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Fabian Neumann 2023-01-06 18:45:36 +01:00
commit 1ebe53cdac
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.git-blame-ignore-revs Normal file
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# SPDX-FileCopyrightText: : 2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
# Exclude pre-commit applications
5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0
92080b1cd2ca5f123158571481722767b99c2b27

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.gitattributes vendored
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# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0

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.github/ISSUE_TEMPLATE/config.yml vendored
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@ -1,5 +1,5 @@
blank_issues_enabled: false
contact_links:
- name: PyPSA Mailing List
url: https://groups.google.com/forum/#!forum/pypsa
about: Please ask and answer general usage questions here.
- name: PyPSA Mailing List
url: https://groups.google.com/forum/#!forum/pypsa
about: Please ask and answer general usage questions here.

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@ -16,7 +16,7 @@ on:
branches:
- master
schedule:
- cron: "0 5 * * TUE"
- cron: "0 5 * * TUE"
env:
CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
@ -28,63 +28,73 @@ jobs:
matrix:
include:
# Matrix required to handle caching with Mambaforge
- os: ubuntu-latest
label: ubuntu-latest
prefix: /usr/share/miniconda3/envs/pypsa-eur
- os: ubuntu-latest
label: ubuntu-latest
prefix: /usr/share/miniconda3/envs/pypsa-eur
- os: macos-latest
label: macos-latest
prefix: /Users/runner/miniconda3/envs/pypsa-eur
- os: macos-latest
label: macos-latest
prefix: /Users/runner/miniconda3/envs/pypsa-eur
- os: windows-latest
label: windows-latest
prefix: C:\Miniconda3\envs\pypsa-eur
- os: windows-latest
label: windows-latest
prefix: C:\Miniconda3\envs\pypsa-eur
name: ${{ matrix.label }}
runs-on: ${{ matrix.os }}
defaults:
run:
shell: bash -l {0}
steps:
- uses: actions/checkout@v2
- name: Setup secrets
run: |
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
- uses: actions/checkout@v2
- name: Add solver to environment
run: |
echo -e " - glpk\n - ipopt<3.13.3" >> envs/environment.yaml
- name: Setup secrets
run: |
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
- name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2
with:
miniforge-variant: Mambaforge
miniforge-version: latest
activate-environment: pypsa-eur
use-mamba: true
- name: Set cache date
run: echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
- name: Add solver to environment
run: |
echo -e "- glpk\n- ipopt" >> envs/environment.yaml
- name: Create environment cache
uses: actions/cache@v2
id: cache
with:
path: ${{ matrix.prefix }}
key: ${{ matrix.label }}-conda-${{ hashFiles('envs/environment.yaml') }}-${{ env.DATE }}-${{ env.CACHE_NUMBER }}
- name: Add solver to environment
run: |
echo -e "- glpk\n- ipopt<3.13.3" >> envs/environment.yaml
if: ${{ matrix.label }} == 'windows-latest'
- 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: Add solver to environment
run: |
echo -e "- glpk\n- ipopt" >> envs/environment.yaml
if: ${{ matrix.label }} != 'windows-latest'
- name: Test snakemake workflow
run: |
conda activate pypsa-eur
conda list
cp test/config.test1.yaml config.yaml
snakemake --cores all solve_all_networks
rm -rf resources/*.nc resources/*.geojson resources/*.h5 networks results
- name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2
with:
miniforge-variant: Mambaforge
miniforge-version: latest
activate-environment: pypsa-eur
use-mamba: true
- name: Set cache date
run: echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
- name: Create environment cache
uses: actions/cache@v2
id: cache
with:
path: ${{ matrix.prefix }}
key: ${{ matrix.label }}-conda-${{ hashFiles('envs/environment.yaml') }}-${{ env.DATE }}-${{ env.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
run: |
conda activate pypsa-eur
conda list
cp test/config.test1.yaml config.yaml
snakemake --cores all solve_all_networks
rm -rf resources/*.nc resources/*.geojson resources/*.h5 networks results

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@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
@ -19,6 +19,7 @@ gurobi.log
/data
/data/links_p_nom.csv
/cutouts
/dask-worker-space
doc/_build

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@ -0,0 +1,92 @@
# SPDX-FileCopyrightText: : 2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
exclude: "^LICENSES"
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v4.4.0
hooks:
- id: check-merge-conflict
- id: end-of-file-fixer
- id: fix-encoding-pragma
- id: mixed-line-ending
- id: trailing-whitespace
- id: check-added-large-files
args: ["--maxkb=2000"]
# Sort package imports alphabetically
- repo: https://github.com/PyCQA/isort
rev: 5.11.4
hooks:
- id: isort
args: ["--profile", "black", "--filter-files"]
# Convert relative imports to absolute imports
- repo: https://github.com/MarcoGorelli/absolufy-imports
rev: v0.3.1
hooks:
- id: absolufy-imports
# Find common spelling mistakes in comments and docstrings
- repo: https://github.com/codespell-project/codespell
rev: v2.2.2
hooks:
- id: codespell
args: ['--ignore-regex="(\b[A-Z]+\b)"', '--ignore-words-list=fom'] # Ignore capital case words, e.g. country codes
types_or: [python, rst, markdown]
files: ^(scripts|doc)/
# Make docstrings PEP 257 compliant
- repo: https://github.com/PyCQA/docformatter
rev: v1.5.1
hooks:
- id: docformatter
args: ["--in-place", "--make-summary-multi-line", "--pre-summary-newline"]
- repo: https://github.com/keewis/blackdoc
rev: v0.3.8
hooks:
- id: blackdoc
# Formatting with "black" coding style
- repo: https://github.com/psf/black
rev: 22.12.0
hooks:
# Format Python files
- id: black
# Format Jupyter Python notebooks
- id: black-jupyter
# Remove output from Jupyter notebooks
- repo: https://github.com/aflc/pre-commit-jupyter
rev: v1.2.1
hooks:
- id: jupyter-notebook-cleanup
args: ["--remove-kernel-metadata"]
# Do YAML formatting (before the linter checks it for misses)
- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
rev: v2.5.0
hooks:
- id: pretty-format-yaml
args: [--autofix, --indent, "2", --preserve-quotes]
# Format Snakemake rule / workflow files
- repo: https://github.com/snakemake/snakefmt
rev: v0.8.0
hooks:
- id: snakefmt
# For cleaning jupyter notebooks
- repo: https://github.com/aflc/pre-commit-jupyter
rev: v1.2.1
hooks:
- id: jupyter-notebook-cleanup
exclude: examples/solve-on-remote.ipynb
# Check for FSFE REUSE compliance (licensing)
- repo: https://github.com/fsfe/reuse-tool
rev: v1.1.0
hooks:
- id: reuse

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@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
@ -7,5 +7,5 @@ version: 2
python:
version: 3.8
install:
- requirements: doc/requirements.txt
- requirements: doc/requirements.txt
system_packages: true

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@ -4,15 +4,15 @@ Upstream-Contact: Tom Brown <tom.brown@kit.edu>
Source: https://github.com/pypsa/pypsa-eur
Files: doc/img/*
Copyright: 2019 Fabian Neumann (KIT)
Copyright: 2019 Fabian Neumann (TUB, KIT)
License: CC-BY-4.0
Files: doc/configtables/*
Copyright: 2019 Fabian Neumann (KIT)
Copyright: 2019 Fabian Neumann (TUB, KIT)
License: CC-BY-4.0
Files: data/*
Copyright: 2017-2020 The PyPSA-Eur Authors
Copyright: 2017-2022 The PyPSA-Eur Authors
License: CC-BY-4.0
Files: .github/*
@ -20,9 +20,9 @@ Copyright: 2019 The PyPSA-Eur Authors
License: CC0-1.0
Files: matplotlibrc
Copyright: : 2017-2020 The PyPSA-Eur Authors
Copyright: : 2017-2022 The PyPSA-Eur Authors
License: CC0-1.0
Files: borg-it
Copyright: : 2017-2020 The PyPSA-Eur Authors
Copyright: : 2017-2022 The PyPSA-Eur Authors
License: CC0-1.0

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@ -16,4 +16,4 @@ notebooks
doc
cutouts
data/bundle
*.nc
*.nc

2
CITATION.cff
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@ -6,7 +6,7 @@ cff-version: 1.1.0
message: "If you use this package, please cite the corresponding manuscript in Energy Strategy Reviews."
title: "PyPSA-Eur: An open optimisation model of the European transmission system"
repository: https://github.com/pypsa/pypsa-eur
version: 0.4.0
version: 0.6.1
license: MIT
journal: Energy Strategy Reviews
doi: 10.1016/j.esr.2018.08.012

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LICENSES/MIT.txt
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@ -1,6 +1,6 @@
MIT License
Copyright 2017-2021 The PyPSA-Eur Authors
Copyright 2017-2022 The PyPSA-Eur Authors
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in

7
README.md
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@ -1,5 +1,5 @@
<!--
SPDX-FileCopyrightText: 2017-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2017-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
-->
@ -8,7 +8,6 @@ SPDX-License-Identifier: CC-BY-4.0
[![Documentation](https://readthedocs.org/projects/pypsa-eur/badge/?version=latest)](https://pypsa-eur.readthedocs.io/en/latest/?badge=latest)
![Size](https://img.shields.io/github/repo-size/pypsa/pypsa-eur)
[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.3520874.svg)](https://doi.org/10.5281/zenodo.3520874)
[![Gitter](https://badges.gitter.im/PyPSA/community.svg)](https://gitter.im/PyPSA/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
[![Snakemake](https://img.shields.io/badge/snakemake-≥5.0.0-brightgreen.svg?style=flat)](https://snakemake.readthedocs.io)
[![REUSE status](https://api.reuse.software/badge/github.com/pypsa/pypsa-eur)](https://api.reuse.software/info/github.com/pypsa/pypsa-eur)
@ -38,9 +37,7 @@ curtailment. We recommend to cluster the network to a couple of
hundred nodes to remove these local inconsistencies. See the
discussion in Section 3.4 "Model validation" of the paper.
![PyPSA-Eur Grid Model](doc/img/base.png)
![PyPSA-Eur Grid Model Simplified](doc/img/elec_s_X.png)
![PyPSA-Eur Grid Model](doc/img/elec.png)
The model building routines are defined through a snakemake workflow. The model is designed to be imported into the open toolbox
[PyPSA](https://github.com/PyPSA/PyPSA) for operational studies as

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Snakefile
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@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -6,65 +6,101 @@ from os.path import normpath, exists
from shutil import copyfile, move
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
HTTP = HTTPRemoteProvider()
if not exists("config.yaml"):
copyfile("config.default.yaml", "config.yaml")
configfile: "config.yaml"
COSTS="data/costs.csv"
ATLITE_NPROCESSES = config['atlite'].get('nprocesses', 4)
run = config.get("run", {})
RDIR = run["name"] + "/" if run.get("name") else ""
CDIR = RDIR if not run.get("shared_cutouts") else ""
COSTS = "resources/" + RDIR + "costs.csv"
ATLITE_NPROCESSES = config["atlite"].get("nprocesses", 4)
wildcard_constraints:
simpl="[a-zA-Z0-9]*|all",
clusters="[0-9]+m?|all",
ll="(v|c)([0-9\.]+|opt|all)|all",
opts="[-+a-zA-Z0-9\.]*"
opts="[-+a-zA-Z0-9\.]*",
rule cluster_all_networks:
input: expand("networks/elec_s{simpl}_{clusters}.nc", **config['scenario'])
input:
expand("networks/" + RDIR + "elec_s{simpl}_{clusters}.nc", **config["scenario"]),
rule extra_components_all_networks:
input: expand("networks/elec_s{simpl}_{clusters}_ec.nc", **config['scenario'])
input:
expand(
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc", **config["scenario"]
),
rule prepare_all_networks:
input: expand("networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", **config['scenario'])
input:
expand(
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
**config["scenario"]
),
rule solve_all_networks:
input: expand("results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", **config['scenario'])
input:
expand(
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
**config["scenario"]
),
if config['enable'].get('prepare_links_p_nom', False):
if config["enable"].get("prepare_links_p_nom", False):
rule prepare_links_p_nom:
output: 'data/links_p_nom.csv'
log: 'logs/prepare_links_p_nom.log'
output:
"data/links_p_nom.csv",
log:
"logs/" + RDIR + "prepare_links_p_nom.log",
threads: 1
resources: mem_mb=500
script: 'scripts/prepare_links_p_nom.py'
resources:
mem_mb=1500,
script:
"scripts/prepare_links_p_nom.py"
datafiles = ['ch_cantons.csv', 'je-e-21.03.02.xls',
'eez/World_EEZ_v8_2014.shp', 'EIA_hydro_generation_2000_2014.csv',
'hydro_capacities.csv', 'naturalearth/ne_10m_admin_0_countries.shp',
'NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp', 'nama_10r_3popgdp.tsv.gz',
'nama_10r_3gdp.tsv.gz', 'corine/g250_clc06_V18_5.tif']
datafiles = [
"ch_cantons.csv",
"je-e-21.03.02.xls",
"eez/World_EEZ_v8_2014.shp",
"hydro_capacities.csv",
"naturalearth/ne_10m_admin_0_countries.shp",
"NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp",
"nama_10r_3popgdp.tsv.gz",
"nama_10r_3gdp.tsv.gz",
"corine/g250_clc06_V18_5.tif",
]
if not config.get('tutorial', False):
if not config.get("tutorial", False):
datafiles.extend(["natura/Natura2000_end2015.shp", "GEBCO_2014_2D.nc"])
if config['enable'].get('retrieve_databundle', True):
if config["enable"].get("retrieve_databundle", True):
rule retrieve_databundle:
output: expand('data/bundle/{file}', file=datafiles)
log: "logs/retrieve_databundle.log"
script: 'scripts/retrieve_databundle.py'
output:
expand("data/bundle/{file}", file=datafiles),
log:
"logs/" + RDIR + "retrieve_databundle.log",
resources:
mem_mb=1000,
script:
"scripts/retrieve_databundle.py"
# Downloading Copernicus Global Land Cover for land cover and land use:
# Website: https://land.copernicus.eu/global/products/lc
@ -104,118 +140,232 @@ rule determine_availability_matrix_MD_UA:
"scripts/determine_availability_matrix_MD_UA.py.ipynb"
rule retrieve_load_data:
input: HTTP.remote("data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv", keep_local=True, static=True)
output: "data/load_raw.csv"
run: move(input[0], output[0])
input:
HTTP.remote(
"data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv",
keep_local=True,
static=True,
),
output:
"data/load_raw.csv",
resources:
mem_mb=5000,
run:
move(input[0], output[0])
rule build_load_data:
input: "data/load_raw.csv"
output: "resources/load.csv"
log: "logs/build_load_data.log"
script: 'scripts/build_load_data.py'
input:
"data/load_raw.csv",
output:
"resources/" + RDIR + "load.csv",
log:
"logs/" + RDIR + "build_load_data.log",
resources:
mem_mb=5000,
script:
"scripts/build_load_data.py"
rule build_powerplants:
input:
base_network="networks/base.nc",
custom_powerplants="data/custom_powerplants.csv"
output: "resources/powerplants.csv"
log: "logs/build_powerplants.log"
base_network="networks/" + RDIR + "base.nc",
custom_powerplants="data/custom_powerplants.csv",
output:
"resources/" + RDIR + "powerplants.csv",
log:
"logs/" + RDIR + "build_powerplants.log",
threads: 1
resources: mem_mb=500
script: "scripts/build_powerplants.py"
resources:
mem_mb=5000,
script:
"scripts/build_powerplants.py"
rule base_network:
input:
eg_buses='data/entsoegridkit/buses.csv',
eg_lines='data/entsoegridkit/lines.csv',
eg_links='data/entsoegridkit/links.csv',
eg_converters='data/entsoegridkit/converters.csv',
eg_transformers='data/entsoegridkit/transformers.csv',
parameter_corrections='data/parameter_corrections.yaml',
links_p_nom='data/links_p_nom.csv',
links_tyndp='data/links_tyndp.csv',
country_shapes='resources/country_shapes.geojson',
offshore_shapes='resources/offshore_shapes.geojson',
europe_shape='resources/europe_shape.geojson'
output: "networks/base.nc"
log: "logs/base_network.log"
benchmark: "benchmarks/base_network"
eg_buses="data/entsoegridkit/buses.csv",
eg_lines="data/entsoegridkit/lines.csv",
eg_links="data/entsoegridkit/links.csv",
eg_converters="data/entsoegridkit/converters.csv",
eg_transformers="data/entsoegridkit/transformers.csv",
parameter_corrections="data/parameter_corrections.yaml",
links_p_nom="data/links_p_nom.csv",
links_tyndp="data/links_tyndp.csv",
country_shapes="resources/" + RDIR + "country_shapes.geojson",
offshore_shapes="resources/" + RDIR + "offshore_shapes.geojson",
europe_shape="resources/" + RDIR + "europe_shape.geojson",
output:
"networks/" + RDIR + "base.nc",
log:
"logs/" + RDIR + "base_network.log",
benchmark:
"benchmarks/" + RDIR + "base_network"
threads: 1
resources: mem_mb=500
script: "scripts/base_network.py"
resources:
mem_mb=1500,
script:
"scripts/base_network.py"
rule build_shapes:
input:
naturalearth='data/bundle/naturalearth/ne_10m_admin_0_countries.shp',
eez='data/bundle/eez/World_EEZ_v8_2014.shp',
nuts3='data/bundle/NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp',
nuts3pop='data/bundle/nama_10r_3popgdp.tsv.gz',
nuts3gdp='data/bundle/nama_10r_3gdp.tsv.gz',
ch_cantons='data/bundle/ch_cantons.csv',
ch_popgdp='data/bundle/je-e-21.03.02.xls'
naturalearth="data/bundle/naturalearth/ne_10m_admin_0_countries.shp",
eez="data/bundle/eez/World_EEZ_v8_2014.shp",
nuts3="data/bundle/NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp",
nuts3pop="data/bundle/nama_10r_3popgdp.tsv.gz",
nuts3gdp="data/bundle/nama_10r_3gdp.tsv.gz",
ch_cantons="data/bundle/ch_cantons.csv",
ch_popgdp="data/bundle/je-e-21.03.02.xls",
output:
country_shapes='resources/country_shapes.geojson',
offshore_shapes='resources/offshore_shapes.geojson',
europe_shape='resources/europe_shape.geojson',
nuts3_shapes='resources/nuts3_shapes.geojson'
log: "logs/build_shapes.log"
country_shapes="resources/" + RDIR + "country_shapes.geojson",
offshore_shapes="resources/" + RDIR + "offshore_shapes.geojson",
europe_shape="resources/" + RDIR + "europe_shape.geojson",
nuts3_shapes="resources/" + RDIR + "nuts3_shapes.geojson",
log:
"logs/" + RDIR + "build_shapes.log",
threads: 1
resources: mem_mb=500
script: "scripts/build_shapes.py"
resources:
mem_mb=1500,
script:
"scripts/build_shapes.py"
rule build_bus_regions:
input:
country_shapes='resources/country_shapes.geojson',
offshore_shapes='resources/offshore_shapes.geojson',
base_network="networks/base.nc"
country_shapes="resources/" + RDIR + "country_shapes.geojson",
offshore_shapes="resources/" + RDIR + "offshore_shapes.geojson",
base_network="networks/" + RDIR + "base.nc",
output:
regions_onshore="resources/regions_onshore.geojson",
regions_offshore="resources/regions_offshore.geojson"
log: "logs/build_bus_regions.log"
regions_onshore="resources/" + RDIR + "regions_onshore.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore.geojson",
log:
"logs/" + RDIR + "build_bus_regions.log",
threads: 1
resources: mem_mb=1000
script: "scripts/build_bus_regions.py"
resources:
mem_mb=1000,
script:
"scripts/build_bus_regions.py"
if config["enable"].get("build_cutout", False):
if config['enable'].get('build_cutout', False):
rule build_cutout:
input:
regions_onshore="resources/regions_onshore.geojson",
regions_offshore="resources/regions_offshore.geojson"
output: "cutouts/{cutout}.nc"
log: "logs/build_cutout/{cutout}.log"
benchmark: "benchmarks/build_cutout_{cutout}"
input:
regions_onshore="resources/" + RDIR + "regions_onshore.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore.geojson",
output:
"cutouts/" + CDIR + "{cutout}.nc",
log:
"logs/" + CDIR + "build_cutout/{cutout}.log",
benchmark:
"benchmarks/" + CDIR + "build_cutout_{cutout}"
threads: ATLITE_NPROCESSES
resources: mem_mb=ATLITE_NPROCESSES * 1000
script: "scripts/build_cutout.py"
resources:
mem_mb=ATLITE_NPROCESSES * 1000,
script:
"scripts/build_cutout.py"
if config['enable'].get('retrieve_cutout', True):
if config["enable"].get("retrieve_cutout", True):
rule retrieve_cutout:
input: HTTP.remote("zenodo.org/record/6350001/files/{cutout}.nc", keep_local=True, static=True)
output: "cutouts/{cutout}.nc"
run: move(input[0], output[0])
input:
HTTP.remote(
"zenodo.org/record/6350001/files/{cutout}.nc",
keep_local=True,
static=True,
),
output:
"cutouts/" + CDIR + "{cutout}.nc",
log:
"logs/" + CDIR + "retrieve_cutout_{cutout}.log",
resources:
mem_mb=5000,
run:
move(input[0], output[0])
if config['enable'].get('build_natura_raster', False):
if config["enable"].get("retrieve_cost_data", True):
rule retrieve_cost_data:
input:
HTTP.remote(
f"raw.githubusercontent.com/PyPSA/technology-data/{config['costs']['version']}/outputs/costs_{config['costs']['year']}.csv",
keep_local=True,
),
output:
COSTS,
log:
"logs/" + RDIR + "retrieve_cost_data.log",
resources:
mem_mb=5000,
run:
move(input[0], output[0])
if config["enable"].get("build_natura_raster", False):
rule build_natura_raster:
input:
natura="data/bundle/natura/Natura2000_end2015.shp",
cutouts=expand("cutouts/{cutouts}.nc", **config['atlite'])
output: "resources/natura.tiff"
log: "logs/build_natura_raster.log"
script: "scripts/build_natura_raster.py"
cutouts=expand("cutouts/" + CDIR + "{cutouts}.nc", **config["atlite"]),
output:
"resources/" + RDIR + "natura.tiff",
resources:
mem_mb=5000,
log:
"logs/" + RDIR + "build_natura_raster.log",
script:
"scripts/build_natura_raster.py"
if config['enable'].get('retrieve_natura_raster', True):
if config["enable"].get("retrieve_natura_raster", True):
rule retrieve_natura_raster:
input: HTTP.remote("zenodo.org/record/4706686/files/natura.tiff", keep_local=True, static=True)
output: "resources/natura.tiff"
run: move(input[0], output[0])
input:
HTTP.remote(
"zenodo.org/record/4706686/files/natura.tiff",
keep_local=True,
static=True,
),
output:
"resources/" + RDIR + "natura.tiff",
resources:
mem_mb=5000,
run:
move(input[0], output[0])
rule retrieve_ship_raster:
input:
HTTP.remote(
"https://zenodo.org/record/6953563/files/shipdensity_global.zip",
keep_local=True,
static=True,
),
output:
"data/shipdensity_global.zip",
resources:
mem_mb=5000,
run:
move(input[0], output[0])
rule build_ship_raster:
input:
ship_density="data/shipdensity_global.zip",
cutouts=expand("cutouts/" + CDIR + "{cutouts}.nc", **config["atlite"]),
output:
"resources/" + RDIR + "shipdensity_raster.nc",
log:
"logs/" + RDIR + "build_ship_raster.log",
resources:
mem_mb=5000,
benchmark:
"benchmarks/" + RDIR + "build_ship_raster"
script:
"scripts/build_ship_raster.py"
# Optional input when having Ukraine (UA) or Moldova (MD) in the countries list
@ -228,136 +378,207 @@ else:
rule build_renewable_profiles:
input:
base_network="networks/base.nc",
base_network="networks/" + RDIR + "base.nc",
corine="data/bundle/corine/g250_clc06_V18_5.tif",
natura="resources/natura.tiff",
gebco=lambda w: ("data/bundle/GEBCO_2014_2D.nc"
if "max_depth" in config["renewable"][w.technology].keys()
else []),
country_shapes='resources/country_shapes.geojson',
offshore_shapes='resources/offshore_shapes.geojson',
regions=lambda w: ("resources/regions_onshore.geojson"
if w.technology in ('onwind', 'solar')
else "resources/regions_offshore.geojson"),
cutout=lambda w: "cutouts/" + config["renewable"][w.technology]['cutout'] + ".nc",
**opt,
output: profile="resources/profile_{technology}.nc",
log: "logs/build_renewable_profile_{technology}.log"
benchmark: "benchmarks/build_renewable_profiles_{technology}"
natura=lambda w: (
"resources/" + RDIR + "natura.tiff"
if config["renewable"][w.technology]["natura"]
else []
),
gebco=lambda w: (
"data/bundle/GEBCO_2014_2D.nc"
if "max_depth" in config["renewable"][w.technology].keys()
else []
),
ship_density=lambda w: (
"resources/" + RDIR + "shipdensity_raster.nc"
if "ship_threshold" in config["renewable"][w.technology].keys()
else []
),
country_shapes="resources/" + RDIR + "country_shapes.geojson",
offshore_shapes="resources/" + RDIR + "offshore_shapes.geojson",
regions=lambda w: (
"resources/" + RDIR + "regions_onshore.geojson"
if w.technology in ("onwind", "solar")
else "resources/" + RDIR + "regions_offshore.geojson"
),
cutout=lambda w: "cutouts/"
+ CDIR
+ config["renewable"][w.technology]["cutout"]
+ ".nc",
output:
profile="resources/" + RDIR + "profile_{technology}.nc",
log:
"logs/" + RDIR + "build_renewable_profile_{technology}.log",
benchmark:
"benchmarks/" + RDIR + "build_renewable_profiles_{technology}"
threads: ATLITE_NPROCESSES
resources: mem_mb=ATLITE_NPROCESSES * 5000
script: "scripts/build_renewable_profiles.py"
resources:
mem_mb=ATLITE_NPROCESSES * 5000,
wildcard_constraints:
technology="(?!hydro).*", # Any technology other than hydro
script:
"scripts/build_renewable_profiles.py"
if 'hydro' in config['renewable'].keys():
rule build_hydro_profile:
input:
country_shapes='resources/country_shapes.geojson',
eia_hydro_generation='data/EIA_hydro_generation_2000_2014.csv',
cutout="cutouts/" + config["renewable"]['hydro']['cutout'] + ".nc"
output: 'resources/profile_hydro.nc'
log: "logs/build_hydro_profile.log"
resources: mem_mb=5000
script: 'scripts/build_hydro_profile.py'
rule build_hydro_profile:
input:
country_shapes="resources/" + RDIR + "country_shapes.geojson",
eia_hydro_generation="data/eia_hydro_annual_generation.csv",
cutout=f"cutouts/" + CDIR + config["renewable"]["hydro"]["cutout"] + ".nc"
if "hydro" in config["renewable"]
else [],
output:
"resources/" + RDIR + "profile_hydro.nc",
log:
"logs/" + RDIR + "build_hydro_profile.log",
resources:
mem_mb=5000,
script:
"scripts/build_hydro_profile.py"
rule add_electricity:
input:
base_network='networks/base.nc',
**{
f"profile_{tech}": "resources/" + RDIR + f"profile_{tech}.nc"
for tech in config["renewable"]
},
**{
f"conventional_{carrier}_{attr}": fn
for carrier, d in config.get("conventional", {None: {}}).items()
for attr, fn in d.items()
if str(fn).startswith("data/")
},
base_network="networks/" + RDIR + "base.nc",
tech_costs=COSTS,
regions="resources/regions_onshore.geojson",
powerplants='resources/powerplants.csv',
hydro_capacities='data/bundle/hydro_capacities.csv',
geth_hydro_capacities='data/geth2015_hydro_capacities.csv',
load='resources/load.csv',
nuts3_shapes='resources/nuts3_shapes.geojson',
regions="resources/" + RDIR + "regions_onshore.geojson",
powerplants="resources/" + RDIR + "powerplants.csv",
hydro_capacities="data/bundle/hydro_capacities.csv",
geth_hydro_capacities="data/geth2015_hydro_capacities.csv",
load="resources/" + RDIR + "load.csv",
nuts3_shapes="resources/" + RDIR + "nuts3_shapes.geojson",
ua_md_gdp='data/GDP_PPP_30arcsec_v3_mapped_default.csv',
**{f"profile_{tech}": f"resources/profile_{tech}.nc"
for tech in config['renewable']}
output: "networks/elec.nc"
log: "logs/add_electricity.log"
benchmark: "benchmarks/add_electricity"
output:
"networks/" + RDIR + "elec.nc",
log:
"logs/" + RDIR + "add_electricity.log",
benchmark:
"benchmarks/" + RDIR + "add_electricity"
threads: 1
resources: mem_mb=5000
script: "scripts/add_electricity.py"
resources:
mem_mb=5000,
script:
"scripts/add_electricity.py"
rule simplify_network:
input:
network='networks/elec.nc',
network="networks/" + RDIR + "elec.nc",
tech_costs=COSTS,
regions_onshore="resources/regions_onshore.geojson",
regions_offshore="resources/regions_offshore.geojson"
regions_onshore="resources/" + RDIR + "regions_onshore.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore.geojson",
output:
network='networks/elec_s{simpl}.nc',
regions_onshore="resources/regions_onshore_elec_s{simpl}.geojson",
regions_offshore="resources/regions_offshore_elec_s{simpl}.geojson",
busmap='resources/busmap_elec_s{simpl}.csv',
connection_costs='resources/connection_costs_s{simpl}.csv'
log: "logs/simplify_network/elec_s{simpl}.log"
benchmark: "benchmarks/simplify_network/elec_s{simpl}"
network="networks/" + RDIR + "elec_s{simpl}.nc",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore_elec_s{simpl}.geojson",
busmap="resources/" + RDIR + "busmap_elec_s{simpl}.csv",
connection_costs="resources/" + RDIR + "connection_costs_s{simpl}.csv",
log:
"logs/" + RDIR + "simplify_network/elec_s{simpl}.log",
benchmark:
"benchmarks/" + RDIR + "simplify_network/elec_s{simpl}"
threads: 1
resources: mem_mb=4000
script: "scripts/simplify_network.py"
resources:
mem_mb=4000,
script:
"scripts/simplify_network.py"
rule cluster_network:
input:
network='networks/elec_s{simpl}.nc',
regions_onshore="resources/regions_onshore_elec_s{simpl}.geojson",
regions_offshore="resources/regions_offshore_elec_s{simpl}.geojson",
busmap=ancient('resources/busmap_elec_s{simpl}.csv'),
custom_busmap=("data/custom_busmap_elec_s{simpl}_{clusters}.csv"
if config["enable"].get("custom_busmap", False) else []),
tech_costs=COSTS
network="networks/" + RDIR + "elec_s{simpl}.nc",
regions_onshore="resources/" + RDIR + "regions_onshore_elec_s{simpl}.geojson",
regions_offshore="resources/" + RDIR + "regions_offshore_elec_s{simpl}.geojson",
busmap=ancient("resources/" + RDIR + "busmap_elec_s{simpl}.csv"),
custom_busmap=(
"data/custom_busmap_elec_s{simpl}_{clusters}.csv"
if config["enable"].get("custom_busmap", False)
else []
),
tech_costs=COSTS,
output:
network='networks/elec_s{simpl}_{clusters}.nc',
regions_onshore="resources/regions_onshore_elec_s{simpl}_{clusters}.geojson",
regions_offshore="resources/regions_offshore_elec_s{simpl}_{clusters}.geojson",
busmap="resources/busmap_elec_s{simpl}_{clusters}.csv",
linemap="resources/linemap_elec_s{simpl}_{clusters}.csv"
log: "logs/cluster_network/elec_s{simpl}_{clusters}.log"
benchmark: "benchmarks/cluster_network/elec_s{simpl}_{clusters}"
network="networks/" + RDIR + "elec_s{simpl}_{clusters}.nc",
regions_onshore="resources/"
+ RDIR
+ "regions_onshore_elec_s{simpl}_{clusters}.geojson",
regions_offshore="resources/"
+ RDIR
+ "regions_offshore_elec_s{simpl}_{clusters}.geojson",
busmap="resources/" + RDIR + "busmap_elec_s{simpl}_{clusters}.csv",
linemap="resources/" + RDIR + "linemap_elec_s{simpl}_{clusters}.csv",
log:
"logs/" + RDIR + "cluster_network/elec_s{simpl}_{clusters}.log",
benchmark:
"benchmarks/" + RDIR + "cluster_network/elec_s{simpl}_{clusters}"
threads: 1
resources: mem_mb=6000
script: "scripts/cluster_network.py"
resources:
mem_mb=6000,
script:
"scripts/cluster_network.py"
rule add_extra_components:
input:
network='networks/elec_s{simpl}_{clusters}.nc',
network="networks/" + RDIR + "elec_s{simpl}_{clusters}.nc",
tech_costs=COSTS,
output: 'networks/elec_s{simpl}_{clusters}_ec.nc'
log: "logs/add_extra_components/elec_s{simpl}_{clusters}.log"
benchmark: "benchmarks/add_extra_components/elec_s{simpl}_{clusters}_ec"
output:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc",
log:
"logs/" + RDIR + "add_extra_components/elec_s{simpl}_{clusters}.log",
benchmark:
"benchmarks/" + RDIR + "add_extra_components/elec_s{simpl}_{clusters}_ec"
threads: 1
resources: mem_mb=3000
script: "scripts/add_extra_components.py"
resources:
mem_mb=3000,
script:
"scripts/add_extra_components.py"
rule prepare_network:
input: 'networks/elec_s{simpl}_{clusters}_ec.nc', tech_costs=COSTS
output: 'networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc'
log: "logs/prepare_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.log"
benchmark: "benchmarks/prepare_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
input:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc",
tech_costs=COSTS,
output:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
log:
"logs/" + RDIR + "prepare_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.log",
benchmark:
(
"benchmarks/"
+ RDIR
+ "prepare_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
)
threads: 1
resources: mem_mb=4000
script: "scripts/prepare_network.py"
resources:
mem_mb=4000,
script:
"scripts/prepare_network.py"
def memory(w):
factor = 3.
for o in w.opts.split('-'):
m = re.match(r'^(\d+)h$', o, re.IGNORECASE)
factor = 3.0
for o in w.opts.split("-"):
m = re.match(r"^(\d+)h$", o, re.IGNORECASE)
if m is not None:
factor /= int(m.group(1))
break
for o in w.opts.split('-'):
m = re.match(r'^(\d+)seg$', o, re.IGNORECASE)
for o in w.opts.split("-"):
m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
if m is not None:
factor *= int(m.group(1)) / 8760
break
if w.clusters.endswith('m'):
if w.clusters.endswith("m"):
return int(factor * (18000 + 180 * int(w.clusters[:-1])))
elif w.clusters == "all":
return int(factor * (18000 + 180 * 4000))
@ -366,44 +587,87 @@ def memory(w):
rule solve_network:
input: "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
input:
"networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
output:
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
log:
solver=normpath("logs/solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_solver.log"),
python="logs/solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_python.log",
memory="logs/solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_memory.log"
benchmark: "benchmarks/solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
solver=normpath(
"logs/"
+ RDIR
+ "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_solver.log"
),
python="logs/"
+ RDIR
+ "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_python.log",
memory="logs/"
+ RDIR
+ "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_memory.log",
benchmark:
"benchmarks/" + RDIR + "solve_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
threads: 4
resources: mem_mb=memory
shadow: "minimal"
script: "scripts/solve_network.py"
resources:
mem_mb=memory,
shadow:
"minimal"
script:
"scripts/solve_network.py"
rule solve_operations_network:
input:
unprepared="networks/elec_s{simpl}_{clusters}_ec.nc",
optimized="results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op.nc"
unprepared="networks/" + RDIR + "elec_s{simpl}_{clusters}_ec.nc",
optimized="results/networks/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
output:
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op.nc",
log:
solver=normpath("logs/solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_solver.log"),
python="logs/solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_python.log",
memory="logs/solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_memory.log"
benchmark: "benchmarks/solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
solver=normpath(
"logs/"
+ RDIR
+ "solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_solver.log"
),
python="logs/"
+ RDIR
+ "solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_python.log",
memory="logs/"
+ RDIR
+ "solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_memory.log",
benchmark:
(
"benchmarks/"
+ RDIR
+ "solve_operations_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}"
)
threads: 4
resources: mem_mb=(lambda w: 5000 + 372 * int(w.clusters))
shadow: "minimal"
script: "scripts/solve_operations_network.py"
resources:
mem_mb=(lambda w: 5000 + 372 * int(w.clusters)),
shadow:
"minimal"
script:
"scripts/solve_operations_network.py"
rule plot_network:
input:
network="results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
tech_costs=COSTS
network="results/networks/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
tech_costs=COSTS,
output:
only_map="results/plots/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}.{ext}",
ext="results/plots/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_ext.{ext}"
log: "logs/plot_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_{ext}.log"
script: "scripts/plot_network.py"
only_map="results/plots/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}.{ext}",
ext="results/plots/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_ext.{ext}",
log:
"logs/"
+ RDIR
+ "plot_network/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_{ext}.log",
script:
"scripts/plot_network.py"
def input_make_summary(w):
@ -414,36 +678,79 @@ def input_make_summary(w):
ll = [l for l in ll if l[0] == w.ll[0]]
else:
ll = w.ll
return ([COSTS] +
expand("results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
ll=ll,
**{k: config["scenario"][k] if getattr(w, k) == "all" else getattr(w, k)
for k in ["simpl", "clusters", "opts"]}))
return [COSTS] + expand(
"results/networks/" + RDIR + "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
ll=ll,
**{
k: config["scenario"][k] if getattr(w, k) == "all" else getattr(w, k)
for k in ["simpl", "clusters", "opts"]
}
)
rule make_summary:
input: input_make_summary
output: directory("results/summaries/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}")
log: "logs/make_summary/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.log",
script: "scripts/make_summary.py"
input:
input_make_summary,
output:
directory(
"results/summaries/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}"
),
log:
"logs/"
+ RDIR
+ "make_summary/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.log",
resources:
mem_mb=1500,
script:
"scripts/make_summary.py"
rule plot_summary:
input: "results/summaries/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}"
output: "results/plots/summary_{summary}_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.{ext}"
log: "logs/plot_summary/{summary}_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}_{ext}.log"
script: "scripts/plot_summary.py"
input:
"results/summaries/"
+ RDIR
+ "elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}",
output:
"results/plots/"
+ RDIR
+ "summary_{summary}_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.{ext}",
log:
"logs/"
+ RDIR
+ "plot_summary/{summary}_elec_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}_{ext}.log",
resources:
mem_mb=1500,
script:
"scripts/plot_summary.py"
def input_plot_p_nom_max(w):
return [("networks/elec_s{simpl}{maybe_cluster}.nc"
.format(maybe_cluster=('' if c == 'full' else ('_' + c)), **w))
for c in w.clusts.split(",")]
return [
(
"results/networks/"
+ RDIR
+ "elec_s{simpl}{maybe_cluster}.nc".format(
maybe_cluster=("" if c == "full" else ("_" + c)), **w
)
)
for c in w.clusts.split(",")
]
rule plot_p_nom_max:
input: input_plot_p_nom_max
output: "results/plots/elec_s{simpl}_cum_p_nom_max_{clusts}_{techs}_{country}.{ext}"
log: "logs/plot_p_nom_max/elec_s{simpl}_{clusts}_{techs}_{country}_{ext}.log"
script: "scripts/plot_p_nom_max.py"
input:
input_plot_p_nom_max,
output:
"results/plots/"
+ RDIR
+ "elec_s{simpl}_cum_p_nom_max_{clusts}_{techs}_{country}.{ext}",
log:
"logs/"
+ RDIR
+ "plot_p_nom_max/elec_s{simpl}_{clusts}_{techs}_{country}_{ext}.log",
resources:
mem_mb=1500,
script:
"scripts/plot_p_nom_max.py"

