM 3 EUR/MWhel DIW DataDoc http://hdl.handle.net/10419/80348
63 nuclear 2030 VOM 8 EUR/MWhel DIW DataDoc http://hdl.handle.net/10419/80348
64 gas 2030 fuel 21.6 EUR/MWhth IEA2011b
65 uranium 2030 fuel 3 EUR/MWhth DIW DataDoc http://hdl.handle.net/10419/80348
66 oil 2030 VOM 3 EUR/MWhel DIW DataDoc http://hdl.handle.net/10419/80348
67 nuclear 2030 fuel 3 EUR/MWhth IEA2011b
68 biomass 2030 fuel 7 EUR/MWhth IEA2011b
69 coal 2030 fuel 8.4 EUR/MWhth IEA2011b
70 lignite 2030 fuel 2.9 EUR/MWhth IEA2011b
71 oil 2030 fuel 50 EUR/MWhth IEA WEM2017 97USD/boe = http://www.iea.org/media/weowebsite/2017/WEM_Documentation_WEO2017.pdf
72 PHS 2030 efficiency 0.75 per unit DIW DataDoc http://hdl.handle.net/10419/80348
73 hydro 2030 efficiency 0.9 per unit DIW DataDoc http://hdl.handle.net/10419/80348
74 ror 2030 efficiency 0.9 per unit DIW DataDoc http://hdl.handle.net/10419/80348
75 OCGT 2030 efficiency 0.39 per unit DIW DataDoc http://hdl.handle.net/10419/80348
76 CCGT 2030 efficiency 0.5 per unit DIW DataDoc http://hdl.handle.net/10419/80348
77 biomass 2030 efficiency 0.468 per unit DIW DataDoc http://hdl.handle.net/10419/80348
78 geothermal 2030 efficiency 0.239 per unit DIW DataDoc http://hdl.handle.net/10419/80348
79 nuclear 2030 efficiency 0.337 per unit DIW DataDoc http://hdl.handle.net/10419/80348
80 gas 2030 CO2 intensity 0.187 tCO2/MWth https://www.eia.gov/environment/emissions/co2_vol_mass.php
81 coal 2030 efficiency 0.464 per unit DIW DataDoc http://hdl.handle.net/10419/80348 PC (Advanced/SuperC)
82 lignite 2030 efficiency 0.447 per unit DIW DataDoc http://hdl.handle.net/10419/80348
83 oil 2030 efficiency 0.393 per unit DIW DataDoc http://hdl.handle.net/10419/80348 CT
84 coal 2030 CO2 intensity 0.354 tCO2/MWth https://www.eia.gov/environment/emissions/co2_vol_mass.php
85 lignite 2030 CO2 intensity 0.334 tCO2/MWth https://www.eia.gov/environment/emissions/co2_vol_mass.php
86 oil 2030 CO2 intensity 0.248 tCO2/MWth https://www.eia.gov/environment/emissions/co2_vol_mass.php
87 geothermal 2030 CO2 intensity 0.026 tCO2/MWth https://www.eia.gov/environment/emissions/co2_vol_mass.php
88 electrolysis 2030 investment 350 EUR/kWel Palzer Thesis
89 electrolysis 2030 FOM 4 %/year NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
90 electrolysis 2030 lifetime 18 years NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
91 electrolysis 2030 efficiency 0.8 per unit NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
92 fuel cell 2030 investment 339 EUR/kWel NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
93 fuel cell 2030 FOM 3 %/year NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
94 fuel cell 2030 lifetime 20 years NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013
95 fuel cell 2030 efficiency 0.58 per unit NREL http://www.nrel.gov/docs/fy09osti/45873.pdf; budischak2013 conservative 2020
96 hydrogen storage 2030 investment 11.2 USD/kWh budischak2013
97 hydrogen storage 2030 lifetime 20 years budischak2013
98 hydrogen underground storage 2030 investment 0.5 EUR/kWh maximum from https://www.nrel.gov/docs/fy10osti/46719.pdf
99 hydrogen underground storage 2030 lifetime 40 years http://www.acatech.de/fileadmin/user_upload/Baumstruktur_nach_Website/Acatech/root/de/Publikationen/Materialien/ESYS_Technologiesteckbrief_Energiespeicher.pdf
100 H2 pipeline 2030 investment 267 EUR/MW/km Welder et al https://doi.org/10.1016/j.ijhydene.2018.12.156
101 H2 pipeline 2030 lifetime 40 years Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
102 H2 pipeline 2030 FOM 5 %/year Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
103 H2 pipeline 2030 efficiency 0.98 per unit Krieg2012 http://juser.fz-juelich.de/record/136392/files/Energie%26Umwelt_144.pdf
104 methanation 2030 investment 1000 EUR/kWH2 Schaber thesis
105 methanation 2030 lifetime 25 years Schaber thesis
106 methanation 2030 FOM 3 %/year Schaber thesis
107 methanation 2030 efficiency 0.6 per unit Palzer; Breyer for DAC
108 helmeth 2030 investment 1000 EUR/kW no source
109 helmeth 2030 lifetime 25 years no source
110 helmeth 2030 FOM 3 %/year no source
111 helmeth 2030 efficiency 0.8 per unit HELMETH press release
112 DAC 2030 investment 250 EUR/(tCO2/a) Fasihi/Climeworks
113 DAC 2030 lifetime 30 years Fasihi
114 DAC 2030 FOM 4 %/year Fasihi
115 battery inverter 2030 investment 411 USD/kWel budischak2013
116 battery inverter 2030 lifetime 20 years budischak2013
117 battery inverter 2030 efficiency 0.9 per unit charge/discharge budischak2013; Lund and Kempton (2008) http://dx.doi.org/10.1016/j.enpol.2008.06.007
118 battery inverter 2030 FOM 3 %/year budischak2013
119 battery storage 2030 investment 192 USD/kWh budischak2013
120 battery storage 2030 lifetime 15 years budischak2013
121 decentral air-sourced heat pump 2030 investment 1050 EUR/kWth HP; Palzer thesis
122 decentral air-sourced heat pump 2030 lifetime 20 years HP; Palzer thesis
123 decentral air-sourced heat pump 2030 FOM 3.5 %/year Palzer thesis
124 decentral air-sourced heat pump 2030 efficiency 3 per unit default for costs
125 decentral air-sourced heat pump 2030 discount rate 0.04 per unit Palzer thesis
126 decentral ground-sourced heat pump 2030 investment 1400 EUR/kWth Palzer thesis
127 decentral ground-sourced heat pump 2030 lifetime 20 years Palzer thesis
128 decentral ground-sourced heat pump 2030 FOM 3.5 %/year Palzer thesis
129 decentral ground-sourced heat pump 2030 efficiency 4 per unit default for costs
130 decentral ground-sourced heat pump 2030 discount rate 0.04 per unit Palzer thesis
131 central air-sourced heat pump 2030 investment 700 EUR/kWth Palzer thesis
132 central air-sourced heat pump 2030 lifetime 20 years Palzer thesis
133 central air-sourced heat pump 2030 FOM 3.5 %/year Palzer thesis
134 central air-sourced heat pump 2030 efficiency 3 per unit default for costs
135 retrofitting I 2030 discount rate 0.04 per unit Palzer thesis
136 retrofitting I 2030 lifetime 50 years Palzer thesis
137 retrofitting I 2030 FOM 1 %/year Palzer thesis
138 retrofitting I 2030 investment 50 EUR/m2/fraction reduction Palzer thesis
139 retrofitting II 2030 discount rate 0.04 per unit Palzer thesis
140 retrofitting II 2030 lifetime 50 years Palzer thesis
141 retrofitting II 2030 FOM 1 %/year Palzer thesis
142 retrofitting II 2030 investment 250 EUR/m2/fraction reduction Palzer thesis
143 water tank charger 2030 efficiency 0.9 per unit HP
144 water tank discharger 2030 efficiency 0.9 per unit HP
145 decentral water tank storage 2030 investment 860 EUR/m3 IWES Interaktion
146 decentral water tank storage 2030 FOM 1 %/year HP
147 decentral water tank storage 2030 lifetime 20 years HP
148 decentral water tank storage 2030 discount rate 0.04 per unit Palzer thesis
149 central water tank storage 2030 investment 30 EUR/m3 IWES Interaktion
150 central water tank storage 2030 FOM 1 %/year HP
151 central water tank storage 2030 lifetime 40 years HP
152 decentral resistive heater 2030 investment 100 EUR/kWhth Schaber thesis
153 decentral resistive heater 2030 lifetime 20 years Schaber thesis
154 decentral resistive heater 2030 FOM 2 %/year Schaber thesis
155 decentral resistive heater 2030 efficiency 0.9 per unit Schaber thesis
156 decentral resistive heater 2030 discount rate 0.04 per unit Palzer thesis
157 central resistive heater 2030 investment 100 EUR/kWhth Schaber thesis
158 central resistive heater 2030 lifetime 20 years Schaber thesis
159 central resistive heater 2030 FOM 2 %/year Schaber thesis
160 central resistive heater 2030 efficiency 0.9 per unit Schaber thesis
161 decentral gas boiler 2030 investment 175 EUR/kWhth Palzer thesis
162 decentral gas boiler 2030 lifetime 20 years Palzer thesis
163 decentral gas boiler 2030 FOM 2 %/year Palzer thesis
164 decentral gas boiler 2030 efficiency 0.9 per unit Palzer thesis
165 decentral gas boiler 2030 discount rate 0.04 per unit Palzer thesis
166 central gas boiler 2030 investment 63 EUR/kWhth Palzer thesis
167 central gas boiler 2030 lifetime 22 years Palzer thesis
168 central gas boiler 2030 FOM 1 %/year Palzer thesis
169 central gas boiler 2030 efficiency 0.9 per unit Palzer thesis
170 decentral CHP 2030 lifetime 25 years HP
171 decentral CHP 2030 investment 1400 EUR/kWel HP
172 decentral CHP 2030 FOM 3 %/year HP
173 decentral CHP 2030 discount rate 0.04 per unit Palzer thesis
174 central CHP 2030 lifetime 25 years HP
175 central CHP 2030 investment 650 EUR/kWel HP
176 central CHP 2030 FOM 3 %/year HP
177 decentral solar thermal 2030 discount rate 0.04 per unit Palzer thesis
178 decentral solar thermal 2030 FOM 1.3 %/year HP
179 decentral solar thermal 2030 investment 270000 EUR/1000m2 HP
180 decentral solar thermal 2030 lifetime 20 years HP
181 central solar thermal 2030 FOM 1.4 %/year HP
182 central solar thermal 2030 investment 140000 EUR/1000m2 HP
183 central solar thermal 2030 lifetime 20 years HP
184 HVAC overhead 2030 investment 400 EUR/MW/km Hagspiel
185 HVAC overhead 2030 lifetime 40 years Hagspiel
186 HVAC overhead 2030 FOM 2 %/year Hagspiel
187 HVDC overhead 2030 investment 400 EUR/MW/km Hagspiel
188 HVDC overhead 2030 lifetime 40 years Hagspiel
189 HVDC overhead 2030 FOM 2 %/year Hagspiel
190 HVDC submarine 2030 investment 2000 EUR/MW/km DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
191 HVDC submarine 2030 lifetime 40 years Hagspiel
192 HVDC submarine 2030 FOM 2 %/year Hagspiel
193 HVDC inverter pair 2030 investment 150000 EUR/MW Hagspiel
194 HVDC inverter pair 2030 lifetime 40 years Hagspiel
195 HVDC inverter pair 2030 FOM 2 %/year Hagspiel

30
data/nuclear_p_max_pu.csv
View File

@ -1,16 +1,16 @@
country,factor country,factor
BE,0.65 BE,0.796
BG,0.89 BG,0.894
CZ,0.82 CZ,0.827
FI,0.92 FI,0.936
FR,0.70 FR,0.71
DE,0.88 DE,0.871
HU,0.90 HU,0.913
NL,0.86 NL,0.868
RO,0.92 RO,0.909
SK,0.89 SK,0.9
SI,0.94 SI,0.913
ES,0.89 ES,0.897
SE,0.82 SE,0.851
CH,0.86 CH,0.87
GB,0.67 GB,0.656

1 country factor
2 BE 0.65 0.796
3 BG 0.89 0.894
4 CZ 0.82 0.827
5 FI 0.92 0.936
6 FR 0.70 0.71
7 DE 0.88 0.871
8 HU 0.90 0.913
9 NL 0.86 0.868
10 RO 0.92 0.909
11 SK 0.89 0.9
12 SI 0.94 0.913
13 ES 0.89 0.897
14 SE 0.82 0.851
15 CH 0.86 0.87
16 GB 0.67 0.656

3
data/override_component_attrs/buses.csv
View File

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

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

13
data/override_component_attrs/links.csv
View File

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

2
data/override_component_attrs/loads.csv
View File

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

4
data/override_component_attrs/stores.csv
View File

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

8
data/unit_commitment.csv Normal file
View File

@ -0,0 +1,8 @@
attribute,OCGT,CCGT,coal,lignite,nuclear
ramp_limit_up,1,1,1,1,0.3
ramp_limit_start_up,0.2,0.45,0.38,0.4,0.5
ramp_limit_shut_down,0.2,0.45,0.38,0.4,0.5
p_min_pu,0.2,0.45,0.325,0.4,0.5
min_up_time,,3,5,7,6
min_down_time,,2,6,6,10
start_up_cost,9.6,34.2,35.64,19.14,16.5
1 attribute OCGT CCGT coal lignite nuclear
2 ramp_limit_up 1 1 1 1 0.3
3 ramp_limit_start_up 0.2 0.45 0.38 0.4 0.5
4 ramp_limit_shut_down 0.2 0.45 0.38 0.4 0.5
5 p_min_pu 0.2 0.45 0.325 0.4 0.5
6 min_up_time 3 5 7 6
7 min_down_time 2 6 6 10
8 start_up_cost 9.6 34.2 35.64 19.14 16.5

2
doc/conf.py
View File

@ -82,7 +82,7 @@ author = "Tom Brown (KIT, TUB, FIAS), Jonas Hoersch (KIT, FIAS), Fabian Hofmann
# The short X.Y version. # The short X.Y version.
version = "0.8" version = "0.8"
# The full version, including alpha/beta/rc tags. # The full version, including alpha/beta/rc tags.
release = "0.8.0" release = "0.8.1"
# The language for content autogenerated by Sphinx. Refer to documentation # The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages. # for a list of supported languages.

35
doc/configtables/clustering.csv
View File

@ -1,17 +1,18 @@
,Unit,Values,Description ,Unit,Values,Description
simplify_network,,, simplify_network,,,
-- to_substations,bool,"{'true','false'}","Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones" -- to_substations,bool,"{'true','false'}","Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones"
-- algorithm,str,"One of {‘kmeans’, ‘hac’, ‘modularity‘}", -- algorithm,str,"One of {‘kmeans’, ‘hac’, ‘modularity‘}",
-- feature,str,"Str in the format ‘carrier1+carrier2+...+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.", -- feature,str,"Str in the format ‘carrier1+carrier2+...+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.",
-- exclude_carriers,list,"List of Str like [ 'solar', 'onwind'] or empy list []","List of carriers which will not be aggregated. If empty, all carriers will be aggregated." -- exclude_carriers,list,"List of Str like [ 'solar', 'onwind'] or empy list []","List of carriers which will not be aggregated. If empty, all carriers will be aggregated."
-- remove stubs,bool,"true/false","Controls whether radial parts of the network should be recursively aggregated. Defaults to true." -- remove stubs,bool,"{'true','false'}",Controls whether radial parts of the network should be recursively aggregated. Defaults to true.
-- remove_stubs_across_borders,bool,"true/false","Controls whether radial parts of the network should be recursively aggregated across borders. Defaults to true." -- remove_stubs_across_borders,bool,"{'true','false'}",Controls whether radial parts of the network should be recursively aggregated across borders. Defaults to true.
cluster_network,,, cluster_network,,,
-- algorithm,str,"One of {‘kmeans’, ‘hac’}", -- algorithm,str,"One of {‘kmeans’, ‘hac’}",
-- feature,str,"Str in the format ‘carrier1+carrier2+...+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.", -- feature,str,"Str in the format ‘carrier1+carrier2+...+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.",
-- exclude_carriers,list,"List of Str like [ 'solar', 'onwind'] or empy list []","List of carriers which will not be aggregated. If empty, all carriers will be aggregated." -- exclude_carriers,list,"List of Str like [ 'solar', 'onwind'] or empy list []","List of carriers which will not be aggregated. If empty, all carriers will be aggregated."
aggregation_strategies,,, -- consider_efficiency_classes,bool,"{'true','false'}","Aggregated each carriers into the top 10-quantile (high), the bottom 90-quantile (low), and everything in between (medium)."
-- generators,,, aggregation_strategies,,,
-- -- {key},str,"{key} can be any of the component of the generator (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}.","Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new generator." -- generators,,,
-- buses,,, -- -- {key},str,"{key} can be any of the component of the generator (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}.","Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new generator."
-- -- {key},str,"{key} can be any of the component of the bus (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}.","Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new bus." -- buses,,,
-- -- {key},str,"{key} can be any of the component of the bus (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}.","Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new bus."

1 Unit Values Description
2 simplify_network
3 -- to_substations bool {'true','false'} Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones
4 -- algorithm str One of {‘kmeans’, ‘hac’, ‘modularity‘}
5 -- feature str Str in the format ‘carrier1+carrier2+...+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.
6 -- exclude_carriers list List of Str like [ 'solar', 'onwind'] or empy list [] List of carriers which will not be aggregated. If empty, all carriers will be aggregated.
7 -- remove stubs bool true/false {'true','false'} Controls whether radial parts of the network should be recursively aggregated. Defaults to true.
8 -- remove_stubs_across_borders bool true/false {'true','false'} Controls whether radial parts of the network should be recursively aggregated across borders. Defaults to true.
9 cluster_network
10 -- algorithm str One of {‘kmeans’, ‘hac’}
11 -- feature str Str in the format ‘carrier1+carrier2+...+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.
12 -- exclude_carriers list List of Str like [ 'solar', 'onwind'] or empy list [] List of carriers which will not be aggregated. If empty, all carriers will be aggregated.
13 aggregation_strategies -- consider_efficiency_classes bool {'true','false'} Aggregated each carriers into the top 10-quantile (high), the bottom 90-quantile (low), and everything in between (medium).
14 -- generators aggregation_strategies
15 -- -- {key} -- generators str {key} can be any of the component of the generator (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}. Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new generator.
16 -- buses -- -- {key} str {key} can be any of the component of the generator (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}. Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new generator.
17 -- -- {key} -- buses str {key} can be any of the component of the bus (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}. Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new bus.
18 -- -- {key} str {key} can be any of the component of the bus (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}. Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new bus.

8
doc/configtables/conventional.csv
View File

@ -1,3 +1,5 @@
,Unit,Values,Description ,Unit,Values,Description
{name},--,"string","For any carrier/technology overwrite attributes as listed below." unit_commitment ,bool,"{true, false}","Allow the overwrite of ramp_limit_up, ramp_limit_start_up, ramp_limit_shut_down, p_min_pu, min_up_time, min_down_time, and start_up_cost of conventional generators. Refer to the CSV file „unit_commitment.csv“."
-- {attribute},--,"string or float","For any attribute, can specify a float or reference to a file path to a CSV file giving floats for each country (2-letter code)." dynamic_fuel_price ,bool,"{true, false}","Consider the monthly fluctuating fuel prices for each conventional generator. Refer to the CSV file ""data/validation/monthly_fuel_price.csv""."
{name},--,string,For any carrier/technology overwrite attributes as listed below.
-- {attribute},--,string or float,"For any attribute, can specify a float or reference to a file path to a CSV file giving floats for each country (2-letter code)."

1 Unit Values Description
2 {name} unit_commitment -- bool string {true, false} For any carrier/technology overwrite attributes as listed below. Allow the overwrite of ramp_limit_up, ramp_limit_start_up, ramp_limit_shut_down, p_min_pu, min_up_time, min_down_time, and start_up_cost of conventional generators. Refer to the CSV file „unit_commitment.csv“.
3 -- {attribute} dynamic_fuel_price -- bool string or float {true, false} For any attribute, can specify a float or reference to a file path to a CSV file giving floats for each country (2-letter code). Consider the monthly fluctuating fuel prices for each conventional generator. Refer to the CSV file "data/validation/monthly_fuel_price.csv".
4 {name} -- string For any carrier/technology overwrite attributes as listed below.
5 -- {attribute} -- string or float For any attribute, can specify a float or reference to a file path to a CSV file giving floats for each country (2-letter code).

14
doc/configtables/hydro.csv
View File

@ -1,6 +1,8 @@
,Unit,Values,Description ,Unit,Values,Description
cutout,--,"Must be 'europe-2013-era5'","Specifies the directory where the relevant weather data ist stored." cutout,--,Must be 'europe-2013-era5',Specifies the directory where the relevant weather data ist stored.
carriers,--,"Any subset of {'ror', 'PHS', 'hydro'}","Specifies the types of hydro power plants to build per-unit availability time series for. 'ror' stands for run-of-river plants, 'PHS' represents pumped-hydro storage, and 'hydro' stands for hydroelectric dams." carriers,--,"Any subset of {'ror', 'PHS', 'hydro'}","Specifies the types of hydro power plants to build per-unit availability time series for. 'ror' stands for run-of-river plants, 'PHS' represents pumped-hydro storage, and 'hydro' stands for hydroelectric dams."
PHS_max_hours,h,float,"Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_." PHS_max_hours,h,float,Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
hydro_max_hours,h,"Any of {float, 'energy_capacity_totals_by_country', 'estimate_by_large_installations'}","Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom`` or heuristically determined. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_." hydro_max_hours,h,"Any of {float, 'energy_capacity_totals_by_country', 'estimate_by_large_installations'}",Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom`` or heuristically determined. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
clip_min_inflow,MW,float,"To avoid too small values in the inflow time series, values below this threshold are set to zero." flatten_dispatch,bool,"{true, false}",Consider an upper limit for the hydro dispatch. The limit is given by the average capacity factor plus the buffer given in ``flatten_dispatch_buffer``
flatten_dispatch_buffer,--,float,"If ``flatten_dispatch`` is true, specify the value added above the average capacity factor."
clip_min_inflow,MW,float,"To avoid too small values in the inflow time series, values below this threshold are set to zero."

1 Unit Values Description
2 cutout -- Must be 'europe-2013-era5' Specifies the directory where the relevant weather data ist stored.
3 carriers -- Any subset of {'ror', 'PHS', 'hydro'} Specifies the types of hydro power plants to build per-unit availability time series for. 'ror' stands for run-of-river plants, 'PHS' represents pumped-hydro storage, and 'hydro' stands for hydroelectric dams.
4 PHS_max_hours h float Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
5 hydro_max_hours h Any of {float, 'energy_capacity_totals_by_country', 'estimate_by_large_installations'} Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom`` or heuristically determined. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
6 clip_min_inflow flatten_dispatch MW bool float {true, false} To avoid too small values in the inflow time series, values below this threshold are set to zero. Consider an upper limit for the hydro dispatch. The limit is given by the average capacity factor plus the buffer given in ``flatten_dispatch_buffer``
7 flatten_dispatch_buffer -- float If ``flatten_dispatch`` is true, specify the value added above the average capacity factor.
8 clip_min_inflow MW float To avoid too small values in the inflow time series, values below this threshold are set to zero.

6
doc/configtables/lines.csv
View File

@ -5,3 +5,9 @@ s_nom_max,MW,"float","Global upper limit for the maximum capacity of each extend
max_extension,MW,"float","Upper limit for the extended capacity of each extendable line." max_extension,MW,"float","Upper limit for the extended capacity of each extendable line."
length_factor,--,float,"Correction factor to account for the fact that buses are *not* connected by lines through air-line distance." length_factor,--,float,"Correction factor to account for the fact that buses are *not* connected by lines through air-line distance."
under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove completely, 'keep': keep with full capacity}","Specifies how to handle lines which are currently under construction." under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove completely, 'keep': keep with full capacity}","Specifies how to handle lines which are currently under construction."
dynamic_line_rating,,,
-- activate,bool,"true or false","Whether to take dynamic line rating into account"
-- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
-- correction_factor,--,"float","Factor to compensate for overestimation of wind speeds in hourly averaged wind data"
-- max_voltage_difference,deg,"float","Maximum voltage angle difference in degrees or 'false' to disable"
-- max_line_rating,--,"float","Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable"

1 Unit Values Description
5 max_extension MW float Upper limit for the extended capacity of each extendable line.
6 length_factor -- float Correction factor to account for the fact that buses are *not* connected by lines through air-line distance.
7 under_construction -- One of {'zero': set capacity to zero, 'remove': remove completely, 'keep': keep with full capacity} Specifies how to handle lines which are currently under construction.
8 dynamic_line_rating
9 -- activate bool true or false Whether to take dynamic line rating into account
10 -- cutout -- Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5. Specifies the directory where the relevant weather data ist stored.
11 -- correction_factor -- float Factor to compensate for overestimation of wind speeds in hourly averaged wind data
12 -- max_voltage_difference deg float Maximum voltage angle difference in degrees or 'false' to disable
13 -- max_line_rating -- float Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable

1
doc/configtables/opts.csv
View File

@ -3,6 +3,7 @@ Trigger, Description, Definition, Status
``nSEG``; e.g. ``4380SEG``, "Apply time series segmentation with `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package to ``n`` adjacent snapshots of varying lengths based on capacity factors of varying renewables, hydro inflow and load.", ``prepare_network``: apply_time_segmentation(), In active use ``nSEG``; e.g. ``4380SEG``, "Apply time series segmentation with `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package to ``n`` adjacent snapshots of varying lengths based on capacity factors of varying renewables, hydro inflow and load.", ``prepare_network``: apply_time_segmentation(), In active use
``Co2L``, Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``. If a float is appended an overall emission limit relative to the emission level given in ``electricity: co2base`` is added (e.g. ``Co2L0.05`` limits emissisions to 5% of what is given in ``electricity: co2base``), ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`__, In active use ``Co2L``, Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``. If a float is appended an overall emission limit relative to the emission level given in ``electricity: co2base`` is added (e.g. ``Co2L0.05`` limits emissisions to 5% of what is given in ``electricity: co2base``), ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`__, In active use
``Ep``, Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well), ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`__, In active use ``Ep``, Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well), ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`__, In active use
``Ept``, Add monthly cost for a carbon-dioxide price based on historical values built by the rule ``build_monthly_prices``, In active use
``CCL``, Add minimum and maximum levels of generator nominal capacity per carrier for individual countries. These can be specified in the file linked at ``electricity: agg_p_nom_limits`` in the configuration. File defaults to ``data/agg_p_nom_minmax.csv``., ``solve_network``, In active use ``CCL``, Add minimum and maximum levels of generator nominal capacity per carrier for individual countries. These can be specified in the file linked at ``electricity: agg_p_nom_limits`` in the configuration. File defaults to ``data/agg_p_nom_minmax.csv``., ``solve_network``, In active use
``EQ``, "Require each country or node to on average produce a minimal share of its total consumption itself. Example: ``EQ0.5c`` demands each country to produce on average at least 50% of its consumption; ``EQ0.5`` demands each node to produce on average at least 50% of its consumption.", ``solve_network``, In active use ``EQ``, "Require each country or node to on average produce a minimal share of its total consumption itself. Example: ``EQ0.5c`` demands each country to produce on average at least 50% of its consumption; ``EQ0.5`` demands each node to produce on average at least 50% of its consumption.", ``solve_network``, In active use
``ATK``, "Require each node to be autarkic. Example: ``ATK`` removes all lines and links. ``ATKc`` removes all cross-border lines and links.", ``prepare_network``, In active use ``ATK``, "Require each node to be autarkic. Example: ``ATK`` removes all lines and links. ``ATKc`` removes all cross-border lines and links.", ``prepare_network``, In active use

Can't render this file because it has a wrong number of fields in line 6.

36
doc/configtables/solving.csv
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@ -1,17 +1,19 @@
,Unit,Values,Description ,Unit,Values,Description
options,,, options,,,
-- load_shedding,bool/float,"{'true','false', float}","Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh." -- clip_p_max_pu,p.u.,float,To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.
-- transmission_losses,int,"[0-9]","Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored." -- load_shedding,bool/float,"{'true','false', float}","Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh."
-- noisy_costs,bool,"{'true','false'}","Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`." -- noisy_costs,bool,"{'true','false'}","Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`."
-- min_iterations,--,int,"Minimum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run." -- skip_iterations,bool,"{'true','false'}","Skip iterating, do not update impedances of branches. Defaults to true."
-- max_iterations,--,int,"Maximum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run." -- rolling_horizon,bool,"{'true','false'}","Whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size `horizon` which are solved consecutively."
-- nhours,--,int,"Specifies the :math:`n` first snapshots to take into account. Must be less than the total number of snapshots. Rather recommended only for debugging." -- seed,--,int,Random seed for increased deterministic behaviour.
-- clip_p_max_pu,p.u.,float,"To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero." -- track_iterations,bool,"{'true','false'}",Flag whether to store the intermediate branch capacities and objective function values are recorded for each iteration in ``network.lines['s_nom_opt_X']`` (where ``X`` labels the iteration)
-- skip_iterations,bool,"{'true','false'}","Skip iterating, do not update impedances of branches. Defaults to true." -- min_iterations,--,int,Minimum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.
-- track_iterations,bool,"{'true','false'}","Flag whether to store the intermediate branch capacities and objective function values are recorded for each iteration in ``network.lines['s_nom_opt_X']`` (where ``X`` labels the iteration)" -- max_iterations,--,int,Maximum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.
-- seed,--,int,"Random seed for increased deterministic behaviour." -- transmission_losses,int,[0-9],"Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored."
solver,,, -- linearized_unit_commitment,bool,"{'true','false'}",Whether to optimise using the linearized unit commitment formulation.
-- name,--,"One of {'gurobi', 'cplex', 'cbc', 'glpk', 'ipopt'}; potentially more possible","Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow." -- horizon,--,int,Number of snapshots to consider in each iteration. Defaults to 100.
-- options,--,"Key listed under ``solver_options``.","Link to specific parameter settings." solver,,,
solver_options,,"dict","Dictionaries with solver-specific parameter settings." -- name,--,"One of {'gurobi', 'cplex', 'cbc', 'glpk', 'ipopt'}; potentially more possible",Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow.
mem,MB,"int","Estimated maximum memory requirement for solving networks." -- options,--,Key listed under ``solver_options``.,Link to specific parameter settings.
solver_options,,dict,Dictionaries with solver-specific parameter settings.
mem,MB,int,Estimated maximum memory requirement for solving networks.

1 Unit Values Description
2 options
3 -- load_shedding -- clip_p_max_pu bool/float p.u. {'true','false', float} float Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh. To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.
4 -- transmission_losses -- load_shedding int bool/float [0-9] {'true','false', float} Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored. Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh.
5 -- noisy_costs bool {'true','false'} Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`.
6 -- min_iterations -- skip_iterations -- bool int {'true','false'} Minimum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run. Skip iterating, do not update impedances of branches. Defaults to true.
7 -- max_iterations -- rolling_horizon -- bool int {'true','false'} Maximum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run. Whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size `horizon` which are solved consecutively.
8 -- nhours -- seed -- int Specifies the :math:`n` first snapshots to take into account. Must be less than the total number of snapshots. Rather recommended only for debugging. Random seed for increased deterministic behaviour.
9 -- clip_p_max_pu -- track_iterations p.u. bool float {'true','false'} To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero. Flag whether to store the intermediate branch capacities and objective function values are recorded for each iteration in ``network.lines['s_nom_opt_X']`` (where ``X`` labels the iteration)
10 -- skip_iterations -- min_iterations bool -- {'true','false'} int Skip iterating, do not update impedances of branches. Defaults to true. Minimum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.
11 -- track_iterations -- max_iterations bool -- {'true','false'} int Flag whether to store the intermediate branch capacities and objective function values are recorded for each iteration in ``network.lines['s_nom_opt_X']`` (where ``X`` labels the iteration) Maximum number of solving iterations in between which resistance and reactence (``x/r``) are updated for branches according to ``s_nom_opt`` of the previous run.
12 -- seed -- transmission_losses -- int int [0-9] Random seed for increased deterministic behaviour. Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored.
13 solver -- linearized_unit_commitment bool {'true','false'} Whether to optimise using the linearized unit commitment formulation.
14 -- name -- horizon -- One of {'gurobi', 'cplex', 'cbc', 'glpk', 'ipopt'}; potentially more possible int Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow. Number of snapshots to consider in each iteration. Defaults to 100.
15 -- options solver -- Key listed under ``solver_options``. Link to specific parameter settings.
16 solver_options -- name -- dict One of {'gurobi', 'cplex', 'cbc', 'glpk', 'ipopt'}; potentially more possible Dictionaries with solver-specific parameter settings. Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow.
17 mem -- options MB -- int Key listed under ``solver_options``. Estimated maximum memory requirement for solving networks. Link to specific parameter settings.
18 solver_options dict Dictionaries with solver-specific parameter settings.
19 mem MB int Estimated maximum memory requirement for solving networks.

18
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@ -1,6 +1,12 @@
,Unit,Values,Description ,Unit,Values,Description
version,--,0.x.x,"Version of PyPSA-Eur. Descriptive only." version,--,0.x.x,Version of PyPSA-Eur. Descriptive only.
tutorial,bool,"{true, false}","Switch to retrieve the tutorial data set instead of the full data set." tutorial,bool,"{true, false}",Switch to retrieve the tutorial data set instead of the full data set.
logging,,, logging,,,
-- level,--,"Any of {'INFO', 'WARNING', 'ERROR'}","Restrict console outputs to all infos, warning or errors only" -- level,--,"Any of {'INFO', 'WARNING', 'ERROR'}","Restrict console outputs to all infos, warning or errors only"
-- format,--,"","Custom format for log messages. See `LogRecord <https://docs.python.org/3/library/logging.html#logging.LogRecord>`_ attributes." -- format,--,,Custom format for log messages. See `LogRecord <https://docs.python.org/3/library/logging.html#logging.LogRecord>`_ attributes.
private,,,
-- keys,,,
-- -- entsoe_api,--,,Optionally specify the ENTSO-E API key. See the guidelines to get `ENTSO-E API key <https://transparency.entsoe.eu/content/static_content/Static%20content/web%20api/Guide.html>`_
remote,,,
-- ssh,--,,Optionally specify the SSH of a remote cluster to be synchronized.
-- path,--,,Optionally specify the file path within the remote cluster to be synchronized.

1 Unit Values Description
2 version -- 0.x.x Version of PyPSA-Eur. Descriptive only.
3 tutorial bool {true, false} Switch to retrieve the tutorial data set instead of the full data set.
4 logging
5 -- level -- Any of {'INFO', 'WARNING', 'ERROR'} Restrict console outputs to all infos, warning or errors only
6 -- format -- Custom format for log messages. See `LogRecord <https://docs.python.org/3/library/logging.html#logging.LogRecord>`_ attributes.
7 private
8 -- keys
9 -- -- entsoe_api -- Optionally specify the ENTSO-E API key. See the guidelines to get `ENTSO-E API key <https://transparency.entsoe.eu/content/static_content/Static%20content/web%20api/Guide.html>`_
10 remote
11 -- ssh -- Optionally specify the SSH of a remote cluster to be synchronized.
12 -- path -- Optionally specify the file path within the remote cluster to be synchronized.

3
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@ -16,12 +16,13 @@ PyPSA-Eur has several configuration options which are documented in this section
Top-level configuration Top-level configuration
======================= =======================
"Private" refers to local, machine-specific settings or data meant for personal use, not to be shared. "Remote" indicates the address of a server used for data exchange, often for clusters and data pushing/pulling.
.. literalinclude:: ../config/config.default.yaml .. literalinclude:: ../config/config.default.yaml
:language: yaml :language: yaml
:start-at: version: :start-at: version:
:end-before: # docs :end-before: # docs
.. csv-table:: .. csv-table::
:header-rows: 1 :header-rows: 1
:widths: 22,7,22,33 :widths: 22,7,22,33

19
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@ -78,10 +78,10 @@ them:
.. note:: .. note::
You can find showcases of the model's capabilities in the Supplementary Materials of the You can find showcases of the model's capabilities in the Supplementary Materials of the
preprint `Benefits of a Hydrogen Network in Europe Joule paper `The potential role of a hydrogen network in Europe
<https://arxiv.org/abs/2207.05816>`_, the Supplementary Materials of the `paper in Joule with a <https://doi.org/10.1016/j.joule.2023.06.016>`_, the Supplementary Materials of another `paper in Joule with a
description of the industry sector description of the industry sector
<https://arxiv.org/abs/2109.09563>`_, or in `a 2021 presentation <https://doi.org/10.1016/j.joule.2022.04.016>`_, or in `a 2021 presentation
at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_. at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_.
The sector-coupled extension of PyPSA-Eur was The sector-coupled extension of PyPSA-Eur was
initially described in the paper `Synergies of sector coupling and transmission initially described in the paper `Synergies of sector coupling and transmission
@ -179,10 +179,13 @@ For sector-coupling studies: ::
@misc{PyPSAEurSec, @misc{PyPSAEurSec,
author = "Fabian Neumann and Elisabeth Zeyen and Marta Victoria and Tom Brown", author = "Fabian Neumann and Elisabeth Zeyen and Marta Victoria and Tom Brown",
title = "The Potential Role of a Hydrogen Network in Europe", title = "The potential role of a hydrogen network in Europe",
year = "2022", journal "Joule",
volume = "7",
pages = "1--25"
year = "2023",
eprint = "2207.05816", eprint = "2207.05816",
url = "https://arxiv.org/abs/2207.05816", doi = "10.1016/j.joule.2022.04.016",
} }
For sector-coupling studies with pathway optimisation: :: For sector-coupling studies with pathway optimisation: ::
@ -224,7 +227,10 @@ The included ``.nc`` files are PyPSA network files which can be imported with Py
n = pypsa.Network(filename) n = pypsa.Network(filename)
Operating Systems
=================
The PyPSA-Eur workflow is continuously tested for Linux, macOS and Windows (WSL only).
.. toctree:: .. toctree::
@ -274,6 +280,7 @@ The included ``.nc`` files are PyPSA network files which can be imported with Py
release_notes release_notes
licenses licenses
validation
limitations limitations
contributing contributing
support support

126
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@ -10,39 +10,131 @@ Release Notes
Upcoming Release Upcoming Release
================ ================
* ``param:`` section in rule definition are added to track changed settings in ``config.yaml``. The goal is to automatically re-execute rules whose parameters have changed. See `Non-file parameters for rules <https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules>`_ in the snakemake documentation. * Updated Global Energy Monitor LNG terminal data to March 2023 version.
* **Important:** The configuration files are now located in the ``config`` directory. This counts for ``config.default.yaml``, ``config.yaml`` as well as the test configuration files which are now located in ``config/test``. Config files that are still in the root directory will be ignored. * For industry distribution, use EPRTR as fallback if ETS data is not available.
* Bugfix: Correct typo in the CPLEX solver configuration in ``config.default.yaml``. PyPSA-Eur 0.8.1 (27th July 2023)
================================
* Bugfix: Error in ``add_electricity`` where carriers were added multiple times to the network, resulting in a non-unique carriers error. **New Features**
* Renamed script file from PyPSA-EUR ``build_load_data`` to ``build_electricity_demand`` and ``retrieve_load_data`` to ``retrieve_electricity_demand``. * Add option to consider dynamic line rating based on wind speeds and
temperature according to `Glaum and Hofmann (2022)
<https://arxiv.org/abs/2208.04716>`_. See configuration section ``lines:
dynamic_line_rating:`` for more details. (https://github.com/PyPSA/pypsa-eur/pull/675)
* Fix docs readthedocs built * Add option to include a piecewise linear approximation of transmission losses,
e.g. by setting ``solving: options: transmission_losses: 2`` for an
approximation with two tangents. (https://github.com/PyPSA/pypsa-eur/pull/664)
* Add plain hydrogen turbine as additional re-electrification option besides * Add plain hydrogen turbine as additional re-electrification option besides
hydrogen fuel cell. Add switches for both re-electrification options under hydrogen fuel cell. Add switches for both re-electrification options under
``sector: hydrogen_turbine:`` and ``sector: hydrogen_fuel_cell:``. ``sector: hydrogen_turbine:`` and ``sector: hydrogen_fuel_cell:``.
(https://github.com/PyPSA/pypsa-eur/pull/647)
* A new function named ``sanitize_carrier`` ensures that all unique carrier names are present in the network's carriers attribute, and adds nice names and colors for each carrier according to the provided configuration dictionary.
* Additional tech_color are added to include previously unlisted carriers.
* Remove ``vresutils`` dependency.
* Added configuration option ``lines: max_extension:`` and ``links: * Added configuration option ``lines: max_extension:`` and ``links:
max_extension:``` to control the maximum capacity addition per line or link in max_extension:``` to control the maximum capacity addition per line or link in
MW. MW. (https://github.com/PyPSA/pypsa-eur/pull/665)
* Add option to include a piecewise linear approximation of transmission losses, * A ``param:`` section in the snakemake rule definitions was added to track
e.g. by setting ``solving: options: transmission_losses: 2`` for an changed settings in ``config.yaml``. The goal is to automatically re-execute
approximation with two tangents. rules where parameters have changed. See `Non-file parameters for rules
<https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#non-file-parameters-for-rules>`_
in the snakemake documentation. (https://github.com/PyPSA/pypsa-eur/pull/663)
* A new function named ``sanitize_carrier`` ensures that all unique carrier
names are present in the network's carriers attribute, and adds nice names and
colors for each carrier according to the provided configuration dictionary.
(https://github.com/PyPSA/pypsa-eur/pull/653,
https://github.com/PyPSA/pypsa-eur/pull/690)
* The configuration settings have been documented in more detail.
(https://github.com/PyPSA/pypsa-eur/pull/685)
**Breaking Changes**
* The configuration files are now located in the ``config`` directory. This
includes the ``config.default.yaml``, ``config.yaml`` as well as the test
configuration files which are now located in the ``config/test`` directory.
Config files that are still in the root directory will be ignored.
(https://github.com/PyPSA/pypsa-eur/pull/640)
* Renamed script and rule name from ``build_load_data`` to
``build_electricity_demand`` and ``retrieve_load_data`` to
``retrieve_electricity_demand``. (https://github.com/PyPSA/pypsa-eur/pull/642,
https://github.com/PyPSA/pypsa-eur/pull/652)
* Updated to new spatial clustering module introduced in PyPSA v0.25.
(https://github.com/PyPSA/pypsa-eur/pull/696)
**Changes**
* Handling networks with links with multiple inputs/outputs no longer requires
to override component attributes.
(https://github.com/PyPSA/pypsa-eur/pull/695)
* Added configuration option ``enable: retrieve:`` to control whether data * Added configuration option ``enable: retrieve:`` to control whether data
retrieval rules from snakemake are enabled or not. Th default setting ``auto`` retrieval rules from snakemake are enabled or not. Th default setting ``auto``
will automatically detect and enable/disable the rules based on internet connectivity. will automatically detect and enable/disable the rules based on internet
connectivity. (https://github.com/PyPSA/pypsa-eur/pull/694)
* Update to ``technology-data`` v0.6.0.
(https://github.com/PyPSA/pypsa-eur/pull/704)
* Handle data bundle extraction paths via ``snakemake.output``.
* Additional technologies are added to ``tech_color`` in the configuration files
to include previously unlisted carriers.
* Doc: Added note that Windows is only tested in CI with WSL.
(https://github.com/PyPSA/pypsa-eur/issues/697)
* Doc: Add support section. (https://github.com/PyPSA/pypsa-eur/pull/656)
* Open ``rasterio`` files with ``rioxarray``.
(https://github.com/PyPSA/pypsa-eur/pull/474)
* Migrate CI to ``micromamba``. (https://github.com/PyPSA/pypsa-eur/pull/700)
**Bugs and Compatibility**
* The new minimum PyPSA version is v0.25.1.
* Removed ``vresutils`` dependency.
(https://github.com/PyPSA/pypsa-eur/pull/662)
* Adapt to new ``powerplantmatching`` version.
(https://github.com/PyPSA/pypsa-eur/pull/687,
https://github.com/PyPSA/pypsa-eur/pull/701)
* Bugfix: Correct typo in the CPLEX solver configuration in
``config.default.yaml``. (https://github.com/PyPSA/pypsa-eur/pull/630)
* Bugfix: Error in ``add_electricity`` where carriers were added multiple times
to the network, resulting in a non-unique carriers error.
* Bugfix of optional reserve constraint.
(https://github.com/PyPSA/pypsa-eur/pull/645)
* Fix broken equity constraints logic.
(https://github.com/PyPSA/pypsa-eur/pull/679)
* Fix addition of load shedding generators.
(https://github.com/PyPSA/pypsa-eur/pull/649)
* Fix automatic building of documentation on readthedocs.org.
(https://github.com/PyPSA/pypsa-eur/pull/658)
* Bugfix: Update network clustering to avoid adding deleted links in clustered
network. (https://github.com/PyPSA/pypsa-eur/pull/678)
* Address ``geopandas`` deprecations.
(https://github.com/PyPSA/pypsa-eur/pull/678)
* Fix bug with underground hydrogen storage creation, where for some small model
regions no cavern storage is available.
(https://github.com/PyPSA/pypsa-eur/pull/672)
PyPSA-Eur 0.8.0 (18th March 2023) PyPSA-Eur 0.8.0 (18th March 2023)