233
config.default.yaml
View File

@ -1,15 +1,18 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
version: 0.4.0
version: 0.6.1
tutorial: false
logging:
level: INFO
format: '%(levelname)s:%(name)s:%(message)s'
summary_dir: results
run:
name: "" # use this to keep track of runs with different settings
shared_cutouts: false # set to true to share the default cutout(s) across runs
scenario:
simpl: ['']
@ -19,10 +22,6 @@ scenario:
countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MD', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK', 'UA']
clustering:
simplify:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
snapshots:
start: "2013-01-01"
end: "2014-01-01"
@ -31,6 +30,7 @@ snapshots:
enable:
prepare_links_p_nom: false
retrieve_databundle: true
retrieve_cost_data: true
build_cutout: false
retrieve_cutout: true
build_natura_raster: false
@ -39,36 +39,58 @@ enable:
electricity:
voltages: [220., 300., 380., 750.]
gaslimit: false # global gas usage limit of X MWh_th
co2limit: 9.59e+7 # 0.05 * co2base
co2base: 1.918e+9
agg_p_nom_limits: data/agg_p_nom_minmax.csv
extendable_carriers:
Generator: []
StorageUnit: [] # battery, H2
Store: [battery, H2]
Link: []
operational_reserve: # like https://genxproject.github.io/GenX/dev/core/#Reserves
activate: false
epsilon_load: 0.02 # share of total load
epsilon_vres: 0.02 # share of total renewable supply
contingency: 4000 # fixed capacity in MW
max_hours:
battery: 6
H2: 168
powerplants_filter: false # use pandas query strings here, e.g. Country not in ['Germany']
custom_powerplants: true # use pandas query strings here, e.g. Country in ['Germany']
conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
renewable_capacities_from_OPSD: [] # onwind, offwind, solar
extendable_carriers:
Generator: [solar, onwind, offwind-ac, offwind-dc, OCGT]
StorageUnit: [] # battery, H2
Store: [battery, H2]
Link: [] # H2 pipeline
# estimate_renewable_capacities_from_capacity_stats:
# # Wind is the Fueltype in ppm.data.Capacity_stats, onwind, offwind-{ac,dc} the carrier in PyPSA-Eur
# Wind: [onwind, offwind-ac, offwind-dc]
# Solar: [solar]
# use pandas query strings here, e.g. Country not in ['Germany']
powerplants_filter: (DateOut >= 2022 or DateOut != DateOut)
# use pandas query strings here, e.g. Country in ['Germany']
custom_powerplants: false
conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
renewable_carriers: [solar, onwind, offwind-ac, offwind-dc, hydro]
estimate_renewable_capacities:
enable: true
# Add capacities from OPSD data
from_opsd: true
# Renewable capacities are based on existing capacities reported by IRENA
year: 2020
# Artificially limit maximum capacities to factor * (IRENA capacities),
# i.e. 110% of <years>'s capacities => expansion_limit: 1.1
# false: Use estimated renewable potentials determine by the workflow
expansion_limit: false
technology_mapping:
# Wind is the Fueltype in powerplantmatching, onwind, offwind-{ac,dc} the carrier in PyPSA-Eur
Offshore: [offwind-ac, offwind-dc]
Onshore: [onwind]
PV: [solar]
atlite:
nprocesses: 4
show_progress: false # false saves time
cutouts:
# use 'base' to determine geographical bounds and time span from config
# base:
# module: era5
# base:
# module: era5
europe-2013-era5:
module: era5 # in priority order
dx: 0.3
@ -78,26 +100,28 @@ atlite:
dx: 0.2
dy: 0.2
sarah_interpolate: false
sarah_dir:
sarah_dir:
features: [influx, temperature]
renewable:
onwind:
cutout: europe-2013-era5
resource:
method: wind
turbine: Vestas_V112_3MW
capacity_per_sqkm: 3 # ScholzPhd Tab 4.3.1: 10MW/km^2
capacity_per_sqkm: 3 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 30% fraction of the already restricted
# area is available for installation of wind generators due to competing land use and likely public
# acceptance issues.
# correction_factor: 0.93
corine:
# Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
# development of the REMix model and application for Europe. ( p.42 / p.28)
grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 31, 32]
grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32]
distance: 1000
distance_grid_codes: [1, 2, 3, 4, 5, 6]
natura: true
excluder_resolution: 100
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-ac:
@ -105,15 +129,19 @@ renewable:
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
capacity_per_sqkm: 2
capacity_per_sqkm: 2 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 20% fraction of the already restricted
# area is available for installation of wind generators due to competing land use and likely public
# acceptance issues.
correction_factor: 0.8855
# proxy for wake losses
# from 10.1016/j.energy.2018.08.153
# until done more rigorously in #153
corine: [44, 255]
natura: true
ship_threshold: 400
max_depth: 50
max_shore_distance: 30000
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-dc:
@ -121,16 +149,19 @@ renewable:
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
# ScholzPhd Tab 4.3.1: 10MW/km^2
capacity_per_sqkm: 2
capacity_per_sqkm: 2 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 20% fraction of the already restricted
# area is available for installation of wind generators due to competing land use and likely public
# acceptance issues.
correction_factor: 0.8855
# proxy for wake losses
# from 10.1016/j.energy.2018.08.153
# until done more rigorously in #153
corine: [44, 255]
natura: true
ship_threshold: 400
max_depth: 50
min_shore_distance: 30000
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
solar:
@ -141,7 +172,7 @@ renewable:
orientation:
slope: 35.
azimuth: 180.
capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2
capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2 and assuming 1% of the area can be used for solar PV panels
# Correction factor determined by comparing uncorrected area-weighted full-load hours to those
# published in Supplementary Data to
# Pietzcker, Robert Carl, et al. "Using the sun to decarbonize the power
@ -150,9 +181,9 @@ renewable:
# This correction factor of 0.854337 may be in order if using reanalysis data.
# for discussion refer to https://github.com/PyPSA/pypsa-eur/pull/304
# correction_factor: 0.854337
corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
natura: true
excluder_resolution: 100
potential: simple # or conservative
clip_p_max_pu: 1.e-2
hydro:
@ -162,6 +193,10 @@ renewable:
hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float
clip_min_inflow: 1.0
conventional:
nuclear:
p_max_pu: "data/nuclear_p_max_pu.csv" # float of file name
lines:
types:
220.: "Al/St 240/40 2-bundle 220.0"
@ -185,17 +220,26 @@ transformers:
type: ''
load:
power_statistics: True # only for files from <2019; set false in order to get ENTSOE transparency data
power_statistics: true # only for files from <2019; set false in order to get ENTSOE transparency data
interpolate_limit: 3 # data gaps up until this size are interpolated linearly
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
manual_adjustments: true # false
scaling_factor: 1.0
costs:
year: 2030
discountrate: 0.07 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
marginal_cost: # EUR/MWh
version: v0.4.0
rooftop_share: 0.14 # based on the potentials, assuming (0.1 kW/m2 and 10 m2/person)
fill_values:
FOM: 0
VOM: 0
efficiency: 1
fuel: 0
investment: 0
lifetime: 25
"CO2 intensity": 0
"discount rate": 0.07
marginal_cost:
solar: 0.01
onwind: 0.015
offwind: 0.015
@ -208,6 +252,29 @@ costs:
emission_prices: # in currency per tonne emission, only used with the option Ep
co2: 0.
clustering:
simplify_network:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
algorithm: kmeans # choose from: [hac, kmeans]
feature: solar+onwind-time # only for hac. choose from: [solar+onwind-time, solar+onwind-cap, solar-time, solar-cap, solar+offwind-cap] etc.
exclude_carriers: []
remove_stubs: true
remove_stubs_across_borders: true
cluster_network:
algorithm: kmeans
feature: solar+onwind-time
exclude_carriers: []
aggregation_strategies:
generators:
p_nom_max: sum # use "min" for more conservative assumptions
p_nom_min: sum
p_min_pu: mean
marginal_cost: mean
committable: any
ramp_limit_up: max
ramp_limit_down: max
efficiency: mean
solving:
options:
formulation: kirchhoff
@ -240,7 +307,7 @@ solving:
plotting:
map:
figsize: [7, 7]
boundaries: [-10.2, 29, 35, 72]
boundaries: [-10.2, 29, 35, 72]
p_nom:
bus_size_factor: 5.e+4
linewidth_factor: 3.e+3
@ -259,50 +326,50 @@ plotting:
AC_carriers: ["AC line", "AC transformer"]
link_carriers: ["DC line", "Converter AC-DC"]
tech_colors:
"onwind" : "#235ebc"
"onshore wind" : "#235ebc"
'offwind' : "#6895dd"
'offwind-ac' : "#6895dd"
'offshore wind' : "#6895dd"
'offshore wind ac' : "#6895dd"
'offwind-dc' : "#74c6f2"
'offshore wind dc' : "#74c6f2"
"hydro" : "#08ad97"
"hydro+PHS" : "#08ad97"
"PHS" : "#08ad97"
"hydro reservoir" : "#08ad97"
'hydroelectricity' : '#08ad97'
"ror" : "#4adbc8"
"run of river" : "#4adbc8"
'solar' : "#f9d002"
'solar PV' : "#f9d002"
'solar thermal' : '#ffef60'
'biomass' : '#0c6013'
'solid biomass' : '#06540d'
'biogas' : '#23932d'
'waste' : '#68896b'
'geothermal' : '#ba91b1'
"OCGT" : "#d35050"
"gas" : "#d35050"
"natural gas" : "#d35050"
"CCGT" : "#b20101"
"nuclear" : "#ff9000"
"coal" : "#707070"
"lignite" : "#9e5a01"
"oil" : "#262626"
"H2" : "#ea048a"
"hydrogen storage" : "#ea048a"
"battery" : "#b8ea04"
"Electric load" : "#f9d002"
"electricity" : "#f9d002"
"lines" : "#70af1d"
"transmission lines" : "#70af1d"
"AC-AC" : "#70af1d"
"AC line" : "#70af1d"
"links" : "#8a1caf"
"HVDC links" : "#8a1caf"
"DC-DC" : "#8a1caf"
"DC link" : "#8a1caf"
"onwind": "#235ebc"
"onshore wind": "#235ebc"
'offwind': "#6895dd"
'offwind-ac': "#6895dd"
'offshore wind': "#6895dd"
'offshore wind ac': "#6895dd"
'offwind-dc': "#74c6f2"
'offshore wind dc': "#74c6f2"
"hydro": "#08ad97"
"hydro+PHS": "#08ad97"
"PHS": "#08ad97"
"hydro reservoir": "#08ad97"
'hydroelectricity': '#08ad97'
"ror": "#4adbc8"
"run of river": "#4adbc8"
'solar': "#f9d002"
'solar PV': "#f9d002"
'solar thermal': '#ffef60'
'biomass': '#0c6013'
'solid biomass': '#06540d'
'biogas': '#23932d'
'waste': '#68896b'
'geothermal': '#ba91b1'
"OCGT": "#d35050"
"gas": "#d35050"
"natural gas": "#d35050"
"CCGT": "#b20101"
"nuclear": "#ff9000"
"coal": "#707070"
"lignite": "#9e5a01"
"oil": "#262626"
"H2": "#ea048a"
"hydrogen storage": "#ea048a"
"battery": "#b8ea04"
"Electric load": "#f9d002"
"electricity": "#f9d002"
"lines": "#70af1d"
"transmission lines": "#70af1d"
"AC-AC": "#70af1d"
"AC line": "#70af1d"
"links": "#8a1caf"
"HVDC links": "#8a1caf"
"DC-DC": "#8a1caf"
"DC link": "#8a1caf"
"load": "#dd2e23"
nice_names:
OCGT: "Open-Cycle Gas"

168
config.tutorial.yaml
View File

@ -1,15 +1,17 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
version: 0.4.0
version: 0.6.1
tutorial: true
logging:
level: INFO
format: '%(levelname)s:%(name)s:%(message)s'
summary_dir: results
run:
name: ""
shared_cutouts: false
scenario:
simpl: ['']
@ -17,11 +19,7 @@ scenario:
clusters: [5]
opts: [Co2L-24H]
countries: ['DE']
clustering:
simplify:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
countries: ['BE']
snapshots:
start: "2013-03-01"
@ -31,6 +29,7 @@ snapshots:
enable:
prepare_links_p_nom: false
retrieve_databundle: true
retrieve_cost_data: true
build_cutout: false
retrieve_cutout: true
build_natura_raster: false
@ -45,7 +44,7 @@ electricity:
Generator: [OCGT]
StorageUnit: [] #battery, H2
Store: [battery, H2]
Link: []
Link: [] # H2 pipeline
max_hours:
battery: 6
@ -57,8 +56,9 @@ electricity:
atlite:
nprocesses: 4
show_progress: false # false saves time
cutouts:
europe-2013-era5-tutorial:
be-03-2013-era5:
module: era5
x: [4., 15.]
y: [46., 56.]
@ -66,7 +66,7 @@ atlite:
renewable:
onwind:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: wind
turbine: Vestas_V112_3MW
@ -75,15 +75,15 @@ renewable:
corine:
# Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
# development of the REMix model and application for Europe. ( p.42 / p.28)
grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 31, 32]
grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32]
distance: 1000
distance_grid_codes: [1, 2, 3, 4, 5, 6]
natura: true
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-ac:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -91,11 +91,13 @@ renewable:
# correction_factor: 0.93
corine: [44, 255]
natura: true
ship_threshold: 400
max_shore_distance: 30000
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-dc:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -104,11 +106,13 @@ renewable:
# correction_factor: 0.93
corine: [44, 255]
natura: true
ship_threshold: 400
min_shore_distance: 30000
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
solar:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: pv
panel: CSi
@ -123,9 +127,9 @@ renewable:
# power." Applied Energy 135 (2014): 704-720.
# This correction factor of 0.854337 may be in order if using reanalysis data.
# correction_factor: 0.854337
corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
natura: true
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
@ -151,16 +155,25 @@ transformers:
type: ''
load:
power_statistics: True # only for files from <2019; set false in order to get ENTSOE transparency data
power_statistics: true # only for files from <2019; set false in order to get ENTSOE transparency data
interpolate_limit: 3 # data gaps up until this size are interpolated linearly
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
manual_adjustments: true # false
scaling_factor: 1.0
costs:
year: 2030
discountrate: 0.07 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
version: v0.4.0
rooftop_share: 0.14
fill_values:
FOM: 0
VOM: 0
efficiency: 1
fuel: 0
investment: 0
lifetime: 25
"CO2 intensity": 0
"discount rate": 0.07
marginal_cost:
solar: 0.01
onwind: 0.015
@ -170,6 +183,27 @@ costs:
emission_prices: # in currency per tonne emission, only used with the option Ep
co2: 0.
clustering:
simplify_network:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
algorithm: kmeans # choose from: [hac, kmeans]
feature: solar+onwind-time # only for hac. choose from: [solar+onwind-time, solar+onwind-cap, solar-time, solar-cap, solar+offwind-cap] etc.
exclude_carriers: []
cluster_network:
algorithm: kmeans
feature: solar+onwind-time
exclude_carriers: []
aggregation_strategies:
generators:
p_nom_max: sum # use "min" for more conservative assumptions
p_nom_min: sum
p_min_pu: mean
marginal_cost: mean
committable: any
ramp_limit_up: max
ramp_limit_down: max
efficiency: mean
solving:
options:
formulation: kirchhoff
@ -186,7 +220,7 @@ solving:
plotting:
map:
figsize: [7, 7]
boundaries: [-10.2, 29, 35, 72]
boundaries: [-10.2, 29, 35, 72]
p_nom:
bus_size_factor: 5.e+4
linewidth_factor: 3.e+3
@ -205,50 +239,50 @@ plotting:
AC_carriers: ["AC line", "AC transformer"]
link_carriers: ["DC line", "Converter AC-DC"]
tech_colors:
"onwind" : "#235ebc"
"onshore wind" : "#235ebc"
'offwind' : "#6895dd"
'offwind-ac' : "#6895dd"
'offshore wind' : "#6895dd"
'offshore wind ac' : "#6895dd"
'offwind-dc' : "#74c6f2"
'offshore wind dc' : "#74c6f2"
"hydro" : "#08ad97"
"hydro+PHS" : "#08ad97"
"PHS" : "#08ad97"
"hydro reservoir" : "#08ad97"
'hydroelectricity' : '#08ad97'
"ror" : "#4adbc8"
"run of river" : "#4adbc8"
'solar' : "#f9d002"
'solar PV' : "#f9d002"
'solar thermal' : '#ffef60'
'biomass' : '#0c6013'
'solid biomass' : '#06540d'
'biogas' : '#23932d'
'waste' : '#68896b'
'geothermal' : '#ba91b1'
"OCGT" : "#d35050"
"gas" : "#d35050"
"natural gas" : "#d35050"
"CCGT" : "#b20101"
"nuclear" : "#ff9000"
"coal" : "#707070"
"lignite" : "#9e5a01"
"oil" : "#262626"
"H2" : "#ea048a"
"hydrogen storage" : "#ea048a"
"battery" : "#b8ea04"
"Electric load" : "#f9d002"
"electricity" : "#f9d002"
"lines" : "#70af1d"
"transmission lines" : "#70af1d"
"AC-AC" : "#70af1d"
"AC line" : "#70af1d"
"links" : "#8a1caf"
"HVDC links" : "#8a1caf"
"DC-DC" : "#8a1caf"
"DC link" : "#8a1caf"
"onwind": "#235ebc"
"onshore wind": "#235ebc"
'offwind': "#6895dd"
'offwind-ac': "#6895dd"
'offshore wind': "#6895dd"
'offshore wind ac': "#6895dd"
'offwind-dc': "#74c6f2"
'offshore wind dc': "#74c6f2"
"hydro": "#08ad97"
"hydro+PHS": "#08ad97"
"PHS": "#08ad97"
"hydro reservoir": "#08ad97"
'hydroelectricity': '#08ad97'
"ror": "#4adbc8"
"run of river": "#4adbc8"
'solar': "#f9d002"
'solar PV': "#f9d002"
'solar thermal': '#ffef60'
'biomass': '#0c6013'
'solid biomass': '#06540d'
'biogas': '#23932d'
'waste': '#68896b'
'geothermal': '#ba91b1'
"OCGT": "#d35050"
"gas": "#d35050"
"natural gas": "#d35050"
"CCGT": "#b20101"
"nuclear": "#ff9000"
"coal": "#707070"
"lignite": "#9e5a01"
"oil": "#262626"
"H2": "#ea048a"
"hydrogen storage": "#ea048a"
"battery": "#b8ea04"
"Electric load": "#f9d002"
"electricity": "#f9d002"
"lines": "#70af1d"
"transmission lines": "#70af1d"
"AC-AC": "#70af1d"
"AC line": "#70af1d"
"links": "#8a1caf"
"HVDC links": "#8a1caf"
"DC-DC": "#8a1caf"
"DC link": "#8a1caf"
nice_names:
OCGT: "Open-Cycle Gas"
CCGT: "Combined-Cycle Gas"