2
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@ -83,7 +83,7 @@ This rule, as a substitute for :mod:`build_natura_raster`, downloads an already
Rule ``retrieve_electricity_demand`` Rule ``retrieve_electricity_demand``
==================================== ====================================
This rule downloads hourly electric load data for each country from the `OPSD platform <data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv>`_. This rule downloads hourly electric load data for each country from the `OPSD platform <https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv>`_.
**Relevant Settings** **Relevant Settings**

53
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@ -0,0 +1,53 @@
..
SPDX-FileCopyrightText: 2019-2023 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
##########################################
Validation
##########################################
The PyPSA-Eur model workflow provides a built-in mechanism for validation. This allows users to contrast the outcomes of network optimization against the historical behaviour of the European power system. The snakemake rule ``validate_elec_networks`` enables this by generating comparative figures that encapsulate key data points such as dispatch carrier, cross-border flows, and market prices per price zone.
These comparisons utilize data from the 2019 ENTSO-E Transparency Platform. To enable this, an ENTSO-E API key must be inserted into the ``config.yaml`` file. Detailed steps for this process can be found in the user guide `here <https://transparency.entsoe.eu/content/static_content/Static%20content/web%20api/Guide.html>`_.
Once the API key is set, the validation workflow can be triggered by running the following command:
snakemake validate_elec_networks --configfile config/config.validation.yaml -c8
The configuration file `config/config.validation.yaml` contains the following parameters:
.. literalinclude:: ../config/config.validation.yaml
:language: yaml
The setup uses monthly varying fuel prices for gas, lignite, coal and oil as well as CO2 prices, which are created by the script ``build_monthly_prices``. Upon completion of the validation process, the resulting network and generated figures will be stored in the ``results/validation`` directory for further analysis.
Results
=======
By the time of writing the comparison with the historical data shows partially accurate, partially improvable results. The following figures show the comparison of the dispatch of the different carriers.
.. image:: ../graphics/validation_seasonal_operation_area_elec_s_37_ec_lv1.0_Ept.png
:width: 100%
:align: center
.. image:: ../graphics/validation_production_bar_elec_s_37_ec_lv1.0_Ept.png
:width: 100%
:align: center
Issues and possible improvements
--------------------------------
**Overestimated dispatch of wind and solar:** Renewable potentials of wind and solar are slightly overestimated in the model. This leads to a higher dispatch of these carriers than in the historical data. In particular, the solar dispatch during winter is overestimated.
**Coal - Lignite fuel switch:** The model has a fuel switch from coal to lignite. This might result from non-captured subsidies for lignite and coal in the model. In order to fix the fuel switch from coal to lignite, a manual cost correction was added to the script ``build_monthly_prices``.
**Planned outages of nuclear power plants:** Planned outages of nuclear power plants are not captured in the model. This leads to a underestimated dispatch of nuclear power plants in winter and a overestimated dispatch in summer. This point is hard to fix, since the planned outages are not published in the ENTSO-E Transparency Platform.
**False classification of run-of-river power plants:** Some run-of-river power plants are classified as hydro power plants in the model. This leads to a general overestimation of the hydro power dispatch. In particular, Swedish hydro power plants are overestimated.
**Load shedding:** Due to constraint NTC's (crossborder capacities), the model has to shed load in some regions. This leads to a high market prices in the regions which drive the average market price up. Further fine-tuning of the NTC's is needed to avoid load shedding.

448
envs/environment.fixed.yaml
View File

@ -12,74 +12,93 @@ dependencies:
- _libgcc_mutex=0.1 - _libgcc_mutex=0.1
- _openmp_mutex=4.5 - _openmp_mutex=4.5
- affine=2.4.0 - affine=2.4.0
- alsa-lib=1.2.8 - alsa-lib=1.2.9
- ampl-mp=3.1.0 - ampl-mp=3.1.0
- amply=0.1.5 - amply=0.1.6
- anyio=3.7.1
- appdirs=1.4.4 - appdirs=1.4.4
- argon2-cffi=21.3.0
- argon2-cffi-bindings=21.2.0
- asttokens=2.2.1 - asttokens=2.2.1
- atlite=0.2.10 - async-lru=2.0.3
- atk-1.0=2.38.0
- atlite=0.2.11
- attr=2.5.1 - attr=2.5.1
- attrs=22.2.0 - attrs=23.1.0
- aws-c-auth=0.7.0
- aws-c-cal=0.6.0
- aws-c-common=0.8.23
- aws-c-compression=0.2.17
- aws-c-event-stream=0.3.1
- aws-c-http=0.7.11
- aws-c-io=0.13.28
- aws-c-mqtt=0.8.14
- aws-c-s3=0.3.13
- aws-c-sdkutils=0.1.11
- aws-checksums=0.1.16
- aws-crt-cpp=0.20.3
- aws-sdk-cpp=1.10.57
- babel=2.12.1
- backcall=0.2.0 - backcall=0.2.0
- backports=1.0 - backports=1.0
- backports.functools_lru_cache=1.6.4 - backports.functools_lru_cache=1.6.5
- beautifulsoup4=4.11.2 - beautifulsoup4=4.12.2
- blosc=1.21.3 - bleach=6.0.0
- bokeh=2.4.3 - blosc=1.21.4
- bokeh=3.2.1
- boost-cpp=1.78.0 - boost-cpp=1.78.0
- bottleneck=1.3.6 - bottleneck=1.3.7
- branca=0.6.0 - branca=0.6.0
- brotli=1.0.9 - brotli=1.0.9
- brotli-bin=1.0.9 - brotli-bin=1.0.9
- brotlipy=0.7.0 - brotli-python=1.0.9
- bzip2=1.0.8 - bzip2=1.0.8
- c-ares=1.18.1 - c-ares=1.19.1
- ca-certificates=2022.12.7 - c-blosc2=2.10.0
- ca-certificates=2023.7.22
- cairo=1.16.0 - cairo=1.16.0
- cartopy=0.21.1 - cartopy=0.21.1
- cdsapi=0.5.1 - cdsapi=0.6.1
- certifi=2022.12.7 - certifi=2023.7.22
- cffi=1.15.1 - cffi=1.15.1
- cfitsio=4.2.0 - cfitsio=4.2.0
- cftime=1.6.2 - cftime=1.6.2
- charset-normalizer=2.1.1 - charset-normalizer=3.2.0
- click=8.1.3 - click=8.1.6
- click-plugins=1.1.1 - click-plugins=1.1.1
- cligj=0.7.2 - cligj=0.7.2
- cloudpickle=2.2.1 - cloudpickle=2.2.1
- coin-or-cbc=2.10.8
- coin-or-cgl=0.60.6
- coin-or-clp=1.17.7
- coin-or-osi=0.108.7
- coin-or-utils=2.11.6
- coincbc=2.10.8
- colorama=0.4.6 - colorama=0.4.6
- configargparse=1.5.3 - comm=0.1.3
- configargparse=1.7
- connection_pool=0.0.3 - connection_pool=0.0.3
- country_converter=0.8.0 - contourpy=1.1.0
- cryptography=39.0.1 - country_converter=1.0.0
- curl=7.88.0 - curl=8.2.0
- cycler=0.11.0 - cycler=0.11.0
- cytoolz=0.12.0 - cytoolz=0.12.2
- dask=2023.2.0 - dask=2023.7.1
- dask-core=2023.2.0 - dask-core=2023.7.1
- datrie=0.8.2 - datrie=0.8.2
- dbus=1.13.6 - dbus=1.13.6
- debugpy=1.6.7
- decorator=5.1.1 - decorator=5.1.1
- defusedxml=0.7.1
- deprecation=2.1.0 - deprecation=2.1.0
- descartes=1.1.0 - descartes=1.1.0
- distributed=2023.2.0 - distributed=2023.7.1
- distro=1.8.0 - distro=1.8.0
- docutils=0.19 - docutils=0.20.1
- dpath=2.1.4 - dpath=2.1.6
- entsoe-py=0.5.8 - entrypoints=0.4
- entsoe-py=0.5.10
- et_xmlfile=1.1.0 - et_xmlfile=1.1.0
- exceptiongroup=1.1.0 - exceptiongroup=1.1.2
- executing=1.2.0 - executing=1.2.0
- expat=2.5.0 - expat=2.5.0
- fftw=3.3.10 - filelock=3.12.2
- filelock=3.9.0 - fiona=1.9.4
- fiona=1.9.1 - flit-core=3.9.0
- folium=0.14.0 - folium=0.14.0
- font-ttf-dejavu-sans-mono=2.37 - font-ttf-dejavu-sans-mono=2.37
- font-ttf-inconsolata=3.000 - font-ttf-inconsolata=3.000
@ -88,293 +107,366 @@ dependencies:
- fontconfig=2.14.2 - fontconfig=2.14.2
- fonts-conda-ecosystem=1 - fonts-conda-ecosystem=1
- fonts-conda-forge=1 - fonts-conda-forge=1
- fonttools=4.38.0 - fonttools=4.41.1
- freetype=2.12.1 - freetype=2.12.1
- freexl=1.0.6 - freexl=1.0.6
- fsspec=2023.1.0 - fribidi=1.0.10
- gdal=3.6.2 - fsspec=2023.6.0
- gdal=3.7.0
- gdk-pixbuf=2.42.10
- geographiclib=1.52 - geographiclib=1.52
- geojson-rewind=1.0.2 - geojson-rewind=1.0.2
- geopandas=0.12.2 - geopandas=0.13.2
- geopandas-base=0.12.2 - geopandas-base=0.13.2
- geopy=2.3.0 - geopy=2.3.0
- geos=3.11.1 - geos=3.11.2
- geotiff=1.7.1 - geotiff=1.7.1
- gettext=0.21.1 - gettext=0.21.1
- gflags=2.2.2
- giflib=5.2.1 - giflib=5.2.1
- gitdb=4.0.10 - gitdb=4.0.10
- gitpython=3.1.30 - gitpython=3.1.32
- glib=2.74.1 - glib=2.76.4
- glib-tools=2.74.1 - glib-tools=2.76.4
- glog=0.6.0
- gmp=6.2.1
- graphite2=1.3.13 - graphite2=1.3.13
- gst-plugins-base=1.22.0 - graphviz=8.1.0
- gstreamer=1.22.0 - gst-plugins-base=1.22.5
- gstreamer-orc=0.4.33 - gstreamer=1.22.5
- harfbuzz=6.0.0 - gtk2=2.24.33
- gts=0.7.6
- harfbuzz=7.3.0
- hdf4=4.2.15 - hdf4=4.2.15
- hdf5=1.12.2 - hdf5=1.14.1
- heapdict=1.0.1
- humanfriendly=10.0 - humanfriendly=10.0
- icu=70.1 - icu=72.1
- idna=3.4 - idna=3.4
- importlib-metadata=6.0.0 - importlib-metadata=6.8.0
- importlib_resources=5.10.2 - importlib_metadata=6.8.0
- importlib_resources=6.0.0
- iniconfig=2.0.0 - iniconfig=2.0.0
- ipopt=3.14.11 - ipopt=3.14.12
- ipython=8.10.0 - ipykernel=6.24.0
- jack=1.9.22 - ipython=8.14.0
- ipython_genutils=0.2.0
- ipywidgets=8.0.7
- jedi=0.18.2 - jedi=0.18.2
- jinja2=3.1.2 - jinja2=3.1.2
- joblib=1.2.0 - joblib=1.3.0
- jpeg=9e
- json-c=0.16 - json-c=0.16
- jsonschema=4.17.3 - json5=0.9.14
- jupyter_core=5.2.0 - jsonschema=4.18.4
- kealib=1.5.0 - jsonschema-specifications=2023.7.1
- jupyter=1.0.0
- jupyter-lsp=2.2.0
- jupyter_client=8.3.0
- jupyter_console=6.6.3
- jupyter_core=5.3.1
- jupyter_events=0.6.3
- jupyter_server=2.7.0
- jupyter_server_terminals=0.4.4
- jupyterlab=4.0.3
- jupyterlab_pygments=0.2.2
- jupyterlab_server=2.24.0
- jupyterlab_widgets=3.0.8
- kealib=1.5.1
- keyutils=1.6.1 - keyutils=1.6.1
- kiwisolver=1.4.4 - kiwisolver=1.4.4
- krb5=1.20.1 - krb5=1.21.1
- lame=3.100 - lame=3.100
- lcms2=2.14 - lcms2=2.15
- ld_impl_linux-64=2.40 - ld_impl_linux-64=2.40
- lerc=4.0.0 - lerc=4.0.0
- libabseil=20230125.3
- libaec=1.0.6 - libaec=1.0.6
- libarchive=3.6.2
- libarrow=12.0.1
- libblas=3.9.0 - libblas=3.9.0
- libbrotlicommon=1.0.9 - libbrotlicommon=1.0.9
- libbrotlidec=1.0.9 - libbrotlidec=1.0.9
- libbrotlienc=1.0.9 - libbrotlienc=1.0.9
- libcap=2.66 - libcap=2.67
- libcblas=3.9.0 - libcblas=3.9.0
- libclang=15.0.7 - libclang=15.0.7
- libclang13=15.0.7 - libclang13=15.0.7
- libcrc32c=1.1.2
- libcups=2.3.3 - libcups=2.3.3
- libcurl=7.88.0 - libcurl=8.2.0
- libdb=6.2.32 - libdeflate=1.18
- libdeflate=1.17
- libedit=3.1.20191231 - libedit=3.1.20191231
- libev=4.33 - libev=4.33
- libevent=2.1.10 - libevent=2.1.12
- libexpat=2.5.0
- libffi=3.4.2 - libffi=3.4.2
- libflac=1.4.2 - libflac=1.4.3
- libgcc-ng=12.2.0 - libgcc-ng=13.1.0
- libgcrypt=1.10.1 - libgcrypt=1.10.1
- libgdal=3.6.2 - libgd=2.3.3
- libgfortran-ng=12.2.0 - libgdal=3.7.0
- libgfortran5=12.2.0 - libgfortran-ng=13.1.0
- libglib=2.74.1 - libgfortran5=13.1.0
- libgomp=12.2.0 - libglib=2.76.4
- libgpg-error=1.46 - libgomp=13.1.0
- libgoogle-cloud=2.12.0
- libgpg-error=1.47
- libgrpc=1.56.2
- libiconv=1.17 - libiconv=1.17
- libjpeg-turbo=2.1.5.1
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- liblapack=3.9.0 - liblapack=3.9.0
- liblapacke=3.9.0 - liblapacke=3.9.0
- libllvm15=15.0.7 - libllvm15=15.0.7
- libnetcdf=4.8.1 - libnetcdf=4.9.2
- libnghttp2=1.51.0 - libnghttp2=1.52.0
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- libnuma=2.0.16
- libogg=1.3.4 - libogg=1.3.4
- libopenblas=0.3.21 - libopenblas=0.3.23
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- libpng=1.6.39 - libpng=1.6.39
- libpq=15.2 - libpq=15.3
- libprotobuf=4.23.3
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- libssh2=1.10.0 - libssh2=1.11.0
- libstdcxx-ng=12.2.0 - libstdcxx-ng=13.1.0
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- locket=1.0.0 - locket=1.0.0
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- munkres=1.1.4 - munkres=1.1.4
- mysql-common=8.0.32 - mysql-common=8.0.33
- mysql-libs=8.0.32 - mysql-libs=8.0.33
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- ncurses=6.3 - nbconvert=7.7.2
- netcdf4=1.6.2 - nbconvert-core=7.7.2
- networkx=3.0 - nbconvert-pandoc=7.7.2
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- ncurses=6.4
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- networkx=3.1
- nomkl=1.0 - nomkl=1.0
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- nspr=4.35 - nspr=4.35
- nss=3.88 - nss=3.89
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- openssl=3.0.8 - openssl=3.1.1
- packaging=23.0 - orc=1.9.0
- pandas=1.5.3 - overrides=7.3.1
- packaging=23.1
- pandas=2.0.3
- pandoc=3.1.3
- pandocfilters=1.5.0
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- pcre2=10.40 - pcre2=10.40
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- pickleshare=0.7.5 - pickleshare=0.7.5
- pillow=9.4.0 - pillow=10.0.0
- pip=23.0 - pip=23.2.1
- pixman=0.40.0 - pixman=0.40.0
- pkgutil-resolve-name=1.3.10 - pkgutil-resolve-name=1.3.10
- plac=1.3.5 - plac=1.3.5
- platformdirs=3.0.0 - platformdirs=3.9.1
- pluggy=1.0.0 - pluggy=1.2.0
- ply=3.11 - ply=3.11
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- poppler=22.12.0 - poppler=23.05.0
- poppler-data=0.4.12 - poppler-data=0.4.12
- postgresql=15.2 - postgresql=15.3
- powerplantmatching=0.5.6 - powerplantmatching=0.5.7
- progressbar2=4.2.0 - progressbar2=4.2.0
- proj=9.1.0 - proj=9.2.1
- prompt-toolkit=3.0.36 - prometheus_client=0.17.1
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- prompt_toolkit=3.0.39
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- pthread-stubs=0.4 - pthread-stubs=0.4
- ptyprocess=0.7.0 - ptyprocess=0.7.0
- pulp=2.7.0 - pulp=2.7.0
- pulseaudio=16.1 - pulseaudio-client=16.1
- pure_eval=0.2.2 - pure_eval=0.2.2
- py-cpuinfo=9.0.0
- pyarrow=12.0.1
- pycountry=22.3.5 - pycountry=22.3.5
- pycparser=2.21 - pycparser=2.21
- pygments=2.14.0 - pygments=2.15.1
- pyomo=6.4.4 - pyomo=6.6.1
- pyopenssl=23.0.0 - pyparsing=3.1.0
- pyparsing=3.0.9 - pyproj=3.6.0
- pyproj=3.4.1
- pypsa=0.22.1
- pyqt=5.15.7 - pyqt=5.15.7
- pyqt5-sip=12.11.0 - pyqt5-sip=12.11.0
- pyrsistent=0.19.3
- pyshp=2.3.1 - pyshp=2.3.1
- pysocks=1.7.1 - pysocks=1.7.1
- pytables=3.7.0 - pytables=3.8.0
- pytest=7.2.1 - pytest=7.4.0
- python=3.10.9 - python=3.10.12
- python-dateutil=2.8.2 - python-dateutil=2.8.2
- python-fastjsonschema=2.16.2 - python-fastjsonschema=2.18.0
- python-utils=3.5.2 - python-json-logger=2.0.7
- python-tzdata=2023.3
- python-utils=3.7.0
- python_abi=3.10 - python_abi=3.10
- pytz=2022.7.1 - pytz=2023.3
- pyxlsb=1.0.10 - pyxlsb=1.0.10
- pyyaml=6.0 - pyyaml=6.0
- pyzmq=25.1.0
- qt-main=5.15.8 - qt-main=5.15.8
- rasterio=1.3.4 - qtconsole=5.4.3
- readline=8.1.2 - qtconsole-base=5.4.3
- requests=2.28.1 - qtpy=2.3.1
- retry=0.9.2 - rasterio=1.3.8
- rich=12.5.1 - rdma-core=28.9
- rioxarray=0.13.3 - re2=2023.03.02
- rtree=1.0.0 - readline=8.2
- s2n=1.0.10 - referencing=0.30.0
- scikit-learn=1.1.1 - requests=2.31.0
- scipy=1.8.1 - reretry=0.11.8
- rfc3339-validator=0.1.4
- rfc3986-validator=0.1.1
- rioxarray=0.14.1
- rpds-py=0.9.2
- rtree=1.0.1
- s2n=1.3.46
- scikit-learn=1.3.0
- scipy=1.11.1
- scotch=6.0.9 - scotch=6.0.9
- seaborn=0.12.2 - seaborn=0.12.2
- seaborn-base=0.12.2 - seaborn-base=0.12.2
- setuptools=67.3.2 - send2trash=1.8.2
- setuptools=68.0.0
- setuptools-scm=7.1.0 - setuptools-scm=7.1.0
- setuptools_scm=7.1.0 - setuptools_scm=7.1.0
- shapely=2.0.1 - shapely=2.0.1
- sip=6.7.7 - sip=6.7.10
- six=1.16.0 - six=1.16.0
- smart_open=6.3.0 - smart_open=6.3.0
- smmap=3.0.5 - smmap=3.0.5
- snakemake-minimal=7.22.0 - snakemake-minimal=7.30.2
- snappy=1.1.9 - snappy=1.1.10
- sniffio=1.3.0
- snuggs=1.4.7 - snuggs=1.4.7
- sortedcontainers=2.4.0 - sortedcontainers=2.4.0
- soupsieve=2.3.2.post1 - soupsieve=2.3.2.post1
- sqlite=3.40.0 - sqlite=3.42.0
- stack_data=0.6.2 - stack_data=0.6.2
- statsmodels=0.13.5 - statsmodels=0.14.0
- stopit=1.1.2 - stopit=1.1.2
- tabula-py=2.6.0 - tabula-py=2.6.0
- tabulate=0.9.0 - tabulate=0.9.0
- tblib=1.7.0 - tblib=1.7.0
- threadpoolctl=3.1.0 - terminado=0.17.1
- threadpoolctl=3.2.0
- throttler=1.2.1 - throttler=1.2.1
- tiledb=2.13.2 - tiledb=2.13.2
- tinycss2=1.2.1
- tk=8.6.12 - tk=8.6.12
- toml=0.10.2 - toml=0.10.2
- tomli=2.0.1 - tomli=2.0.1
- toolz=0.12.0 - toolz=0.12.0
- toposort=1.9 - toposort=1.10
- tornado=6.2 - tornado=6.3.2
- tqdm=4.64.1 - tqdm=4.65.0
- traitlets=5.9.0 - traitlets=5.9.0
- typing-extensions=4.4.0 - typing-extensions=4.7.1
- typing_extensions=4.4.0 - typing_extensions=4.7.1
- tzcode=2022g - typing_utils=0.1.0
- tzdata=2022g - tzcode=2023c
- tzdata=2023c
- ucx=1.14.1
- unicodedata2=15.0.0 - unicodedata2=15.0.0
- unidecode=1.3.6 - unidecode=1.3.6
- unixodbc=2.3.10 - unixodbc=2.3.10
- urllib3=1.26.14 - urllib3=2.0.4
- wcwidth=0.2.6 - wcwidth=0.2.6
- wheel=0.38.4 - webencodings=0.5.1
- wrapt=1.14.1 - websocket-client=1.6.1
- xarray=2023.2.0 - wheel=0.41.0
- widgetsnbextension=4.0.8
- wrapt=1.15.0
- xarray=2023.7.0
- xcb-util=0.4.0 - xcb-util=0.4.0
- xcb-util-image=0.4.0 - xcb-util-image=0.4.0
- xcb-util-keysyms=0.4.0 - xcb-util-keysyms=0.4.0
- xcb-util-renderutil=0.3.9 - xcb-util-renderutil=0.3.9
- xcb-util-wm=0.4.1 - xcb-util-wm=0.4.1
- xerces-c=3.2.4 - xerces-c=3.2.4
- xkeyboard-config=2.39
- xlrd=2.0.1 - xlrd=2.0.1
- xorg-fixesproto=5.0 - xorg-fixesproto=5.0
- xorg-inputproto=2.3.2 - xorg-inputproto=2.3.2
- xorg-kbproto=1.0.7 - xorg-kbproto=1.0.7
- xorg-libice=1.0.10 - xorg-libice=1.1.1
- xorg-libsm=1.2.3 - xorg-libsm=1.2.4
- xorg-libx11=1.7.2 - xorg-libx11=1.8.6
- xorg-libxau=1.0.9 - xorg-libxau=1.0.11
- xorg-libxdmcp=1.1.3 - xorg-libxdmcp=1.1.3
- xorg-libxext=1.3.4 - xorg-libxext=1.3.4
- xorg-libxfixes=5.0.3 - xorg-libxfixes=5.0.3
- xorg-libxi=1.7.10 - xorg-libxi=1.7.10
- xorg-libxrender=0.9.10 - xorg-libxrender=0.9.11
- xorg-libxtst=1.2.3 - xorg-libxtst=1.2.3
- xorg-recordproto=1.14.2 - xorg-recordproto=1.14.2
- xorg-renderproto=0.11.1 - xorg-renderproto=0.11.1
- xorg-xextproto=7.3.0 - xorg-xextproto=7.3.0
- xorg-xf86vidmodeproto=2.3.1
- xorg-xproto=7.0.31 - xorg-xproto=7.0.31
- xyzservices=2022.9.0 - xyzservices=2023.7.0
- xz=5.2.6 - xz=5.2.6
- yaml=0.2.5 - yaml=0.2.5
- yte=1.5.1 - yte=1.5.1
- zict=2.2.0 - zeromq=4.3.4
- zipp=3.13.0 - zict=3.0.0
- zipp=3.16.2
- zlib=1.2.13 - zlib=1.2.13
- zlib-ng=2.0.7
- zstd=1.5.2 - zstd=1.5.2
- pip: - pip:
- countrycode==0.2 - gurobipy==10.0.2
- highspy==1.5.0.dev0 - linopy==0.2.2
- pybind11==2.10.3 - pypsa==0.25.1
- tsam==2.2.2 - tsam==2.3.0
- validators==0.20.0