50
data/eia_hydro_annual_generation.csv Normal file
View File

@ -0,0 +1,50 @@
https://www.eia.gov/international/data/world/electricity/electricity-generation?pd=2&p=000000000000000000000000000000g&u=1&f=A&v=mapbubble&a=-&i=none&vo=value&t=R&g=000000000000002&l=73-1028i008017kg6368g80a4k000e0ag00gg0004g8g0ho00g000400008&s=315532800000&e=1577836800000&ev=false&
Report generated on: 03-28-2022 11:20:48
"API","","1980","1981","1982","1983","1984","1985","1986","1987","1988","1989","1990","1991","1992","1993","1994","1995","1996","1997","1998","1999","2000","2001","2002","2003","2004","2005","2006","2007","2008","2009","2010","2011","2012","2013","2014","2015","2016","2017","2018","2019","2020"
"","hydroelectricity net generation (billion kWh)","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","","",""
"INTL.33-12-EURO-BKWH.A"," Europe","458.018","464.155","459.881","473.685","481.241","476.739","459.535","491.085","534.517","465.365","474.466","475.47","509.041","526.448","531.815","543.743","529.114164","543.845616","562.441501","569.308453","591.206662","587.371195","541.542535","506.19703","544.536443","545.176179","537.335934","540.934407","567.557921","564.244482","619.96477","543.05273","600.46622","631.86431","619.59229","615.53013","629.98906","562.59258","619.31106","610.62616","670.925"
"INTL.33-12-ALB-BKWH.A"," Albania","2.919","3.018","3.093","3.167","3.241","3.315","3.365","3.979","3.713","3.846","2.82","3.483","3.187","3.281","3.733","4.162","5.669","4.978","4.872","5.231","4.548","3.519","3.477","5.117","5.411","5.319","4.951","2.76","3.759","5.201","7.49133","4.09068","4.67775","6.88941","4.67676","5.83605","7.70418","4.47975","8.46648","5.15394","5.281"
"INTL.33-12-AUT-BKWH.A"," Austria","28.501","30.008","29.893","29.577","28.384","30.288","30.496","25.401","35.151","34.641","31.179","31.112","34.483","36.336","35.349","36.696","33.874","35.744","36.792","40.292","41.418","40.05","39.825","32.883","36.394","36.31","35.48","36.732","37.969","40.487","36.466","32.511","41.862","40.138","39.001","35.255","37.954","36.462","35.73","40.43655","45.344"
"INTL.33-12-BEL-BKWH.A"," Belgium","0.274","0.377","0.325","0.331","0.348","0.282","0.339","0.425","0.354","0.3","0.263","0.226","0.338","0.252","0.342","0.335","0.237","0.30195","0.38511","0.338","0.455","0.437","0.356","0.245","0.314","0.285","0.355","0.385","0.406","0.325","0.298","0.193","0.353","0.376","0.289","0.314","0.367","0.268","0.311","0.108","1.29"
"INTL.33-12-BIH-BKWH.A"," Bosnia and Herzegovina","--","--","--","--","--","--","--","--","--","--","--","--","3.374","2.343","3.424","3.607","5.104","4.608","4.511","5.477","5.043","5.129","5.215","4.456","5.919","5.938","5.798","3.961","4.818","6.177","7.946","4.343","4.173","7.164","5.876","5.495","5.585","3.7521","6.35382","6.02019","6.1"
"INTL.33-12-BGR-BKWH.A"," Bulgaria","3.674","3.58","3.018","3.318","3.226","2.214","2.302","2.512","2.569","2.662","1.859","2.417","2.042","1.923","1.453","2.291","2.89","2.726","3.066","2.725","2.646","1.72","2.172","2.999","3.136","4.294","4.196","2.845","2.796","3.435","4.98168","2.84328","3.14622","3.99564","4.55598","5.59845","3.8412","2.79972","5.09553","3.34917","3.37"
"INTL.33-12-HRV-BKWH.A"," Croatia","--","--","--","--","--","--","--","--","--","--","--","--","4.298","4.302","4.881","5.212","7.156","5.234","5.403","6.524","5.794","6.482","5.311","4.827","6.888","6.27","5.94","4.194","5.164","6.663","9.035","4.983","4.789","8.536","8.917","6.327","6.784","5.255","7.62399","5.87268","3.4"
"INTL.33-12-CYP-BKWH.A"," Cyprus","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0"
"INTL.33-12-CZE-BKWH.A"," Czech Republic","--","--","--","--","--","--","--","--","--","--","--","--","--","1.355","1.445","1.982","1.949","1.68201","1.382","1.664","1.7404","2.033","2.467","1.369","1.999","2.356","2.525","2.068","2.004","2.405","2.775","1.95","2.107","2.704","1.909","1.779","1.983","1.852","1.615","1.98792","3.4"
"INTL.33-12-DNK-BKWH.A"," Denmark","0.03","0.031","0.028","0.036","0.028","0.027","0.029","0.029","0.032","0.027","0.027","0.026","0.028","0.027","0.033","0.03","0.019","0.019","0.02673","0.031","0.03","0.028","0.032","0.021","0.027","0.023","0.023","0.028","0.026","0.019","0.021","0.017","0.017","0.013","0.015","0.018","0.019","0.018","0.015","0.01584","0.02"
"INTL.33-12-EST-BKWH.A"," Estonia","--","--","--","--","--","--","--","--","--","--","--","--","0.001","0.001","0.003","0.002","0.002","0.003","0.004","0.004","0.005","0.007","0.006","0.013","0.022","0.022","0.014","0.021","0.028","0.032","0.027","0.03","0.042","0.026","0.027","0.027","0.035","0.026","0.015","0.01881","0.04"
"INTL.33-12-FRO-BKWH.A"," Faroe Islands","0.049","0.049","0.049","0.049","0.049","0.049","0.049","0.049","0.062","0.071","0.074","0.074","0.083","0.073","0.075","0.075","0.069564","0.075066","0.076501","0.069453","0.075262","0.075195","0.095535","0.08483","0.093443","0.097986","0.099934","0.103407","0.094921","0.091482","0.06676","0.092","0.099","0.091","0.121","0.132","0.105","0.11","0.107","0.102","0.11"
"INTL.33-12-FIN-BKWH.A"," Finland","10.115","13.518","12.958","13.445","13.115","12.211","12.266","13.658","13.229","12.9","10.75","13.065","14.956","13.341","11.669","12.796","11.742","12.11958","14.9","12.652","14.513","13.073","10.668","9.495","14.919","13.646","11.379","14.035","16.941","12.559","12.743","12.278","16.667","12.672","13.24","16.584","15.634","14.61","13.137","12.31461","15.56"
"INTL.33-12-CSK-BKWH.A"," Former Czechoslovakia","4.8","4.2","3.7","3.9","3.2","4.3","4","4.853","4.355","4.229","3.919","3.119","3.602","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
"INTL.33-12-SCG-BKWH.A"," Former Serbia and Montenegro","--","--","--","--","--","--","--","--","--","--","--","--","11.23","10.395","11.016","12.071","14.266","12.636","12.763","13.243","11.88","12.326","11.633","9.752","11.01","11.912","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
"INTL.33-12-YUG-BKWH.A"," Former Yugoslavia","27.868","25.044","23.295","21.623","25.645","24.363","27.474","25.98","25.612","23.256","19.601","18.929","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
"INTL.33-12-FRA-BKWH.A"," France","68.253","70.358","68.6","67.515","64.01","60.248","60.953","68.623","73.952","45.744","52.796","56.277","68.313","64.3","78.057","72.196","64.43","63.151","61.479","71.832","66.466","73.888","59.992","58.567","59.276","50.965","55.741","57.029","63.017","56.428","61.945","45.184","59.099","71.042","62.993","54.876","60.094","49.389","64.485","56.98242","64.84"
"INTL.33-12-DEU-BKWH.A"," Germany","--","--","--","--","--","--","--","--","--","--","--","14.742","17.223","17.699","19.731","21.562","21.737","17.18343","17.044","19.451","21.515","22.506","22.893","19.071","20.866","19.442","19.808","20.957","20.239","18.841","20.678","17.323","21.331","22.66","19.31","18.664","20.214","19.985","17.815","19.86039","24.75"
"INTL.33-12-DDR-BKWH.A"," Germany, East","1.658","1.718","1.748","1.683","1.748","1.758","1.767","1.726","1.719","1.551","1.389","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
"INTL.33-12-DEUW-BKWH.A"," Germany, West","17.125","17.889","17.694","16.713","16.434","15.354","16.526","18.36","18.128","16.482","15.769","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--"
"INTL.33-12-GIB-BKWH.A"," Gibraltar","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0"
"INTL.33-12-GRC-BKWH.A"," Greece","3.396","3.398","3.551","2.331","2.852","2.792","3.222","2.768","2.354","1.888","1.751","3.068","2.181","2.26","2.573","3.494","4.305","3.84318","3.68","4.546","3.656","2.076","2.772","4.718","4.625","4.967","5.806","2.565","3.279","5.32","7.431","3.998","4.387","6.337","4.464","5.782","5.543","3.962","5.035","3.9798","3.43"
"INTL.33-12-HUN-BKWH.A"," Hungary","0.111","0.166","0.158","0.153","0.179","0.153","0.152","0.167","0.167","0.156","0.176","0.192","0.156","0.164","0.159","0.161","0.205","0.21384","0.15345","0.179","0.176","0.184","0.192","0.169","0.203","0.2","0.184","0.208","0.211","0.226","0.184","0.216","0.206","0.208","0.294","0.227","0.253","0.214","0.216","0.21681","0.24"
"INTL.33-12-ISL-BKWH.A"," Iceland","3.053","3.085","3.407","3.588","3.738","3.667","3.846","3.918","4.169","4.217","4.162","4.162","4.267","4.421","4.47","4.635","4.724","5.15493","5.565","5.987","6.292","6.512","6.907","7.017","7.063","6.949","7.22","8.31","12.303","12.156","12.51","12.382","12.214","12.747","12.554","13.541","13.092","13.892","13.679","13.32441","12.46"
"INTL.33-12-IRL-BKWH.A"," Ireland","0.833","0.855","0.792","0.776","0.68","0.824","0.91","0.673","0.862","0.684","0.69","0.738","0.809","0.757","0.911","0.706","0.715","0.67122","0.907","0.838","0.838","0.59","0.903","0.592","0.624","0.625","0.717","0.66","0.959","0.893","0.593","0.699","0.795","0.593","0.701","0.798","0.674","0.685","0.687","0.87813","1.21"
"INTL.33-12-ITA-BKWH.A"," Italy","44.997","42.782","41.216","40.96","41.923","40.616","40.626","39.05","40.205","33.647","31.31","41.817","41.778","41.011","44.212","37.404","41.617","41.18697","40.808","44.911","43.763","46.343","39.125","33.303","41.915","35.706","36.624","32.488","41.207","48.647","50.506","45.36477","41.45625","52.24626","57.95955","45.08163","42.00768","35.83701","48.29913","45.31824","47.72"
"INTL.33-12-XKS-BKWH.A"," Kosovo","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","0.075","0.119","0.154","0.104","0.095","0.142","0.149","0.139","0.243","0.177","0.27027","0.2079","0.26"
"INTL.33-12-LVA-BKWH.A"," Latvia","--","--","--","--","--","--","--","--","--","--","--","--","2.498","2.846","3.272","2.908","1.841","2.922","2.99","2.729","2.791","2.805","2.438","2.243","3.078","3.293","2.671","2.706","3.078","3.422","3.488","2.857","3.677","2.838","1.953","1.841","2.523","4.356","2.417","2.08692","2.59"
"INTL.33-12-LTU-BKWH.A"," Lithuania","--","--","--","--","--","--","--","--","--","--","--","--","0.308","0.389","0.447","0.369","0.323","0.291","0.413","0.409","0.336","0.322","0.35","0.323","0.417","0.446193","0.393","0.417","0.398","0.42","0.535","0.475","0.419","0.516","0.395","0.346","0.45","0.597","0.427","0.34254","1.06"
"INTL.33-12-LUX-BKWH.A"," Luxembourg","0.086","0.095","0.084","0.083","0.088","0.071","0.084","0.101","0.097","0.072","0.07","0.083","0.069","0.066","0.117","0.087","0.059","0.082","0.114","0.084","0.119","0.117","0.098","0.078","0.103","0.093","0.11","0.116","0.131","0.105","0.104","0.061","0.095","0.114","0.104","0.095","0.111","0.082","0.089","0.10593","1.09"
"INTL.33-12-MLT-BKWH.A"," Malta","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0","0"
"INTL.33-12-MNE-BKWH.A"," Montenegro","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","1.733","1.271","1.524","2.05","2.723","1.192","1.462","2.479","1.734","1.476","1.825","1.014","2.09187","1.78","1.8"
"INTL.33-12-NLD-BKWH.A"," Netherlands","0","0","0","0","0","0.003","0.003","0.001","0.002","0.037","0.119","0.079","0.119","0.091","0.1","0.087","0.079","0.09108","0.111","0.089","0.141","0.116","0.109","0.071","0.094","0.087","0.105","0.106","0.101","0.097","0.105","0.057","0.104","0.114","0.112","0.093","0.1","0.061","0.072","0.07326","0.05"
"INTL.33-12-MKD-BKWH.A"," North Macedonia","--","--","--","--","--","--","--","--","--","--","--","--","0.817","0.517","0.696","0.793","0.842","0.891","1.072","1.375","1.158","0.62","0.749","1.36","1.467","1.477","1.634","1","0.832","1.257","2.407","1.419","1.031","1.568","1.195","1.846","1.878","1.099","1.773","1.15236","1.24"
"INTL.33-12-NOR-BKWH.A"," Norway","82.717","91.876","91.507","104.704","104.895","101.464","95.321","102.341","107.919","117.369","119.933","109.032","115.505","118.024","110.398","120.315","102.823","108.677","114.546","120.237","140.4","119.258","128.078","104.425","107.693","134.331","118.175","132.319","137.654","124.03","116.257","119.78","141.189","127.551","134.844","136.662","142.244","141.651","138.202","123.66288","141.69"
"INTL.33-12-POL-BKWH.A"," Poland","2.326","2.116","1.528","1.658","1.394","1.833","1.534","1.644","1.775","1.593","1.403","1.411","1.492","1.473","1.716","1.868","1.912","1.941","2.286","2.133","2.085","2.302","2.256","1.654","2.06","2.179","2.022","2.328","2.13","2.351","2.9","2.313","2.02","2.421","2.165","1.814","2.117","2.552","1.949","1.93842","2.93"
"INTL.33-12-PRT-BKWH.A"," Portugal","7.873","4.934","6.82","7.897","9.609","10.512","8.364","9.005","12.037","5.72","9.065","8.952","4.599","8.453","10.551","8.26","14.613","12.97395","12.853","7.213","11.21","13.894","7.722","15.566","9.77","4.684","10.892","9.991","6.73","8.201","15.954","11.423","5.589","13.652","15.471","8.615","15.608","5.79","12.316","8.6526","13.96"
"INTL.33-12-ROU-BKWH.A"," Romania","12.506","12.605","11.731","9.934","11.208","11.772","10.688","11.084","13.479","12.497","10.87","14.107","11.583","12.64","12.916","16.526","15.597","17.334","18.69","18.107","14.63","14.774","15.886","13.126","16.348","20.005","18.172","15.806","17.023","15.379","19.684","14.581","11.945","14.807","18.618","16.467","17.848","14.349","17.48736","15.65289","15.53"
"INTL.33-12-SRB-BKWH.A"," Serbia","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","--","10.855","9.937","9.468","10.436","11.772","8.58","9.193","10.101","10.893","9.979","10.684","9.061","10.53261","10.07028","9.66"
"INTL.33-12-SVK-BKWH.A"," Slovakia","--","--","--","--","--","--","--","--","--","--","--","--","--","3.432","4.311","4.831","4.185","4.023","4.224","4.429","4.569","4.878","5.215","3.4452","4.059","4.592","4.355","4.406","4","4.324","5.184","3.211","3.687","4.329","3.762","3.701","4.302","4.321","3.506","4.27383","4.67"
"INTL.33-12-SVN-BKWH.A"," Slovenia","--","--","--","--","--","--","--","--","--","--","--","--","3.379","2.974","3.348","3.187","3.616","3.046","3.4","3.684","3.771","3.741","3.265","2.916","4.033","3.426","3.555","3.233","3.978","4.666","4.452","3.506","3.841","4.562","6.011","3.75","4.443","3.814","4.643","4.43421","5.24"
"INTL.33-12-ESP-BKWH.A"," Spain","29.16","21.64","25.99","26.696","31.088","30.895","26.105","27.016","34.76","19.046","25.16","27.01","18.731","24.133","27.898","22.881","39.404","34.43","33.665","22.634","29.274","40.617","22.691","40.643","31.359","18.209","25.699","27.036","23.13","26.147","41.576","30.07","20.192","36.45","38.815","27.656","35.77","18.007","33.743","24.23025","33.34"
"INTL.33-12-SWE-BKWH.A"," Sweden","58.133","59.006","54.369","62.801","67.106","70.095","60.134","70.95","69.016","70.911","71.778","62.603","73.588","73.905","58.508","67.421","51.2226","68.365","74.25","70.974","77.798","78.269","65.696","53.005","59.522","72.075","61.106","65.497","68.378","65.193","66.279","66.047","78.333","60.81","63.227","74.734","61.645","64.651","61.79","64.46583","71.6"
"INTL.33-12-CHE-BKWH.A"," Switzerland","32.481","35.13","35.974","35.069","29.871","31.731","32.576","34.328","35.437","29.477","29.497","31.756","32.373","35.416","38.678","34.817","28.458","33.70257","33.136","39.604","36.466","40.895","34.862","34.471","33.411","30.914","30.649","34.898","35.676","35.366","35.704","32.069","38.218","38.08","37.659","37.879","34.281","33.754","34.637","37.6596","40.62"
"INTL.33-12-TUR-BKWH.A"," Turkey","11.159","12.308","13.81","11.13","13.19","11.822","11.637","18.314","28.447","17.61","22.917","22.456","26.302","33.611","30.28","35.186","40.07","39.41784","41.80671","34.33","30.57","23.77","33.346","34.977","45.623","39.165","43.802","35.492","32.937","35.598","51.423","51.155","56.669","58.225","39.75","65.856","66.686","57.824","59.49","87.99714","77.39"
"INTL.33-12-GBR-BKWH.A"," United Kingdom","3.921","4.369","4.543","4.548","3.992","4.08","4.767","4.13","4.915","4.732","5.119","4.534","5.329","4.237","5.043","4.79","3.359","4.127","5.067","5.283","5.035","4.015","4.74","3.195","4.795","4.873","4.547","5.026","5.094","5.178","3.566","5.655","5.286","4.667","5.832","6.246","5.342","5.836","5.189","5.89941","7.64"
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data/links_tyndp.csv
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@ -24,3 +24,5 @@ Gridlink,Kingsnorth (UK),Warande (FR),160,,1400,in permitting,,https://tyndp.ent
NeuConnect,Grain (UK),Fedderwarden (DE),680,,1400,in permitting,,https://tyndp.entsoe.eu/tyndp2018/projects/projects/309,0.716666666666667,51.44,8.046524,53.562763
NordBalt,Klaipeda (LT),Nybro (SE),450,,700,built,,https://en.wikipedia.org/wiki/NordBalt,21.256667,55.681667,15.854167,56.767778
Estlink 1,Harku (EE),Espoo (FI),105,,350,built,,https://en.wikipedia.org/wiki/Estlink,24.560278,59.384722,24.551667,60.203889
Greenlink,Waterford (IE),Pembroke (UK),,180,500,under construction,,https://tyndp2022-project-platform.azurewebsites.net/projectsheets/transmission/286,-6.987,52.260,-4.986,51.686
Celtic Interconnector,Aghada (IE),La Martyre (FR),,572,700,under consideration,,https://tyndp2022-project-platform.azurewebsites.net/projectsheets/transmission/107,-8.16642,51.91413,-4.184,48.459

1 Name Converterstation 1 Converterstation 2 Length (given) (km) Length (distance*1.2) (km) Power (MW) status replaces Ref x1 y1 x2 y2
24 NeuConnect Grain (UK) Fedderwarden (DE) 680 1400 in permitting https://tyndp.entsoe.eu/tyndp2018/projects/projects/309 0.716666666666667 51.44 8.046524 53.562763
25 NordBalt Klaipeda (LT) Nybro (SE) 450 700 built https://en.wikipedia.org/wiki/NordBalt 21.256667 55.681667 15.854167 56.767778
26 Estlink 1 Harku (EE) Espoo (FI) 105 350 built https://en.wikipedia.org/wiki/Estlink 24.560278 59.384722 24.551667 60.203889
27 Greenlink Waterford (IE) Pembroke (UK) 180 500 under construction https://tyndp2022-project-platform.azurewebsites.net/projectsheets/transmission/286 -6.987 52.260 -4.986 51.686
28 Celtic Interconnector Aghada (IE) La Martyre (FR) 572 700 under consideration https://tyndp2022-project-platform.azurewebsites.net/projectsheets/transmission/107 -8.16642 51.91413 -4.184 48.459

16
data/nuclear_p_max_pu.csv Normal file
View File

@ -0,0 +1,16 @@
country,factor
BE,0.65
BG,0.89
CZ,0.82
FI,0.92
FR,0.70
DE,0.88
HU,0.90
NL,0.86
RO,0.92
SK,0.89
SI,0.94
ES,0.89
SE,0.82
CH,0.86
GB,0.67
1 country factor
2 BE 0.65
3 BG 0.89
4 CZ 0.82
5 FI 0.92
6 FR 0.70
7 DE 0.88
8 HU 0.90
9 NL 0.86
10 RO 0.92
11 SK 0.89
12 SI 0.94
13 ES 0.89
14 SE 0.82
15 CH 0.86
16 GB 0.67

9
data/parameter_corrections.yaml
View File

@ -36,12 +36,20 @@ Link:
"5583": "7428" # bus0 == bus1 to remove link in remove_unconnected_components (Sardinia)
"13588": "7428" # bus0 == bus1 to remove link in remove_unconnected_components (Sardinia)
"T23": "6355" # bus0 == bus1 to remove link in remove_unconnected_components (NordBalt)
"14815": "5939" # Kainachtal
"8706": "6448"
bus1:
index:
"12931": "8152" # BorWin3
"5582": "2382" # combine link 5583 + 5582 in 5582 (Sardinia)
"13589": "1349" # combine link 13589 + 13588 in 13589 (Sardinia)
"14820": "6354" # NordBalt
"14810": "6365" # Skagerrak
"8708": "6448"
"8394": "6695"
"14813": "7052"
"8009": "5939"
"5601": "7052" # Link Sweden - Lübeck
length:
index:
"5582": 26.39 # new length of combined links (sum)
@ -53,6 +61,7 @@ Line:
bus0:
index:
"14573": "7179" #fix bus-id substation in PT (220/380kV issue)
"14756": "8577" # Deeside connection
v_nom:
index:
"14573": 220 # 220/380kV issue of substation in PT

2
doc/Makefile
View File

@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT

4
doc/_static/theme_overrides.css vendored
View File

@ -1,4 +1,4 @@
/* SPDX-FileCopyrightText: 2017-2020 The PyPSA-Eur Authors
/* SPDX-FileCopyrightText: 2017-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: MIT
*/
@ -71,4 +71,4 @@
.wy-nav-content {
max-width: 910px !important;
}
}
}

4
doc/cloudcomputing.rst
View File

@ -72,7 +72,7 @@ Step 3 - Installation of Cloud SDK
- Download Google Cloud SDK `SDK <https://cloud.google.com/sdk>`_. Check that you are logged in in your Google account. The link should lead you to the Windows installation of Google Cloud SDK.
- Follow the "Quickstart for Windows - Before you begin" steps.
- After the successfull installation and initialization, close the Google Cloud SDK reopen it again. Type the following command into the "Google Cloud SDK Shell":
- After the successful installation and initialization, close the Google Cloud SDK reopen it again. Type the following command into the "Google Cloud SDK Shell":
.. code:: bash
@ -107,7 +107,7 @@ Make sure that your instance is operating for the next steps.
- Click on the advanced setting. SSH -> Authentication.
- Option 1. Click on the Tools button and "Install Public Key into Server..". Somewhere in your folder structure must be a public key. I found it with the following folder syntax on my local windows computer -> :\Users\...\.ssh (there should be a PKK file).
- Option 2. Click on the Tools button and "Generate new key pair...". Save the private key at a folder you remember and add it to the "private key file" field in WinSCP. Upload the public key to the metadeta of your instance.
- Click ok and save. Then click Login. If successfull WinSCP will open on the left side your local computer folder structure and on the right side the folder strucutre of your VM. (If you followed Option 2 and its not initially working. Stop your instance, refresh the website, reopen the WinSCP field. Afterwards your your Login should be successfull)
- Click ok and save. Then click Login. If successful WinSCP will open on the left side your local computer folder structure and on the right side the folder structure of your VM. (If you followed Option 2 and its not initially working. Stop your instance, refresh the website, reopen the WinSCP field. Afterwards your your Login should be successful)
If you had struggle with the above steps, you could also try `this video <https://www.youtube.com/watch?v=lYx1oQkEF0E>`_.

186
doc/conf.py
View File

@ -1,3 +1,4 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: 20017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -16,19 +17,19 @@
# All configuration values have a default; values that are commented out
# serve to show the default.
import sys
import os
import shlex
import sys
# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
sys.path.insert(0, os.path.abspath('../scripts'))
sys.path.insert(0, os.path.abspath("../scripts"))
# -- General configuration ------------------------------------------------
# If your documentation needs a minimal Sphinx version, state it here.
#needs_sphinx = '1.0'
# needs_sphinx = '1.0'
# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
@ -36,47 +37,47 @@ sys.path.insert(0, os.path.abspath('../scripts'))
extensions = [
#'sphinx.ext.autodoc',
#'sphinx.ext.autosummary',
'sphinx.ext.intersphinx',
'sphinx.ext.todo',
'sphinx.ext.mathjax',
'sphinx.ext.napoleon',
'sphinx.ext.graphviz',
"sphinx.ext.intersphinx",
"sphinx.ext.todo",
"sphinx.ext.mathjax",
"sphinx.ext.napoleon",
"sphinx.ext.graphviz",
#'sphinx.ext.pngmath',
#'sphinxcontrib.tikz',
#'rinoh.frontend.sphinx',
'sphinx.ext.imgconverter', # for SVG conversion
"sphinx.ext.imgconverter", # for SVG conversion
]
autodoc_default_flags = ['members']
autodoc_default_flags = ["members"]
autosummary_generate = True
# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']
templates_path = ["_templates"]
# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
# source_suffix = ['.rst', '.md']
source_suffix = '.rst'
source_suffix = ".rst"
# The encoding of source files.
#source_encoding = 'utf-8-sig'
# source_encoding = 'utf-8-sig'
# The master toctree document.
master_doc = 'index'
master_doc = "index"
# General information about the project.
project = u'PyPSA-Eur'
copyright = u'2017-2020 Jonas Hoersch (KIT, FIAS), Fabian Hofmann (FIAS), David Schlachtberger (FIAS), Tom Brown (KIT, FIAS); 2019-2020 Fabian Neumann (KIT)'
author = u'Jonas Hoersch (KIT, FIAS), Fabian Hofmann (FIAS), David Schlachtberger (FIAS), Tom Brown (KIT, FIAS), Fabian Neumann (KIT)'
project = "PyPSA-Eur"
copyright = "2017-2022 Jonas Hoersch (KIT, FIAS), Fabian Hofmann (TUB, FIAS), David Schlachtberger (FIAS), Tom Brown (TUB, KIT, FIAS); 2019-2022 Fabian Neumann (TUB, KIT)"
author = "Jonas Hoersch (KIT, FIAS), Fabian Hofmann (TUB, FIAS), David Schlachtberger (FIAS), Tom Brown (TUB, KIT, FIAS), Fabian Neumann (TUB, KIT)"
# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = u'0.3'
version = "0.6"
# The full version, including alpha/beta/rc tags.
release = u'0.4.0'
release = "0.6.1"
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
@ -87,37 +88,37 @@ language = None
# There are two options for replacing |today|: either, you set today to some
# non-false value, then it is used:
#today = ''
# today = ''
# Else, today_fmt is used as the format for a strftime call.
#today_fmt = '%B %d, %Y'
# today_fmt = '%B %d, %Y'
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
exclude_patterns = ['_build']
exclude_patterns = ["_build"]
# The reST default role (used for this markup: `text`) to use for all
# documents.
#default_role = None
# default_role = None
# If true, '()' will be appended to :func: etc. cross-reference text.
#add_function_parentheses = True
# add_function_parentheses = True
# If true, the current module name will be prepended to all description
# unit titles (such as .. function::).
#add_module_names = True
# add_module_names = True
# If true, sectionauthor and moduleauthor directives will be shown in the
# output. They are ignored by default.
#show_authors = False
# show_authors = False
# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'
pygments_style = "sphinx"
# A list of ignored prefixes for module index sorting.
#modindex_common_prefix = []
# modindex_common_prefix = []
# If true, keep warnings as "system message" paragraphs in the built documents.
#keep_warnings = False
# keep_warnings = False
# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = True
@ -127,35 +128,35 @@ todo_include_todos = True
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
html_theme = 'sphinx_rtd_theme'
html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
html_theme_options = {
'display_version': True,
'sticky_navigation': True,
"display_version": True,
"sticky_navigation": True,
}
# Add any paths that contain custom themes here, relative to this directory.
#html_theme_path = []
# html_theme_path = []
# The name for this set of Sphinx documents. If None, it defaults to
# "<project> v<release> documentation".
#html_title = None
# html_title = None
# A shorter title for the navigation bar. Default is the same as html_title.
#html_short_title = None
# html_short_title = None
# The name of an image file (relative to this directory) to place at the top
# of the sidebar.
#html_logo = None
# html_logo = None
# The name of an image file (within the static path) to use as favicon of the
# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32
# pixels large.
#html_favicon = None
# html_favicon = None
# These folders are copied to the documentation's HTML output
html_static_path = ["_static"]
@ -167,130 +168,127 @@ html_css_files = ["theme_overrides.css"]
# Add any extra paths that contain custom files (such as robots.txt or
# .htaccess) here, relative to this directory. These files are copied
# directly to the root of the documentation.
#html_extra_path = []
# html_extra_path = []
# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
# using the given strftime format.
#html_last_updated_fmt = '%b %d, %Y'
# html_last_updated_fmt = '%b %d, %Y'
# If true, SmartyPants will be used to convert quotes and dashes to
# typographically correct entities.
#html_use_smartypants = True
# html_use_smartypants = True
# Custom sidebar templates, maps document names to template names.
#html_sidebars = {}
# html_sidebars = {}
# Additional templates that should be rendered to pages, maps page names to
# template names.
#html_additional_pages = {}
# html_additional_pages = {}
# If false, no module index is generated.
#html_domain_indices = True
# html_domain_indices = True
# If false, no index is generated.
#html_use_index = True
# html_use_index = True
# If true, the index is split into individual pages for each letter.
#html_split_index = False
# html_split_index = False
# If true, links to the reST sources are added to the pages.
#html_show_sourcelink = True
# html_show_sourcelink = True
# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
#html_show_sphinx = True
# html_show_sphinx = True
# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
#html_show_copyright = True
# html_show_copyright = True
# If true, an OpenSearch description file will be output, and all pages will
# contain a <link> tag referring to it. The value of this option must be the
# base URL from which the finished HTML is served.
#html_use_opensearch = ''
# html_use_opensearch = ''
# This is the file name suffix for HTML files (e.g. ".xhtml").
#html_file_suffix = None
# html_file_suffix = None
# Language to be used for generating the HTML full-text search index.
# Sphinx supports the following languages:
# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja'
# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr'
#html_search_language = 'en'
# html_search_language = 'en'
# A dictionary with options for the search language support, empty by default.
# Now only 'ja' uses this config value
#html_search_options = {'type': 'default'}
# html_search_options = {'type': 'default'}
# The name of a javascript file (relative to the configuration directory) that
# implements a search results scorer. If empty, the default will be used.
#html_search_scorer = 'scorer.js'
# html_search_scorer = 'scorer.js'
# Output file base name for HTML help builder.
htmlhelp_basename = 'PyPSAEurdoc'
htmlhelp_basename = "PyPSAEurdoc"
# -- Options for LaTeX output ---------------------------------------------
latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
# Latex figure (float) alignment
#'figure_align': 'htbp',
# The paper size ('letterpaper' or 'a4paper').
#'papersize': 'letterpaper',
# The font size ('10pt', '11pt' or '12pt').
#'pointsize': '10pt',
# Additional stuff for the LaTeX preamble.
#'preamble': '',
# Latex figure (float) alignment
#'figure_align': 'htbp',
}
# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'PyPSA-Eur.tex', u'PyPSA-Eur Documentation',
u'author', 'manual'),
(master_doc, "PyPSA-Eur.tex", "PyPSA-Eur Documentation", "author", "manual"),
]
#Added for rinoh http://www.mos6581.org/rinohtype/quickstart.html
rinoh_documents = [(master_doc, # top-level file (index.rst)
'PyPSA-Eur', # output (target.pdf)
'PyPSA-Eur Documentation', # document title
'author')] # document author
# Added for rinoh http://www.mos6581.org/rinohtype/quickstart.html
rinoh_documents = [
(
master_doc, # top-level file (index.rst)
"PyPSA-Eur", # output (target.pdf)
"PyPSA-Eur Documentation", # document title
"author",
)
] # document author
# The name of an image file (relative to this directory) to place at the top of
# the title page.
#latex_logo = None
# latex_logo = None
# For "manual" documents, if this is true, then toplevel headings are parts,
# not chapters.
#latex_use_parts = False
# latex_use_parts = False
# If true, show page references after internal links.
#latex_show_pagerefs = False
# latex_show_pagerefs = False
# If true, show URL addresses after external links.
#latex_show_urls = False
# latex_show_urls = False
# Documents to append as an appendix to all manuals.
#latex_appendices = []
# latex_appendices = []
# If false, no module index is generated.
#latex_domain_indices = True
# latex_domain_indices = True
# -- Options for manual page output ---------------------------------------
# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'pypsa-eur', u'PyPSA-Eur Documentation',
[author], 1)
]
man_pages = [(master_doc, "pypsa-eur", "PyPSA-Eur Documentation", [author], 1)]
# If true, show URL addresses after external links.
#man_show_urls = False
# man_show_urls = False
# -- Options for Texinfo output -------------------------------------------
@ -299,23 +297,29 @@ man_pages = [
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'PyPSA-Eur', u'PyPSA-Eur Documentation',
author, 'PyPSA-Eur', 'One line description of project.',
'Miscellaneous'),
(
master_doc,
"PyPSA-Eur",
"PyPSA-Eur Documentation",
author,
"PyPSA-Eur",
"One line description of project.",
"Miscellaneous",
),
]
# Documents to append as an appendix to all manuals.
#texinfo_appendices = []
# texinfo_appendices = []
# If false, no module index is generated.
#texinfo_domain_indices = True
# texinfo_domain_indices = True
# How to display URL addresses: 'footnote', 'no', or 'inline'.
#texinfo_show_urls = 'footnote'
# texinfo_show_urls = 'footnote'
# If true, do not generate a @detailmenu in the "Top" node's menu.
#texinfo_no_detailmenu = False
# texinfo_no_detailmenu = False
# Example configuration for intersphinx: refer to the Python standard library.
intersphinx_mapping = {'https://docs.python.org/': None}
intersphinx_mapping = {"https://docs.python.org/": None}

14
doc/configtables/clustering.csv
View File

@ -1,3 +1,15 @@
,Unit,Values,Description
simplify,,,
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"
-- 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’}.",
-- 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."
cluster_network,,,
-- 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’}.",
-- 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,,,
-- generators,,,
-- -- {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."
-- 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 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 cluster_network
8 -- algorithm str One of {‘kmeans’, ‘hac’}
9 -- 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’}.
10 -- 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.
11 aggregation_strategies
12 -- generators
13 -- -- {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.
14 -- buses
15 -- -- {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.

11
doc/configtables/costs.csv
View File

@ -1,8 +1,9 @@
,Unit,Values,Description
year,--,"YYYY; e.g. '2030'","Year for which to retrieve cost assumptions of ``data/costs.csv``."
discountrate,--,float,"Default discount rate if not specified for a technology in ``data/costs.csv``."
USD2013_to_EUR2013,--,float,"Exchange rate from USD :math:`_{2013}` to EUR :math:`_{2013}` from `ECB <https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html>`_"
capital_cost,EUR/MW,"Keys should be in the 'technology' column of ``data/costs.csv``. Values can be any float.","For the given technologies, assumptions about their capital investment costs are set to the corresponding value. Optional; overwrites cost assumptions from ``data/costs.csv``."
marginal_cost,EUR/MWh,"Keys should be in the 'technology' column of ``data/costs.csv``. Values can be any float.","For the given technologies, assumptions about their marginal operating costs are set to the corresponding value. Optional; overwrites cost assumptions from ``data/costs.csv``."
year,--,"YYYY; e.g. '2030'","Year for which to retrieve cost assumptions of ``resources/costs.csv``."
version,--,"vX.X.X; e.g. 'v0.1.0'","Version of ``technology-data`` repository to use."
rooftop_share,--,float,"Share of rooftop PV when calculating capital cost of solar (joint rooftop and utility-scale PV)."
fill_values,--,float,"Default values if not specified for a technology in ``resources/costs.csv``."
capital_cost,EUR/MW,"Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float.","For the given technologies, assumptions about their capital investment costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``."
marginal_cost,EUR/MWh,"Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float.","For the given technologies, assumptions about their marginal operating costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``."
emission_prices,,,"Specify exogenous prices for emission types listed in ``network.carriers`` to marginal costs."
-- co2,EUR/t,float,"Exogenous price of carbon-dioxide added to the marginal costs of fossil-fuelled generators according to their carbon intensity. Added through the keyword ``Ep`` in the ``{opts}`` wildcard only in the rule :mod:`prepare_network``."

1 Unit Values Description
2 year -- YYYY; e.g. '2030' Year for which to retrieve cost assumptions of ``data/costs.csv``. Year for which to retrieve cost assumptions of ``resources/costs.csv``.
3 discountrate version -- float vX.X.X; e.g. 'v0.1.0' Default discount rate if not specified for a technology in ``data/costs.csv``. Version of ``technology-data`` repository to use.
4 USD2013_to_EUR2013 rooftop_share -- float Exchange rate from USD :math:`_{2013}` to EUR :math:`_{2013}` from `ECB <https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html>`_ Share of rooftop PV when calculating capital cost of solar (joint rooftop and utility-scale PV).
5 capital_cost fill_values EUR/MW -- Keys should be in the 'technology' column of ``data/costs.csv``. Values can be any float. float For the given technologies, assumptions about their capital investment costs are set to the corresponding value. Optional; overwrites cost assumptions from ``data/costs.csv``. Default values if not specified for a technology in ``resources/costs.csv``.
6 marginal_cost capital_cost EUR/MWh EUR/MW Keys should be in the 'technology' column of ``data/costs.csv``. Values can be any float. Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float. For the given technologies, assumptions about their marginal operating costs are set to the corresponding value. Optional; overwrites cost assumptions from ``data/costs.csv``. For the given technologies, assumptions about their capital investment costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``.
7 marginal_cost EUR/MWh Keys should be in the 'technology' column of ``resources/costs.csv``. Values can be any float. For the given technologies, assumptions about their marginal operating costs are set to the corresponding value. Optional; overwrites cost assumptions from ``resources/costs.csv``.
8 emission_prices Specify exogenous prices for emission types listed in ``network.carriers`` to marginal costs.
9 -- co2 EUR/t float Exogenous price of carbon-dioxide added to the marginal costs of fossil-fuelled generators according to their carbon intensity. Added through the keyword ``Ep`` in the ``{opts}`` wildcard only in the rule :mod:`prepare_network``.

30
doc/configtables/electricity.csv
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@ -1,19 +1,29 @@
,Unit,Values,Description
voltages,kV,"Any subset of {220., 300., 380.}",Voltage levels to consider when
voltages,kV,"Any subset of {220., 300., 380.}",Voltage levels to consider
gaslimit,MWhth,"float or false",Global gas usage limit
co2limit,:math:`t_{CO_2-eq}/a`,float,Cap on total annual system carbon dioxide emissions
co2base,:math:`t_{CO_2-eq}/a`,float,Reference value of total annual system carbon dioxide emissions if relative emission reduction target is specified in ``{opts}`` wildcard.
agg_p_nom_limits,file,path,Reference to ``.csv`` file specifying per carrier generator nominal capacity constraints for individual countries if ``'CCL'`` is in ``{opts}`` wildcard. Defaults to ``data/agg_p_nom_minmax.csv``.
extendable_carriers,,,
-- Generator,--,"Any subset of {'OCGT','CCGT'}",Places extendable conventional power plants (OCGT and/or CCGT) where gas power plants are located today without capacity limits.
-- StorageUnit,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
-- Store,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
-- Link,--,Any subset of {'H2 pipeline'},Adds extendable links (H2 pipelines only) at every connection where there are lines or HVDC links without capacity limits and with zero initial capacity. Hydrogen pipelines require hydrogen storage to be modelled as ``Store``.
operational_reserve,,,"Settings for reserve requirements following like `GenX <https://genxproject.github.io/GenX/dev/core/#Reserves>`_"
-- activate,bool,"true or false","Whether to take operational reserve requirements into account during optimisation"
-- epsilon_load,--,float,share of total load
-- epsilon_vres,--,float,share of total renewable supply
-- contingency,MW,float,fixed reserve capacity
max_hours,,,
-- battery,h,float,Maximum state of charge capacity of the battery in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
-- H2,h,float,Maximum state of charge capacity of the hydrogen storage in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
extendable_carriers,,,
-- Generator,--,"Any extendable carrier","Defines existing or non-existing conventional and renewable power plants to be extendable during the optimization. Conventional generators can only be built/expanded where already existent today. If a listed conventional carrier is not included in the ``conventional_carriers`` list, the lower limit of the capacity expansion is set to 0."
-- StorageUnit,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
-- Store,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
-- Link,--,Any subset of {'H2 pipeline'},Adds extendable links (H2 pipelines only) at every connection where there are lines or HVDC links without capacity limits and with zero initial capacity. Hydrogen pipelines require hydrogen storage to be modelled as ``Store``.
powerplants_filter,--,"use `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ strings here, e.g. Country not in ['Germany']",Filter query for the default powerplant database.
custom_powerplants,--,"use `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ strings here, e.g. Country in ['Germany']",Filter query for the custom powerplant database.
conventional_carriers,--,"Any subset of {nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass}",List of conventional power plants to include in the model from ``resources/powerplants.csv``.
renewable_capacities_from_OPSD,,"[solar, onwind, offwind]",List of carriers (offwind-ac and offwind-dc are included in offwind) whose capacities 'p_nom' are aligned to the `OPSD renewable power plant list <https://data.open-power-system-data.org/renewable_power_plants/>`_
estimate_renewable_capacities_from_capacitiy_stats,,,
"-- Fueltype [ppm], e.g. Wind",,"list of fueltypes strings in PyPSA-Eur, e.g. [onwind, offwind-ac, offwind-dc]",converts ppm Fueltype to PyPSA-EUR Fueltype
conventional_carriers,--,"Any subset of {nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass}","List of conventional power plants to include in the model from ``resources/powerplants.csv``. If an included carrier is also listed in `extendable_carriers`, the capacity is taken as a lower bound."
renewable_carriers,--,"Any subset of {solar, onwind, offwind-ac, offwind-dc, hydro}",List of renewable generators to include in the model.
estimate_renewable_capacities,,,
-- enable,,bool,"Activate routine to estimate renewable capacities"
-- from_opsd,--,bool,"Add capacities from OPSD data"
-- year,--,bool,"Renewable capacities are based on existing capacities reported by IRENA for the specified year"
-- expansion_limit,--,float or false,"Artificially limit maximum capacities to factor * (IRENA capacities), i.e. 110% of <years>'s capacities => expansion_limit: 1.1 false: Use estimated renewable potentials determine by the workflow"
-- technology_mapping,,,"Mapping between powerplantmatching and PyPSA-Eur technology names"

1 Unit Values Description
2 voltages kV Any subset of {220., 300., 380.} Voltage levels to consider when Voltage levels to consider
3 gaslimit MWhth float or false Global gas usage limit
4 co2limit :math:`t_{CO_2-eq}/a` float Cap on total annual system carbon dioxide emissions
5 co2base :math:`t_{CO_2-eq}/a` float Reference value of total annual system carbon dioxide emissions if relative emission reduction target is specified in ``{opts}`` wildcard.
6 agg_p_nom_limits file path Reference to ``.csv`` file specifying per carrier generator nominal capacity constraints for individual countries if ``'CCL'`` is in ``{opts}`` wildcard. Defaults to ``data/agg_p_nom_minmax.csv``.
7 extendable_carriers operational_reserve Settings for reserve requirements following like `GenX <https://genxproject.github.io/GenX/dev/core/#Reserves>`_
8 -- Generator -- activate -- bool Any subset of {'OCGT','CCGT'} true or false Places extendable conventional power plants (OCGT and/or CCGT) where gas power plants are located today without capacity limits. Whether to take operational reserve requirements into account during optimisation
9 -- StorageUnit -- epsilon_load -- Any subset of {'battery','H2'} float Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity. share of total load
10 -- Store -- epsilon_vres -- Any subset of {'battery','H2'} float Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity. share of total renewable supply
11 -- Link -- contingency -- MW Any subset of {'H2 pipeline'} float Adds extendable links (H2 pipelines only) at every connection where there are lines or HVDC links without capacity limits and with zero initial capacity. Hydrogen pipelines require hydrogen storage to be modelled as ``Store``. fixed reserve capacity
12 max_hours
13 -- battery h float Maximum state of charge capacity of the battery in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
14 -- H2 h float Maximum state of charge capacity of the hydrogen storage in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
15 extendable_carriers
16 -- Generator -- Any extendable carrier Defines existing or non-existing conventional and renewable power plants to be extendable during the optimization. Conventional generators can only be built/expanded where already existent today. If a listed conventional carrier is not included in the ``conventional_carriers`` list, the lower limit of the capacity expansion is set to 0.
17 -- StorageUnit -- Any subset of {'battery','H2'} Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
18 -- Store -- Any subset of {'battery','H2'} Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
19 -- Link -- Any subset of {'H2 pipeline'} Adds extendable links (H2 pipelines only) at every connection where there are lines or HVDC links without capacity limits and with zero initial capacity. Hydrogen pipelines require hydrogen storage to be modelled as ``Store``.
20 powerplants_filter -- use `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ strings here, e.g. Country not in ['Germany'] Filter query for the default powerplant database.
21 custom_powerplants -- use `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ strings here, e.g. Country in ['Germany'] Filter query for the custom powerplant database.
22 conventional_carriers -- Any subset of {nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass} List of conventional power plants to include in the model from ``resources/powerplants.csv``. List of conventional power plants to include in the model from ``resources/powerplants.csv``. If an included carrier is also listed in `extendable_carriers`, the capacity is taken as a lower bound.
23 renewable_capacities_from_OPSD renewable_carriers -- [solar, onwind, offwind] Any subset of {solar, onwind, offwind-ac, offwind-dc, hydro} List of carriers (offwind-ac and offwind-dc are included in offwind) whose capacities 'p_nom' are aligned to the `OPSD renewable power plant list <https://data.open-power-system-data.org/renewable_power_plants/>`_ List of renewable generators to include in the model.
24 estimate_renewable_capacities_from_capacitiy_stats estimate_renewable_capacities
25 -- Fueltype [ppm], e.g. Wind -- enable list of fueltypes strings in PyPSA-Eur, e.g. [onwind, offwind-ac, offwind-dc] bool converts ppm Fueltype to PyPSA-EUR Fueltype Activate routine to estimate renewable capacities
26 -- from_opsd -- bool Add capacities from OPSD data
27 -- year -- bool Renewable capacities are based on existing capacities reported by IRENA for the specified year
28 -- expansion_limit -- float or false Artificially limit maximum capacities to factor * (IRENA capacities), i.e. 110% of <years>'s capacities => expansion_limit: 1.1 false: Use estimated renewable potentials determine by the workflow
29 -- technology_mapping Mapping between powerplantmatching and PyPSA-Eur technology names

2
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@ -1,6 +1,6 @@
,Unit,Values,Description
url,--,string,"Link to open power system data time series data."
power_statistics,bool,"{true, false}",Whether to load the electricity consumption data of the ENTSOE power statistics (only for files from 2019 and before) or from the ENTSOE transparency data (only has load data from 2015 onwards).
power_statistics,bool,"{true, false}",Whether to load the electricity consumption data of the ENTSOE power statistics (only for files from 2019 and before) or from the ENTSOE transparency data (only has load data from 2015 onwards).
interpolate_limit,hours,integer,"Maximum gap size (consecutive nans) which interpolated linearly."
time_shift_for_large_gaps,string,string,"Periods which are used for copying time-slices in order to fill large gaps of nans. Have to be valid ``pandas`` period strings."
manual_adjustments,bool,"{true, false}","Whether to adjust the load data manually according to the function in :func:`manual_adjustment`."

1 Unit Values Description
2 url -- string Link to open power system data time series data.
3 power_statistics bool {true, false} Whether to load the electricity consumption data of the ENTSOE power statistics (only for files from 2019 and before) or from the ENTSOE transparency data (only has load data from 2015 onwards). Whether to load the electricity consumption data of the ENTSOE power statistics (only for files from 2019 and before) or from the ENTSOE transparency data (only has load data from 2015 onwards).
4 interpolate_limit hours integer Maximum gap size (consecutive nans) which interpolated linearly.
5 time_shift_for_large_gaps string string Periods which are used for copying time-slices in order to fill large gaps of nans. Have to be valid ``pandas`` period strings.
6 manual_adjustments bool {true, false} Whether to adjust the load data manually according to the function in :func:`manual_adjustment`.

2
doc/configtables/offwind-ac.csv
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@ -6,8 +6,8 @@ resource,,,
capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
corine,--,"Any *realistic* subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement."
natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
ship_threshold,--,float,"Ship density threshold from which areas are excluded."
max_depth,m,float,"Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential."
min_shore_distance,m,float,"Minimum distance to the shore below which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential."
potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
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."
keep_all_available_areas,bool,"{'true', 'false'}","Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed."

1 Unit Values Description
6 capacity_per_sqkm :math:`MW/km^2` float Allowable density of wind turbine placement.
7 corine -- Any *realistic* subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_ Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement.
8 natura bool {true, false} Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``.
9 ship_threshold -- float Ship density threshold from which areas are excluded.
10 max_depth m float Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential.
11 min_shore_distance m float Minimum distance to the shore below which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential.
12 potential -- One of {'simple', 'conservative'} Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`
13 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.
keep_all_available_areas bool {'true', 'false'} Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed.

2
doc/configtables/offwind-dc.csv
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@ -6,8 +6,8 @@ resource,,,
capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
corine,--,"Any *realistic* subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_","Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement."
natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
ship_threshold,--,float,"Ship density threshold from which areas are excluded."
max_depth,m,float,"Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential."
min_shore_distance,m,float,"Minimum distance to the shore below which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential."
potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
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."
keep_all_available_areas,bool,"{'true', 'false'}","Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed."

1 Unit Values Description
6 capacity_per_sqkm :math:`MW/km^2` float Allowable density of wind turbine placement.
7 corine -- Any *realistic* subset of the `CORINE Land Cover code list <http://www.eea.europa.eu/data-and-maps/data/corine-land-cover-2006-raster-1/corine-land-cover-classes-and/clc_legend.csv/at_download/file>`_ Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement.
8 natura bool {true, false} Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``.
9 ship_threshold -- float Ship density threshold from which areas are excluded.
10 max_depth m float Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential.
11 min_shore_distance m float Minimum distance to the shore below which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential.
12 potential -- One of {'simple', 'conservative'} Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`
13 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.
keep_all_available_areas bool {'true', 'false'} Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed.

1
doc/configtables/onwind.csv
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@ -11,4 +11,3 @@ corine,,,
natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
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."
keep_all_available_areas,bool,"{'true', 'false'}","Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed."

1 Unit Values Description
11 natura bool {true, false} Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``.
12 potential -- One of {'simple', 'conservative'} Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`
13 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.
keep_all_available_areas bool {'true', 'false'} Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed.

3
doc/configtables/opts.csv
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@ -8,4 +8,5 @@ Trigger, Description, Definition, Status
``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
``BAU``, Add a per-``carrier`` minimal overall capacity; i.e. at least ``40GW`` of ``OCGT`` in Europe; configured in ``electricity: BAU_mincapacities``, ``solve_network``: `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L66>`__, Untested
``SAFE``, Add a capacity reserve margin of a certain fraction above the peak demand to which renewable generators and storage do *not* contribute. Ignores network., ``solve_network`` `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L73>`__, Untested
``carrier+{c|p}factor``, "Alter the capital cost (``c``) or installable potential (``p``) of a carrier by a factor. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values.", ``prepare_network``, In active use
``carrier+{c|p|m}factor``,"Alter the capital cost (``c``), installable potential (``p``) or marginal costs (``m``) of a carrier by a factor. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values.", ``prepare_network``, In active use
``CH4L``,"Add an overall absolute gas limit. If configured in ``electricity: gaslimit`` it is given in MWh thermal, if a float is appended, the overall gaslimit is assumed to be given in TWh thermal (e.g. ``CH4L200`` limits gas dispatch to 200 TWh termal)", ``prepare_network``: ``add_gaslimit()``, In active use

1 Trigger Description Definition Status
8 ``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
9 ``BAU`` Add a per-``carrier`` minimal overall capacity; i.e. at least ``40GW`` of ``OCGT`` in Europe; configured in ``electricity: BAU_mincapacities`` ``solve_network``: `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L66>`__ Untested
10 ``SAFE`` Add a capacity reserve margin of a certain fraction above the peak demand to which renewable generators and storage do *not* contribute. Ignores network. ``solve_network`` `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L73>`__ Untested
11 ``carrier+{c|p}factor`` ``carrier+{c|p|m}factor`` Alter the capital cost (``c``) or installable potential (``p``) of a carrier by a factor. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values. Alter the capital cost (``c``), installable potential (``p``) or marginal costs (``m``) of a carrier by a factor. Example: ``solar+c0.5`` reduces the capital cost of solar to 50\% of original values. ``prepare_network`` In active use
12 ``CH4L`` Add an overall absolute gas limit. If configured in ``electricity: gaslimit`` it is given in MWh thermal, if a float is appended, the overall gaslimit is assumed to be given in TWh thermal (e.g. ``CH4L200`` limits gas dispatch to 200 TWh termal) ``prepare_network``: ``add_gaslimit()`` In active use

2
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@ -1,4 +1,4 @@
,Unit,Values,Description
start,--,"str or datetime-like; e.g. YYYY-MM-DD","Left bound of date range"
end,--,"str or datetime-like; e.g. YYYY-MM-DD","Right bound of date range"
closed,--,"One of {None, ‘left’, ‘right’}","Make the time interval closed to the ``left``, ``right``, or both sides ``None``."
closed,--,"One of {None, ‘left’, ‘right’}","Make the time interval closed to the ``left``, ``right``, or open on both sides ``None``."