4
envs/environment.yaml
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@ -10,7 +10,6 @@ dependencies:
- python>=3.8 - python>=3.8
- pip - pip
# - pypsa>=0.23
- atlite>=0.2.9 - atlite>=0.2.9
- dask - dask
@ -54,6 +53,7 @@ dependencies:
- descartes - descartes
- rasterio!=1.2.10 - rasterio!=1.2.10
- pip: - pip:
- tsam>=1.1.0 - tsam>=1.1.0
- git+https://github.com/PyPSA/PyPSA.git@master - pypsa>=0.25.1

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1
matplotlibrc
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@ -4,3 +4,4 @@
font.family: sans-serif font.family: sans-serif
font.sans-serif: Ubuntu, DejaVu Sans font.sans-serif: Ubuntu, DejaVu Sans
image.cmap: viridis image.cmap: viridis
figure.autolayout : True

196
rules/build_electricity.smk
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@ -62,6 +62,9 @@ rule base_network:
params: params:
countries=config["countries"], countries=config["countries"],
snapshots=config["snapshots"], snapshots=config["snapshots"],
lines=config["lines"],
links=config["links"],
transformers=config["transformers"],
input: input:
eg_buses="data/entsoegridkit/buses.csv", eg_buses="data/entsoegridkit/buses.csv",
eg_lines="data/entsoegridkit/lines.csv", eg_lines="data/entsoegridkit/lines.csv",
@ -203,75 +206,117 @@ rule build_ship_raster:
"../scripts/build_ship_raster.py" "../scripts/build_ship_raster.py"
# rule build_renewable_profiles: rule build_renewable_profiles:
# params: params:
# renewable=config["renewable"], renewable=config["renewable"],
# input: input:
# base_network=RESOURCES + "networks/base.nc", base_network=RESOURCES + "networks/base.nc",
# corine=ancient("data/bundle/corine/g250_clc06_V18_5.tif"), corine=ancient("data/bundle/corine/g250_clc06_V18_5.tif"),
# natura=lambda w: ( natura=lambda w: (
# RESOURCES + "natura.tiff" RESOURCES + "natura.tiff"
# if config["renewable"][w.technology]["natura"] if config["renewable"][w.technology]["natura"]
# else [] else []
# ), ),
# gebco=ancient( gebco=ancient(
# lambda w: ( lambda w: (
# "data/bundle/GEBCO_2014_2D.nc" "data/bundle/GEBCO_2014_2D.nc"
# if config["renewable"][w.technology].get("max_depth") if config["renewable"][w.technology].get("max_depth")
# else [] else []
# ) )
# ), ),
# ship_density=lambda w: ( ship_density=lambda w: (
# RESOURCES + "shipdensity_raster.tif" RESOURCES + "shipdensity_raster.tif"
# if "ship_threshold" in config["renewable"][w.technology].keys() if "ship_threshold" in config["renewable"][w.technology].keys()
# else [] else []
# ), ),
# country_shapes=RESOURCES + "country_shapes.geojson", country_shapes=RESOURCES + "country_shapes.geojson",
# offshore_shapes=RESOURCES + "offshore_shapes.geojson", offshore_shapes=RESOURCES + "offshore_shapes.geojson",
# regions=lambda w: ( regions=lambda w: (
# RESOURCES + "regions_onshore.geojson" RESOURCES + "regions_onshore.geojson"
# if w.technology in ("onwind", "solar") if w.technology in ("onwind", "solar")
# else RESOURCES + "regions_offshore.geojson" else RESOURCES + "regions_offshore.geojson"
# ), ),
# cutout=lambda w: "cutouts/" cutout=lambda w: "cutouts/"
# + CDIR + CDIR
# + config["renewable"][w.technology]["cutout"] + config["renewable"][w.technology]["cutout"]
# + ".nc", + ".nc",
# output: output:
# profile=RESOURCES + "profile_{technology}.nc", profile=RESOURCES + "profile_{technology}.nc",
# log: log:
# LOGS + "build_renewable_profile_{technology}.log", LOGS + "build_renewable_profile_{technology}.log",
# benchmark: benchmark:
# BENCHMARKS + "build_renewable_profiles_{technology}" BENCHMARKS + "build_renewable_profiles_{technology}"
# threads: ATLITE_NPROCESSES threads: ATLITE_NPROCESSES
# resources: resources:
# mem_mb=ATLITE_NPROCESSES * 5000, mem_mb=ATLITE_NPROCESSES * 5000,
# wildcard_constraints: wildcard_constraints:
# technology="(?!hydro).*", # Any technology other than hydro technology="(?!hydro).*", # Any technology other than hydro
# conda: conda:
# "../envs/environment.yaml" "../envs/environment.yaml"
# script: script:
# "../scripts/build_renewable_profiles.py" "../scripts/build_renewable_profiles.py"
# rule build_hydro_profile: rule build_monthly_prices:
# params: input:
# hydro=config["renewable"]["hydro"], co2_price_raw="data/validation/emission-spot-primary-market-auction-report-2019-data.xls",
# countries=config["countries"], fuel_price_raw="data/validation/energy-price-trends-xlsx-5619002.xlsx",
# input: output:
# country_shapes=RESOURCES + "country_shapes.geojson", co2_price=RESOURCES + "co2_price.csv",
# eia_hydro_generation="data/eia_hydro_annual_generation.csv", fuel_price=RESOURCES + "monthly_fuel_price.csv",
# cutout=f"cutouts/" + CDIR + config["renewable"]["hydro"]["cutout"] + ".nc", log:
# output: LOGS + "build_monthly_prices.log",
# RESOURCES + "profile_hydro.nc", threads: 1
# log: resources:
# LOGS + "build_hydro_profile.log", mem_mb=5000,
# resources: conda:
# mem_mb=5000, "../envs/environment.yaml"
# conda: script:
# "../envs/environment.yaml" "../scripts/build_monthly_prices.py"
# script:
# "../scripts/build_hydro_profile.py"
rule build_hydro_profile:
params:
hydro=config["renewable"]["hydro"],
countries=config["countries"],
input:
country_shapes=RESOURCES + "country_shapes.geojson",
eia_hydro_generation="data/eia_hydro_annual_generation.csv",
cutout=f"cutouts/" + CDIR + config["renewable"]["hydro"]["cutout"] + ".nc",
output:
RESOURCES + "profile_hydro.nc",
log:
LOGS + "build_hydro_profile.log",
resources:
mem_mb=5000,
conda:
"../envs/environment.yaml"
script:
"../scripts/build_hydro_profile.py"
if config["lines"]["dynamic_line_rating"]["activate"]:
rule build_line_rating:
input:
base_network=RESOURCES + "networks/base.nc",
cutout="cutouts/"
+ CDIR
+ config["lines"]["dynamic_line_rating"]["cutout"]
+ ".nc",
output:
output=RESOURCES + "networks/line_rating.nc",
log:
LOGS + "build_line_rating.log",
benchmark:
BENCHMARKS + "build_line_rating"
threads: ATLITE_NPROCESSES
resources:
mem_mb=ATLITE_NPROCESSES * 1000,
conda:
"../envs/environment.yaml"
script:
"../scripts/build_line_rating.py"
rule add_electricity: rule add_electricity:
@ -281,7 +326,7 @@ rule add_electricity:
countries=config["countries"], countries=config["countries"],
renewable=config["renewable"], renewable=config["renewable"],
electricity=config["electricity"], electricity=config["electricity"],
conventional=config.get("conventional", {}), conventional=config["conventional"],
costs=config["costs"], costs=config["costs"],
input: input:
**{ **{
@ -291,15 +336,21 @@ rule add_electricity:
**{ **{
f"conventional_{carrier}_{attr}": fn f"conventional_{carrier}_{attr}": fn
for carrier, d in config.get("conventional", {None: {}}).items() for carrier, d in config.get("conventional", {None: {}}).items()
if carrier in config["electricity"]["conventional_carriers"]
for attr, fn in d.items() for attr, fn in d.items()
if str(fn).startswith("data/") if str(fn).startswith("data/")
}, },
base_network=RESOURCES + "networks/base.nc", base_network=RESOURCES + "networks/base.nc",
line_rating=RESOURCES + "networks/line_rating.nc"
if config["lines"]["dynamic_line_rating"]["activate"]
else RESOURCES + "networks/base.nc",
tech_costs=COSTS, tech_costs=COSTS,
regions=RESOURCES + "regions_onshore.geojson", regions=RESOURCES + "regions_onshore.geojson",
powerplants=RESOURCES + "powerplants.csv", powerplants=RESOURCES + "powerplants.csv",
hydro_capacities=ancient("data/bundle/hydro_capacities.csv"), hydro_capacities=ancient("data/bundle/hydro_capacities.csv"),
geth_hydro_capacities="data/geth2015_hydro_capacities.csv", geth_hydro_capacities="data/geth2015_hydro_capacities.csv",
unit_commitment="data/unit_commitment.csv",
fuel_price=RESOURCES + "monthly_fuel_price.csv",
load=RESOURCES + "load.csv", load=RESOURCES + "load.csv",
nuts3_shapes=RESOURCES + "nuts3_shapes.geojson", nuts3_shapes=RESOURCES + "nuts3_shapes.geojson",
output: output:
@ -310,7 +361,7 @@ rule add_electricity:
BENCHMARKS + "add_electricity" BENCHMARKS + "add_electricity"
threads: 1 threads: 1
resources: resources:
mem_mb=5000, mem_mb=10000,
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -344,7 +395,7 @@ rule simplify_network:
BENCHMARKS + "simplify_network/elec_s{simpl}" BENCHMARKS + "simplify_network/elec_s{simpl}"
threads: 1 threads: 1
resources: resources:
mem_mb=4000, mem_mb=12000,
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -385,7 +436,7 @@ rule cluster_network:
BENCHMARKS + "cluster_network/elec_s{simpl}_{clusters}" BENCHMARKS + "cluster_network/elec_s{simpl}_{clusters}"
threads: 1 threads: 1
resources: resources:
mem_mb=6000, mem_mb=10000,
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -408,7 +459,7 @@ rule add_extra_components:
BENCHMARKS + "add_extra_components/elec_s{simpl}_{clusters}_ec" BENCHMARKS + "add_extra_components/elec_s{simpl}_{clusters}_ec"
threads: 1 threads: 1
resources: resources:
mem_mb=3000, mem_mb=4000,
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -427,6 +478,7 @@ rule prepare_network:
input: input:
RESOURCES + "networks/elec_s{simpl}_{clusters}_ec.nc", RESOURCES + "networks/elec_s{simpl}_{clusters}_ec.nc",
tech_costs=COSTS, tech_costs=COSTS,
co2_price=RESOURCES + "co2_price.csv",
output: output:
RESOURCES + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", RESOURCES + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
log: log:

8
rules/build_sector.smk
View File

@ -86,7 +86,7 @@ if config["sector"]["gas_network"] or config["sector"]["H2_retrofit"]:
rule build_gas_input_locations: rule build_gas_input_locations:
input: input:
lng=HTTP.remote( lng=HTTP.remote(
"https://globalenergymonitor.org/wp-content/uploads/2022/09/Europe-Gas-Tracker-August-2022.xlsx", "https://globalenergymonitor.org/wp-content/uploads/2023/07/Europe-Gas-Tracker-2023-03-v3.xlsx",
keep_local=True, keep_local=True,
), ),
entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson", entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
@ -295,7 +295,7 @@ rule build_biomass_potentials:
"../scripts/build_biomass_potentials.py" "../scripts/build_biomass_potentials.py"
if config["sector"]["biomass_transport"]: if config["sector"]["biomass_transport"] or config["sector"]["biomass_spatial"]:
rule build_biomass_transport_costs: rule build_biomass_transport_costs:
input: input:
@ -320,9 +320,8 @@ if config["sector"]["biomass_transport"]:
build_biomass_transport_costs_output = rules.build_biomass_transport_costs.output build_biomass_transport_costs_output = rules.build_biomass_transport_costs.output
if not config["sector"]["biomass_transport"]: if not (config["sector"]["biomass_transport"] or config["sector"]["biomass_spatial"]):
# this is effecively an `else` statement which is however not liked by snakefmt # this is effecively an `else` statement which is however not liked by snakefmt
build_biomass_transport_costs_output = {} build_biomass_transport_costs_output = {}
@ -724,7 +723,6 @@ rule prepare_sector_network:
**build_biomass_transport_costs_output, **build_biomass_transport_costs_output,
**gas_infrastructure, **gas_infrastructure,
**build_sequestration_potentials_output, **build_sequestration_potentials_output,
overrides="data/override_component_attrs",
network=RESOURCES + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", network=RESOURCES + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
energy_totals_name=RESOURCES + "energy_totals.csv", energy_totals_name=RESOURCES + "energy_totals.csv",
eurostat=input_eurostat, eurostat=input_eurostat,

15
rules/collect.smk
View File

@ -82,3 +82,18 @@ rule plot_networks:
+ "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf", + "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
**config["scenario"] **config["scenario"]
), ),
rule validate_elec_networks:
input:
expand(
RESULTS
+ "figures/.statistics_plots_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}",
**config["scenario"]
),
expand(
RESULTS
+ "figures/.validation_{kind}_plots_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}",
**config["scenario"],
kind=["production", "prices", "cross_border"]
),

4
rules/common.smk
View File

@ -15,8 +15,8 @@ def memory(w):
if m is not None: if m is not None:
factor *= int(m.group(1)) / 8760 factor *= int(m.group(1)) / 8760
break break
if w.clusters.endswith("m"): if w.clusters.endswith("m") or w.clusters.endswith("c"):
return int(factor * (18000 + 180 * int(w.clusters[:-1]))) return int(factor * (55000 + 600 * int(w.clusters[:-1])))
elif w.clusters == "all": elif w.clusters == "all":
return int(factor * (18000 + 180 * 4000)) return int(factor * (18000 + 180 * 4000))
else: else:

77
rules/postprocess.smk
View File

@ -13,7 +13,6 @@ rule plot_network:
foresight=config["foresight"], foresight=config["foresight"],
plotting=config["plotting"], plotting=config["plotting"],
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson", regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
@ -35,12 +34,38 @@ rule plot_network:
script: script:
"../scripts/plot_network.py" "../scripts/plot_network.py"
rule plot_network_perfect:
params:
foresight=config["foresight"],
plotting=config["plotting"],
input:
network=RESULTS
+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
regions=RESOURCES + "regions_onshore_elec_s{simpl}_{clusters}.geojson",
output:
map=RESULTS
+ "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}-costs-all_{year}.pdf",
today=RESULTS
+ "maps/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{year}-today.pdf",
threads: 2
resources:
mem_mb=10000,
benchmark:
BENCHMARKS
+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{year}_brownfield_all_years_benchmark.txt",
conda:
"../envs/environment.yaml"
script:
"../scripts/plot_network.py"
rule copy_config: rule copy_config:
params: params:
RDIR=RDIR, RDIR=RDIR,
output: output:
RESULTS + "config/config.yaml", RESULTS + "config.yaml",
threads: 1 threads: 1
resources: resources:
mem_mb=1000, mem_mb=1000,
@ -52,22 +77,6 @@ rule copy_config:
"../scripts/copy_config.py" "../scripts/copy_config.py"
rule copy_conda_env:
output:
RESULTS + "config/environment.yaml",
threads: 1
resources:
mem_mb=500,
log:
LOGS + "copy_conda_env.log",
benchmark:
BENCHMARKS + "copy_conda_env"
conda:
"../envs/environment.yaml"
shell:
"conda env export -f {output} --no-builds"
rule make_summary: rule make_summary:
params: params:
foresight=config["foresight"], foresight=config["foresight"],
@ -76,7 +85,6 @@ rule make_summary:
scenario=config["scenario"], scenario=config["scenario"],
RDIR=RDIR, RDIR=RDIR,
input: input:
overrides="data/override_component_attrs",
networks=expand( networks=expand(
RESULTS RESULTS
+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
@ -147,3 +155,34 @@ rule plot_summary:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
"../scripts/plot_summary.py" "../scripts/plot_summary.py"
STATISTICS_BARPLOTS = [
"capacity_factor",
"installed_capacity",
"optimal_capacity",
"capital_expenditure",
"operational_expenditure",
"curtailment",
"supply",
"withdrawal",
"market_value",
]
rule plot_elec_statistics:
params:
plotting=config["plotting"],
barplots=STATISTICS_BARPLOTS,
input:
network=RESULTS + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
output:
**{
f"{plot}_bar": RESULTS
+ f"figures/statistics_{plot}_bar_elec_s{{simpl}}_{{clusters}}_ec_l{{ll}}_{{opts}}.pdf"
for plot in STATISTICS_BARPLOTS
},
barplots_touch=RESULTS
+ "figures/.statistics_plots_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}",
script:
"../scripts/plot_statistics.py"

42
rules/retrieve.smk
View File

@ -158,7 +158,11 @@ if config["enable"]["retrieve"]:
rule retrieve_electricity_demand: rule retrieve_electricity_demand:
input: input:
HTTP.remote( HTTP.remote(
"data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv", "data.open-power-system-data.org/time_series/{version}/time_series_60min_singleindex.csv".format(
version="2019-06-05"
if config["snapshots"]["end"] < "2019"
else "2020-10-06"
),
keep_local=True, keep_local=True,
static=True, static=True,
), ),
@ -191,3 +195,39 @@ if config["enable"]["retrieve"]:
retries: 2 retries: 2
run: run:
move(input[0], output[0]) move(input[0], output[0])
if config["enable"]["retrieve"]:
rule retrieve_monthly_co2_prices:
input:
HTTP.remote(
"https://www.eex.com/fileadmin/EEX/Downloads/EUA_Emission_Spot_Primary_Market_Auction_Report/Archive_Reports/emission-spot-primary-market-auction-report-2019-data.xls",
keep_local=True,
static=True,
),
output:
"data/validation/emission-spot-primary-market-auction-report-2019-data.xls",
log:
LOGS + "retrieve_monthly_co2_prices.log",
resources:
mem_mb=5000,
retries: 2
run:
move(input[0], output[0])
if config["enable"]["retrieve"]:
rule retrieve_monthly_fuel_prices:
output:
"data/validation/energy-price-trends-xlsx-5619002.xlsx",
log:
LOGS + "retrieve_monthly_fuel_prices.log",
resources:
mem_mb=5000,
retries: 2
conda:
"../envs/environment.yaml"
script:
"../scripts/retrieve_monthly_fuel_prices.py"

5
rules/solve_electricity.smk
View File

@ -13,6 +13,7 @@ rule solve_network:
), ),
input: input:
network=RESOURCES + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", network=RESOURCES + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
config=RESULTS + "config.yaml",
output: output:
network=RESULTS + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", network=RESULTS + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
log: log:
@ -26,6 +27,7 @@ rule solve_network:
threads: 4 threads: 4
resources: resources:
mem_mb=memory, mem_mb=memory,
walltime=config["solving"].get("walltime", "12:00:00"),
shadow: shadow:
"minimal" "minimal"
conda: conda:
@ -55,7 +57,8 @@ rule solve_operations_network:
) )
threads: 4 threads: 4
resources: resources:
mem_mb=(lambda w: 5000 + 372 * int(w.clusters)), mem_mb=(lambda w: 10000 + 372 * int(w.clusters)),
walltime=config["solving"].get("walltime", "12:00:00"),
shadow: shadow:
"minimal" "minimal"
conda: conda:

6
rules/solve_myopic.smk
View File

@ -10,7 +10,6 @@ rule add_existing_baseyear:
existing_capacities=config["existing_capacities"], existing_capacities=config["existing_capacities"],
costs=config["costs"], costs=config["costs"],
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
powerplants=RESOURCES + "powerplants.csv", powerplants=RESOURCES + "powerplants.csv",
@ -52,7 +51,6 @@ rule add_brownfield:
H2_retrofit_capacity_per_CH4=config["sector"]["H2_retrofit_capacity_per_CH4"], H2_retrofit_capacity_per_CH4=config["sector"]["H2_retrofit_capacity_per_CH4"],
threshold_capacity=config["existing_capacities"]["threshold_capacity"], threshold_capacity=config["existing_capacities"]["threshold_capacity"],
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
network_p=solved_previous_horizon, #solved network at previous time step network_p=solved_previous_horizon, #solved network at previous time step
@ -91,11 +89,10 @@ rule solve_sector_network_myopic:
"co2_sequestration_potential", 200 "co2_sequestration_potential", 200
), ),
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
costs="data/costs_{planning_horizons}.csv", costs="data/costs_{planning_horizons}.csv",
config=RESULTS + "config/config.yaml", config=RESULTS + "config.yaml",
output: output:
RESULTS RESULTS
+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
@ -109,6 +106,7 @@ rule solve_sector_network_myopic:
threads: 4 threads: 4
resources: resources:
mem_mb=config["solving"]["mem"], mem_mb=config["solving"]["mem"],
walltime=config["solving"].get("walltime", "12:00:00"),
benchmark: benchmark:
( (
BENCHMARKS BENCHMARKS

6
rules/solve_overnight.smk
View File

@ -12,12 +12,9 @@ rule solve_sector_network:
"co2_sequestration_potential", 200 "co2_sequestration_potential", 200
), ),
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
costs="data/costs_{}.csv".format(config["costs"]["year"]), config=RESULTS + "config.yaml",
config=RESULTS + "config/config.yaml",
#env=RDIR + 'config/environment.yaml',
output: output:
RESULTS RESULTS
+ "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "postnetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
@ -31,6 +28,7 @@ rule solve_sector_network:
threads: config["solving"]["solver"].get("threads", 4) threads: config["solving"]["solver"].get("threads", 4)
resources: resources:
mem_mb=config["solving"]["mem"], mem_mb=config["solving"]["mem"],
walltime=config["solving"].get("walltime", "12:00:00"),
benchmark: benchmark:
( (
RESULTS RESULTS

5
rules/solve_perfect.smk
View File

@ -3,7 +3,6 @@
# SPDX-License-Identifier: MIT # SPDX-License-Identifier: MIT
rule add_existing_baseyear: rule add_existing_baseyear:
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
powerplants=RESOURCES + "powerplants.csv", powerplants=RESOURCES + "powerplants.csv",
@ -41,7 +40,6 @@ rule add_existing_baseyear:
rule add_brownfield: rule add_brownfield:
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc", + "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
network_p=solved_previous_horizon, #solved network at previous time step network_p=solved_previous_horizon, #solved network at previous time step
@ -82,7 +80,6 @@ rule prepare_perfect_foresight:
+ "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_" + "elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_"
+ "{}.nc".format(str(config["scenario"]["planning_horizons"][0])) + "{}.nc".format(str(config["scenario"]["planning_horizons"][0]))
), ),
overrides="data/override_component_attrs",
output: output:
RESULTS RESULTS
+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc", + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
@ -105,7 +102,6 @@ rule prepare_perfect_foresight:
rule solve_sector_network_perfect: rule solve_sector_network_perfect:
input: input:
overrides="data/override_component_attrs",
network=RESULTS network=RESULTS
+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc", + "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_brownfield_all_years.nc",
costs="data/costs_2030.csv", costs="data/costs_2030.csv",
@ -148,7 +144,6 @@ rule make_summary_perfect:
for ll in config["scenario"]["ll"] for ll in config["scenario"]["ll"]
}, },
costs="data/costs_2020.csv", costs="data/costs_2020.csv",
overrides="data/override_component_attrs",
output: output:
nodal_costs=RESULTS + "csvs/nodal_costs.csv", nodal_costs=RESULTS + "csvs/nodal_costs.csv",
nodal_capacities=RESULTS + "csvs/nodal_capacities.csv", nodal_capacities=RESULTS + "csvs/nodal_capacities.csv",

117
rules/validate.smk Normal file
View File

@ -0,0 +1,117 @@
# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
PRODUCTION_PLOTS = [
"production_bar",
"production_deviation_bar",
"seasonal_operation_area",
]
CROSS_BORDER_PLOTS = ["trade_time_series", "cross_border_bar"]
PRICES_PLOTS = ["price_bar", "price_line"]
rule build_electricity_production:
"""
This rule builds the electricity production for each country and technology from ENTSO-E data.
The data is used for validation of the optimization results.
"""
params:
snapshots=config["snapshots"],
countries=config["countries"],
output:
RESOURCES + "historical_electricity_production.csv",
log:
LOGS + "build_electricity_production.log",
resources:
mem_mb=5000,
script:
"../scripts/build_electricity_production.py"
rule build_cross_border_flows:
"""
This rule builds the cross-border flows from ENTSO-E data.
The data is used for validation of the optimization results.
"""
params:
snapshots=config["snapshots"],
countries=config["countries"],
input:
network=RESOURCES + "networks/base.nc",
output:
RESOURCES + "historical_cross_border_flows.csv",
log:
LOGS + "build_cross_border_flows.log",
resources:
mem_mb=5000,
script:
"../scripts/build_cross_border_flows.py"
rule build_electricity_prices:
"""
This rule builds the electricity prices from ENTSO-E data.
The data is used for validation of the optimization results.
"""
params:
snapshots=config["snapshots"],
countries=config["countries"],
output:
RESOURCES + "historical_electricity_prices.csv",
log:
LOGS + "build_electricity_prices.log",
resources:
mem_mb=5000,
script:
"../scripts/build_electricity_prices.py"
rule plot_validation_electricity_production:
input:
network=RESULTS + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
electricity_production=RESOURCES + "historical_electricity_production.csv",
output:
**{
plot: RESULTS
+ f"figures/validation_{plot}_elec_s{{simpl}}_{{clusters}}_ec_l{{ll}}_{{opts}}.pdf"
for plot in PRODUCTION_PLOTS
},
plots_touch=RESULTS
+ "figures/.validation_production_plots_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}",
script:
"../scripts/plot_validation_electricity_production.py"
rule plot_validation_cross_border_flows:
params:
countries=config["countries"],
input:
network=RESULTS + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
cross_border_flows=RESOURCES + "historical_cross_border_flows.csv",
output:
**{
plot: RESULTS
+ f"figures/validation_{plot}_elec_s{{simpl}}_{{clusters}}_ec_l{{ll}}_{{opts}}.pdf"
for plot in CROSS_BORDER_PLOTS
},
plots_touch=RESULTS
+ "figures/.validation_cross_border_plots_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}",
script:
"../scripts/plot_validation_cross_border_flows.py"
rule plot_validation_electricity_prices:
input:
network=RESULTS + "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
electricity_prices=RESOURCES + "historical_electricity_prices.csv",
output:
**{
plot: RESULTS
+ f"figures/validation_{plot}_elec_s{{simpl}}_{{clusters}}_ec_l{{ll}}_{{opts}}.pdf"
for plot in PRICES_PLOTS
},
plots_touch=RESULTS
+ "figures/.validation_prices_plots_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}",
script:
"../scripts/plot_validation_electricity_prices.py"

114
scripts/_helpers.py
View File

@ -72,92 +72,6 @@ def configure_logging(snakemake, skip_handlers=False):
logging.basicConfig(**kwargs) logging.basicConfig(**kwargs)
def load_network(import_name=None, custom_components=None):
"""
Helper for importing a pypsa.Network with additional custom components.
Parameters
----------
import_name : str
As in pypsa.Network(import_name)
custom_components : dict
Dictionary listing custom components.
For using ``snakemake.params['override_components']``
in ``config/config.yaml`` define:
.. code:: yaml
override_components:
ShadowPrice:
component: ["shadow_prices","Shadow price for a global constraint.",np.nan]
attributes:
name: ["string","n/a","n/a","Unique name","Input (required)"]
value: ["float","n/a",0.,"shadow value","Output"]
Returns
-------
pypsa.Network
"""
import pypsa
from pypsa.descriptors import Dict
override_components = None
override_component_attrs = None
if custom_components is not None:
override_components = pypsa.components.components.copy()
override_component_attrs = Dict(
{k: v.copy() for k, v in pypsa.components.component_attrs.items()}
)
for k, v in custom_components.items():
override_components.loc[k] = v["component"]
override_component_attrs[k] = pd.DataFrame(
columns=["type", "unit", "default", "description", "status"]
)
for attr, val in v["attributes"].items():
override_component_attrs[k].loc[attr] = val
return pypsa.Network(
import_name=import_name,
override_components=override_components,
override_component_attrs=override_component_attrs,
)
def load_network_for_plots(fn, tech_costs, config, combine_hydro_ps=True):
import pypsa
from add_electricity import load_costs, update_transmission_costs
n = pypsa.Network(fn)
n.loads["carrier"] = n.loads.bus.map(n.buses.carrier) + " load"
n.stores["carrier"] = n.stores.bus.map(n.buses.carrier)
n.links["carrier"] = (
n.links.bus0.map(n.buses.carrier) + "-" + n.links.bus1.map(n.buses.carrier)
)
n.lines["carrier"] = "AC line"
n.transformers["carrier"] = "AC transformer"
n.lines["s_nom"] = n.lines["s_nom_min"]
n.links["p_nom"] = n.links["p_nom_min"]
if combine_hydro_ps:
n.storage_units.loc[
n.storage_units.carrier.isin({"PHS", "hydro"}), "carrier"
] = "hydro+PHS"
# if the carrier was not set on the heat storage units
# bus_carrier = n.storage_units.bus.map(n.buses.carrier)
# n.storage_units.loc[bus_carrier == "heat","carrier"] = "water tanks"
Nyears = n.snapshot_weightings.objective.sum() / 8760.0
costs = load_costs(tech_costs, config["costs"], config["electricity"], Nyears)
update_transmission_costs(n, costs)
return n
def update_p_nom_max(n): def update_p_nom_max(n):
# if extendable carriers (solar/onwind/...) have capacity >= 0, # if extendable carriers (solar/onwind/...) have capacity >= 0,
# e.g. existing assets from the OPSD project are included to the network, # e.g. existing assets from the OPSD project are included to the network,
@ -367,34 +281,6 @@ def mock_snakemake(rulename, configfiles=[], **wildcards):
return snakemake return snakemake
def override_component_attrs(directory):
"""
Tell PyPSA that links can have multiple outputs by overriding the
component_attrs. This can be done for as many buses as you need with format
busi for i = 2,3,4,5,.... See https://pypsa.org/doc/components.html#link-
with-multiple-outputs-or-inputs.
Parameters
----------
directory : string
Folder where component attributes to override are stored
analogous to ``pypsa/component_attrs``, e.g. `links.csv`.
Returns
-------
Dictionary of overridden component attributes.
"""
attrs = Dict({k: v.copy() for k, v in component_attrs.items()})
for component, list_name in components.list_name.items():
fn = f"{directory}/{list_name}.csv"
if os.path.isfile(fn):
overrides = pd.read_csv(fn, index_col=0, na_values="n/a")
attrs[component] = overrides.combine_first(attrs[component])
return attrs
def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None): 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 Give a 24*7 long list of weekly hourly profiles, generate this for each

7
scripts/add_brownfield.py
View File

@ -16,7 +16,7 @@ idx = pd.IndexSlice
import numpy as np import numpy as np
import pypsa import pypsa
from _helpers import override_component_attrs, update_config_with_sector_opts from _helpers import update_config_with_sector_opts
from add_existing_baseyear import add_build_year_to_new_assets from add_existing_baseyear import add_build_year_to_new_assets
@ -147,12 +147,11 @@ if __name__ == "__main__":
year = int(snakemake.wildcards.planning_horizons) year = int(snakemake.wildcards.planning_horizons)
overrides = override_component_attrs(snakemake.input.overrides) n = pypsa.Network(snakemake.input.network)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
add_build_year_to_new_assets(n, year) add_build_year_to_new_assets(n, year)
n_p = pypsa.Network(snakemake.input.network_p, override_component_attrs=overrides) n_p = pypsa.Network(snakemake.input.network_p)
add_brownfield(n, n_p, year) add_brownfield(n, n_p, year)

128
scripts/add_electricity.py
View File

@ -2,8 +2,6 @@
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors # SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
# #
# SPDX-License-Identifier: MIT # SPDX-License-Identifier: MIT
# coding: utf-8
""" """
Adds electrical generators and existing hydro storage units to a base network. Adds electrical generators and existing hydro storage units to a base network.
@ -206,7 +204,6 @@ def load_costs(tech_costs, config, max_hours, Nyears=1.0):
* costs["investment"] * costs["investment"]
* Nyears * Nyears
) )
costs.at["OCGT", "fuel"] = costs.at["gas", "fuel"] costs.at["OCGT", "fuel"] = costs.at["gas", "fuel"]
costs.at["CCGT", "fuel"] = costs.at["gas", "fuel"] costs.at["CCGT", "fuel"] = costs.at["gas", "fuel"]
@ -362,7 +359,6 @@ def attach_wind_and_solar(
n, costs, input_profiles, carriers, extendable_carriers, line_length_factor=1 n, costs, input_profiles, carriers, extendable_carriers, line_length_factor=1
): ):
add_missing_carriers(n, carriers) add_missing_carriers(n, carriers)
for car in carriers: for car in carriers:
if car == "hydro": if car == "hydro":
continue continue
@ -421,26 +417,54 @@ def attach_conventional_generators(
extendable_carriers, extendable_carriers,
conventional_params, conventional_params,
conventional_inputs, conventional_inputs,
unit_commitment=None,
fuel_price=None,
): ):
carriers = list(set(conventional_carriers) | set(extendable_carriers["Generator"])) carriers = list(set(conventional_carriers) | set(extendable_carriers["Generator"]))
add_missing_carriers(n, carriers) add_missing_carriers(n, carriers)
add_co2_emissions(n, costs, carriers) add_co2_emissions(n, costs, carriers)
# Replace carrier "natural gas" with the respective technology (OCGT or
# CCGT) to align with PyPSA names of "carriers" and avoid filtering "natural
# gas" powerplants in ppl.query("carrier in @carriers")
ppl.loc[ppl["carrier"] == "natural gas", "carrier"] = ppl.loc[
ppl["carrier"] == "natural gas", "technology"
]
ppl = ( ppl = (
ppl.query("carrier in @carriers") ppl.query("carrier in @carriers")
.join(costs, on="carrier", rsuffix="_r") .join(costs, on="carrier", rsuffix="_r")
.rename(index=lambda s: "C" + str(s)) .rename(index=lambda s: "C" + str(s))
) )
ppl["efficiency"] = ppl.efficiency.fillna(ppl.efficiency_r) ppl["efficiency"] = ppl.efficiency.fillna(ppl.efficiency_r)
ppl["marginal_cost"] = (
ppl.carrier.map(costs.VOM) + ppl.carrier.map(costs.fuel) / ppl.efficiency
)
logger.info( if unit_commitment is not None:
"Adding {} generators with capacities [GW] \n{}".format( committable_attrs = ppl.carrier.isin(unit_commitment).to_frame("committable")
len(ppl), ppl.groupby("carrier").p_nom.sum().div(1e3).round(2) for attr in unit_commitment.index:
default = pypsa.components.component_attrs["Generator"].default[attr]
committable_attrs[attr] = ppl.carrier.map(unit_commitment.loc[attr]).fillna(
default
)
else:
committable_attrs = {}
if fuel_price is not None:
fuel_price = fuel_price.assign(
OCGT=fuel_price["gas"], CCGT=fuel_price["gas"]
).drop("gas", axis=1)
missing_carriers = list(set(carriers) - set(fuel_price))
fuel_price = fuel_price.assign(**costs.fuel[missing_carriers])
fuel_price = fuel_price.reindex(ppl.carrier, axis=1)
fuel_price.columns = ppl.index
marginal_cost = fuel_price.div(ppl.efficiency).add(ppl.carrier.map(costs.VOM))
else:
marginal_cost = (
ppl.carrier.map(costs.VOM) + ppl.carrier.map(costs.fuel) / ppl.efficiency
) )
)
# Define generators using modified ppl DataFrame
caps = ppl.groupby("carrier").p_nom.sum().div(1e3).round(2)
logger.info(f"Adding {len(ppl)} generators with capacities [GW] \n{caps}")
n.madd( n.madd(
"Generator", "Generator",
@ -451,13 +475,14 @@ def attach_conventional_generators(
p_nom=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0), p_nom=ppl.p_nom.where(ppl.carrier.isin(conventional_carriers), 0),
p_nom_extendable=ppl.carrier.isin(extendable_carriers["Generator"]), p_nom_extendable=ppl.carrier.isin(extendable_carriers["Generator"]),
efficiency=ppl.efficiency, efficiency=ppl.efficiency,
marginal_cost=ppl.marginal_cost, marginal_cost=marginal_cost,
capital_cost=ppl.capital_cost, capital_cost=ppl.capital_cost,
build_year=ppl.datein.fillna(0).astype(int), build_year=ppl.datein.fillna(0).astype(int),
lifetime=(ppl.dateout - ppl.datein).fillna(np.inf), lifetime=(ppl.dateout - ppl.datein).fillna(np.inf),
**committable_attrs,
) )
for carrier in conventional_params: for carrier in set(conventional_params) & set(carriers):
# Generators with technology affected # Generators with technology affected
idx = n.generators.query("carrier == @carrier").index idx = n.generators.query("carrier == @carrier").index
@ -591,6 +616,14 @@ def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **par
hydro.max_hours > 0, hydro.country.map(max_hours_country) hydro.max_hours > 0, hydro.country.map(max_hours_country)
).fillna(6) ).fillna(6)
flatten_dispatch = params.get("flatten_dispatch", False)
if flatten_dispatch:
buffer = params.get("flatten_dispatch_buffer", 0.2)
average_capacity_factor = inflow_t[hydro.index].mean() / hydro["p_nom"]
p_max_pu = (average_capacity_factor + buffer).clip(upper=1)
else:
p_max_pu = 1
n.madd( n.madd(
"StorageUnit", "StorageUnit",
hydro.index, hydro.index,
@ -600,7 +633,7 @@ def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **par
max_hours=hydro_max_hours, max_hours=hydro_max_hours,
capital_cost=costs.at["hydro", "capital_cost"], capital_cost=costs.at["hydro", "capital_cost"],
marginal_cost=costs.at["hydro", "marginal_cost"], marginal_cost=costs.at["hydro", "marginal_cost"],
p_max_pu=1.0, # dispatch p_max_pu=p_max_pu, # dispatch
p_min_pu=0.0, # store p_min_pu=0.0, # store
efficiency_dispatch=costs.at["hydro", "efficiency"], efficiency_dispatch=costs.at["hydro", "efficiency"],
efficiency_store=0.0, efficiency_store=0.0,
@ -690,13 +723,14 @@ def attach_OPSD_renewables(n, tech_map):
{"Solar": "PV"} {"Solar": "PV"}
) )
df = df.query("Fueltype in @tech_map").powerplant.convert_country_to_alpha2() df = df.query("Fueltype in @tech_map").powerplant.convert_country_to_alpha2()
df = df.dropna(subset=["lat", "lon"])
for fueltype, carriers in tech_map.items(): for fueltype, carriers in tech_map.items():
gens = n.generators[lambda df: df.carrier.isin(carriers)] gens = n.generators[lambda df: df.carrier.isin(carriers)]
buses = n.buses.loc[gens.bus.unique()] buses = n.buses.loc[gens.bus.unique()]
gens_per_bus = gens.groupby("bus").p_nom.count() gens_per_bus = gens.groupby("bus").p_nom.count()
caps = map_country_bus(df.query("Fueltype == @fueltype and lat == lat"), buses) caps = map_country_bus(df.query("Fueltype == @fueltype"), buses)
caps = caps.groupby(["bus"]).Capacity.sum() caps = caps.groupby(["bus"]).Capacity.sum()
caps = caps / gens_per_bus.reindex(caps.index, fill_value=1) caps = caps / gens_per_bus.reindex(caps.index, fill_value=1)
@ -745,6 +779,30 @@ def estimate_renewable_capacities(n, year, tech_map, expansion_limit, countries)
) )
def attach_line_rating(
n, rating, s_max_pu, correction_factor, max_voltage_difference, max_line_rating
):
# TODO: Only considers overhead lines
n.lines_t.s_max_pu = (rating / n.lines.s_nom[rating.columns]) * correction_factor
if max_voltage_difference:
x_pu = (
n.lines.type.map(n.line_types["x_per_length"])
* n.lines.length
/ (n.lines.v_nom**2)
)
# need to clip here as cap values might be below 1
# -> would mean the line cannot be operated at actual given pessimistic ampacity
s_max_pu_cap = (
np.deg2rad(max_voltage_difference) / (x_pu * n.lines.s_nom)
).clip(lower=1)
n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(
lower=1, upper=s_max_pu_cap, axis=1
)
if max_line_rating:
n.lines_t.s_max_pu = n.lines_t.s_max_pu.clip(upper=max_line_rating)
n.lines_t.s_max_pu *= s_max_pu
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
@ -782,6 +840,20 @@ if __name__ == "__main__":
conventional_inputs = { conventional_inputs = {
k: v for k, v in snakemake.input.items() if k.startswith("conventional_") k: v for k, v in snakemake.input.items() if k.startswith("conventional_")
} }
if params.conventional["unit_commitment"]:
unit_commitment = pd.read_csv(snakemake.input.unit_commitment, index_col=0)
else:
unit_commitment = None
if params.conventional["dynamic_fuel_price"]:
fuel_price = pd.read_csv(
snakemake.input.fuel_price, index_col=0, header=0, parse_dates=True
)
fuel_price = fuel_price.reindex(n.snapshots).fillna(method="ffill")
else:
fuel_price = None
attach_conventional_generators( attach_conventional_generators(
n, n,
costs, costs,
@ -790,6 +862,8 @@ if __name__ == "__main__":
extendable_carriers, extendable_carriers,
params.conventional, params.conventional,
conventional_inputs, conventional_inputs,
unit_commitment=unit_commitment,
fuel_price=fuel_price,
) )
attach_wind_and_solar( attach_wind_and_solar(
@ -802,15 +876,16 @@ if __name__ == "__main__":
) )
if "hydro" in renewable_carriers: if "hydro" in renewable_carriers:
para = params.renewable["hydro"] p = params.renewable["hydro"]
carriers = p.pop("carriers", [])
attach_hydro( attach_hydro(
n, n,
costs, costs,
ppl, ppl,
snakemake.input.profile_hydro, snakemake.input.profile_hydro,
snakemake.input.hydro_capacities, snakemake.input.hydro_capacities,
para.pop("carriers", []), carriers,
**para, **p,
) )
estimate_renewable_caps = params.electricity["estimate_renewable_capacities"] estimate_renewable_caps = params.electricity["estimate_renewable_capacities"]
@ -827,6 +902,23 @@ if __name__ == "__main__":
update_p_nom_max(n) update_p_nom_max(n)
line_rating_config = snakemake.config["lines"]["dynamic_line_rating"]
if line_rating_config["activate"]:
rating = xr.open_dataarray(snakemake.input.line_rating).to_pandas().transpose()
s_max_pu = snakemake.config["lines"]["s_max_pu"]
correction_factor = line_rating_config["correction_factor"]
max_voltage_difference = line_rating_config["max_voltage_difference"]
max_line_rating = line_rating_config["max_line_rating"]
attach_line_rating(
n,
rating,
s_max_pu,
correction_factor,
max_voltage_difference,
max_line_rating,
)
sanitize_carriers(n, snakemake.config) sanitize_carriers(n, snakemake.config)
n.meta = snakemake.config n.meta = snakemake.config