1 Unit Values Description
2 start -- str or datetime-like; e.g. YYYY-MM-DD Left bound of date range
3 end -- str or datetime-like; e.g. YYYY-MM-DD Right bound of date range
4 closed -- One of {None, ‘left’, ‘right’} Make the time interval closed to the ``left``, ``right``, or both sides ``None``. Make the time interval closed to the ``left``, ``right``, or open on both sides ``None``.

1
doc/configtables/solar.csv
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@ -12,4 +12,3 @@ corine,--,"Any subset of the `CORINE Land Cover code list <http://www.eea.europa
natura,bool,"{true, false}","Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``."
potential,--,"One of {'simple', 'conservative'}","Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`"
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."
keep_all_available_areas,bool,"{'true', 'false'}","Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed."

1 Unit Values Description
12 natura bool {true, false} Switch to exclude `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas. Area is excluded if ``true``.
13 potential -- One of {'simple', 'conservative'} Method to compute the maximal installable potential for a node; confer :ref:`renewableprofiles`
14 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.
keep_all_available_areas bool {'true', 'false'} Use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed.

59
doc/configuration.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -28,13 +28,24 @@ Top-level configuration
.. _scenario:
``scenario``
============
``run``
=======
It is common conduct to analyse energy system optimisation models for **multiple scenarios** for a variety of reasons,
e.g. assessing their sensitivity towards changing the temporal and/or geographical resolution or investigating how
investment changes as more ambitious greenhouse-gas emission reduction targets are applied.
The ``run`` section is used for running and storing scenarios with different configurations which are not covered by :ref:`wildcards`. It determines the path at which resources, networks and results are stored. Therefore the user can run different configurations within the same directory. If a run with a non-empty name should use cutouts shared across runs, set ``shared_cutouts`` to `true`.
.. literalinclude:: ../config.default.yaml
:language: yaml
:start-at: run:
:end-before: scenario:
``scenario``
============
The ``scenario`` section is an extraordinary section of the config file
that is strongly connected to the :ref:`wildcards` and is designed to
facilitate running multiple scenarios through a single command
@ -91,9 +102,6 @@ Specifies the temporal range to build an energy system model for as arguments to
:widths: 25,7,22,30
:file: configtables/electricity.csv
.. warning::
Carriers in ``conventional_carriers`` must not also be in ``extendable_carriers``.
.. _atlite_cf:
``atlite``
@ -174,7 +182,7 @@ Define and specify the ``atlite.Cutout`` used for calculating renewable potentia
.. literalinclude:: ../config.default.yaml
:language: yaml
:start-at: hydro:
:end-before: lines:
:end-before: conventional:
.. csv-table::
:header-rows: 1
@ -183,6 +191,17 @@ Define and specify the ``atlite.Cutout`` used for calculating renewable potentia
.. _lines_cf:
``conventional``
=============
Define additional generator attribute for conventional carrier types. If a scalar value is given it is applied to all generators. However if a string starting with "data/" is given, the value is interpreted as a path to a csv file with country specific values. Then, the values are read in and applied to all generators of the given carrier in the given country. Note that the value(s) overwrite the existing values in the corresponding section of the ``generators`` dataframe.
.. literalinclude:: ../config.default.yaml
:language: yaml
:start-at: conventional:
:end-before: lines:
``lines``
=============
@ -233,8 +252,7 @@ Define and specify the ``atlite.Cutout`` used for calculating renewable potentia
.. literalinclude:: ../config.default.yaml
:language: yaml
:start-at: load:
:end-before: costs:
:lines: 212-217
.. csv-table::
:header-rows: 1
@ -249,7 +267,7 @@ Define and specify the ``atlite.Cutout`` used for calculating renewable potentia
.. literalinclude:: ../config.default.yaml
:language: yaml
:start-after: scaling_factor:
:end-before: solving:
:end-before: clustering:
.. csv-table::
:header-rows: 1
@ -257,8 +275,25 @@ Define and specify the ``atlite.Cutout`` used for calculating renewable potentia
:file: configtables/costs.csv
.. note::
To change cost assumptions in more detail (i.e. other than ``marginal_cost`` and ``capital_cost``), consider modifying cost assumptions directly in ``data/costs.csv`` as this is not yet supported through the config file.
You can also build multiple different cost databases. Make a renamed copy of ``data/costs.csv`` (e.g. ``data/costs-optimistic.csv``) and set the variable ``COSTS=data/costs-optimistic.csv`` in the ``Snakefile``.
To change cost assumptions in more detail (i.e. other than ``marginal_cost`` and ``capital_cost``), consider modifying cost assumptions directly in ``resources/costs.csv`` as this is not yet supported through the config file.
You can also build multiple different cost databases. Make a renamed copy of ``resources/costs.csv`` (e.g. ``data/costs-optimistic.csv``) and set the variable ``COSTS=data/costs-optimistic.csv`` in the ``Snakefile``.
.. _clustering_cf:
``clustering``
==============
.. literalinclude:: ../config.default.yaml
:language: yaml
:start-after: co2:
:end-before: solving:
.. csv-table::
:header-rows: 1
:widths: 25,7,22,30
:file: configtables/clustering.csv
.. _solving_cf:

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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -16,10 +16,20 @@ to our `GitHub repository <https://github.com/PyPSA/PyPSA-Eur>`_.
* If you start working on a feature in the code, let us know by opening an issue or a draft pull request.
This helps all of us to keep an overview on what is being done and helps to avoid a situation where we
are doing the same work twice in parallel.
* We encourage you to use the `PEP 8 coding style <https://www.python.org/dev/peps/pep-0008/>`_.
For linting, formatting and checking your code contributions
against our guidelines (e.g. we use `Black <https://github.com/psf/black>`_ as code style
use `pre-commit <https://pre-commit.com/index.html>`_:
1. Installation ``conda install -c conda-forge pre-commit`` or ``pip install pre-commit``
2. Usage:
* To automatically activate ``pre-commit`` on every ``git commit``: Run ``pre-commit install``
* To manually run it: ``pre-commit run --all``
Note that installing `pre-commit` locally is not strictly necessary. If you create a Pull Request the `pre-commit CI` will be triggered automatically and take care of the checks.
For all code contributions we follow the four eyes principle (two person principle), i.e. all suggested code
including our own are reviewed by a second person before they are incoporated into our repository.
including our own are reviewed by a second person before they are incorporated into our repository.
If you are unfamiliar with pull requests, the GitHub help pages have a nice `guide <https://help.github.com/en/articles/about-pull-requests>`_.

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..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -7,7 +7,13 @@
Cost Assumptions
##################
The database of cost assumptions is stored in ``data/costs.csv``.
The database of cost assumptions is retrieved from the repository
`PyPSA/technology-data <https://github.com/pypsa/technology-data>`_ and then
saved to ``resources/costs.csv``. Cost assumptions of previous PyPSA-Eur
versions can be restored by setting in the ``Snakefile``:
``COSTS="data/costs.csv"``.
The ``config.yaml`` provides options to choose a reference year (``costs: year:``) and use a specific version of the repository ``costs: version:``.
It includes cost assumptions for all included technologies for specific
years from various sources, namely for
@ -30,24 +36,12 @@ with a discount rate of :math:`r` over the economic lifetime :math:`n` using the
Based on the parameters above the ``marginal_cost`` and ``capital_cost`` of the system components are calculated.
.. note::
Another great resource for cost assumptions is the `cost database from the Danish Energy Agency <https://ens.dk/en/our-services/projections-and-models/technology-data>`_.
Modifying Cost Assumptions
==========================
Some cost assumptions (e.g. marginal cost and capital cost) can be directly overwritten in the ``config.yaml`` (cf. Section :ref:`costs_cf` in :ref:`config`).
To change cost assumptions in more detail, modify cost assumptions directly in ``data/costs.csv`` as this is not yet supported through the config file.
To change cost assumptions in more detail, modify cost assumptions directly in ``resources/costs.csv`` as this is not yet supported through the config file.
You can also build multiple different cost databases. Make a renamed copy of ``data/costs.csv`` (e.g. ``data/costs-optimistic.csv``) and set the variable ``COSTS=data/costs-optimistic.csv`` in the ``Snakefile``.
Default Cost Assumptions
========================
.. csv-table::
:header-rows: 1
:widths: 10,3,5,4,6,8
:file: ../data/costs.csv
You can also build multiple different cost databases. Make a renamed copy of ``resources/costs.csv`` (e.g. ``data/costs-optimistic.csv``) and set the variable ``COSTS=data/costs-optimistic.csv`` in the ``Snakefile``.

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..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -22,10 +22,6 @@ PyPSA-Eur: An Open Optimisation Model of the European Transmission System
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3520874.svg
:target: https://doi.org/10.5281/zenodo.3520874
.. image:: https://badges.gitter.im/PyPSA/community.svg
:target: https://gitter.im/PyPSA/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge
:alt: Chat on Gitter
.. image:: https://img.shields.io/badge/snakemake-≥5.0.0-brightgreen.svg?style=flat
:target: https://snakemake.readthedocs.io
:alt: Snakemake
@ -41,7 +37,7 @@ It contains alternating current lines at and above 220 kV voltage level and all
The model is suitable both for operational studies and generation and transmission expansion planning studies. The continental scope and highly resolved spatial scale enables a proper description of the long-range smoothing effects for renewable power generation and their varying resource availability.
.. image:: img/base.png
.. image:: img/elec.png
:width: 50%
:align: center
@ -199,7 +195,7 @@ The included ``.nc`` files are PyPSA network files which can be imported with Py
import pypsa
filename = "elec_s_1024_ec.nc" # example
filename = "elec_s_1024_ec.nc" # example
n = pypsa.Network(filename)
Licence

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..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -71,6 +71,7 @@ PyPSA is known to work with the free software
- `Ipopt <https://coin-or.github.io/Ipopt/INSTALL.html>`_
- `Cbc <https://projects.coin-or.org/Cbc#DownloadandInstall>`_
- `GLPK <https://www.gnu.org/software/glpk/>`_ (`WinGLKP <http://winglpk.sourceforge.net/>`_)
- `HiGHS <https://highs.dev/>`_
and the non-free, commercial software (for some of which free academic licenses are available)
@ -102,6 +103,8 @@ It might be the case that you can only retrieve solutions by using a commercial
conda activate pypsa-eur
conda install -c conda-forge ipopt glpk
.. warning::
On Windows, new versions of ``ipopt`` have caused problems. Consider downgrading to version 3.11.1.
.. _defaultconfig:

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REM SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
REM SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
REM SPDX-License-Identifier: MIT
@ECHO OFF

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..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -30,7 +30,7 @@ The :ref:`tutorial` uses a smaller cutout than required for the full model (30 M
.. note::
To download cutouts yourself from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ you need to `set up the CDS API <https://cds.climate.copernicus.eu/api-how-to>`_.
**Relevant Settings**

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SPDX-License-Identifier: CC-BY-4.0
@ -7,6 +7,222 @@
Release Notes
##########################################
Upcoming Release
================
* Carriers of generators can now be excluded from aggregation in clustering network and simplify network.
* Bugfix in the reserve constraint will increase demand related reserve requirements
PyPSA-Eur 0.6.1 (20th September 2022)
=====================================
* Individual commits are now tested against pre-commit hooks. This includes
black style formatting, sorting of package imports, Snakefile formatting and
others. Installation instructions can for the pre-commit can be found `here
<https://pre-commit.com/>`_.
* Pre-commit CI is now part of the repository's CI.
* The software now supports running the workflow with different settings within
the same directory. A new config section ``run`` was created that specifies
under which scenario ``name`` the created resources, networks and results
should be stored. If ``name`` is not specified, the workflow uses the default
paths. The entry ``shared_cutouts`` specifies whether the run should use
cutouts from the default root directory or use run-specific cutouts.
* The heuristic distribution of today's renewable capacity installations is now
enabled by default.
* The marginal costs of conventional generators are now taking the plant-specific
efficiency into account where available.
PyPSA-Eur 0.6.0 (10th September 2022)
=====================================
* Functionality to consider shipping routes when calculating the available area
for offshore technologies were added. Data for the shipping density comes from
the `Global Shipping Traffic Density dataset
<https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density>`_.
* When transforming all transmission lines to a unified voltage level of 380kV,
the workflow now preserves the transmission capacity rather than electrical
impedance and reactance.
* Memory resources are now specified for all rules.
* Filtering of power plant data was adjusted to new versions of
``powerplantmatching``.
* The resolution of land exclusion calculation is now a configurable option. See
setting ``excluder_resolution``.
PyPSA-Eur 0.5.0 (27th July 2022)
=====================================
**New Features**
* New network topology extracted from the ENTSO-E interactive map.
* Added existing renewable capacities for all countries based on IRENA
statistics (IRENASTAT) using new ``powerplantmatching`` version:
* The corresponding ``config`` entries changed, cf. ``config.default.yaml``:
* old: ``estimate_renewable_capacities_from_capacity_stats``
* new: ``estimate_renewable_capacities``
* The estimation is endabled by setting the subkey ``enable`` to ``True``.
* Configuration of reference year for capacities can be configured (default:
``2020``)
* The list of renewables provided by the OPSD database can be used as a basis,
using the tag ``from_opsd: True``. This adds the renewables from the
database and fills up the missing capacities with the heuristic
distribution.
* Uniform expansion limit of renewable build-up based on existing capacities
can be configured using ``expansion_limit`` option (default: ``false``;
limited to determined renewable potentials)
* Distribution of country-level capacities proportional to maximum annual
energy yield for each bus region
* The config key ``renewable_capacities_from_OPSD`` is deprecated and was moved
under the section, ``estimate_renewable_capacities``. To enable it, set
``from_opsd`` to ``True``.
* Add operational reserve margin constraint analogous to `GenX implementation
<https://genxproject.github.io/GenX/dev/core/#Reserves>`_. Can be activated
with config setting ``electricity: operational_reserve:``.
* Implement country-specific Energy Availability Factors (EAFs) for nuclear
power plants based on IAEA 2018-2020 reported country averages. These are
specified ``data/nuclear_p_max_pu.csv`` and translate to static ``p_max_pu``
values.
* Add function to add global constraint on use of gas in :mod:`prepare_network`.
This can be activated by including the keyword ``CH4L`` in the ``{opts}``
wildcard which enforces the limit set in ``electricity: gaslimit:`` given in
MWh thermal. Alternatively, it is possible to append a number in the ``{opts}``
wildcard, e.g. ``CH4L200`` which limits the gas use to 200 TWh thermal.
* Add option to alter marginal costs of a carrier through ``{opts}`` wildcard:
``<carrier>+m<factor>``, e.g. ``gas+m2.5``, will multiply the default marginal
cost for gas by factor 2.5.
* Hierarchical clustering was introduced. Distance metric is calculated from
renewable potentials on hourly (feature entry ends with ``-time``) or annual
(feature entry in config end with ``-cap``) values.
* Greedy modularity clustering was introduced. Distance metric is based on electrical distance taking into account the impedance of all transmission lines of the network.
* Techno-economic parameters of technologies (e.g. costs and efficiencies) will
now be retrieved from a separate repository `PyPSA/technology-data
<https://github.com/pypsa/technology-data>`_ that collects assumptions from a
variety of sources. It is activated by default with ``enable:
retrieve_cost_data: true`` and controlled with ``costs: year:`` and ``costs:
version:``. The location of this data changed from ``data/costs.csv`` to
``resources/costs.csv`` [`#184
<https://github.com/PyPSA/pypsa-eur/pull/184>`_].
* A new section ``conventional`` was added to the config file. This section
contains configurations for conventional carriers.
* Add configuration option to implement arbitrary generator attributes for
conventional generation technologies.
* Add option to set CO2 emission prices through ``{opts}`` wildcard: ``Ep<number>``,
e.g. ``Ep180``, will set the EUR/tCO2 price.
**Changes**
* Add an efficiency factor of 88.55% to offshore wind capacity factors as a
proxy for wake losses. More rigorous modelling is `planned
<https://github.com/PyPSA/pypsa-eur/issues/153>`_ [`#277
<https://github.com/PyPSA/pypsa-eur/pull/277>`_].
* Following discussion in `#285
<https://github.com/PyPSA/pypsa-eur/issues/285>`_ we have disabled the
correction factor for solar PV capacity factors by default while satellite
data is used. A correction factor of 0.854337 is recommended if reanalysis
data like ERA5 is used.
* The default deployment density of AC- and DC-connected offshore wind capacity
is reduced from 3 MW/sqkm to a more conservative estimate of 2 MW/sqkm [`#280
<https://github.com/PyPSA/pypsa-eur/pull/280>`_].
* The inclusion of renewable carriers is now specified in the config entry
``renewable_carriers``. Before this was done by commenting/uncommenting
sub-sections in the ``renewable`` config section.
* Now, all carriers that should be extendable have to be listed in the config
entry ``extendable_carriers``. Before, renewable carriers were always set to
be extendable. For backwards compatibility, the workflow is still looking at
the listed carriers under the ``renewable`` key. In the future, all of them
have to be listed under ``extendable_carriers``.
* It is now possible to set conventional power plants as extendable by adding
them to the list of extendable ``Generator`` carriers in the config.
* Listing conventional carriers in ``extendable_carriers`` but not in
``conventional_carriers``, sets the corresponding conventional power plants as
extendable without a lower capacity bound of today's capacities.
* Now, conventional carriers have an assigned capital cost by default.
* The ``build_year`` and ``lifetime`` column are now defined for conventional
power plants.
* Use updated SARAH-2 and ERA5 cutouts with slightly wider scope to east and
additional variables.
* Resource definitions for memory usage now follow `Snakemake standard resource
definition
<https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#standard-resources>`_
``mem_mb`` rather than ``mem``.
* The powerplants that have been shut down by 2021 are filtered out.
* Updated historical `EIA hydro generation data <https://www.eia.gov/international/data/world>`_.
* Network building is made deterministic by supplying a fixed random state to
network clustering routines.
* Clustering strategies for generator and bus attributes can now be specified directly in the ``config.yaml``.
* Iterative solving with impedance updates is skipped if there are no expandable
lines.
* The unused argument ``simple_hvdc_costs`` in :mod:`add_electricity` was
removed.
* Switch from Germany to Belgium for continuous integration and tutorial to save
resources.
* It is now possible to skip the progressbar for land eligibility calculations for additional speedup.
**Bugs and Compatibility**
* Fix crs bug. Change crs 4236 to 4326.
* ``powerplantmatching>=0.5.1`` is now required for ``IRENASTATS``.
* Update rasterio version to correctly calculate exclusion raster.
* It is now possible to run the workflow with only landlocked countries.
* Bugfixes for manual load adjustments across years.
* Enable parallel computing with new dask version.
* Restore compatibility of ``mock_snakemake`` with latest Snakemake versions.
* Script ``build_bus_regions``: move voronoi partition from vresutils to script.
* Script ``add_electricity``: remove ``vresutils.costdata.annuity`` dependency.
* Fix the plot_network snakemake rule.
* Compatibility with pandas 1.4. Address deprecations.
* Restore Windows compatibility by using ``shutil.move`` rather than ``mv``.
Synchronisation Release - Ukraine and Moldova (17th March 2022)
===============================================================
@ -42,59 +258,6 @@ This release is not on the ``master`` branch. It can be used with
git checkout synchronisation-release
On March 16, 2022, the transmission networks of Ukraine and Moldova have
successfully been `synchronised with the continental European grid <https://www.entsoe.eu/news/2022/03/16/continental-europe-successful-synchronisation-with-ukraine-and-moldova-power-systems/>`_. We have taken
this as an opportunity to add the power systems of Ukraine and Moldova to
PyPSA-Eur. This includes:
.. image:: img/synchronisation.png
:width: 500
* the transmission network topology from the `ENTSO-E interactive map <https://www.entsoe.eu/data/map/>`_.
* existing power plants (incl. nuclear, coal, gas and hydro) from the `powerplantmatching <https://github.com/fresna/powerplantmatching>`_ tool
* country-level load time series from ENTSO-E through the `OPSD platform <https://data.open-power-system-data.org/time_series/2020-10-06>`_, which are then distributed heuristically to substations by GDP and population density.
* wind and solar profiles based on ERA5 and SARAH-2 weather data
* hydro profiles based on historical `EIA generation data <https://www.eia.gov/international/data/world>`_
* a simplified calculation of wind and solar potentials based on the `Copernicus Land Cover dataset <https://land.copernicus.eu/global/products/lc>`_.
* electrical characteristics of 750 kV transmission lines
The Crimean power system is currently disconnected from the main Ukrainian grid and, hence, not included.
This release is not on the ``master`` branch. It can be used with
.. code-block:: bash
git clone https://github.com/pypsa/pypsa-eur
git checkout synchronisation-release
Upcoming Regular Release
========================
* Add an efficiency factor of 88.55% to offshore wind capacity factors
as a proxy for wake losses. More rigorous modelling is `planned <https://github.com/PyPSA/pypsa-eur/issues/153>`_
[`#277 <https://github.com/PyPSA/pypsa-eur/pull/277>`_].
* The default deployment density of AC- and DC-connected offshore wind capacity is reduced from 3 MW/sqkm
to a more conservative estimate of 2 MW/sqkm [`#280 <https://github.com/PyPSA/pypsa-eur/pull/280>`_].
* Following discussion in `#285 <https://github.com/PyPSA/pypsa-eur/issues/285>`_ we have disabled the
correction factor for solar PV capacity factors by default while satellite data is used.
A correction factor of 0.854337 is recommended if reanalysis data like ERA5 is used.
* Resource definitions for memory usage now follow [Snakemake standard resource definition](https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#standard-resources) ```mem_mb`` rather than ``mem``.
* Network building is made deterministic by supplying a fixed random state to network clustering routines.
* New network topology extracted from the ENTSO-E interactive map.
PyPSA-Eur 0.4.0 (22th September 2021)
=====================================
@ -109,7 +272,7 @@ PyPSA-Eur 0.4.0 (22th September 2021)
(~factor 2). A lot of the code which calculated the land-use availability is now
outsourced and does not rely on ``glaes``, ``geokit`` anymore. This facilitates
the environment building and version compatibility of ``gdal``, ``libgdal`` with
other packages [`#224 <https://github.com/PyPSA/pypsa-eur/pull/224>`_].
other packages [`#224 <https://github.com/PyPSA/pypsa-eur/pull/224>`_].
* Implemented changes to ``n.snapshot_weightings`` in new PyPSA version v0.18
(cf. `PyPSA/PyPSA/#227 <https://github.com/PyPSA/PyPSA/pull/227>`_)
@ -132,17 +295,17 @@ PyPSA-Eur 0.4.0 (22th September 2021)
used or maintained.
* The connection cost of generators in :mod:`simplify_network` are now reported
in ``resources/connection_costs_s{simpl}.csv``
in ``resources/connection_costs_s{simpl}.csv``
[`#261 <https://github.com/PyPSA/pypsa-eur/pull/261>`_].
* The tutorial cutout was renamed from ``cutouts/europe-2013-era5.nc`` to
``cutouts/europe-2013-era5-tutorial.nc`` to accomodate tutorial and productive
``cutouts/be-03-2013-era5.nc`` to accommodate tutorial and productive
cutouts side-by-side.
* The flag ``keep_all_available_areas`` in the configuration for renewable
potentials was deprecated and now defaults to ``True``.
potentials was deprecated and now defaults to ``True``.
* Update dependencies in ``envs/environment.yaml``
* Update dependencies in ``envs/environment.yaml``
[`#257 <https://github.com/PyPSA/pypsa-eur/pull/257>`_]
* Continuous integration testing switches to Github Actions from Travis CI
@ -171,7 +334,7 @@ PyPSA-Eur 0.4.0 (22th September 2021)
* Value for ``co2base`` in ``config.yaml`` adjusted to 1.487e9 t CO2-eq
(from 3.1e9 t CO2-eq). The new value represents emissions related to the
electricity sector for EU+UK+Balkan. The old value was too high and used when
the emissions wildcard in ``{opts}`` was used
the emissions wildcard in ``{opts}`` was used
[`#233 <https://github.com/PyPSA/pypsa-eur/pull/233>`_].
* Add escape in :mod:`base_network` if all TYNDP links are already
@ -179,11 +342,11 @@ PyPSA-Eur 0.4.0 (22th September 2021)
[`#246 <https://github.com/PyPSA/pypsa-eur/pull/246>`_].
* In :mod:`solve_operations_network` the optimised capacities are now
fixed for all extendable links, not only HVDC links
fixed for all extendable links, not only HVDC links
[`#244 <https://github.com/PyPSA/pypsa-eur/pull/244>`_].
* The ``focus_weights`` are now also considered when pre-clustering in
the :mod:`simplify_network` rule
the :mod:`simplify_network` rule
[`#241 <https://github.com/PyPSA/pypsa-eur/pull/241>`_].
* in :mod:`build_renewable_profile` where offshore wind profiles could
@ -203,14 +366,13 @@ PyPSA-Eur 0.4.0 (22th September 2021)
load shedding generators are only added at the AC buses, excluding buses for H2
and battery stores [`#269 <https://github.com/PyPSA/pypsa-eur/pull/269>`_].
* Delete duplicated capital costs at battery discharge link
* Delete duplicated capital costs at battery discharge link
[`#240 <https://github.com/PyPSA/pypsa-eur/pull/240>`_].
* Propagate the solver log file name to the solver. Previously, the
PyPSA network solving functions were not told about the solver logfile specified
in the Snakemake file [`#247 <https://github.com/PyPSA/pypsa-eur/pull/247>`_]
PyPSA-Eur 0.3.0 (7th December 2020)
===================================
@ -221,7 +383,7 @@ Using the ``{opts}`` wildcard for scenarios:
* An option is introduced which adds constraints such that each country or node produces on average a minimal share of its total consumption itself.
For example ``EQ0.5c`` set in the ``{opts}`` wildcard requires each country to produce on average at least 50% of its consumption. Additionally,
the option ``ATK`` requires autarky at each node and removes all means of power transmission through lines and links. ``ATKc`` only removes
cross-border transfer capacities.
cross-border transfer capacities.
[`#166 <https://github.com/PyPSA/pypsa-eur/pull/166>`_].
* Added an option to alter the capital cost (``c``) or installable potentials (``p``) of carriers by a factor via ``carrier+{c,p}factor`` in the ``{opts}`` wildcard.
@ -308,7 +470,7 @@ Other:
[`#191 <https://github.com/PyPSA/pypsa-eur/pull/191>`_].
* Raise a warning if ``tech_colors`` in the config are not defined for all carriers
[`#178 <https://github.com/PyPSA/pypsa-eur/pull/178>`_].
[`#178 <https://github.com/PyPSA/pypsa-eur/pull/178>`_].
PyPSA-Eur 0.2.0 (8th June 2020)
@ -330,7 +492,7 @@ PyPSA-Eur 0.2.0 (8th June 2020)
* Removed the ``id`` column for custom power plants in ``data/custom_powerplants.csv`` to avoid custom power plants with conflicting ids getting attached to the wrong bus [`#131 <https://github.com/PyPSA/pypsa-eur/pull/131>`_].
* Add option ``renewables: {carrier}: keep_all_available_areas:`` to use all availabe weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed [`#150 <https://github.com/PyPSA/pypsa-eur/pull/150>`_].
* Add option ``renewables: {carrier}: keep_all_available_areas:`` to use all available weather cells for renewable profile and potential generation. The default ignores weather cells where only less than 1 MW can be installed [`#150 <https://github.com/PyPSA/pypsa-eur/pull/150>`_].
* Added a function ``_helpers.load_network()`` which loads a network with overridden components specified in ``snakemake.config['override_components']`` [`#128 <https://github.com/PyPSA/pypsa-eur/pull/128>`_].
@ -386,7 +548,7 @@ Release Process
``conda env export -n pypsa-eur -f envs/environment.fixed.yaml --no-builds``
from an up-to-date `pypsa-eur` environment.
* Update version number in ``doc/conf.py`` and ``*config.*.yaml``.
* Update version number in ``doc/conf.py``, ``CITATION.cff`` and ``*config.*.yaml``.
* Open, review and merge pull request for branch ``release-v0.x.x``.
Make sure to close issues and PRs or the release milestone with it (e.g. closes #X).

4
doc/requirements.txt
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@ -1,4 +1,4 @@
# SPDX-FileCopyrightText: : 2019-2021 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2019-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
@ -18,4 +18,4 @@ pyyaml
seaborn
memory_profiler
tables
descartes
descartes

2
doc/simplification.rst
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@ -1,7 +1,7 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

2
doc/simplification/add_extra_components.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

2
doc/simplification/cluster_network.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

2
doc/simplification/prepare_network.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

2
doc/simplification/simplify_network.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

2
doc/solving.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

2
doc/solving/solve_network.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

2
doc/solving/solve_operations_network.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0

58
doc/tutorial.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -43,51 +43,63 @@ For more information on the data dependencies of PyPSA-Eur, continue reading :re
How to customise PyPSA-Eur?
===========================
The model can be adapted to only include selected countries (e.g. Germany) instead of all European countries to limit the spatial scope.
The model can be adapted to only include selected countries (e.g. Belgium) instead of all European countries to limit the spatial scope.
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 20
:start-at: countries:
:end-before: snapshots:
Likewise, the example's temporal scope can be restricted (e.g. to a single month).
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 24-27
:start-at: snapshots:
:end-before: enable:
It is also possible to allow less or more carbon-dioxide emissions. Here, we limit the emissions of Germany 100 Megatonnes per year.
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 38,40
:start-at: electricity:
:end-before: exentable_carriers:
PyPSA-Eur also includes a database of existing conventional powerplants.
We can select which types of powerplants we like to be included with fixed capacities:
We can select which types of powerplants we like to be included:
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 38,54
:start-at: extendable_carriers:
:end-before: max_hours:
To accurately model the temporal and spatial availability of renewables such as wind and solar energy, we rely on historical weather data.
It is advisable to adapt the required range of coordinates to the selection of countries.
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 56-63
:start-at: atlite:
:end-before: renewable:
We can also decide which weather data source should be used to calculate potentials and capacity factor time-series for each carrier.
For example, we may want to use the ERA-5 dataset for solar and not the default SARAH-2 dataset.
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 65,108-109
:start-at: be-03-2013-era5:
:end-at: module:
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:start-at: solar:
:end-at: cutout:
Finally, it is possible to pick a solver. For instance, this tutorial uses the open-source solvers CBC and Ipopt and does not rely
on the commercial solvers Gurobi or CPLEX (for which free academic licenses are available).
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 171,181-182
:start-at: solver:
:end-before: plotting:
.. note::
@ -116,21 +128,12 @@ clustered down to 6 buses and every 24 hours aggregated to one snapshot. The com
orders ``snakemake`` to run the script ``solve_network`` that produces the solved network and stores it in ``.../pypsa-eur/results/networks`` with the name ``elec_s_6_ec_lcopt_Co2L-24H.nc``:
.. code::
rule solve_network:
input: "networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
[...]
script: "scripts/solve_network.py"
.. literalinclude:: ../Snakefile
:start-at: rule solve_network:
:end-before: rule solve_operations_network:
.. until https://github.com/snakemake/snakemake/issues/46 closed
.. warning::
On Windows the previous command may currently cause a ``MissingRuleException`` due to problems with output files in subfolders.
This is an `open issue <https://github.com/snakemake/snakemake/issues/46>`_ at `snakemake <https://snakemake.readthedocs.io/>`_.
Windows users should add the option ``--keep-target-files`` to the command or instead run ``snakemake -j 1 solve_all_networks``.
This triggers a workflow of multiple preceding jobs that depend on each rule's inputs and outputs:
.. graphviz::
@ -218,7 +221,7 @@ A job (here ``simplify_network``) will display its attributes and normally some
[<DATETIME>]
rule simplify_network:
input: networks/elec.nc, data/costs.csv, resources/regions_onshore.geojson, resources/regions_offshore.geojson
input: networks/elec.nc, resources/costs.csv, resources/regions_onshore.geojson, resources/regions_offshore.geojson
output: networks/elec_s.nc, resources/regions_onshore_elec_s.geojson, resources/regions_offshore_elec_s.geojson, resources/clustermaps_elec_s.h5
jobid: 3
benchmark: benchmarks/simplify_network/elec_s
@ -247,7 +250,7 @@ Once the whole worktree is finished, it should show state so in the terminal:
You will notice that many intermediate stages are saved, namely the outputs of each individual ``snakemake`` rule.
You can produce any output file occuring in the ``Snakefile`` by running
You can produce any output file occurring in the ``Snakefile`` by running
.. code:: bash
@ -271,9 +274,8 @@ the wildcards given in ``scenario`` in the configuration file ``config.yaml`` ar
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 14-18
In this example we would not only solve a 6-node model of Germany but also a 2-node model.
:start-at: scenario:
:end-before: countries:
How to analyse solved networks?
===============================
@ -286,4 +288,4 @@ The solved networks can be analysed just like any other PyPSA network (e.g. in J
network = pypsa.Network("results/networks/elec_s_6_ec_lcopt_Co2L-24H.nc")
For inspiration, read the `examples section in the PyPSA documentation <https://pypsa.readthedocs.io/en/latest/examples.html>`_.
For inspiration, read the `examples section in the PyPSA documentation <https://pypsa.readthedocs.io/en/latest/examples-basic.html>`_.

5
doc/wildcards.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2022 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -123,7 +123,7 @@ These cutouts will be stored in a folder specified by ``{cutout}``.
The ``{technology}`` wildcard
=============================
The ``{technology}`` wildcard specifies for which renewable energy technology to produce availablity time
The ``{technology}`` wildcard specifies for which renewable energy technology to produce availability time
series and potentials using the rule :mod:`build_renewable_profiles`.
It can take the values ``onwind``, ``offwind-ac``, ``offwind-dc``, and ``solar`` but **not** ``hydro``
(since hydroelectric plant profiles are created by a different rule).
@ -155,4 +155,5 @@ formats depends on the used backend. To query the supported file types on your s
.. code:: python
import matplotlib.pyplot as plt
plt.gcf().canvas.get_supported_filetypes()

729
envs/environment.fixed.yaml
View File

@ -1,311 +1,430 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
name: pypsa-eur
channels:
- bioconda
- conda-forge
- defaults
- bioconda
- http://conda.anaconda.org/gurobi
- conda-forge
- defaults
dependencies:
- _libgcc_mutex=0.1
- _openmp_mutex=4.5
- affine=2.3.0
- alsa-lib=1.2.3
- amply=0.1.4
- appdirs=1.4.4
- atlite=0.2.5
- attrs=21.2.0
- backcall=0.2.0
- backports=1.0
- backports.functools_lru_cache=1.6.4
- beautifulsoup4=4.10.0
- blosc=1.21.0
- bokeh=2.3.3
- boost-cpp=1.74.0
- bottleneck=1.3.2
- brotlipy=0.7.0
- bzip2=1.0.8
- c-ares=1.17.2
- ca-certificates=2021.5.30
- cairo=1.16.0
- cartopy=0.19.0.post1
- cdsapi=0.5.1
- certifi=2021.5.30
- cffi=1.14.6
- cfitsio=3.470
- cftime=1.5.0
- chardet=4.0.0
- charset-normalizer=2.0.0
- click=7.1.2
- click-plugins=1.1.1
- cligj=0.7.2
- cloudpickle=2.0.0
- coincbc=2.10.5
- colorama=0.4.4
- conda=4.10.3
- conda-package-handling=1.7.3
- configargparse=1.5.2
- connection_pool=0.0.3
- country_converter=0.7.3
- cryptography=3.4.7
- curl=7.79.0
- cycler=0.10.0
- cytoolz=0.11.0
- dask=2021.3.1
- dask-core=2021.3.1
- datrie=0.8.2
- dbus=1.13.6
- decorator=4.4.2
- deprecation=2.1.0
- descartes=1.1.0
- distributed=2021.4.1
- distro=1.5.0
- docutils=0.17.1
- entsoe-py=0.3.7
- et_xmlfile=1.0.1
- expat=2.4.1
- filelock=3.0.12
- fiona=1.8.18
- fontconfig=2.13.1
- freetype=2.10.4
- freexl=1.0.6
- fsspec=2021.8.1
- gdal=3.2.1
- geographiclib=1.52
- geopandas=0.9.0
- geopandas-base=0.9.0
- geopy=2.2.0
- geos=3.9.1
- geotiff=1.6.0
- gettext=0.19.8.1
- giflib=5.2.1
- gitdb=4.0.7
- gitpython=3.1.23
- glib=2.68.4
- glib-tools=2.68.4
- graphite2=1.3.13
- gst-plugins-base=1.18.5
- gstreamer=1.18.5
- harfbuzz=2.9.1
- hdf4=4.2.15
- hdf5=1.10.6
- heapdict=1.0.1
- icu=68.1
- idna=3.1
- importlib-metadata=4.8.1
- iniconfig=1.1.1
- ipython=7.27.0
- ipython_genutils=0.2.0
- jdcal=1.4.1
- jedi=0.18.0
- jinja2=3.0.1
- joblib=1.0.1
- jpeg=9d
- json-c=0.15
- jsonschema=3.2.0
- jupyter_core=4.8.1
- kealib=1.4.14
- kiwisolver=1.3.2
- krb5=1.19.2
- lcms2=2.12
- ld_impl_linux-64=2.36.1
- libarchive=3.5.1
- libblas=3.9.0
- libcblas=3.9.0
- libclang=11.1.0
- libcurl=7.79.0
- libdap4=3.20.6
- libedit=3.1.20191231
- libev=4.33
- libevent=2.1.10
- libffi=3.4.2
- libgcc-ng=11.2.0
- libgdal=3.2.1
- libgfortran-ng=11.2.0
- libgfortran5=11.2.0
- libglib=2.68.4
- libgomp=11.2.0
- libiconv=1.16
- libkml=1.3.0
- liblapack=3.9.0
- libllvm11=11.1.0
- libnetcdf=4.7.4
- libnghttp2=1.43.0
- libogg=1.3.4
- libopenblas=0.3.17
- libopus=1.3.1
- libpng=1.6.37
- libpq=13.3
- librttopo=1.1.0
- libsolv=0.7.19
- libspatialindex=1.9.3
- libspatialite=5.0.1
- libssh2=1.10.0
- libstdcxx-ng=11.2.0
- libtiff=4.2.0
- libuuid=2.32.1
- libvorbis=1.3.7
- libwebp-base=1.2.1
- libxcb=1.13
- libxkbcommon=1.0.3
- libxml2=2.9.12
- libxslt=1.1.33
- locket=0.2.0
- lxml=4.6.3
- lz4-c=1.9.3
- lzo=2.10
- mamba=0.15.3
- mapclassify=2.4.3
- markupsafe=2.0.1
- matplotlib=3.4.3
- matplotlib-base=3.4.3
- matplotlib-inline=0.1.3
- memory_profiler=0.58.0
- mock=4.0.3
- more-itertools=8.10.0
- msgpack-python=1.0.2
- munch=2.5.0
- mysql-common=8.0.25
- mysql-libs=8.0.25
- nbformat=5.1.3
- ncurses=6.2
- netcdf4=1.5.6
- networkx=2.6.3
- nspr=4.30
- nss=3.69
- numexpr=2.7.3
- numpy=1.21.2
- olefile=0.46
- openjdk=11.0.9.1
- openjpeg=2.4.0
- openpyxl=3.0.8
- openssl=1.1.1l
- packaging=21.0
- pandas=1.2.5
- parso=0.8.2
- partd=1.2.0
- patsy=0.5.1
- pcre=8.45
- pexpect=4.8.0
- pickleshare=0.7.5
- pillow=8.2.0
- pip=21.2.4
- pixman=0.40.0
- pluggy=1.0.0
- ply=3.11
- poppler=0.89.0
- poppler-data=0.4.11
- postgresql=13.3
- powerplantmatching=0.4.8
- progressbar2=3.53.1
- proj=7.2.0
- prompt-toolkit=3.0.20
- psutil=5.8.0
- pthread-stubs=0.4
- ptyprocess=0.7.0
- pulp=2.5.0
- py=1.10.0
- pycosat=0.6.3
- pycountry=20.7.3
- pycparser=2.20
- pygments=2.10.0
- pyomo=6.1.2
- pyopenssl=20.0.1
- pyparsing=2.4.7
- pyproj=3.1.0
- pypsa=0.18.0
- pyqt=5.12.3
- pyqt-impl=5.12.3
- pyqt5-sip=4.19.18
- pyqtchart=5.12
- pyqtwebengine=5.12.1
- pyrsistent=0.17.3
- pyshp=2.1.3
- pysocks=1.7.1
- pytables=3.6.1
- pytest=6.2.5
- python=3.9.7
- python-dateutil=2.8.2
- python-utils=2.5.6
- python_abi=3.9
- pytz=2021.1
- pyyaml=5.4.1
- qt=5.12.9
- rasterio=1.2.6
- ratelimiter=1.2.0
- readline=8.1
- reproc=14.2.3
- reproc-cpp=14.2.3
- requests=2.26.0
- rtree=0.9.7
- ruamel_yaml=0.15.80
- scikit-learn=0.24.2
- scipy=1.7.1
- seaborn=0.11.2
- seaborn-base=0.11.2
- setuptools=58.0.4
- setuptools-scm=6.3.2
- setuptools_scm=6.3.2
- shapely=1.7.1
- six=1.16.0
- smart_open=5.2.1
- smmap=3.0.5
- snakemake-minimal=6.8.0
- snuggs=1.4.7
- sortedcontainers=2.4.0
- soupsieve=2.0.1
- sqlite=3.36.0
- statsmodels=0.12.2
- stopit=1.1.2
- tabula-py=2.2.0
- tabulate=0.8.9
- tblib=1.7.0
- threadpoolctl=2.2.0
- tiledb=2.2.9
- tk=8.6.11
- toml=0.10.2
- tomli=1.2.1
- toolz=0.11.1
- toposort=1.6
- tornado=6.1
- tqdm=4.62.3
- traitlets=5.1.0
- typing_extensions=3.10.0.2
- tzcode=2021a
- tzdata=2021a
- urllib3=1.26.6
- wcwidth=0.2.5
- wheel=0.37.0
- wrapt=1.12.1
- xarray=0.19.0
- xerces-c=3.2.3
- xlrd=2.0.1
- xorg-fixesproto=5.0
- xorg-inputproto=2.3.2
- xorg-kbproto=1.0.7
- xorg-libice=1.0.10
- xorg-libsm=1.2.3
- xorg-libx11=1.7.2
- xorg-libxau=1.0.9
- xorg-libxdmcp=1.1.3
- xorg-libxext=1.3.4
- xorg-libxfixes=5.0.3
- xorg-libxi=1.7.10
- xorg-libxrender=0.9.10
- xorg-libxtst=1.2.3
- xorg-recordproto=1.14.2
- xorg-renderproto=0.11.1
- xorg-xextproto=7.3.0
- xorg-xproto=7.0.31
- xz=5.2.5
- yaml=0.2.5
- zict=2.0.0
- zipp=3.5.0
- zlib=1.2.11
- zstd=1.4.9
- pip:
- countrycode==0.2
- sklearn==0.0
- tsam==1.1.1
- vresutils==0.3.1
- _libgcc_mutex=0.1
- _openmp_mutex=4.5
- abseil-cpp=20210324.2
- affine=2.3.1
- alsa-lib=1.2.3.2
- altair=4.2.0
- ampl-mp=3.1.0
- amply=0.1.5
- anyio=3.6.1
- appdirs=1.4.4
- argon2-cffi=21.3.0
- argon2-cffi-bindings=21.2.0
- arrow-cpp=8.0.0
- asttokens=2.0.5
- atlite=0.2.9
- attrs=21.4.0
- aws-c-cal=0.5.11
- aws-c-common=0.6.2
- aws-c-event-stream=0.2.7
- aws-c-io=0.10.5
- aws-checksums=0.1.11
- aws-sdk-cpp=1.8.186
- babel=2.10.3
- backcall=0.2.0
- backports=1.0
- backports.functools_lru_cache=1.6.4
- beautifulsoup4=4.11.1
- bleach=5.0.1
- blinker=1.4
- blosc=1.21.1
- bokeh=2.4.3
- boost-cpp=1.74.0
- bottleneck=1.3.5
- branca=0.5.0
- brotli=1.0.9
- brotli-bin=1.0.9
- brotlipy=0.7.0
- bzip2=1.0.8
- c-ares=1.18.1
- ca-certificates=2022.6.15.1
- cachetools=5.0.0
- cairo=1.16.0
- cartopy=0.20.1
- cdsapi=0.5.1
- certifi=2022.6.15.1
- cffi=1.15.1
- cfitsio=4.0.0
- cftime=1.6.1
- charset-normalizer=2.1.0
- click=8.0.4
- click-plugins=1.1.1
- cligj=0.7.2
- cloudpickle=2.1.0
- 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=0.4.5
- colorcet=3.0.0
- commonmark=0.9.1
- configargparse=1.5.3
- connection_pool=0.0.3
- country_converter=0.7.4
- cryptography=37.0.4
- curl=7.83.1
- cycler=0.11.0
- cytoolz=0.12.0
- dask=2022.7.0
- dask-core=2022.7.0
- dataclasses=0.8
- datrie=0.8.2
- dbus=1.13.6
- debugpy=1.6.0
- decorator=5.1.1
- defusedxml=0.7.1
- deprecation=2.1.0
- descartes=1.1.0
- distributed=2022.7.0
- distro=1.6.0
- docutils=0.19
- dpath=2.0.6
- entrypoints=0.4
- entsoe-py=0.5.4
- et_xmlfile=1.0.1
- executing=0.8.3
- expat=2.4.8
- filelock=3.7.1
- fiona=1.8.20
- flit-core=3.7.1
- folium=0.12.1.post1
- font-ttf-dejavu-sans-mono=2.37
- font-ttf-inconsolata=3.000
- font-ttf-source-code-pro=2.038
- font-ttf-ubuntu=0.83
- fontconfig=2.14.0
- fonts-conda-ecosystem=1
- fonts-conda-forge=1
- fonttools=4.34.4
- freetype=2.10.4
- freexl=1.0.6
- fsspec=2022.5.0
- future=0.18.2
- gdal=3.3.3
- geographiclib=1.52
- geojson-rewind=1.0.2
- geopandas=0.11.0
- geopandas-base=0.11.0
- geopy=2.2.0
- geos=3.10.0
- geotiff=1.7.0
- gettext=0.19.8.1
- gflags=2.2.2
- giflib=5.2.1
- gitdb=4.0.9
- gitpython=3.1.27
- glog=0.6.0
- gmp=6.2.1
- graphite2=1.3.13
- grpc-cpp=1.45.2
- gst-plugins-base=1.18.5
- gstreamer=1.18.5
- harfbuzz=2.9.1
- hdf4=4.2.15
- hdf5=1.12.1
- heapdict=1.0.1
- icu=68.2
- idna=3.3
- importlib-metadata=4.11.4
- importlib_metadata=4.11.4
- importlib_resources=5.8.0
- iniconfig=1.1.1
- ipykernel=6.15.1
- ipython=8.4.0
- ipython_genutils=0.2.0
- ipywidgets=7.7.1
- jedi=0.18.1
- jinja2=3.1.2
- joblib=1.1.0
- jpeg=9e
- json-c=0.15
- json5=0.9.5
- jsonschema=4.7.2
- jupyter_client=7.3.4
- jupyter_core=4.10.0
- jupyter_server=1.18.1
- kealib=1.4.15
- keyutils=1.6.1
- kiwisolver=1.4.4
- krb5=1.19.3
- lcms2=2.12
- ld_impl_linux-64=2.36.1
- lerc=3.0
- libblas=3.9.0
- libbrotlicommon=1.0.9
- libbrotlidec=1.0.9
- libbrotlienc=1.0.9
- libcblas=3.9.0
- libclang=11.1.0
- libcrc32c=1.1.2
- libcurl=7.83.1
- libdap4=3.20.6
- libdeflate=1.12
- libedit=3.1.20191231
- libev=4.33
- libevent=2.1.10
- libffi=3.4.2
- libgcc-ng=12.1.0
- libgdal=3.3.3
- libgfortran-ng=12.1.0
- libgfortran5=12.1.0
- libglib=2.72.1
- libgomp=12.1.0
- libgoogle-cloud=1.40.2
- libiconv=1.16
- libkml=1.3.0
- liblapack=3.9.0
- liblapacke=3.9.0
- libllvm11=11.1.0
- libnetcdf=4.8.1
- libnghttp2=1.47.0
- libnsl=2.0.0
- libogg=1.3.4
- libopenblas=0.3.20
- libopus=1.3.1
- libpng=1.6.37
- libpq=13.5
- libprotobuf=3.20.1
- librttopo=1.1.0
- libsodium=1.0.18
- libspatialindex=1.9.3
- libspatialite=5.0.1
- libssh2=1.10.0
- libstdcxx-ng=12.1.0
- libthrift=0.16.0
- libtiff=4.4.0
- libutf8proc=2.7.0
- libuuid=2.32.1
- libvorbis=1.3.7
- libwebp=1.2.2
- libwebp-base=1.2.2
- libxcb=1.13
- libxkbcommon=1.0.3
- libxml2=2.9.12
- libxslt=1.1.33
- libzip=1.9.2
- libzlib=1.2.12
- locket=1.0.0
- lxml=4.8.0
- lz4=4.0.0
- lz4-c=1.9.3
- lzo=2.10
- mapclassify=2.4.3
- markdown=3.4.1
- markupsafe=2.1.1
- matplotlib=3.5.2
- matplotlib-base=3.5.2
- matplotlib-inline=0.1.3
- memory_profiler=0.60.0
- metis=5.1.0
- mistune=0.8.4
- msgpack-python=1.0.4
- mumps-include=5.2.1
- mumps-seq=5.2.1
- munch=2.5.0
- munkres=1.1.4
- mysql-common=8.0.29
- mysql-libs=8.0.29
- nbclassic=0.4.3
- nbclient=0.6.6
- nbconvert=6.5.0
- nbconvert-core=6.5.0
- nbconvert-pandoc=6.5.0
- nbformat=5.4.0
- ncurses=6.3
- nest-asyncio=1.5.5
- netcdf4=1.6.0
- networkx=2.8.4
- nomkl=1.0
- notebook=6.4.12
- notebook-shim=0.1.0
- nspr=4.32
- nss=3.78
- numexpr=2.8.3
- numpy=1.23.1
- openjdk=11.0.9.1
- openjpeg=2.4.0
- openpyxl=3.0.9
- openssl=1.1.1q
- orc=1.7.5
- packaging=21.3
- pandas=1.4.3
- pandoc=2.18
- pandocfilters=1.5.0
- parquet-cpp=1.5.1
- parso=0.8.3
- partd=1.2.0
- patsy=0.5.2
- pcre=8.45
- pexpect=4.8.0
- pickleshare=0.7.5
- pillow=9.2.0
- pip=22.1.2
- pixman=0.40.0
- plac=1.3.5
- pluggy=1.0.0
- ply=3.11
- poppler=21.09.0
- poppler-data=0.4.11
- postgresql=13.5
- powerplantmatching=0.5.4
- progressbar2=4.0.0
- proj=8.1.1
- prometheus_client=0.14.1
- prompt-toolkit=3.0.30
- protobuf=3.20.1
- psutil=5.9.1
- pthread-stubs=0.4
- ptyprocess=0.7.0
- pulp=2.6.0
- pure_eval=0.2.2
- py=1.11.0
- pyarrow=8.0.0
- pycountry=20.7.3
- pycparser=2.21
- pyct=0.4.6
- pyct-core=0.4.6
- pydeck=0.7.1
- pygments=2.12.0
- pympler=0.9
- pyomo=6.4.1
- pyopenssl=22.0.0
- pyparsing=3.0.9
- pyproj=3.2.1
- pypsa=0.20.0
- pyqt=5.12.3
- pyqt-impl=5.12.3
- pyqt5-sip=4.19.18
- pyqtchart=5.12
- pyqtwebengine=5.12.1
- pyrsistent=0.18.1
- pyshp=2.3.0
- pysocks=1.7.1
- pytables=3.7.0
- pytest=7.1.2
- python=3.9.13
- python-dateutil=2.8.2
- python-fastjsonschema=2.16.1
- python-tzdata=2022.1
- python-utils=3.3.3
- python_abi=3.9
- pytz=2022.1
- pytz-deprecation-shim=0.1.0.post0
- pyviz_comms=2.2.0
- pyxlsb=1.0.9
- pyyaml=6.0
- pyzmq=23.2.0
- qt=5.12.9
- rasterio=1.2.9
- ratelimiter=1.2.0
- re2=2022.06.01
- readline=8.1.2
- requests=2.28.1
- retry=0.9.2
- rich=12.5.1
- rtree=1.0.0
- s2n=1.0.10
- scikit-learn=1.1.1
- scipy=1.8.1
- scotch=6.0.9
- seaborn=0.11.2
- seaborn-base=0.11.2
- semver=2.13.0
- send2trash=1.8.0
- setuptools=63.2.0
- setuptools-scm=7.0.5
- setuptools_scm=7.0.5
- shapely=1.8.0
- six=1.16.0
- smart_open=6.0.0
- smmap=3.0.5
- snakemake-minimal=7.8.5
- snappy=1.1.9
- sniffio=1.2.0
- snuggs=1.4.7
- sortedcontainers=2.4.0
- soupsieve=2.3.1
- sqlite=3.39.1
- stack_data=0.3.0
- statsmodels=0.13.2
- stopit=1.1.2
- streamlit=1.10.0
- tabula-py=2.2.0
- tabulate=0.8.10
- tblib=1.7.0
- tenacity=8.0.1
- terminado=0.15.0
- threadpoolctl=3.1.0
- tiledb=2.3.4
- tinycss2=1.1.1
- tk=8.6.12
- toml=0.10.2
- tomli=2.0.1
- toolz=0.12.0
- toposort=1.7
- tornado=6.1
- tqdm=4.64.0
- traitlets=5.3.0
- typing-extensions=4.3.0
- typing_extensions=4.3.0
- tzcode=2022a
- tzdata=2022a
- tzlocal=4.2
- unicodedata2=14.0.0
- unidecode=1.3.4
- unixodbc=2.3.10
- urllib3=1.26.10
- validators=0.18.2
- watchdog=2.1.9
- wcwidth=0.2.5
- webencodings=0.5.1
- websocket-client=1.3.3
- wheel=0.37.1
- widgetsnbextension=3.6.1
- wrapt=1.14.1
- xarray=2022.3.0
- xerces-c=3.2.3
- xlrd=2.0.1
- xorg-fixesproto=5.0
- xorg-inputproto=2.3.2
- xorg-kbproto=1.0.7
- xorg-libice=1.0.10
- xorg-libsm=1.2.3
- xorg-libx11=1.7.2
- xorg-libxau=1.0.9
- xorg-libxdmcp=1.1.3
- xorg-libxext=1.3.4
- xorg-libxfixes=5.0.3
- xorg-libxi=1.7.10
- xorg-libxrender=0.9.10
- xorg-libxtst=1.2.3
- xorg-recordproto=1.14.2
- xorg-renderproto=0.11.1
- xorg-xextproto=7.3.0
- xorg-xproto=7.0.31
- xyzservices=2022.6.0
- xz=5.2.5
- yaml=0.2.5
- yte=1.5.1
- zeromq=4.3.4
- zict=2.2.0
- zipp=3.8.0
- zlib=1.2.12
- zstd=1.5.2
- pip:
- countrycode==0.2
- tsam==2.1.0
- vresutils==0.3.1

90
envs/environment.yaml
View File

@ -1,60 +1,62 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
name: pypsa-eur
channels:
- conda-forge
- bioconda
- conda-forge
- bioconda
dependencies:
- python>=3.8
- pip
- python>=3.8
- pip
- pypsa>=0.19.2
- atlite>=0.2.5
- dask
- jupyter
- nbconvert
- pypsa>=0.21.3
- atlite>=0.2.9
- dask
- jupyter
- nbconvert
# Dependencies of the workflow itself
- xlrd
- openpyxl
- pycountry
- seaborn
- snakemake-minimal
- memory_profiler
- yaml
- pytables
- lxml
- powerplantmatching>=0.4.8
- numpy
- pandas
- geopandas
- xarray
- netcdf4
- networkx
- scipy
- shapely
- progressbar2
- pyomo
- matplotlib
- proj
- xlrd
- openpyxl
- pycountry
- seaborn
- snakemake-minimal
- memory_profiler
- yaml
- pytables
- lxml
- powerplantmatching>=0.5.5
- numpy<1.24
- pandas
- geopandas>=0.11.0
- xarray
- netcdf4
- networkx
- scipy
- shapely<2.0
- progressbar2
- pyomo
- matplotlib<3.6
- proj
- fiona
- country_converter
# Keep in conda environment when calling ipython
- ipython
- ipython
# GIS dependencies:
- cartopy
- descartes
- rasterio
- cartopy
- descartes
- rasterio!=1.2.10
# PyPSA-Eur-Sec Dependencies
- geopy
- tqdm
- pytz
- country_converter
- tabula-py
- geopy
- tqdm
- pytz
- tabula-py
- pyxlsb
- pip:
- vresutils>=0.3.1
- tsam>=1.1.0
- pip:
- vresutils>=0.3.1
- tsam>=1.1.0