21
scripts/add_existing_baseyear.py
View File

@ -21,7 +21,7 @@ import country_converter as coco
import numpy as np import numpy as np
import pypsa import pypsa
import xarray as xr import xarray as xr
from _helpers import override_component_attrs, update_config_with_sector_opts from _helpers import update_config_with_sector_opts
from add_electricity import sanitize_carriers from add_electricity import sanitize_carriers
from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs
@ -129,10 +129,14 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
"Oil": "oil", "Oil": "oil",
"OCGT": "OCGT", "OCGT": "OCGT",
"CCGT": "CCGT", "CCGT": "CCGT",
"Natural Gas": "gas",
"Bioenergy": "urban central solid biomass CHP", "Bioenergy": "urban central solid biomass CHP",
} }
# Replace Fueltype "Natural Gas" with the respective technology (OCGT or CCGT)
df_agg.loc[df_agg["Fueltype"] == "Natural Gas", "Fueltype"] = df_agg.loc[
df_agg["Fueltype"] == "Natural Gas", "Technology"
]
fueltype_to_drop = [ fueltype_to_drop = [
"Hydro", "Hydro",
"Wind", "Wind",
@ -601,12 +605,13 @@ if __name__ == "__main__":
snakemake = mock_snakemake( snakemake = mock_snakemake(
"add_existing_baseyear", "add_existing_baseyear",
configfiles="config/test/config.myopic.yaml",
simpl="", simpl="",
clusters="45", clusters="5",
ll="v1.0", ll="v1.5",
opts="", opts="",
sector_opts="8760H-T-H-B-I-A-solar+p3-dist1", sector_opts="24H-T-H-B-I-A-solar+p3-dist1",
planning_horizons=2020, planning_horizons=2030,
) )
logging.basicConfig(level=snakemake.config["logging"]["level"]) logging.basicConfig(level=snakemake.config["logging"]["level"])
@ -618,8 +623,8 @@ if __name__ == "__main__":
baseyear = snakemake.params.baseyear baseyear = snakemake.params.baseyear
overrides = override_component_attrs(snakemake.input.overrides) n = pypsa.Network(snakemake.input.network)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
# define spatial resolution of carriers # define spatial resolution of carriers
spatial = define_spatial(n.buses[n.buses.carrier == "AC"].index, options) 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)

2
scripts/base_network.py
View File

@ -337,7 +337,7 @@ def _load_lines_from_eg(buses, eg_lines):
) )
lines["length"] /= 1e3 lines["length"] /= 1e3
lines["carrier"] = "AC"
lines = _remove_dangling_branches(lines, buses) lines = _remove_dangling_branches(lines, buses)
return lines return lines

65
scripts/build_cross_border_flows.py Normal file
View File

@ -0,0 +1,65 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import logging
import pandas as pd
import pypsa
from _helpers import configure_logging
from entsoe import EntsoePandasClient
from entsoe.exceptions import InvalidBusinessParameterError, NoMatchingDataError
from requests import HTTPError
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_cross_border_flows")
configure_logging(snakemake)
api_key = snakemake.config["private"]["keys"]["entsoe_api"]
client = EntsoePandasClient(api_key=api_key)
n = pypsa.Network(snakemake.input.network)
start = pd.Timestamp(snakemake.params.snapshots["start"], tz="Europe/Brussels")
end = pd.Timestamp(snakemake.params.snapshots["end"], tz="Europe/Brussels")
branches = n.branches().query("carrier in ['AC', 'DC']")
c = n.buses.country
branch_countries = pd.concat([branches.bus0.map(c), branches.bus1.map(c)], axis=1)
branch_countries = branch_countries.query("bus0 != bus1")
branch_countries = branch_countries.apply(sorted, axis=1, result_type="broadcast")
country_pairs = branch_countries.drop_duplicates().reset_index(drop=True)
flows = []
unavailable_borders = []
for from_country, to_country in country_pairs.values:
try:
flow_directed = client.query_crossborder_flows(
from_country, to_country, start=start, end=end
)
flow_reverse = client.query_crossborder_flows(
to_country, from_country, start=start, end=end
)
flow = (flow_directed - flow_reverse).rename(
f"{from_country} - {to_country}"
)
flow = flow.tz_localize(None).resample("1h").mean()
flow = flow.loc[start.tz_localize(None) : end.tz_localize(None)]
flows.append(flow)
except (HTTPError, NoMatchingDataError, InvalidBusinessParameterError):
unavailable_borders.append(f"{from_country}-{to_country}")
if unavailable_borders:
logger.warning(
"Historical electricity cross-border flows for countries"
f" {', '.join(unavailable_borders)} not available."
)
flows = pd.concat(flows, axis=1)
flows.to_csv(snakemake.output[0])

21
scripts/build_electricity_demand.py
View File

@ -80,11 +80,9 @@ def load_timeseries(fn, years, countries, powerstatistics=True):
def rename(s): def rename(s):
return s[: -len(pattern)] return s[: -len(pattern)]
def date_parser(x):
return dateutil.parser.parse(x, ignoretz=True)
return ( return (
pd.read_csv(fn, index_col=0, parse_dates=[0], date_parser=date_parser) pd.read_csv(fn, index_col=0, parse_dates=[0])
.tz_localize(None)
.filter(like=pattern) .filter(like=pattern)
.rename(columns=rename) .rename(columns=rename)
.dropna(how="all", axis=0) .dropna(how="all", axis=0)
@ -168,6 +166,7 @@ def manual_adjustment(load, fn_load, powerstatistics):
by the corresponding ratio of total energy consumptions reported by by the corresponding ratio of total energy consumptions reported by
IEA Data browser [0] for the year 2013. IEA Data browser [0] for the year 2013.
2. For the ENTSOE transparency load data (if powerstatistics is False) 2. For the ENTSOE transparency load data (if powerstatistics is False)
Albania (AL) and Macedonia (MK) do not exist in the data set. Both get the Albania (AL) and Macedonia (MK) do not exist in the data set. Both get the
@ -176,6 +175,9 @@ def manual_adjustment(load, fn_load, powerstatistics):
[0] https://www.iea.org/data-and-statistics?country=WORLD&fuel=Electricity%20and%20heat&indicator=TotElecCons [0] https://www.iea.org/data-and-statistics?country=WORLD&fuel=Electricity%20and%20heat&indicator=TotElecCons
Bosnia and Herzegovina (BA) does not exist in the data set for 2019. It gets the
electricity consumption data from Croatia (HR) for the year 2019, scaled by the
factors derived from https://energy.at-site.be/eurostat-2021/
Parameters Parameters
---------- ----------
@ -264,9 +266,17 @@ def manual_adjustment(load, fn_load, powerstatistics):
load["AL"] = load.ME * (5.7 / 2.9) load["AL"] = load.ME * (5.7 / 2.9)
if "MK" not in load and "MK" in countries: if "MK" not in load and "MK" in countries:
load["MK"] = load.ME * (6.7 / 2.9) load["MK"] = load.ME * (6.7 / 2.9)
if "BA" not in load and "BA" in countries:
load["BA"] = load.HR * (11.0 / 16.2)
copy_timeslice( copy_timeslice(
load, "BG", "2018-10-27 21:00", "2018-10-28 22:00", Delta(weeks=1) load, "BG", "2018-10-27 21:00", "2018-10-28 22:00", Delta(weeks=1)
) )
copy_timeslice(
load, "LU", "2019-01-02 11:00", "2019-01-05 05:00", Delta(weeks=-1)
)
copy_timeslice(
load, "LU", "2019-02-05 20:00", "2019-02-06 19:00", Delta(weeks=-1)
)
return load return load
@ -291,6 +301,9 @@ if __name__ == "__main__":
if snakemake.params.load["manual_adjustments"]: if snakemake.params.load["manual_adjustments"]:
load = manual_adjustment(load, snakemake.input[0], powerstatistics) load = manual_adjustment(load, snakemake.input[0], powerstatistics)
if load.empty:
logger.warning("Build electricity demand time series is empty.")
logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.") logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.")
load = load.interpolate(method="linear", limit=interpolate_limit) load = load.interpolate(method="linear", limit=interpolate_limit)

52
scripts/build_electricity_prices.py Normal file
View File

@ -0,0 +1,52 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import logging
import pandas as pd
from _helpers import configure_logging
from entsoe import EntsoePandasClient
from entsoe.exceptions import NoMatchingDataError
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_cross_border_flows")
configure_logging(snakemake)
api_key = snakemake.config["private"]["keys"]["entsoe_api"]
client = EntsoePandasClient(api_key=api_key)
start = pd.Timestamp(snakemake.params.snapshots["start"], tz="Europe/Brussels")
end = pd.Timestamp(snakemake.params.snapshots["end"], tz="Europe/Brussels")
countries = snakemake.params.countries
prices = []
unavailable_countries = []
for country in countries:
country_code = country
try:
gen = client.query_day_ahead_prices(country, start=start, end=end)
gen = gen.tz_localize(None).resample("1h").mean()
gen = gen.loc[start.tz_localize(None) : end.tz_localize(None)]
prices.append(gen)
except NoMatchingDataError:
unavailable_countries.append(country)
if unavailable_countries:
logger.warning(
f"Historical electricity prices for countries {', '.join(unavailable_countries)} not available."
)
keys = [c for c in countries if c not in unavailable_countries]
prices = pd.concat(prices, keys=keys, axis=1)
prices.to_csv(snakemake.output[0])

73
scripts/build_electricity_production.py Normal file
View File

@ -0,0 +1,73 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import logging
import pandas as pd
from _helpers import configure_logging
from entsoe import EntsoePandasClient
from entsoe.exceptions import NoMatchingDataError
logger = logging.getLogger(__name__)
carrier_grouper = {
"Waste": "Biomass",
"Hydro Pumped Storage": "Hydro",
"Hydro Water Reservoir": "Hydro",
"Hydro Run-of-river and poundage": "Run of River",
"Fossil Coal-derived gas": "Gas",
"Fossil Gas": "Gas",
"Fossil Oil": "Oil",
"Fossil Oil shale": "Oil",
"Fossil Brown coal/Lignite": "Lignite",
"Fossil Peat": "Lignite",
"Fossil Hard coal": "Coal",
"Wind Onshore": "Onshore Wind",
"Wind Offshore": "Offshore Wind",
"Other renewable": "Other",
"Marine": "Other",
}
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_electricity_production")
configure_logging(snakemake)
api_key = snakemake.config["private"]["keys"]["entsoe_api"]
client = EntsoePandasClient(api_key=api_key)
start = pd.Timestamp(snakemake.params.snapshots["start"], tz="Europe/Brussels")
end = pd.Timestamp(snakemake.params.snapshots["end"], tz="Europe/Brussels")
countries = snakemake.params.countries
generation = []
unavailable_countries = []
for country in countries:
country_code = country
try:
gen = client.query_generation(country, start=start, end=end, nett=True)
gen = gen.tz_localize(None).resample("1h").mean()
gen = gen.loc[start.tz_localize(None) : end.tz_localize(None)]
gen = gen.rename(columns=carrier_grouper).groupby(level=0, axis=1).sum()
generation.append(gen)
except NoMatchingDataError:
unavailable_countries.append(country)
if unavailable_countries:
logger.warning(
f"Historical electricity production for countries {', '.join(unavailable_countries)} not available."
)
keys = [c for c in countries if c not in unavailable_countries]
generation = pd.concat(generation, keys=keys, axis=1)
generation.to_csv(snakemake.output[0])

22
scripts/build_industrial_distribution_key.py
View File

@ -92,6 +92,20 @@ def prepare_hotmaps_database(regions):
gdf.rename(columns={"index_right": "bus"}, inplace=True) gdf.rename(columns={"index_right": "bus"}, inplace=True)
gdf["country"] = gdf.bus.str[:2] gdf["country"] = gdf.bus.str[:2]
# the .sjoin can lead to duplicates if a geom is in two regions
if gdf.index.duplicated().any():
import pycountry
# get all duplicated entries
duplicated_i = gdf.index[gdf.index.duplicated()]
# convert from raw data country name to iso-2-code
s = df.loc[duplicated_i, "Country"].apply(lambda x: pycountry.countries.lookup(x).alpha_2)
# Get a boolean mask where gdf's country column matches s's values for the same index
mask = gdf['country'] == gdf.index.map(s)
# Filter gdf using the mask
gdf_filtered = gdf[mask]
# concat not duplicated and filtered gdf
gdf = pd.concat([gdf.drop(duplicated_i), gdf_filtered]).sort_index()
return gdf return gdf
@ -115,7 +129,9 @@ def build_nodal_distribution_key(hotmaps, regions, countries):
facilities = hotmaps.query("country == @country and Subsector == @sector") facilities = hotmaps.query("country == @country and Subsector == @sector")
if not facilities.empty: if not facilities.empty:
emissions = facilities["Emissions_ETS_2014"] emissions = facilities["Emissions_ETS_2014"].fillna(
hotmaps["Emissions_EPRTR_2014"]
)
if emissions.sum() == 0: if emissions.sum() == 0:
key = pd.Series(1 / len(facilities), facilities.index) key = pd.Series(1 / len(facilities), facilities.index)
else: else:
@ -130,7 +146,7 @@ def build_nodal_distribution_key(hotmaps, regions, countries):
return keys return keys
#%%
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
@ -138,7 +154,7 @@ if __name__ == "__main__":
snakemake = mock_snakemake( snakemake = mock_snakemake(
"build_industrial_distribution_key", "build_industrial_distribution_key",
simpl="", simpl="",
clusters=48, clusters=128,
) )
logging.basicConfig(level=snakemake.config["logging"]["level"]) logging.basicConfig(level=snakemake.config["logging"]["level"])

156
scripts/build_line_rating.py Executable file
View File

@ -0,0 +1,156 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Adds dynamic line rating timeseries to the base network.
Relevant Settings
-----------------
.. code:: yaml
lines:
cutout:
line_rating:
.. seealso::
Documentation of the configuration file ``config.yaml`
Inputs
------
- ``data/cutouts``:
- ``networks/base.nc``: confer :ref:`base`
Outputs
-------
- ``resources/line_rating.nc``
Description
-----------
The rule :mod:`build_line_rating` calculates the line rating for transmission lines.
The line rating provides the maximal capacity of a transmission line considering the heat exchange with the environment.
The following heat gains and losses are considered:
- heat gain through resistive losses
- heat gain through solar radiation
- heat loss through radiation of the trasnmission line
- heat loss through forced convection with wind
- heat loss through natural convection
With a heat balance considering the maximum temperature threshold of the transmission line,
the maximal possible capacity factor "s_max_pu" for each transmission line at each time step is calculated.
"""
import logging
import re
import atlite
import geopandas as gpd
import numpy as np
import pandas as pd
import pypsa
import xarray as xr
from _helpers import configure_logging
from shapely.geometry import LineString as Line
from shapely.geometry import Point
def calculate_resistance(T, R_ref, T_ref=293, alpha=0.00403):
"""
Calculates the resistance at other temperatures than the reference
temperature.
Parameters
----------
T : Temperature at which resistance is calculated in [°C] or [K]
R_ref : Resistance at reference temperature in [Ohm] or [Ohm/Per Length Unit]
T_ref : Reference temperature in [°C] or [K]
alpha: Temperature coefficient in [1/K]
Defaults are:
* T_ref : 20 °C
* alpha : 0.00403 1/K
Returns
-------
Resistance of at given temperature.
"""
R = R_ref * (1 + alpha * (T - T_ref))
return R
def calculate_line_rating(n, cutout):
"""
Calculates the maximal allowed power flow in each line for each time step
considering the maximal temperature.
Parameters
----------
n : pypsa.Network object containing information on grid
Returns
-------
xarray DataArray object with maximal power.
"""
relevant_lines = n.lines[(n.lines["underground"] == False)]
buses = relevant_lines[["bus0", "bus1"]].values
x = n.buses.x
y = n.buses.y
shapes = [Line([Point(x[b0], y[b0]), Point(x[b1], y[b1])]) for (b0, b1) in buses]
shapes = gpd.GeoSeries(shapes, index=relevant_lines.index)
if relevant_lines.r_pu.eq(0).all():
# Overwrite standard line resistance with line resistance obtained from line type
r_per_length = n.line_types["r_per_length"]
R = (
relevant_lines.join(r_per_length, on=["type"])["r_per_length"] / 1000
) # in meters
# If line type with bundles is given retrieve number of conductors per bundle
relevant_lines["n_bundle"] = (
relevant_lines["type"]
.where(relevant_lines["type"].str.contains("bundle"))
.dropna()
.apply(lambda x: int(re.findall(r"(\d+)-bundle", x)[0]))
)
# Set default number of bundles per line
relevant_lines["n_bundle"].fillna(1, inplace=True)
R *= relevant_lines["n_bundle"]
R = calculate_resistance(T=353, R_ref=R)
Imax = cutout.line_rating(shapes, R, D=0.0218, Ts=353, epsilon=0.8, alpha=0.8)
line_factor = relevant_lines.eval("v_nom * n_bundle * num_parallel") / 1e3 # in mW
da = xr.DataArray(
data=np.sqrt(3) * Imax * line_factor.values.reshape(-1, 1),
attrs=dict(
description="Maximal possible power in MW for given line considering line rating"
),
)
return da
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake(
"build_line_rating",
network="elec",
simpl="",
clusters="5",
ll="v1.0",
opts="Co2L-4H",
)
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.base_network)
time = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout = atlite.Cutout(snakemake.input.cutout).sel(time=time)
da = calculate_line_rating(n, cutout)
da.to_netcdf(snakemake.output[0])

122
scripts/build_monthly_prices.py Normal file
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@ -0,0 +1,122 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue May 16 10:37:35 2023.
This script extracts monthly fuel prices of oil, gas, coal and lignite,
as well as CO2 prices
Inputs
------
- ``data/energy-price-trends-xlsx-5619002.xlsx``: energy price index of fossil fuels
- ``emission-spot-primary-market-auction-report-2019-data.xls``: CO2 Prices spot primary auction
Outputs
-------
- ``data/validation/monthly_fuel_price.csv``
- ``data/validation/CO2_price_2019.csv``
Description
-----------
The rule :mod:`build_monthly_prices` collects monthly fuel prices and CO2 prices
and translates them from different input sources to pypsa syntax
Data sources:
[1] Fuel price index. Destatis
https://www.destatis.de/EN/Home/_node.html
[2] average annual fuel price lignite, ENTSO-E
https://2020.entsos-tyndp-scenarios.eu/fuel-commodities-and-carbon-prices/
[3] CO2 Prices, Emission spot primary auction, EEX
https://www.eex.com/en/market-data/environmental-markets/eua-primary-auction-spot-download
Data was accessed at 16.5.2023
"""
import logging
import pandas as pd
from _helpers import configure_logging
logger = logging.getLogger(__name__)
# keywords in datasheet
keywords = {
"coal": " GP09-051 Hard coal",
"lignite": " GP09-052 Lignite and lignite briquettes",
"oil": " GP09-0610 10 Mineral oil, crude",
"gas": "GP09-062 Natural gas",
}
# sheet names to pypsa syntax
sheet_name_map = {
"coal": "5.1 Hard coal and lignite",
"lignite": "5.1 Hard coal and lignite",
"oil": "5.2 Mineral oil",
"gas": "5.3.1 Natural gas - indices",
}
# import fuel price 2015 in Eur/MWh
# source lignite, price for 2020, scaled by price index, ENTSO-E [3]
price_2020 = (
pd.Series({"coal": 3.0, "oil": 10.6, "gas": 5.6, "lignite": 1.1}) * 3.6
) # Eur/MWh
# manual adjustment of coal price
price_2020["coal"] = 2.4 * 3.6
price_2020["lignite"] = 1.6 * 3.6
def get_fuel_price():
price = {}
for carrier, keyword in keywords.items():
sheet_name = sheet_name_map[carrier]
df = pd.read_excel(
snakemake.input.fuel_price_raw,
sheet_name=sheet_name,
index_col=0,
skiprows=6,
nrows=18,
)
df = df.dropna(axis=0).iloc[:, :12]
start, end = df.index[0], str(int(df.index[-1][:4]) + 1)
df = df.stack()
df.index = pd.date_range(start=start, end=end, freq="MS", inclusive="left")
scale = price_2020[carrier] / df["2020"].mean() # scale to 2020 price
df = df.mul(scale)
price[carrier] = df
return pd.concat(price, axis=1)
def get_co2_price():
# emission price
co2_price = pd.read_excel(snakemake.input.co2_price_raw, index_col=1, header=5)
return co2_price["Auction Price €/tCO2"]
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_monthly_prices")
configure_logging(snakemake)
fuel_price = get_fuel_price()
fuel_price.to_csv(snakemake.output.fuel_price)
co2_price = get_co2_price()
co2_price.to_csv(snakemake.output.co2_price)

17
scripts/build_natura_raster.py
View File

@ -54,6 +54,23 @@ logger = logging.getLogger(__name__)
def determine_cutout_xXyY(cutout_name): def determine_cutout_xXyY(cutout_name):
"""
Determine the full extent of a cutout.
Since the coordinates of the cutout data are given as the
center of the grid cells, the extent of the cutout is
calculated by adding/subtracting half of the grid cell size.
Parameters
----------
cutout_name : str
Path to the cutout.
Returns
-------
A list of extent coordinates in the order [x, X, y, Y].
"""
cutout = atlite.Cutout(cutout_name) cutout = atlite.Cutout(cutout_name)
assert cutout.crs.to_epsg() == 4326 assert cutout.crs.to_epsg() == 4326
x, X, y, Y = cutout.extent x, X, y, Y = cutout.extent

10
scripts/build_powerplants.py
View File

@ -98,13 +98,15 @@ def add_custom_powerplants(ppl, custom_powerplants, custom_ppl_query=False):
def replace_natural_gas_technology(df): def replace_natural_gas_technology(df):
mapping = {"Steam Turbine": "OCGT", "Combustion Engine": "OCGT"} mapping = {"Steam Turbine": "CCGT", "Combustion Engine": "OCGT"}
tech = df.Technology.replace(mapping).fillna("OCGT") tech = df.Technology.replace(mapping).fillna("CCGT")
return df.Technology.where(df.Fueltype != "Natural Gas", tech) return df.Technology.mask(df.Fueltype == "Natural Gas", tech)
def replace_natural_gas_fueltype(df): def replace_natural_gas_fueltype(df):
return df.Fueltype.where(df.Fueltype != "Natural Gas", df.Technology) return df.Fueltype.mask(
(df.Technology == "OCGT") | (df.Technology == "CCGT"), "Natural Gas"
)
if __name__ == "__main__": if __name__ == "__main__":

8
scripts/build_renewable_profiles.py
View File

@ -186,6 +186,7 @@ import time
import atlite import atlite
import geopandas as gpd import geopandas as gpd
import numpy as np import numpy as np
import pandas as pd
import xarray as xr import xarray as xr
from _helpers import configure_logging from _helpers import configure_logging
from dask.distributed import Client from dask.distributed import Client
@ -222,7 +223,8 @@ if __name__ == "__main__":
else: else:
client = None client = None
cutout = atlite.Cutout(snakemake.input.cutout) sns = pd.date_range(freq="h", **snakemake.config["snapshots"])
cutout = atlite.Cutout(snakemake.input.cutout).sel(time=sns)
regions = gpd.read_file(snakemake.input.regions) regions = gpd.read_file(snakemake.input.regions)
assert not regions.empty, ( assert not regions.empty, (
f"List of regions in {snakemake.input.regions} is empty, please " f"List of regions in {snakemake.input.regions} is empty, please "
@ -372,4 +374,6 @@ if __name__ == "__main__":
ds["profile"] = ds["profile"].where(ds["profile"] >= min_p_max_pu, 0) ds["profile"] = ds["profile"].where(ds["profile"] >= min_p_max_pu, 0)
ds.to_netcdf(snakemake.output.profile) ds.to_netcdf(snakemake.output.profile)
client.shutdown()
if client is not None:
client.shutdown()

4
scripts/build_sequestration_potentials.py
View File

@ -28,9 +28,7 @@ def allocate_sequestration_potential(
overlay["share"] = area(overlay) / overlay["area_sqkm"] overlay["share"] = area(overlay) / overlay["area_sqkm"]
adjust_cols = overlay.columns.difference({"name", "area_sqkm", "geometry", "share"}) adjust_cols = overlay.columns.difference({"name", "area_sqkm", "geometry", "share"})
overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0) overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0)
gdf_regions = overlay.groupby("name").sum() return overlay.dissolve("name", aggfunc="sum")[attr]
gdf_regions.drop(["area_sqkm", "share"], axis=1, inplace=True)
return gdf_regions.squeeze()
if __name__ == "__main__": if __name__ == "__main__":

24
scripts/cluster_network.py
View File

@ -461,7 +461,7 @@ if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake("cluster_network", simpl="", clusters="37c") snakemake = mock_snakemake("cluster_network", simpl="", clusters="37")
configure_logging(snakemake) configure_logging(snakemake)
params = snakemake.params params = snakemake.params
@ -483,6 +483,23 @@ if __name__ == "__main__":
else: else:
n_clusters = int(snakemake.wildcards.clusters) n_clusters = int(snakemake.wildcards.clusters)
if params.cluster_network.get("consider_efficiency_classes", False):
carriers = []
for c in aggregate_carriers:
gens = n.generators.query("carrier == @c")
low = gens.efficiency.quantile(0.10)
high = gens.efficiency.quantile(0.90)
if low >= high:
carriers += [c]
else:
labels = ["low", "medium", "high"]
suffix = pd.cut(
gens.efficiency, bins=[0, low, high, 1], labels=labels
).astype(str)
carriers += [f"{c} {label} efficiency" for label in labels]
n.generators.carrier.update(gens.carrier + " " + suffix + " efficiency")
aggregate_carriers = carriers
if n_clusters == len(n.buses): if n_clusters == len(n.buses):
# Fast-path if no clustering is necessary # Fast-path if no clustering is necessary
busmap = n.buses.index.to_series() busmap = n.buses.index.to_series()
@ -524,6 +541,11 @@ if __name__ == "__main__":
update_p_nom_max(clustering.network) update_p_nom_max(clustering.network)
if params.cluster_network.get("consider_efficiency_classes"):
labels = [f" {label} efficiency" for label in ["low", "medium", "high"]]
nc = clustering.network
nc.generators["carrier"] = nc.generators.carrier.replace(labels, "", regex=True)
clustering.network.meta = dict( clustering.network.meta = dict(
snakemake.config, **dict(wildcards=dict(snakemake.wildcards)) snakemake.config, **dict(wildcards=dict(snakemake.wildcards))
) )

14
scripts/copy_config.py
View File

@ -11,25 +11,13 @@ from shutil import copy
import yaml import yaml
files = {
"config/config.yaml": "config.yaml",
"Snakefile": "Snakefile",
"scripts/solve_network.py": "solve_network.py",
"scripts/prepare_sector_network.py": "prepare_sector_network.py",
}
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake("copy_config") snakemake = mock_snakemake("copy_config")
basepath = Path(f"results/{snakemake.params.RDIR}config/") with open(snakemake.output[0], "w") as yaml_file:
for f, name in files.items():
copy(f, basepath / name)
with open(basepath / "config.snakemake.yaml", "w") as yaml_file:
yaml.dump( yaml.dump(
snakemake.config, snakemake.config,
yaml_file, yaml_file,

10
scripts/make_summary.py
View File

@ -16,7 +16,6 @@ import sys
import numpy as np import numpy as np
import pandas as pd import pandas as pd
import pypsa import pypsa
from _helpers import override_component_attrs
from prepare_sector_network import prepare_costs from prepare_sector_network import prepare_costs
idx = pd.IndexSlice idx = pd.IndexSlice
@ -300,9 +299,9 @@ def calculate_energy(n, label, energy):
) )
# remove values where bus is missing (bug in nomopyomo) # remove values where bus is missing (bug in nomopyomo)
no_bus = c.df.index[c.df["bus" + port] == ""] no_bus = c.df.index[c.df["bus" + port] == ""]
totals.loc[no_bus] = n.component_attrs[c.name].loc[ totals.loc[no_bus] = float(
"p" + port, "default" n.component_attrs[c.name].loc["p" + port, "default"]
] )
c_energies -= totals.groupby(c.df.carrier).sum() c_energies -= totals.groupby(c.df.carrier).sum()
c_energies = pd.concat([c_energies], keys=[c.list_name]) c_energies = pd.concat([c_energies], keys=[c.list_name])
@ -659,8 +658,7 @@ def make_summaries(networks_dict):
for label, filename in networks_dict.items(): for label, filename in networks_dict.items():
logger.info(f"Make summary for scenario {label}, using {filename}") logger.info(f"Make summary for scenario {label}, using {filename}")
overrides = override_component_attrs(snakemake.input.overrides) n = pypsa.Network(filename)
n = pypsa.Network(filename, override_component_attrs=overrides)
assign_carriers(n) assign_carriers(n)
assign_locations(n) assign_locations(n)

4
scripts/plot_network.py
View File

@ -20,7 +20,6 @@ import geopandas as gpd
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import pandas as pd import pandas as pd
import pypsa import pypsa
from _helpers import override_component_attrs
from make_summary import assign_carriers from make_summary import assign_carriers
from plot_summary import preferred_order, rename_techs from plot_summary import preferred_order, rename_techs
from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
@ -930,8 +929,7 @@ if __name__ == "__main__":
logging.basicConfig(level=snakemake.config["logging"]["level"]) logging.basicConfig(level=snakemake.config["logging"]["level"])
overrides = override_component_attrs(snakemake.input.overrides) n = pypsa.Network(snakemake.input.network)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
regions = gpd.read_file(snakemake.input.regions).set_index("name") regions = gpd.read_file(snakemake.input.regions).set_index("name")

116
scripts/plot_statistics.py Normal file
View File

@ -0,0 +1,116 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import matplotlib.pyplot as plt
import pypsa
import seaborn as sns
from _helpers import configure_logging
sns.set_theme("paper", style="whitegrid")
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake(
"plot_elec_statistics",
simpl="",
opts="Ept-12h",
clusters="37",
ll="v1.0",
)
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
n.loads.carrier = "load"
n.carriers.loc["load", ["nice_name", "color"]] = "Load", "darkred"
colors = n.carriers.set_index("nice_name").color.where(
lambda s: s != "", "lightgrey"
)
# %%
def rename_index(ds):
specific = ds.index.map(lambda x: f"{x[1]}\n({x[0]})")
generic = ds.index.get_level_values("carrier")
duplicated = generic.duplicated(keep=False)
index = specific.where(duplicated, generic)
return ds.set_axis(index)
def plot_static_per_carrier(ds, ax, drop_zero=True):
if drop_zero:
ds = ds[ds != 0]
ds = ds.dropna()
c = colors[ds.index.get_level_values("carrier")]
ds = ds.pipe(rename_index)
label = f"{ds.attrs['name']} [{ds.attrs['unit']}]"
ds.plot.barh(color=c.values, xlabel=label, ax=ax)
ax.grid(axis="y")
fig, ax = plt.subplots()
ds = n.statistics.capacity_factor().dropna()
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.capacity_factor_bar)
fig, ax = plt.subplots()
ds = n.statistics.installed_capacity().dropna()
ds = ds.drop("Line")
ds = ds.drop(("Generator", "Load"))
ds = ds / 1e3
ds.attrs["unit"] = "GW"
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.installed_capacity_bar)
fig, ax = plt.subplots()
ds = n.statistics.optimal_capacity()
ds = ds.drop("Line")
ds = ds.drop(("Generator", "Load"))
ds = ds / 1e3
ds.attrs["unit"] = "GW"
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.optimal_capacity_bar)
fig, ax = plt.subplots()
ds = n.statistics.capex()
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.capital_expenditure_bar)
fig, ax = plt.subplots()
ds = n.statistics.opex()
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.operational_expenditure_bar)
fig, ax = plt.subplots()
ds = n.statistics.curtailment()
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.curtailment_bar)
fig, ax = plt.subplots()
ds = n.statistics.supply()
ds = ds.drop("Line")
ds = ds / 1e6
ds.attrs["unit"] = "TWh"
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.supply_bar)
fig, ax = plt.subplots()
ds = n.statistics.withdrawal()
ds = ds.drop("Line")
ds = ds / -1e6
ds.attrs["unit"] = "TWh"
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.withdrawal_bar)
fig, ax = plt.subplots()
ds = n.statistics.market_value()
plot_static_per_carrier(ds, ax)
fig.savefig(snakemake.output.market_value_bar)
# touch file
with open(snakemake.output.barplots_touch, "a"):
pass

242
scripts/plot_validation_cross_border_flows.py Normal file
View File

@ -0,0 +1,242 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import country_converter as coco
import matplotlib.pyplot as plt
import pandas as pd
import pypsa
import seaborn as sns
from _helpers import configure_logging
sns.set_theme("paper", style="whitegrid")
cc = coco.CountryConverter()
color_country = {
"AL": "#440154",
"AT": "#482677",
"BA": "#43398e",
"BE": "#3953a4",
"BG": "#2c728e",
"CH": "#228b8d",
"CZ": "#1f9d8a",
"DE": "#29af7f",
"DK": "#3fbc73",
"EE": "#5ec962",
"ES": "#84d44b",
"FI": "#addc30",
"FR": "#d8e219",
"GB": "#fde725",
"GR": "#f0f921",
"HR": "#f1c25e",
"HU": "#f4a784",
"IE": "#f78f98",
"IT": "#f87ea0",
"LT": "#f87a9a",
"LU": "#f57694",
"LV": "#f3758d",
"ME": "#f37685",
"MK": "#f37b7c",
"NL": "#FF6666",
"NO": "#FF3333",
"PL": "#eb0000",
"PT": "#d70000",
"RO": "#c00000",
"RS": "#a50000",
"SE": "#8a0000",
"SI": "#6f0000",
"SK": "#550000",
}
def sort_one_country(country, df):
indices = [link for link in df.columns if country in link]
df_country = df[indices].copy()
for link in df_country.columns:
if country in link[5:]:
df_country[link] = -df_country[link]
link_reverse = str(link[5:] + " - " + link[:2])
df_country = df_country.rename(columns={link: link_reverse})
return df_country.reindex(sorted(df_country.columns), axis=1)
def cross_border_time_series(countries, data):
fig, ax = plt.subplots(2 * len(countries), 1, figsize=(15, 10 * len(countries)))
axis = 0
for country in countries:
ymin = 0
ymax = 0
for df in data:
df_country = sort_one_country(country, df)
df_neg, df_pos = df_country.clip(upper=0), df_country.clip(lower=0)
color = [color_country[link[5:]] for link in df_country.columns]
df_pos.plot.area(
ax=ax[axis], stacked=True, linewidth=0.0, color=color, ylim=[-1, 1]
)
df_neg.plot.area(
ax=ax[axis], stacked=True, linewidth=0.0, color=color, ylim=[-1, 1]
)
if (axis % 2) == 0:
title = "Historic"
else:
title = "Optimized"
ax[axis].set_title(
title + " Import / Export for " + cc.convert(country, to="name_short")
)
# Custom legend elements
legend_elements = []
for link in df_country.columns:
legend_elements = legend_elements + [
plt.fill_between(
[],
[],
color=color_country[link[5:]],
label=cc.convert(link[5:], to="name_short"),
)
]
# Create the legend
ax[axis].legend(handles=legend_elements, loc="upper right")
# rescale the y axis
neg_min = df_neg.sum(axis=1).min() * 1.2
if neg_min < ymin:
ymin = neg_min
pos_max = df_pos.sum(axis=1).max() * 1.2
if pos_max < ymax:
ymax = pos_max
axis = axis + 1
for x in range(axis - 2, axis):
ax[x].set_ylim([neg_min, pos_max])
fig.savefig(snakemake.output.trade_time_series, bbox_inches="tight")
def cross_border_bar(countries, data):
df_positive = pd.DataFrame()
df_negative = pd.DataFrame()
color = []
for country in countries:
order = 0
for df in data:
df_country = sort_one_country(country, df)
df_neg, df_pos = df_country.clip(upper=0), df_country.clip(lower=0)
if (order % 2) == 0:
title = "Historic"
else:
title = "Optimized"
df_positive_new = pd.DataFrame(data=df_pos.sum()).T.rename(
{0: title + " " + cc.convert(country, to="name_short")}
)
df_negative_new = pd.DataFrame(data=df_neg.sum()).T.rename(
{0: title + " " + cc.convert(country, to="name_short")}
)
df_positive = pd.concat([df_positive_new, df_positive])
df_negative = pd.concat([df_negative_new, df_negative])
order = order + 1
color = [color_country[link[5:]] for link in df_positive.columns]
fig, ax = plt.subplots(figsize=(15, 60))
df_positive.plot.barh(ax=ax, stacked=True, color=color, zorder=2)
df_negative.plot.barh(ax=ax, stacked=True, color=color, zorder=2)
plt.grid(axis="x", zorder=0)
plt.grid(axis="y", zorder=0)
# Custom legend elements
legend_elements = []
for country in list(color_country.keys()):
legend_elements = legend_elements + [
plt.fill_between(
[],
[],
color=color_country[country],
label=cc.convert(country, to="name_short"),
)
]
# Create the legend
plt.legend(handles=legend_elements, loc="upper right")
fig.savefig(snakemake.output.cross_border_bar, bbox_inches="tight")
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake(
"plot_electricity_prices",
simpl="",
opts="Ept-12h",
clusters="37",
ll="v1.0",
)
configure_logging(snakemake)
countries = snakemake.params.countries
n = pypsa.Network(snakemake.input.network)
n.loads.carrier = "load"
historic = pd.read_csv(
snakemake.input.cross_border_flows,
index_col=0,
header=0,
parse_dates=True,
)
if len(historic.index) > len(n.snapshots):
historic = historic.resample(n.snapshots.inferred_freq).mean().loc[n.snapshots]
# Preparing network data to be shaped similar to ENTSOE datastructure
optimized_links = n.links_t.p0.rename(
columns=dict(n.links.bus0.str[:2] + " - " + n.links.bus1.str[:2])
)
optimized_lines = n.lines_t.p0.rename(
columns=dict(n.lines.bus0.str[:2] + " - " + n.lines.bus1.str[:2])
)
optimized = pd.concat([optimized_links, optimized_lines], axis=1)
# Drop internal country connection
optimized.drop(
[c for c in optimized.columns if c[:2] == c[5:]], axis=1, inplace=True
)
# align columns name
for c1 in optimized.columns:
for c2 in optimized.columns:
if c1[:2] == c2[5:] and c2[:2] == c1[5:]:
optimized = optimized.rename(columns={c1: c2})
optimized = optimized.groupby(lambda x: x, axis=1).sum()
cross_border_bar(countries, [historic, optimized])
cross_border_time_series(countries, [historic, optimized])
# touch file
with open(snakemake.output.plots_touch, "a"):
pass