239
scripts/_helpers.py
View File

@ -1,10 +1,14 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
import pandas as pd
from pathlib import Path
import pandas as pd
REGION_COLS = ["geometry", "name", "x", "y", "country"]
def configure_logging(snakemake, skip_handlers=False):
"""
@ -27,21 +31,26 @@ def configure_logging(snakemake, skip_handlers=False):
import logging
kwargs = snakemake.config.get('logging', dict())
kwargs = snakemake.config.get("logging", dict()).copy()
kwargs.setdefault("level", "INFO")
if skip_handlers is False:
fallback_path = Path(__file__).parent.joinpath('..', 'logs', f"{snakemake.rule}.log")
logfile = snakemake.log.get('python', snakemake.log[0] if snakemake.log
else fallback_path)
fallback_path = Path(__file__).parent.joinpath(
"..", "logs", f"{snakemake.rule}.log"
)
logfile = snakemake.log.get(
"python", snakemake.log[0] if snakemake.log else fallback_path
)
kwargs.update(
{'handlers': [
# Prefer the 'python' log, otherwise take the first log for each
# Snakemake rule
logging.FileHandler(logfile),
logging.StreamHandler()
{
"handlers": [
# Prefer the 'python' log, otherwise take the first log for each
# Snakemake rule
logging.FileHandler(logfile),
logging.StreamHandler(),
]
})
}
)
logging.basicConfig(**kwargs)
@ -79,138 +88,199 @@ def load_network(import_name=None, custom_components=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()})
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_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)
return pypsa.Network(
import_name=import_name,
override_components=override_components,
override_component_attrs=override_component_attrs,
)
def pdbcast(v, h):
return pd.DataFrame(v.values.reshape((-1, 1)) * h.values,
index=v.index, columns=h.index)
return pd.DataFrame(
v.values.reshape((-1, 1)) * h.values, index=v.index, columns=h.index
)
def load_network_for_plots(fn, tech_costs, config, combine_hydro_ps=True):
import pypsa
from add_electricity import update_transmission_costs, load_costs
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.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']
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'
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.
costs = load_costs(Nyears, tech_costs, config['costs'], config['electricity'])
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):
# if extendable carriers (solar/onwind/...) have capacity >= 0,
# e.g. existing assets from the OPSD project are included to the network,
# the installed capacity might exceed the expansion limit.
# Hence, we update the assumptions.
n.generators.p_nom_max = n.generators[['p_nom_min', 'p_nom_max']].max(1)
n.generators.p_nom_max = n.generators[["p_nom_min", "p_nom_max"]].max(1)
def aggregate_p_nom(n):
return pd.concat([
n.generators.groupby("carrier").p_nom_opt.sum(),
n.storage_units.groupby("carrier").p_nom_opt.sum(),
n.links.groupby("carrier").p_nom_opt.sum(),
n.loads_t.p.groupby(n.loads.carrier,axis=1).sum().mean()
])
return pd.concat(
[
n.generators.groupby("carrier").p_nom_opt.sum(),
n.storage_units.groupby("carrier").p_nom_opt.sum(),
n.links.groupby("carrier").p_nom_opt.sum(),
n.loads_t.p.groupby(n.loads.carrier, axis=1).sum().mean(),
]
)
def aggregate_p(n):
return pd.concat([
n.generators_t.p.sum().groupby(n.generators.carrier).sum(),
n.storage_units_t.p.sum().groupby(n.storage_units.carrier).sum(),
n.stores_t.p.sum().groupby(n.stores.carrier).sum(),
-n.loads_t.p.sum().groupby(n.loads.carrier).sum()
])
return pd.concat(
[
n.generators_t.p.sum().groupby(n.generators.carrier).sum(),
n.storage_units_t.p.sum().groupby(n.storage_units.carrier).sum(),
n.stores_t.p.sum().groupby(n.stores.carrier).sum(),
-n.loads_t.p.sum().groupby(n.loads.carrier).sum(),
]
)
def aggregate_e_nom(n):
return pd.concat([
(n.storage_units["p_nom_opt"]*n.storage_units["max_hours"]).groupby(n.storage_units["carrier"]).sum(),
n.stores["e_nom_opt"].groupby(n.stores.carrier).sum()
])
return pd.concat(
[
(n.storage_units["p_nom_opt"] * n.storage_units["max_hours"])
.groupby(n.storage_units["carrier"])
.sum(),
n.stores["e_nom_opt"].groupby(n.stores.carrier).sum(),
]
)
def aggregate_p_curtailed(n):
return pd.concat([
((n.generators_t.p_max_pu.sum().multiply(n.generators.p_nom_opt) - n.generators_t.p.sum())
.groupby(n.generators.carrier).sum()),
((n.storage_units_t.inflow.sum() - n.storage_units_t.p.sum())
.groupby(n.storage_units.carrier).sum())
])
return pd.concat(
[
(
(
n.generators_t.p_max_pu.sum().multiply(n.generators.p_nom_opt)
- n.generators_t.p.sum()
)
.groupby(n.generators.carrier)
.sum()
),
(
(n.storage_units_t.inflow.sum() - n.storage_units_t.p.sum())
.groupby(n.storage_units.carrier)
.sum()
),
]
)
def aggregate_costs(n, flatten=False, opts=None, existing_only=False):
components = dict(Link=("p_nom", "p0"),
Generator=("p_nom", "p"),
StorageUnit=("p_nom", "p"),
Store=("e_nom", "p"),
Line=("s_nom", None),
Transformer=("s_nom", None))
components = dict(
Link=("p_nom", "p0"),
Generator=("p_nom", "p"),
StorageUnit=("p_nom", "p"),
Store=("e_nom", "p"),
Line=("s_nom", None),
Transformer=("s_nom", None),
)
costs = {}
for c, (p_nom, p_attr) in zip(
n.iterate_components(components.keys(), skip_empty=False),
components.values()
n.iterate_components(components.keys(), skip_empty=False), components.values()
):
if c.df.empty: continue
if not existing_only: p_nom += "_opt"
costs[(c.list_name, 'capital')] = (c.df[p_nom] * c.df.capital_cost).groupby(c.df.carrier).sum()
if c.df.empty:
continue
if not existing_only:
p_nom += "_opt"
costs[(c.list_name, "capital")] = (
(c.df[p_nom] * c.df.capital_cost).groupby(c.df.carrier).sum()
)
if p_attr is not None:
p = c.pnl[p_attr].sum()
if c.name == 'StorageUnit':
if c.name == "StorageUnit":
p = p.loc[p > 0]
costs[(c.list_name, 'marginal')] = (p*c.df.marginal_cost).groupby(c.df.carrier).sum()
costs[(c.list_name, "marginal")] = (
(p * c.df.marginal_cost).groupby(c.df.carrier).sum()
)
costs = pd.concat(costs)
if flatten:
assert opts is not None
conv_techs = opts['conv_techs']
conv_techs = opts["conv_techs"]
costs = costs.reset_index(level=0, drop=True)
costs = costs['capital'].add(
costs['marginal'].rename({t: t + ' marginal' for t in conv_techs}),
fill_value=0.
costs = costs["capital"].add(
costs["marginal"].rename({t: t + " marginal" for t in conv_techs}),
fill_value=0.0,
)
return costs
def progress_retrieve(url, file):
import urllib
from progressbar import ProgressBar
pbar = ProgressBar(0, 100)
def dlProgress(count, blockSize, totalSize):
pbar.update( int(count * blockSize * 100 / totalSize) )
pbar.update(int(count * blockSize * 100 / totalSize))
urllib.request.urlretrieve(url, file, reporthook=dlProgress)
def get_aggregation_strategies(aggregation_strategies):
# default aggregation strategies that cannot be defined in .yaml format must be specified within
# the function, otherwise (when defaults are passed in the function's definition) they get lost
# when custom values are specified in the config.
import numpy as np
from pypsa.networkclustering import _make_consense
bus_strategies = dict(country=_make_consense("Bus", "country"))
bus_strategies.update(aggregation_strategies.get("buses", {}))
generator_strategies = {"build_year": lambda x: 0, "lifetime": lambda x: np.inf}
generator_strategies.update(aggregation_strategies.get("generators", {}))
return bus_strategies, generator_strategies
def mock_snakemake(rulename, **wildcards):
"""
This function is expected to be executed from the 'scripts'-directory of '
@ -227,20 +297,24 @@ def mock_snakemake(rulename, **wildcards):
keyword arguments fixing the wildcards. Only necessary if wildcards are
needed.
"""
import snakemake as sm
import os
import snakemake as sm
from packaging.version import Version, parse
from pypsa.descriptors import Dict
from snakemake.script import Snakemake
script_dir = Path(__file__).parent.resolve()
assert Path.cwd().resolve() == script_dir, \
f'mock_snakemake has to be run from the repository scripts directory {script_dir}'
assert (
Path.cwd().resolve() == script_dir
), f"mock_snakemake has to be run from the repository scripts directory {script_dir}"
os.chdir(script_dir.parent)
for p in sm.SNAKEFILE_CHOICES:
if os.path.exists(p):
snakefile = p
break
workflow = sm.Workflow(snakefile, overwrite_configfiles=[])
kwargs = dict(rerun_triggers=[]) if parse(sm.__version__) > Version("7.7.0") else {}
workflow = sm.Workflow(snakefile, overwrite_configfiles=[], **kwargs)
workflow.include(snakefile)
workflow.global_resources = {}
rule = workflow.get_rule(rulename)
@ -254,9 +328,18 @@ def mock_snakemake(rulename, **wildcards):
io[i] = os.path.abspath(io[i])
make_accessable(job.input, job.output, job.log)
snakemake = Snakemake(job.input, job.output, job.params, job.wildcards,
job.threads, job.resources, job.log,
job.dag.workflow.config, job.rule.name, None,)
snakemake = Snakemake(
job.input,
job.output,
job.params,
job.wildcards,
job.threads,
job.resources,
job.log,
job.dag.workflow.config,
job.rule.name,
None,
)
# create log and output dir if not existent
for path in list(snakemake.log) + list(snakemake.output):
Path(path).parent.mkdir(parents=True, exist_ok=True)

911
scripts/add_electricity.py
View File

File diff suppressed because it is too large Load Diff

240
scripts/add_extra_components.py
View File

@ -1,4 +1,5 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -13,7 +14,7 @@ Relevant Settings
costs:
year:
USD2013_to_EUR2013:
version:
dicountrate:
emission_prices:
@ -32,7 +33,7 @@ Relevant Settings
Inputs
------
- ``data/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``resources/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
Outputs
-------
@ -50,14 +51,16 @@ The rule :mod:`add_extra_components` attaches additional extendable components t
- ``Stores`` of carrier 'H2' and/or 'battery' in combination with ``Links``. If this option is chosen, the script adds extra buses with corresponding carrier where energy ``Stores`` are attached and which are connected to the corresponding power buses via two links, one each for charging and discharging. This leads to three investment variables for the energy capacity, charging and discharging capacity of the storage unit.
"""
import logging
from _helpers import configure_logging
import pypsa
import pandas as pd
import numpy as np
from add_electricity import (load_costs, add_nice_carrier_names,
_add_missing_carriers_from_costs)
import pandas as pd
import pypsa
from _helpers import configure_logging
from add_electricity import (
_add_missing_carriers_from_costs,
add_nice_carrier_names,
load_costs,
)
idx = pd.IndexSlice
@ -65,8 +68,8 @@ logger = logging.getLogger(__name__)
def attach_storageunits(n, costs, elec_opts):
carriers = elec_opts['extendable_carriers']['StorageUnit']
max_hours = elec_opts['max_hours']
carriers = elec_opts["extendable_carriers"]["StorageUnit"]
max_hours = elec_opts["max_hours"]
_add_missing_carriers_from_costs(n, costs, carriers)
@ -76,128 +79,168 @@ def attach_storageunits(n, costs, elec_opts):
lookup_dispatch = {"H2": "fuel cell", "battery": "battery inverter"}
for carrier in carriers:
n.madd("StorageUnit", buses_i, ' ' + carrier,
bus=buses_i,
carrier=carrier,
p_nom_extendable=True,
capital_cost=costs.at[carrier, 'capital_cost'],
marginal_cost=costs.at[carrier, 'marginal_cost'],
efficiency_store=costs.at[lookup_store[carrier], 'efficiency'],
efficiency_dispatch=costs.at[lookup_dispatch[carrier], 'efficiency'],
max_hours=max_hours[carrier],
cyclic_state_of_charge=True)
roundtrip_correction = 0.5 if carrier == "battery" else 1
n.madd(
"StorageUnit",
buses_i,
" " + carrier,
bus=buses_i,
carrier=carrier,
p_nom_extendable=True,
capital_cost=costs.at[carrier, "capital_cost"],
marginal_cost=costs.at[carrier, "marginal_cost"],
efficiency_store=costs.at[lookup_store[carrier], "efficiency"]
** roundtrip_correction,
efficiency_dispatch=costs.at[lookup_dispatch[carrier], "efficiency"]
** roundtrip_correction,
max_hours=max_hours[carrier],
cyclic_state_of_charge=True,
)
def attach_stores(n, costs, elec_opts):
carriers = elec_opts['extendable_carriers']['Store']
carriers = elec_opts["extendable_carriers"]["Store"]
_add_missing_carriers_from_costs(n, costs, carriers)
buses_i = n.buses.index
bus_sub_dict = {k: n.buses[k].values for k in ['x', 'y', 'country']}
bus_sub_dict = {k: n.buses[k].values for k in ["x", "y", "country"]}
if 'H2' in carriers:
if "H2" in carriers:
h2_buses_i = n.madd("Bus", buses_i + " H2", carrier="H2", **bus_sub_dict)
n.madd("Store", h2_buses_i,
bus=h2_buses_i,
carrier='H2',
e_nom_extendable=True,
e_cyclic=True,
capital_cost=costs.at["hydrogen storage", "capital_cost"])
n.madd(
"Store",
h2_buses_i,
bus=h2_buses_i,
carrier="H2",
e_nom_extendable=True,
e_cyclic=True,
capital_cost=costs.at["hydrogen storage underground", "capital_cost"],
)
n.madd("Link", h2_buses_i + " Electrolysis",
bus0=buses_i,
bus1=h2_buses_i,
carrier='H2 electrolysis',
p_nom_extendable=True,
efficiency=costs.at["electrolysis", "efficiency"],
capital_cost=costs.at["electrolysis", "capital_cost"],
marginal_cost=costs.at["electrolysis", "marginal_cost"])
n.madd(
"Link",
h2_buses_i + " Electrolysis",
bus0=buses_i,
bus1=h2_buses_i,
carrier="H2 electrolysis",
p_nom_extendable=True,
efficiency=costs.at["electrolysis", "efficiency"],
capital_cost=costs.at["electrolysis", "capital_cost"],
marginal_cost=costs.at["electrolysis", "marginal_cost"],
)
n.madd("Link", h2_buses_i + " Fuel Cell",
bus0=h2_buses_i,
bus1=buses_i,
carrier='H2 fuel cell',
p_nom_extendable=True,
efficiency=costs.at["fuel cell", "efficiency"],
#NB: fixed cost is per MWel
capital_cost=costs.at["fuel cell", "capital_cost"] * costs.at["fuel cell", "efficiency"],
marginal_cost=costs.at["fuel cell", "marginal_cost"])
n.madd(
"Link",
h2_buses_i + " Fuel Cell",
bus0=h2_buses_i,
bus1=buses_i,
carrier="H2 fuel cell",
p_nom_extendable=True,
efficiency=costs.at["fuel cell", "efficiency"],
# NB: fixed cost is per MWel
capital_cost=costs.at["fuel cell", "capital_cost"]
* costs.at["fuel cell", "efficiency"],
marginal_cost=costs.at["fuel cell", "marginal_cost"],
)
if 'battery' in carriers:
b_buses_i = n.madd("Bus", buses_i + " battery", carrier="battery", **bus_sub_dict)
if "battery" in carriers:
b_buses_i = n.madd(
"Bus", buses_i + " battery", carrier="battery", **bus_sub_dict
)
n.madd("Store", b_buses_i,
bus=b_buses_i,
carrier='battery',
e_cyclic=True,
e_nom_extendable=True,
capital_cost=costs.at['battery storage', 'capital_cost'],
marginal_cost=costs.at["battery", "marginal_cost"])
n.madd(
"Store",
b_buses_i,
bus=b_buses_i,
carrier="battery",
e_cyclic=True,
e_nom_extendable=True,
capital_cost=costs.at["battery storage", "capital_cost"],
marginal_cost=costs.at["battery", "marginal_cost"],
)
n.madd("Link", b_buses_i + " charger",
bus0=buses_i,
bus1=b_buses_i,
carrier='battery charger',
efficiency=costs.at['battery inverter', 'efficiency'],
capital_cost=costs.at['battery inverter', 'capital_cost'],
p_nom_extendable=True,
marginal_cost=costs.at["battery inverter", "marginal_cost"])
n.madd(
"Link",
b_buses_i + " charger",
bus0=buses_i,
bus1=b_buses_i,
carrier="battery charger",
# the efficiencies are "round trip efficiencies"
efficiency=costs.at["battery inverter", "efficiency"] ** 0.5,
capital_cost=costs.at["battery inverter", "capital_cost"],
p_nom_extendable=True,
marginal_cost=costs.at["battery inverter", "marginal_cost"],
)
n.madd("Link", b_buses_i + " discharger",
bus0=b_buses_i,
bus1=buses_i,
carrier='battery discharger',
efficiency=costs.at['battery inverter','efficiency'],
p_nom_extendable=True,
marginal_cost=costs.at["battery inverter", "marginal_cost"])
n.madd(
"Link",
b_buses_i + " discharger",
bus0=b_buses_i,
bus1=buses_i,
carrier="battery discharger",
efficiency=costs.at["battery inverter", "efficiency"] ** 0.5,
p_nom_extendable=True,
marginal_cost=costs.at["battery inverter", "marginal_cost"],
)
def attach_hydrogen_pipelines(n, costs, elec_opts):
ext_carriers = elec_opts['extendable_carriers']
as_stores = ext_carriers.get('Store', [])
ext_carriers = elec_opts["extendable_carriers"]
as_stores = ext_carriers.get("Store", [])
if 'H2 pipeline' not in ext_carriers.get('Link',[]): return
if "H2 pipeline" not in ext_carriers.get("Link", []):
return
assert 'H2' in as_stores, ("Attaching hydrogen pipelines requires hydrogen "
"storage to be modelled as Store-Link-Bus combination. See "
"`config.yaml` at `electricity: extendable_carriers: Store:`.")
assert "H2" in as_stores, (
"Attaching hydrogen pipelines requires hydrogen "
"storage to be modelled as Store-Link-Bus combination. See "
"`config.yaml` at `electricity: extendable_carriers: Store:`."
)
# determine bus pairs
attrs = ["bus0","bus1","length"]
candidates = pd.concat([n.lines[attrs], n.links.query('carrier=="DC"')[attrs]])\
.reset_index(drop=True)
attrs = ["bus0", "bus1", "length"]
candidates = pd.concat(
[n.lines[attrs], n.links.query('carrier=="DC"')[attrs]]
).reset_index(drop=True)
# remove bus pair duplicates regardless of order of bus0 and bus1
h2_links = candidates[~pd.DataFrame(np.sort(candidates[['bus0', 'bus1']])).duplicated()]
h2_links = candidates[
~pd.DataFrame(np.sort(candidates[["bus0", "bus1"]])).duplicated()
]
h2_links.index = h2_links.apply(lambda c: f"H2 pipeline {c.bus0}-{c.bus1}", axis=1)
# add pipelines
n.madd("Link",
h2_links.index,
bus0=h2_links.bus0.values + " H2",
bus1=h2_links.bus1.values + " H2",
p_min_pu=-1,
p_nom_extendable=True,
length=h2_links.length.values,
capital_cost=costs.at['H2 pipeline','capital_cost']*h2_links.length,
efficiency=costs.at['H2 pipeline','efficiency'],
carrier="H2 pipeline")
n.madd(
"Link",
h2_links.index,
bus0=h2_links.bus0.values + " H2",
bus1=h2_links.bus1.values + " H2",
p_min_pu=-1,
p_nom_extendable=True,
length=h2_links.length.values,
capital_cost=costs.at["H2 pipeline", "capital_cost"] * h2_links.length,
efficiency=costs.at["H2 pipeline", "efficiency"],
carrier="H2 pipeline",
)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('add_extra_components', network='elec',
simpl='', clusters=5)
snakemake = mock_snakemake("add_extra_components", simpl="", clusters=5)
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
elec_config = snakemake.config['electricity']
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], elec_config, Nyears)
elec_config = snakemake.config["electricity"]
Nyears = n.snapshot_weightings.objective.sum() / 8760.0
costs = load_costs(
snakemake.input.tech_costs, snakemake.config["costs"], elec_config, Nyears
)
attach_storageunits(n, costs, elec_config)
attach_stores(n, costs, elec_config)
@ -205,4 +248,5 @@ if __name__ == "__main__":
add_nice_carrier_names(n, snakemake.config)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