63
scripts/plot_validation_electricity_prices.py Normal file
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@ -0,0 +1,63 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import matplotlib.pyplot as plt
import pandas as pd
import pypsa
import seaborn as sns
from _helpers import configure_logging
from pypsa.statistics import get_bus_and_carrier
sns.set_theme("paper", style="whitegrid")
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake(
"plot_electricity_prices",
simpl="",
opts="Ept-12h",
clusters="37",
ll="v1.0",
)
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
n.loads.carrier = "load"
historic = pd.read_csv(
snakemake.input.electricity_prices,
index_col=0,
header=0,
parse_dates=True,
)
if len(historic.index) > len(n.snapshots):
historic = historic.resample(n.snapshots.inferred_freq).mean().loc[n.snapshots]
optimized = n.buses_t.marginal_price.groupby(n.buses.country, axis=1).mean()
data = pd.concat([historic, optimized], keys=["Historic", "Optimized"], axis=1)
data.columns.names = ["Kind", "Country"]
fig, ax = plt.subplots(figsize=(6, 6))
df = data.mean().unstack().T
df.plot.barh(ax=ax, xlabel="Electricity Price [€/MWh]", ylabel="")
ax.grid(axis="y")
fig.savefig(snakemake.output.price_bar, bbox_inches="tight")
fig, ax = plt.subplots()
df = data.groupby(level="Kind", axis=1).mean()
df.plot(ax=ax, xlabel="", ylabel="Electricity Price [€/MWh]", alpha=0.8)
ax.grid(axis="x")
fig.savefig(snakemake.output.price_line, bbox_inches="tight")
# touch file
with open(snakemake.output.plots_touch, "a"):
pass

144
scripts/plot_validation_electricity_production.py Normal file
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@ -0,0 +1,144 @@
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import matplotlib.pyplot as plt
import pandas as pd
import pypsa
import seaborn as sns
from _helpers import configure_logging
from pypsa.statistics import get_bus_and_carrier
sns.set_theme("paper", style="whitegrid")
carrier_groups = {
"Offshore Wind (AC)": "Offshore Wind",
"Offshore Wind (DC)": "Offshore Wind",
"Open-Cycle Gas": "Gas",
"Combined-Cycle Gas": "Gas",
"Reservoir & Dam": "Hydro",
"Pumped Hydro Storage": "Hydro",
}
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake(
"plot_validation_electricity_production",
simpl="",
opts="Ept",
clusters="37c",
ll="v1.0",
)
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
n.loads.carrier = "load"
historic = pd.read_csv(
snakemake.input.electricity_production,
index_col=0,
header=[0, 1],
parse_dates=True,
)
colors = n.carriers.set_index("nice_name").color.where(
lambda s: s != "", "lightgrey"
)
colors["Offshore Wind"] = colors["Offshore Wind (AC)"]
colors["Gas"] = colors["Combined-Cycle Gas"]
colors["Hydro"] = colors["Reservoir & Dam"]
colors["Other"] = "lightgray"
if len(historic.index) > len(n.snapshots):
historic = historic.resample(n.snapshots.inferred_freq).mean().loc[n.snapshots]
optimized = n.statistics.dispatch(
groupby=get_bus_and_carrier, aggregate_time=False
).T
optimized = optimized[["Generator", "StorageUnit"]].droplevel(0, axis=1)
optimized = optimized.rename(columns=n.buses.country, level=0)
optimized = optimized.rename(columns=carrier_groups, level=1)
optimized = optimized.groupby(axis=1, level=[0, 1]).sum()
data = pd.concat([historic, optimized], keys=["Historic", "Optimized"], axis=1)
data.columns.names = ["Kind", "Country", "Carrier"]
data = data.mul(n.snapshot_weightings.generators, axis=0)
# total production per carrier
fig, ax = plt.subplots(figsize=(6, 6))
df = data.groupby(level=["Kind", "Carrier"], axis=1).sum().sum().unstack().T
df = df / 1e6 # TWh
df.plot.barh(ax=ax, xlabel="Electricity Production [TWh]", ylabel="")
ax.grid(axis="y")
fig.savefig(snakemake.output.production_bar, bbox_inches="tight")
# highest diffs
fig, ax = plt.subplots(figsize=(6, 10))
df = data.sum() / 1e6 # TWh
df = df["Optimized"] - df["Historic"]
df = df.dropna().sort_values()
df = pd.concat([df.iloc[:5], df.iloc[-5:]])
c = colors[df.index.get_level_values(1)]
df.plot.barh(
xlabel="Optimized Production - Historic Production [TWh]", ax=ax, color=c.values
)
ax.set_title("Strongest Deviations")
ax.grid(axis="y")
fig.savefig(snakemake.output.production_deviation_bar, bbox_inches="tight")
# seasonal operation
fig, axes = plt.subplots(3, 1, figsize=(9, 9))
df = (
data.groupby(level=["Kind", "Carrier"], axis=1)
.sum()
.resample("1W")
.mean()
.clip(lower=0)
)
df = df / 1e3
order = (
(df["Historic"].diff().abs().sum() / df["Historic"].sum()).sort_values().index
)
c = colors[order]
optimized = df["Optimized"].reindex(order, axis=1, level=1)
historical = df["Historic"].reindex(order, axis=1, level=1)
kwargs = dict(color=c, legend=False, ylabel="Production [GW]", xlabel="")
optimized.plot.area(ax=axes[0], **kwargs, title="Optimized")
historical.plot.area(ax=axes[1], **kwargs, title="Historic")
diff = optimized - historical
diff.clip(lower=0).plot.area(
ax=axes[2], **kwargs, title="$\Delta$ (Optimized - Historic)"
)
lim = axes[2].get_ylim()[1]
diff.clip(upper=0).plot.area(ax=axes[2], **kwargs)
axes[2].set_ylim(bottom=-lim, top=lim)
h, l = axes[0].get_legend_handles_labels()
fig.legend(
h[::-1],
l[::-1],
loc="center left",
bbox_to_anchor=(1, 0.5),
ncol=1,
frameon=False,
labelspacing=1,
)
fig.savefig(snakemake.output.seasonal_operation_area, bbox_inches="tight")
# touch file
with open(snakemake.output.plots_touch, "a"):
pass

31
scripts/prepare_network.py
View File

@ -65,6 +65,7 @@ import pandas as pd
import pypsa import pypsa
from _helpers import configure_logging from _helpers import configure_logging
from add_electricity import load_costs, update_transmission_costs from add_electricity import load_costs, update_transmission_costs
from pypsa.descriptors import expand_series
idx = pd.IndexSlice idx = pd.IndexSlice
@ -103,10 +104,30 @@ def add_emission_prices(n, emission_prices={"co2": 0.0}, exclude_co2=False):
).sum(axis=1) ).sum(axis=1)
gen_ep = n.generators.carrier.map(ep) / n.generators.efficiency gen_ep = n.generators.carrier.map(ep) / n.generators.efficiency
n.generators["marginal_cost"] += gen_ep n.generators["marginal_cost"] += gen_ep
n.generators_t["marginal_cost"] += gen_ep[n.generators_t["marginal_cost"].columns]
su_ep = n.storage_units.carrier.map(ep) / n.storage_units.efficiency_dispatch su_ep = n.storage_units.carrier.map(ep) / n.storage_units.efficiency_dispatch
n.storage_units["marginal_cost"] += su_ep n.storage_units["marginal_cost"] += su_ep
def add_dynamic_emission_prices(n):
co2_price = pd.read_csv(snakemake.input.co2_price, index_col=0, parse_dates=True)
co2_price = co2_price[~co2_price.index.duplicated()]
co2_price = (
co2_price.reindex(n.snapshots).fillna(method="ffill").fillna(method="bfill")
)
emissions = (
n.generators.carrier.map(n.carriers.co2_emissions) / n.generators.efficiency
)
co2_cost = expand_series(emissions, n.snapshots).T.mul(co2_price.iloc[:, 0], axis=0)
static = n.generators.marginal_cost
dynamic = n.get_switchable_as_dense("Generator", "marginal_cost")
marginal_cost = dynamic + co2_cost.reindex(columns=dynamic.columns, fill_value=0)
n.generators_t.marginal_cost = marginal_cost.loc[:, marginal_cost.ne(static).any()]
def set_line_s_max_pu(n, s_max_pu=0.7): def set_line_s_max_pu(n, s_max_pu=0.7):
n.lines["s_max_pu"] = s_max_pu n.lines["s_max_pu"] = s_max_pu
logger.info(f"N-1 security margin of lines set to {s_max_pu}") logger.info(f"N-1 security margin of lines set to {s_max_pu}")
@ -253,12 +274,13 @@ def set_line_nom_max(
n.links.p_nom_max.clip(upper=p_nom_max_set, inplace=True) n.links.p_nom_max.clip(upper=p_nom_max_set, inplace=True)
# %%
if __name__ == "__main__": if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake( snakemake = mock_snakemake(
"prepare_network", simpl="", clusters="40", ll="v0.3", opts="Co2L-24H" "prepare_network", simpl="", clusters="37", ll="v1.0", opts="Ept"
) )
configure_logging(snakemake) configure_logging(snakemake)
@ -332,7 +354,12 @@ if __name__ == "__main__":
c.df.loc[sel, attr] *= factor c.df.loc[sel, attr] *= factor
for o in opts: for o in opts:
if "Ep" in o: if "Ept" in o:
logger.info(
"Setting time dependent emission prices according spot market price"
)
add_dynamic_emission_prices(n)
elif "Ep" in o:
m = re.findall("[0-9]*\.?[0-9]+$", o) m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0: if len(m) > 0:
logger.info("Setting emission prices according to wildcard value.") logger.info("Setting emission prices according to wildcard value.")

39
scripts/prepare_sector_network.py
View File

@ -17,11 +17,7 @@ import numpy as np
import pandas as pd import pandas as pd
import pypsa import pypsa
import xarray as xr import xarray as xr
from _helpers import ( from _helpers import generate_periodic_profiles, update_config_with_sector_opts
generate_periodic_profiles,
override_component_attrs,
update_config_with_sector_opts,
)
from add_electricity import calculate_annuity, sanitize_carriers from add_electricity import calculate_annuity, sanitize_carriers
from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2 from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
from networkx.algorithms import complement from networkx.algorithms import complement
@ -2163,12 +2159,11 @@ def add_biomass(n, costs):
) )
if options["biomass_transport"]: if options["biomass_transport"]:
transport_costs = pd.read_csv(
snakemake.input.biomass_transport_costs,
index_col=0,
).squeeze()
# add biomass transport # add biomass transport
transport_costs = pd.read_csv(
snakemake.input.biomass_transport_costs, index_col=0
)
transport_costs = transport_costs.squeeze()
biomass_transport = create_network_topology( biomass_transport = create_network_topology(
n, "biomass transport ", bidirectional=False n, "biomass transport ", bidirectional=False
) )
@ -2192,6 +2187,27 @@ def add_biomass(n, costs):
carrier="solid biomass transport", carrier="solid biomass transport",
) )
elif options["biomass_spatial"]:
# add artificial biomass generators at nodes which include transport costs
transport_costs = pd.read_csv(
snakemake.input.biomass_transport_costs, index_col=0
)
transport_costs = transport_costs.squeeze()
bus_transport_costs = spatial.biomass.nodes.to_series().apply(
lambda x: transport_costs[x[:2]]
)
average_distance = 200 # km #TODO: validate this assumption
n.madd(
"Generator",
spatial.biomass.nodes,
bus=spatial.biomass.nodes,
carrier="solid biomass",
p_nom=10000,
marginal_cost=costs.at["solid biomass", "fuel"]
+ bus_transport_costs * average_distance,
)
# AC buses with district heating # AC buses with district heating
urban_central = n.buses.index[n.buses.carrier == "urban central heat"] urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
if not urban_central.empty and options["chp"]: if not urban_central.empty and options["chp"]:
@ -3283,8 +3299,7 @@ if __name__ == "__main__":
investment_year = int(snakemake.wildcards.planning_horizons[-4:]) investment_year = int(snakemake.wildcards.planning_horizons[-4:])
overrides = override_component_attrs(snakemake.input.overrides) n = pypsa.Network(snakemake.input.network)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0) pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
nhours = n.snapshot_weightings.generators.sum() nhours = n.snapshot_weightings.generators.sum()

35
scripts/retrieve_monthly_fuel_prices.py Normal file
View File

@ -0,0 +1,35 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Retrieve monthly fuel prices from Destatis.
"""
import logging
logger = logging.getLogger(__name__)
from pathlib import Path
from _helpers import configure_logging, progress_retrieve
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("retrieve_monthly_fuel_prices")
rootpath = ".."
else:
rootpath = "."
configure_logging(snakemake)
url = "https://www.destatis.de/EN/Themes/Economy/Prices/Publications/Downloads-Energy-Price-Trends/energy-price-trends-xlsx-5619002.xlsx?__blob=publicationFile"
to_fn = Path(rootpath) / Path(snakemake.output[0])
logger.info(f"Downloading monthly fuel prices from '{url}'.")
disable_progress = snakemake.config["run"].get("disable_progressbar", False)
progress_retrieve(url, to_fn, disable=disable_progress)
logger.info(f"Monthly fuel prices available at {to_fn}")

1
scripts/simplify_network.py
View File

@ -613,6 +613,7 @@ if __name__ == "__main__":
"substation_lv", "substation_lv",
"substation_off", "substation_off",
"geometry", "geometry",
"underground",
] ]
n.buses.drop(remove, axis=1, inplace=True, errors="ignore") n.buses.drop(remove, axis=1, inplace=True, errors="ignore")
n.lines.drop(remove, axis=1, errors="ignore", inplace=True) n.lines.drop(remove, axis=1, errors="ignore", inplace=True)

91
scripts/solve_network.py
View File

@ -33,11 +33,7 @@ import numpy as np
import pandas as pd import pandas as pd
import pypsa import pypsa
import xarray as xr import xarray as xr
from _helpers import ( from _helpers import configure_logging, update_config_with_sector_opts
configure_logging,
override_component_attrs,
update_config_with_sector_opts,
)
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
pypsa.pf.logger.setLevel(logging.WARNING) pypsa.pf.logger.setLevel(logging.WARNING)
@ -152,7 +148,7 @@ def prepare_network(
if "clip_p_max_pu" in solve_opts: if "clip_p_max_pu" in solve_opts:
for df in ( for df in (
n.generators_t.p_max_pu, n.generators_t.p_max_pu,
n.generators_t.p_min_pu, # TODO: check if this can be removed n.generators_t.p_min_pu,
n.storage_units_t.inflow, n.storage_units_t.inflow,
): ):
df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True) df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)
@ -280,13 +276,13 @@ def add_EQ_constraints(n, o, scaling=1e-1):
float_regex = "[0-9]*\.?[0-9]+" float_regex = "[0-9]*\.?[0-9]+"
level = float(re.findall(float_regex, o)[0]) level = float(re.findall(float_regex, o)[0])
if o[-1] == "c": if o[-1] == "c":
ggrouper = n.generators.bus.map(n.buses.country).to_xarray() ggrouper = n.generators.bus.map(n.buses.country)
lgrouper = n.loads.bus.map(n.buses.country).to_xarray() lgrouper = n.loads.bus.map(n.buses.country)
sgrouper = n.storage_units.bus.map(n.buses.country).to_xarray() sgrouper = n.storage_units.bus.map(n.buses.country)
else: else:
ggrouper = n.generators.bus.to_xarray() ggrouper = n.generators.bus
lgrouper = n.loads.bus.to_xarray() lgrouper = n.loads.bus
sgrouper = n.storage_units.bus.to_xarray() sgrouper = n.storage_units.bus
load = ( load = (
n.snapshot_weightings.generators n.snapshot_weightings.generators
@ n.loads_t.p_set.groupby(lgrouper, axis=1).sum() @ n.loads_t.p_set.groupby(lgrouper, axis=1).sum()
@ -300,7 +296,7 @@ def add_EQ_constraints(n, o, scaling=1e-1):
p = n.model["Generator-p"] p = n.model["Generator-p"]
lhs_gen = ( lhs_gen = (
(p * (n.snapshot_weightings.generators * scaling)) (p * (n.snapshot_weightings.generators * scaling))
.groupby(ggrouper) .groupby(ggrouper.to_xarray())
.sum() .sum()
.sum("snapshot") .sum("snapshot")
) )
@ -309,7 +305,7 @@ def add_EQ_constraints(n, o, scaling=1e-1):
spillage = n.model["StorageUnit-spill"] spillage = n.model["StorageUnit-spill"]
lhs_spill = ( lhs_spill = (
(spillage * (-n.snapshot_weightings.stores * scaling)) (spillage * (-n.snapshot_weightings.stores * scaling))
.groupby(sgrouper) .groupby(sgrouper.to_xarray())
.sum() .sum()
.sum("snapshot") .sum("snapshot")
) )
@ -599,47 +595,46 @@ def extra_functionality(n, snapshots):
def solve_network(n, config, solving, opts="", **kwargs): def solve_network(n, config, solving, opts="", **kwargs):
set_of_options = solving["solver"]["options"] set_of_options = solving["solver"]["options"]
solver_options = solving["solver_options"][set_of_options] if set_of_options else {}
solver_name = solving["solver"]["name"]
cf_solving = solving["options"] cf_solving = solving["options"]
track_iterations = cf_solving.get("track_iterations", False)
min_iterations = cf_solving.get("min_iterations", 4) kwargs["multi_investment_periods"] = True if config["foresight"] == "perfect" else False
max_iterations = cf_solving.get("max_iterations", 6) kwargs["solver_options"] = (
transmission_losses = cf_solving.get("transmission_losses", 0) solving["solver_options"][set_of_options] if set_of_options else {}
multi_horizon = True if config["foresight"] == "perfect" else False )
kwargs["solver_name"] = solving["solver"]["name"]
kwargs["extra_functionality"] = extra_functionality
kwargs["transmission_losses"] = cf_solving.get("transmission_losses", False)
kwargs["linearized_unit_commitment"] = cf_solving.get(
"linearized_unit_commitment", False
)
kwargs["assign_all_duals"] = cf_solving.get("assign_all_duals", False)
rolling_horizon = cf_solving.pop("rolling_horizon", False)
skip_iterations = cf_solving.pop("skip_iterations", False)
if not n.lines.s_nom_extendable.any():
skip_iterations = True
logger.info("No expandable lines found. Skipping iterative solving.")
# add to network for extra_functionality # add to network for extra_functionality
n.config = config n.config = config
n.opts = opts n.opts = opts
skip_iterations = cf_solving.get("skip_iterations", False) if rolling_horizon:
if not n.lines.s_nom_extendable.any(): kwargs["horizon"] = cf_solving.get("horizon", 365)
skip_iterations = True kwargs["overlap"] = cf_solving.get("overlap", 0)
logger.info("No expandable lines found. Skipping iterative solving.") n.optimize.optimize_with_rolling_horizon(**kwargs)
status, condition = "", ""
if skip_iterations: elif skip_iterations:
status, condition = n.optimize( status, condition = n.optimize(**kwargs)
solver_name=solver_name,
transmission_losses=transmission_losses,
extra_functionality=extra_functionality,
multi_investment_periods=multi_horizon,
**solver_options,
**kwargs,
)
else: else:
kwargs["track_iterations"] = (cf_solving.get("track_iterations", False),)
kwargs["min_iterations"] = (cf_solving.get("min_iterations", 4),)
kwargs["max_iterations"] = (cf_solving.get("max_iterations", 6),)
status, condition = n.optimize.optimize_transmission_expansion_iteratively( status, condition = n.optimize.optimize_transmission_expansion_iteratively(
solver_name=solver_name, **kwargs
track_iterations=track_iterations,
min_iterations=min_iterations,
max_iterations=max_iterations,
transmission_losses=transmission_losses,
extra_functionality=extra_functionality,
multi_investment_periods=multi_horizon,
**solver_options,
**kwargs,
) )
if status != "ok": if status != "ok" and not rolling_horizon:
logger.warning( logger.warning(
f"Solving status '{status}' with termination condition '{condition}'" f"Solving status '{status}' with termination condition '{condition}'"
) )
@ -677,11 +672,7 @@ if __name__ == "__main__":
np.random.seed(solve_opts.get("seed", 123)) np.random.seed(solve_opts.get("seed", 123))
if "overrides" in snakemake.input.keys(): n = pypsa.Network(snakemake.input.network)
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
else:
n = pypsa.Network(snakemake.input.network)
n = prepare_network( n = prepare_network(
n, n,

12
scripts/solve_operations_network.py
View File

@ -11,11 +11,7 @@ import logging
import numpy as np import numpy as np
import pypsa import pypsa
from _helpers import ( from _helpers import configure_logging, update_config_with_sector_opts
configure_logging,
override_component_attrs,
update_config_with_sector_opts,
)
from solve_network import prepare_network, solve_network from solve_network import prepare_network, solve_network
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -45,11 +41,7 @@ if __name__ == "__main__":
np.random.seed(solve_opts.get("seed", 123)) np.random.seed(solve_opts.get("seed", 123))
if "overrides" in snakemake.input: n = pypsa.Network(snakemake.input.network)
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
else:
n = pypsa.Network(snakemake.input.network)
n.optimize.fix_optimal_capacities() n.optimize.fix_optimal_capacities()
n = prepare_network(n, solve_opts, config=snakemake.config) n = prepare_network(n, solve_opts, config=snakemake.config)