550
scripts/base_network.py
View File

@ -1,10 +1,14 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Creates the network topology from a `ENTSO-E map extract <https://github.com/PyPSA/GridKit/tree/master/entsoe>`_ (March 2022) as a PyPSA network.
Creates the network topology from a `ENTSO-E map extract.
<https://github.com/PyPSA/GridKit/tree/master/entsoe>`_ (March 2022) as a PyPSA
network.
Relevant Settings
-----------------
@ -59,25 +63,24 @@ Outputs
Description
-----------
"""
import logging
from _helpers import configure_logging
import pypsa
import yaml
import pandas as pd
import geopandas as gpd
import numpy as np
import networkx as nx
from scipy import spatial
from scipy.sparse import csgraph
from itertools import product
from shapely.geometry import Point, LineString
import shapely, shapely.prepared, shapely.wkt
import geopandas as gpd
import networkx as nx
import numpy as np
import pandas as pd
import pypsa
import shapely
import shapely.prepared
import shapely.wkt
import yaml
from _helpers import configure_logging
from scipy import spatial
from scipy.sparse import csgraph
from shapely.geometry import LineString, Point
logger = logging.getLogger(__name__)
@ -97,48 +100,73 @@ def _get_country(df):
def _find_closest_links(links, new_links, distance_upper_bound=1.5):
treecoords = np.asarray([np.asarray(shapely.wkt.loads(s).coords)[[0, -1]].flatten()
for s in links.geometry])
querycoords = np.vstack([new_links[['x1', 'y1', 'x2', 'y2']],
new_links[['x2', 'y2', 'x1', 'y1']]])
treecoords = np.asarray(
[
np.asarray(shapely.wkt.loads(s).coords)[[0, -1]].flatten()
for s in links.geometry
]
)
querycoords = np.vstack(
[new_links[["x1", "y1", "x2", "y2"]], new_links[["x2", "y2", "x1", "y1"]]]
)
tree = spatial.KDTree(treecoords)
dist, ind = tree.query(querycoords, distance_upper_bound=distance_upper_bound)
found_b = ind < len(links)
found_i = np.arange(len(new_links)*2)[found_b] % len(new_links)
return pd.DataFrame(dict(D=dist[found_b],
i=links.index[ind[found_b] % len(links)]),
index=new_links.index[found_i]).sort_values(by='D')\
[lambda ds: ~ds.index.duplicated(keep='first')]\
.sort_index()['i']
found_i = np.arange(len(new_links) * 2)[found_b] % len(new_links)
return (
pd.DataFrame(
dict(D=dist[found_b], i=links.index[ind[found_b] % len(links)]),
index=new_links.index[found_i],
)
.sort_values(by="D")[lambda ds: ~ds.index.duplicated(keep="first")]
.sort_index()["i"]
)
def _load_buses_from_eg(eg_buses, europe_shape, config_elec):
buses = (pd.read_csv(eg_buses, quotechar="'",
true_values=['t'], false_values=['f'],
dtype=dict(bus_id="str"))
.set_index("bus_id")
.drop(['station_id'], axis=1)
.rename(columns=dict(voltage='v_nom')))
buses = (
pd.read_csv(
eg_buses,
quotechar="'",
true_values=["t"],
false_values=["f"],
dtype=dict(bus_id="str"),
)
.set_index("bus_id")
.drop(["station_id"], axis=1)
.rename(columns=dict(voltage="v_nom"))
)
buses['carrier'] = buses.pop('dc').map({True: 'DC', False: 'AC'})
buses['under_construction'] = buses['under_construction'].fillna(False).astype(bool)
buses["carrier"] = buses.pop("dc").map({True: "DC", False: "AC"})
buses["under_construction"] = buses["under_construction"].fillna(False).astype(bool)
# remove all buses outside of all countries including exclusive economic zones (offshore)
europe_shape = gpd.read_file(europe_shape).loc[0, 'geometry']
europe_shape = gpd.read_file(europe_shape).loc[0, "geometry"]
europe_shape_prepped = shapely.prepared.prep(europe_shape)
buses_in_europe_b = buses[['x', 'y']].apply(lambda p: europe_shape_prepped.contains(Point(p)), axis=1)
buses_in_europe_b = buses[["x", "y"]].apply(
lambda p: europe_shape_prepped.contains(Point(p)), axis=1
)
buses_with_v_nom_to_keep_b = buses.v_nom.isin(config_elec['voltages']) | buses.v_nom.isnull()
logger.info("Removing buses with voltages {}".format(pd.Index(buses.v_nom.unique()).dropna().difference(config_elec['voltages'])))
buses_with_v_nom_to_keep_b = (
buses.v_nom.isin(config_elec["voltages"]) | buses.v_nom.isnull()
)
logger.info(
"Removing buses with voltages {}".format(
pd.Index(buses.v_nom.unique()).dropna().difference(config_elec["voltages"])
)
)
return pd.DataFrame(buses.loc[buses_in_europe_b & buses_with_v_nom_to_keep_b])
def _load_transformers_from_eg(buses, eg_transformers):
transformers = (pd.read_csv(eg_transformers, quotechar="'",
true_values=['t'], false_values=['f'],
dtype=dict(transformer_id='str', bus0='str', bus1='str'))
.set_index('transformer_id'))
transformers = pd.read_csv(
eg_transformers,
quotechar="'",
true_values=["t"],
false_values=["f"],
dtype=dict(transformer_id="str", bus0="str", bus1="str"),
).set_index("transformer_id")
transformers = _remove_dangling_branches(transformers, buses)
@ -146,33 +174,40 @@ def _load_transformers_from_eg(buses, eg_transformers):
def _load_converters_from_eg(buses, eg_converters):
converters = (pd.read_csv(eg_converters, quotechar="'",
true_values=['t'], false_values=['f'],
dtype=dict(converter_id='str', bus0='str', bus1='str'))
.set_index('converter_id'))
converters = pd.read_csv(
eg_converters,
quotechar="'",
true_values=["t"],
false_values=["f"],
dtype=dict(converter_id="str", bus0="str", bus1="str"),
).set_index("converter_id")
converters = _remove_dangling_branches(converters, buses)
converters['carrier'] = 'B2B'
converters["carrier"] = "B2B"
return converters
def _load_links_from_eg(buses, eg_links):
links = (pd.read_csv(eg_links, quotechar="'", true_values=['t'], false_values=['f'],
dtype=dict(link_id='str', bus0='str', bus1='str', under_construction="bool"))
.set_index('link_id'))
links = pd.read_csv(
eg_links,
quotechar="'",
true_values=["t"],
false_values=["f"],
dtype=dict(link_id="str", bus0="str", bus1="str", under_construction="bool"),
).set_index("link_id")
links['length'] /= 1e3
links["length"] /= 1e3
# Skagerrak Link is connected to 132kV bus which is removed in _load_buses_from_eg.
# Connect to neighboring 380kV bus
links.loc[links.bus1=='6396', 'bus1'] = '6398'
links.loc[links.bus1 == "6396", "bus1"] = "6398"
links = _remove_dangling_branches(links, buses)
# Add DC line parameters
links['carrier'] = 'DC'
links["carrier"] = "DC"
return links
@ -181,15 +216,21 @@ def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
links_tyndp = pd.read_csv(links_tyndp)
# remove all links from list which lie outside all of the desired countries
europe_shape = gpd.read_file(europe_shape).loc[0, 'geometry']
europe_shape = gpd.read_file(europe_shape).loc[0, "geometry"]
europe_shape_prepped = shapely.prepared.prep(europe_shape)
x1y1_in_europe_b = links_tyndp[['x1', 'y1']].apply(lambda p: europe_shape_prepped.contains(Point(p)), axis=1)
x2y2_in_europe_b = links_tyndp[['x2', 'y2']].apply(lambda p: europe_shape_prepped.contains(Point(p)), axis=1)
x1y1_in_europe_b = links_tyndp[["x1", "y1"]].apply(
lambda p: europe_shape_prepped.contains(Point(p)), axis=1
)
x2y2_in_europe_b = links_tyndp[["x2", "y2"]].apply(
lambda p: europe_shape_prepped.contains(Point(p)), axis=1
)
is_within_covered_countries_b = x1y1_in_europe_b & x2y2_in_europe_b
if not is_within_covered_countries_b.all():
logger.info("TYNDP links outside of the covered area (skipping): " +
", ".join(links_tyndp.loc[~ is_within_covered_countries_b, "Name"]))
logger.info(
"TYNDP links outside of the covered area (skipping): "
+ ", ".join(links_tyndp.loc[~is_within_covered_countries_b, "Name"])
)
links_tyndp = links_tyndp.loc[is_within_covered_countries_b]
if links_tyndp.empty:
@ -197,25 +238,32 @@ def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
has_replaces_b = links_tyndp.replaces.notnull()
oids = dict(Bus=_get_oid(buses), Link=_get_oid(links))
keep_b = dict(Bus=pd.Series(True, index=buses.index),
Link=pd.Series(True, index=links.index))
for reps in links_tyndp.loc[has_replaces_b, 'replaces']:
for comps in reps.split(':'):
oids_to_remove = comps.split('.')
keep_b = dict(
Bus=pd.Series(True, index=buses.index), Link=pd.Series(True, index=links.index)
)
for reps in links_tyndp.loc[has_replaces_b, "replaces"]:
for comps in reps.split(":"):
oids_to_remove = comps.split(".")
c = oids_to_remove.pop(0)
keep_b[c] &= ~oids[c].isin(oids_to_remove)
buses = buses.loc[keep_b['Bus']]
links = links.loc[keep_b['Link']]
buses = buses.loc[keep_b["Bus"]]
links = links.loc[keep_b["Link"]]
links_tyndp["j"] = _find_closest_links(links, links_tyndp, distance_upper_bound=0.20)
links_tyndp["j"] = _find_closest_links(
links, links_tyndp, distance_upper_bound=0.20
)
# Corresponds approximately to 20km tolerances
if links_tyndp["j"].notnull().any():
logger.info("TYNDP links already in the dataset (skipping): " + ", ".join(links_tyndp.loc[links_tyndp["j"].notnull(), "Name"]))
logger.info(
"TYNDP links already in the dataset (skipping): "
+ ", ".join(links_tyndp.loc[links_tyndp["j"].notnull(), "Name"])
)
links_tyndp = links_tyndp.loc[links_tyndp["j"].isnull()]
if links_tyndp.empty: return buses, links
if links_tyndp.empty:
return buses, links
tree = spatial.KDTree(buses[['x', 'y']])
tree = spatial.KDTree(buses[["x", "y"]])
_, ind0 = tree.query(links_tyndp[["x1", "y1"]])
ind0_b = ind0 < len(buses)
links_tyndp.loc[ind0_b, "bus0"] = buses.index[ind0[ind0_b]]
@ -224,24 +272,42 @@ def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
ind1_b = ind1 < len(buses)
links_tyndp.loc[ind1_b, "bus1"] = buses.index[ind1[ind1_b]]
links_tyndp_located_b = links_tyndp["bus0"].notnull() & links_tyndp["bus1"].notnull()
links_tyndp_located_b = (
links_tyndp["bus0"].notnull() & links_tyndp["bus1"].notnull()
)
if not links_tyndp_located_b.all():
logger.warning("Did not find connected buses for TYNDP links (skipping): " + ", ".join(links_tyndp.loc[~links_tyndp_located_b, "Name"]))
logger.warning(
"Did not find connected buses for TYNDP links (skipping): "
+ ", ".join(links_tyndp.loc[~links_tyndp_located_b, "Name"])
)
links_tyndp = links_tyndp.loc[links_tyndp_located_b]
logger.info("Adding the following TYNDP links: " + ", ".join(links_tyndp["Name"]))
links_tyndp = links_tyndp[["bus0", "bus1"]].assign(
carrier='DC',
carrier="DC",
p_nom=links_tyndp["Power (MW)"],
length=links_tyndp["Length (given) (km)"].fillna(links_tyndp["Length (distance*1.2) (km)"]),
length=links_tyndp["Length (given) (km)"].fillna(
links_tyndp["Length (distance*1.2) (km)"]
),
under_construction=True,
underground=False,
geometry=(links_tyndp[["x1", "y1", "x2", "y2"]]
.apply(lambda s: str(LineString([[s.x1, s.y1], [s.x2, s.y2]])), axis=1)),
tags=('"name"=>"' + links_tyndp["Name"] + '", ' +
'"ref"=>"' + links_tyndp["Ref"] + '", ' +
'"status"=>"' + links_tyndp["status"] + '"')
geometry=(
links_tyndp[["x1", "y1", "x2", "y2"]].apply(
lambda s: str(LineString([[s.x1, s.y1], [s.x2, s.y2]])), axis=1
)
),
tags=(
'"name"=>"'
+ links_tyndp["Name"]
+ '", '
+ '"ref"=>"'
+ links_tyndp["Ref"]
+ '", '
+ '"status"=>"'
+ links_tyndp["status"]
+ '"'
),
)
links_tyndp.index = "T" + links_tyndp.index.astype(str)
@ -252,13 +318,25 @@ def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
def _load_lines_from_eg(buses, eg_lines):
lines = (pd.read_csv(eg_lines, quotechar="'", true_values=['t'], false_values=['f'],
dtype=dict(line_id='str', bus0='str', bus1='str',
underground="bool", under_construction="bool"))
.set_index('line_id')
.rename(columns=dict(voltage='v_nom', circuits='num_parallel')))
lines = (
pd.read_csv(
eg_lines,
quotechar="'",
true_values=["t"],
false_values=["f"],
dtype=dict(
line_id="str",
bus0="str",
bus1="str",
underground="bool",
under_construction="bool",
),
)
.set_index("line_id")
.rename(columns=dict(voltage="v_nom", circuits="num_parallel"))
)
lines['length'] /= 1e3
lines["length"] /= 1e3
lines = _remove_dangling_branches(lines, buses)
@ -269,18 +347,20 @@ def _apply_parameter_corrections(n, parameter_corrections):
with open(parameter_corrections) as f:
corrections = yaml.safe_load(f)
if corrections is None: return
if corrections is None:
return
for component, attrs in corrections.items():
df = n.df(component)
oid = _get_oid(df)
if attrs is None: continue
if attrs is None:
continue
for attr, repls in attrs.items():
for i, r in repls.items():
if i == 'oid':
if i == "oid":
r = oid.map(repls["oid"]).dropna()
elif i == 'index':
elif i == "index":
r = pd.Series(repls["index"])
else:
raise NotImplementedError()
@ -289,78 +369,87 @@ def _apply_parameter_corrections(n, parameter_corrections):
def _set_electrical_parameters_lines(lines, config):
v_noms = config['electricity']['voltages']
linetypes = config['lines']['types']
v_noms = config["electricity"]["voltages"]
linetypes = config["lines"]["types"]
for v_nom in v_noms:
lines.loc[lines["v_nom"] == v_nom, 'type'] = linetypes[v_nom]
lines.loc[lines["v_nom"] == v_nom, "type"] = linetypes[v_nom]
lines['s_max_pu'] = config['lines']['s_max_pu']
lines["s_max_pu"] = config["lines"]["s_max_pu"]
return lines
def _set_lines_s_nom_from_linetypes(n):
n.lines['s_nom'] = (
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
n.lines['v_nom'] * n.lines.num_parallel
n.lines["s_nom"] = (
np.sqrt(3)
* n.lines["type"].map(n.line_types.i_nom)
* n.lines["v_nom"]
* n.lines.num_parallel
)
def _set_electrical_parameters_links(links, config, links_p_nom):
if links.empty: return links
if links.empty:
return links
p_max_pu = config['links'].get('p_max_pu', 1.)
links['p_max_pu'] = p_max_pu
links['p_min_pu'] = -p_max_pu
p_max_pu = config["links"].get("p_max_pu", 1.0)
links["p_max_pu"] = p_max_pu
links["p_min_pu"] = -p_max_pu
links_p_nom = pd.read_csv(links_p_nom)
# filter links that are not in operation anymore
removed_b = links_p_nom.Remarks.str.contains('Shut down|Replaced', na=False)
removed_b = links_p_nom.Remarks.str.contains("Shut down|Replaced", na=False)
links_p_nom = links_p_nom[~removed_b]
# find closest link for all links in links_p_nom
links_p_nom['j'] = _find_closest_links(links, links_p_nom)
links_p_nom["j"] = _find_closest_links(links, links_p_nom)
links_p_nom = links_p_nom.groupby(['j'],as_index=False).agg({'Power (MW)': 'sum'})
links_p_nom = links_p_nom.groupby(["j"], as_index=False).agg({"Power (MW)": "sum"})
p_nom = links_p_nom.dropna(subset=["j"]).set_index("j")["Power (MW)"]
# Don't update p_nom if it's already set
p_nom_unset = p_nom.drop(links.index[links.p_nom.notnull()], errors='ignore') if "p_nom" in links else p_nom
p_nom_unset = (
p_nom.drop(links.index[links.p_nom.notnull()], errors="ignore")
if "p_nom" in links
else p_nom
)
links.loc[p_nom_unset.index, "p_nom"] = p_nom_unset
return links
def _set_electrical_parameters_converters(converters, config):
p_max_pu = config['links'].get('p_max_pu', 1.)
converters['p_max_pu'] = p_max_pu
converters['p_min_pu'] = -p_max_pu
p_max_pu = config["links"].get("p_max_pu", 1.0)
converters["p_max_pu"] = p_max_pu
converters["p_min_pu"] = -p_max_pu
converters['p_nom'] = 2000
converters["p_nom"] = 2000
# Converters are combined with links
converters['under_construction'] = False
converters['underground'] = False
converters["under_construction"] = False
converters["underground"] = False
return converters
def _set_electrical_parameters_transformers(transformers, config):
config = config['transformers']
config = config["transformers"]
## Add transformer parameters
transformers["x"] = config.get('x', 0.1)
transformers["s_nom"] = config.get('s_nom', 2000)
transformers['type'] = config.get('type', '')
transformers["x"] = config.get("x", 0.1)
transformers["s_nom"] = config.get("s_nom", 2000)
transformers["type"] = config.get("type", "")
return transformers
def _remove_dangling_branches(branches, buses):
return pd.DataFrame(branches.loc[branches.bus0.isin(buses.index) & branches.bus1.isin(buses.index)])
return pd.DataFrame(
branches.loc[branches.bus0.isin(buses.index) & branches.bus1.isin(buses.index)]
)
def _remove_unconnected_components(network):
@ -370,44 +459,62 @@ def _remove_unconnected_components(network):
component_sizes = component.value_counts()
components_to_remove = component_sizes.iloc[1:]
logger.info("Removing {} unconnected network components with less than {} buses. In total {} buses."
.format(len(components_to_remove), components_to_remove.max(), components_to_remove.sum()))
logger.info(
"Removing {} unconnected network components with less than {} buses. In total {} buses.".format(
len(components_to_remove),
components_to_remove.max(),
components_to_remove.sum(),
)
)
return network[component == component_sizes.index[0]]
def _set_countries_and_substations(n, config, country_shapes, offshore_shapes):
buses = n.buses
def buses_in_shape(shape):
shape = shapely.prepared.prep(shape)
return pd.Series(
np.fromiter((shape.contains(Point(x, y))
for x, y in buses.loc[:,["x", "y"]].values),
dtype=bool, count=len(buses)),
index=buses.index
np.fromiter(
(
shape.contains(Point(x, y))
for x, y in buses.loc[:, ["x", "y"]].values
),
dtype=bool,
count=len(buses),
),
index=buses.index,
)
countries = config['countries']
country_shapes = gpd.read_file(country_shapes).set_index('name')['geometry']
offshore_shapes = gpd.read_file(offshore_shapes).set_index('name')['geometry']
substation_b = buses['symbol'].str.contains('substation|converter station', case=False)
countries = config["countries"]
country_shapes = gpd.read_file(country_shapes).set_index("name")["geometry"]
# reindexing necessary for supporting empty geo-dataframes
offshore_shapes = gpd.read_file(offshore_shapes)
offshore_shapes = offshore_shapes.reindex(columns=["name", "geometry"]).set_index(
"name"
)["geometry"]
substation_b = buses["symbol"].str.contains(
"substation|converter station", case=False
)
def prefer_voltage(x, which):
index = x.index
if len(index) == 1:
return pd.Series(index, index)
key = (x.index[0]
if x['v_nom'].isnull().all()
else getattr(x['v_nom'], 'idx' + which)())
key = (
x.index[0]
if x["v_nom"].isnull().all()
else getattr(x["v_nom"], "idx" + which)()
)
return pd.Series(key, index)
gb = buses.loc[substation_b].groupby(['x', 'y'], as_index=False,
group_keys=False, sort=False)
bus_map_low = gb.apply(prefer_voltage, 'min')
gb = buses.loc[substation_b].groupby(
["x", "y"], as_index=False, group_keys=False, sort=False
)
bus_map_low = gb.apply(prefer_voltage, "min")
lv_b = (bus_map_low == bus_map_low.index).reindex(buses.index, fill_value=False)
bus_map_high = gb.apply(prefer_voltage, 'max')
bus_map_high = gb.apply(prefer_voltage, "max")
hv_b = (bus_map_high == bus_map_high.index).reindex(buses.index, fill_value=False)
onshore_b = pd.Series(False, buses.index)
@ -418,47 +525,66 @@ def _set_countries_and_substations(n, config, country_shapes, offshore_shapes):
onshore_country_b = buses_in_shape(onshore_shape)
onshore_b |= onshore_country_b
buses.loc[onshore_country_b, 'country'] = country
buses.loc[onshore_country_b, "country"] = country
if country not in offshore_shapes.index: continue
if country not in offshore_shapes.index:
continue
offshore_country_b = buses_in_shape(offshore_shapes[country])
offshore_b |= offshore_country_b
buses.loc[offshore_country_b, 'country'] = country
buses.loc[offshore_country_b, "country"] = country
# Only accept buses as low-voltage substations (where load is attached), if
# they have at least one connection which is not under_construction
has_connections_b = pd.Series(False, index=buses.index)
for b, df in product(('bus0', 'bus1'), (n.lines, n.links)):
has_connections_b |= ~ df.groupby(b).under_construction.min()
for b, df in product(("bus0", "bus1"), (n.lines, n.links)):
has_connections_b |= ~df.groupby(b).under_construction.min()
buses['substation_lv'] = lv_b & onshore_b & (~ buses['under_construction']) & has_connections_b
buses['substation_off'] = (offshore_b | (hv_b & onshore_b)) & (~ buses['under_construction'])
buses["substation_lv"] = (
lv_b & onshore_b & (~buses["under_construction"]) & has_connections_b
)
buses["substation_off"] = (offshore_b | (hv_b & onshore_b)) & (
~buses["under_construction"]
)
c_nan_b = buses.country.isnull()
if c_nan_b.sum() > 0:
c_tag = _get_country(buses.loc[c_nan_b])
c_tag.loc[~c_tag.isin(countries)] = np.nan
n.buses.loc[c_nan_b, 'country'] = c_tag
n.buses.loc[c_nan_b, "country"] = c_tag
c_tag_nan_b = n.buses.country.isnull()
# Nearest country in path length defines country of still homeless buses
# Work-around until commit 705119 lands in pypsa release
n.transformers['length'] = 0.
graph = n.graph(weight='length')
n.transformers.drop('length', axis=1, inplace=True)
n.transformers["length"] = 0.0
graph = n.graph(weight="length")
n.transformers.drop("length", axis=1, inplace=True)
for b in n.buses.index[c_tag_nan_b]:
df = (pd.DataFrame(dict(pathlength=nx.single_source_dijkstra_path_length(graph, b, cutoff=200)))
.join(n.buses.country).dropna())
assert not df.empty, "No buses with defined country within 200km of bus `{}`".format(b)
n.buses.at[b, 'country'] = df.loc[df.pathlength.idxmin(), 'country']
df = (
pd.DataFrame(
dict(
pathlength=nx.single_source_dijkstra_path_length(
graph, b, cutoff=200
)
)
)
.join(n.buses.country)
.dropna()
)
assert (
not df.empty
), "No buses with defined country within 200km of bus `{}`".format(b)
n.buses.at[b, "country"] = df.loc[df.pathlength.idxmin(), "country"]
logger.warning("{} buses are not in any country or offshore shape,"
" {} have been assigned from the tag of the entsoe map,"
" the rest from the next bus in terms of pathlength."
.format(c_nan_b.sum(), c_nan_b.sum() - c_tag_nan_b.sum()))
logger.warning(
"{} buses are not in any country or offshore shape,"
" {} have been assigned from the tag of the entsoe map,"
" the rest from the next bus in terms of pathlength.".format(
c_nan_b.sum(), c_nan_b.sum() - c_tag_nan_b.sum()
)
)
return buses
@ -467,11 +593,13 @@ def _replace_b2b_converter_at_country_border_by_link(n):
# Affects only the B2B converter in Lithuania at the Polish border at the moment
buscntry = n.buses.country
linkcntry = n.links.bus0.map(buscntry)
converters_i = n.links.index[(n.links.carrier == 'B2B') & (linkcntry == n.links.bus1.map(buscntry))]
converters_i = n.links.index[
(n.links.carrier == "B2B") & (linkcntry == n.links.bus1.map(buscntry))
]
def findforeignbus(G, i):
cntry = linkcntry.at[i]
for busattr in ('bus0', 'bus1'):
for busattr in ("bus0", "bus1"):
b0 = n.links.at[i, busattr]
for b1 in G[b0]:
if buscntry[b1] != cntry:
@ -484,67 +612,93 @@ def _replace_b2b_converter_at_country_border_by_link(n):
if busattr is not None:
comp, line = next(iter(G[b0][b1]))
if comp != "Line":
logger.warning("Unable to replace B2B `{}` expected a Line, but found a {}"
.format(i, comp))
logger.warning(
"Unable to replace B2B `{}` expected a Line, but found a {}".format(
i, comp
)
)
continue
n.links.at[i, busattr] = b1
n.links.at[i, 'p_nom'] = min(n.links.at[i, 'p_nom'], n.lines.at[line, 's_nom'])
n.links.at[i, 'carrier'] = 'DC'
n.links.at[i, 'underwater_fraction'] = 0.
n.links.at[i, 'length'] = n.lines.at[line, 'length']
n.links.at[i, "p_nom"] = min(
n.links.at[i, "p_nom"], n.lines.at[line, "s_nom"]
)
n.links.at[i, "carrier"] = "DC"
n.links.at[i, "underwater_fraction"] = 0.0
n.links.at[i, "length"] = n.lines.at[line, "length"]
n.remove("Line", line)
n.remove("Bus", b0)
logger.info("Replacing B2B converter `{}` together with bus `{}` and line `{}` by an HVDC tie-line {}-{}"
.format(i, b0, line, linkcntry.at[i], buscntry.at[b1]))
logger.info(
"Replacing B2B converter `{}` together with bus `{}` and line `{}` by an HVDC tie-line {}-{}".format(
i, b0, line, linkcntry.at[i], buscntry.at[b1]
)
)
def _set_links_underwater_fraction(n, offshore_shapes):
if n.links.empty: return
if n.links.empty:
return
if not hasattr(n.links, 'geometry'):
n.links['underwater_fraction'] = 0.
if not hasattr(n.links, "geometry"):
n.links["underwater_fraction"] = 0.0
else:
offshore_shape = gpd.read_file(offshore_shapes).unary_union
links = gpd.GeoSeries(n.links.geometry.dropna().map(shapely.wkt.loads))
n.links['underwater_fraction'] = links.intersection(offshore_shape).length / links.length
n.links["underwater_fraction"] = (
links.intersection(offshore_shape).length / links.length
)
def _adjust_capacities_of_under_construction_branches(n, config):
lines_mode = config['lines'].get('under_construction', 'undef')
if lines_mode == 'zero':
n.lines.loc[n.lines.under_construction, 'num_parallel'] = 0.
n.lines.loc[n.lines.under_construction, 's_nom'] = 0.
elif lines_mode == 'remove':
lines_mode = config["lines"].get("under_construction", "undef")
if lines_mode == "zero":
n.lines.loc[n.lines.under_construction, "num_parallel"] = 0.0
n.lines.loc[n.lines.under_construction, "s_nom"] = 0.0
elif lines_mode == "remove":
n.mremove("Line", n.lines.index[n.lines.under_construction])
elif lines_mode != 'keep':
logger.warning("Unrecognized configuration for `lines: under_construction` = `{}`. Keeping under construction lines.")
elif lines_mode != "keep":
logger.warning(
"Unrecognized configuration for `lines: under_construction` = `{}`. Keeping under construction lines."
)
links_mode = config['links'].get('under_construction', 'undef')
if links_mode == 'zero':
n.links.loc[n.links.under_construction, "p_nom"] = 0.
elif links_mode == 'remove':
links_mode = config["links"].get("under_construction", "undef")
if links_mode == "zero":
n.links.loc[n.links.under_construction, "p_nom"] = 0.0
elif links_mode == "remove":
n.mremove("Link", n.links.index[n.links.under_construction])
elif links_mode != 'keep':
logger.warning("Unrecognized configuration for `links: under_construction` = `{}`. Keeping under construction links.")
elif links_mode != "keep":
logger.warning(
"Unrecognized configuration for `links: under_construction` = `{}`. Keeping under construction links."
)
if lines_mode == 'remove' or links_mode == 'remove':
if lines_mode == "remove" or links_mode == "remove":
# We might need to remove further unconnected components
n = _remove_unconnected_components(n)
return n
def base_network(eg_buses, eg_converters, eg_transformers, eg_lines, eg_links,
links_p_nom, links_tyndp, europe_shape, country_shapes, offshore_shapes,
parameter_corrections, config):
def base_network(
eg_buses,
eg_converters,
eg_transformers,
eg_lines,
eg_links,
links_p_nom,
links_tyndp,
europe_shape,
country_shapes,
offshore_shapes,
parameter_corrections,
config,
):
buses = _load_buses_from_eg(eg_buses, europe_shape, config['electricity'])
buses = _load_buses_from_eg(eg_buses, europe_shape, config["electricity"])
links = _load_links_from_eg(buses, eg_links)
if config['links'].get('include_tyndp'):
if config["links"].get("include_tyndp"):
buses, links = _add_links_from_tyndp(buses, links, links_tyndp, europe_shape)
converters = _load_converters_from_eg(buses, eg_converters)
@ -558,9 +712,9 @@ def base_network(eg_buses, eg_converters, eg_transformers, eg_lines, eg_links,
converters = _set_electrical_parameters_converters(converters, config)
n = pypsa.Network()
n.name = 'PyPSA-Eur'
n.name = "PyPSA-Eur"
n.set_snapshots(pd.date_range(freq='h', **config['snapshots']))
n.set_snapshots(pd.date_range(freq="h", **config["snapshots"]))
n.import_components_from_dataframe(buses, "Bus")
n.import_components_from_dataframe(lines, "Line")
@ -584,14 +738,28 @@ def base_network(eg_buses, eg_converters, eg_transformers, eg_lines, eg_links,
return n
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('base_network')
snakemake = mock_snakemake("base_network")
configure_logging(snakemake)
n = base_network(snakemake.input.eg_buses, snakemake.input.eg_converters, snakemake.input.eg_transformers, snakemake.input.eg_lines, snakemake.input.eg_links,
snakemake.input.links_p_nom, snakemake.input.links_tyndp, snakemake.input.europe_shape, snakemake.input.country_shapes, snakemake.input.offshore_shapes,
snakemake.input.parameter_corrections, snakemake.config)
n = base_network(
snakemake.input.eg_buses,
snakemake.input.eg_converters,
snakemake.input.eg_transformers,
snakemake.input.eg_lines,
snakemake.input.eg_links,
snakemake.input.links_p_nom,
snakemake.input.links_tyndp,
snakemake.input.europe_shape,
snakemake.input.country_shapes,
snakemake.input.offshore_shapes,
snakemake.input.parameter_corrections,
snakemake.config,
)
n.meta = snakemake.config
n.export_to_netcdf(snakemake.output[0])

144
scripts/build_bus_regions.py
View File

@ -1,9 +1,11 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Creates Voronoi shapes for each bus representing both onshore and offshore regions.
Creates Voronoi shapes for each bus representing both onshore and offshore
regions.
Relevant Settings
-----------------
@ -38,41 +40,94 @@ Outputs
Description
-----------
"""
import logging
from _helpers import configure_logging
import pypsa
import os
import pandas as pd
import geopandas as gpd
from vresutils.graph import voronoi_partition_pts
import geopandas as gpd
import numpy as np
import pandas as pd
import pypsa
from _helpers import REGION_COLS, configure_logging
from scipy.spatial import Voronoi
from shapely.geometry import Polygon
logger = logging.getLogger(__name__)
def save_to_geojson(s, fn):
if os.path.exists(fn):
os.unlink(fn)
schema = {**gpd.io.file.infer_schema(s), 'geometry': 'Unknown'}
s.to_file(fn, driver='GeoJSON', schema=schema)
def voronoi_partition_pts(points, outline):
"""
Compute the polygons of a voronoi partition of `points` within the
polygon `outline`. Taken from
https://github.com/FRESNA/vresutils/blob/master/vresutils/graph.py
Attributes
----------
points : Nx2 - ndarray[dtype=float]
outline : Polygon
Returns
-------
polygons : N - ndarray[dtype=Polygon|MultiPolygon]
"""
points = np.asarray(points)
if len(points) == 1:
polygons = [outline]
else:
xmin, ymin = np.amin(points, axis=0)
xmax, ymax = np.amax(points, axis=0)
xspan = xmax - xmin
yspan = ymax - ymin
# to avoid any network positions outside all Voronoi cells, append
# the corners of a rectangle framing these points
vor = Voronoi(
np.vstack(
(
points,
[
[xmin - 3.0 * xspan, ymin - 3.0 * yspan],
[xmin - 3.0 * xspan, ymax + 3.0 * yspan],
[xmax + 3.0 * xspan, ymin - 3.0 * yspan],
[xmax + 3.0 * xspan, ymax + 3.0 * yspan],
],
)
)
)
polygons = []
for i in range(len(points)):
poly = Polygon(vor.vertices[vor.regions[vor.point_region[i]]])
if not poly.is_valid:
poly = poly.buffer(0)
poly = poly.intersection(outline)
polygons.append(poly)
return polygons
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_bus_regions')
snakemake = mock_snakemake("build_bus_regions")
configure_logging(snakemake)
countries = snakemake.config['countries']
countries = snakemake.config["countries"]
n = pypsa.Network(snakemake.input.base_network)
country_shapes = gpd.read_file(snakemake.input.country_shapes).set_index('name')['geometry']
offshore_shapes = gpd.read_file(snakemake.input.offshore_shapes).set_index('name')['geometry']
country_shapes = gpd.read_file(snakemake.input.country_shapes).set_index("name")[
"geometry"
]
offshore_shapes = gpd.read_file(snakemake.input.offshore_shapes)
offshore_shapes = offshore_shapes.reindex(columns=REGION_COLS).set_index("name")[
"geometry"
]
onshore_regions = []
offshore_regions = []
@ -82,27 +137,42 @@ if __name__ == "__main__":
onshore_shape = country_shapes[country]
onshore_locs = n.buses.loc[c_b & n.buses.substation_lv, ["x", "y"]]
onshore_regions.append(gpd.GeoDataFrame({
'name': onshore_locs.index,
'x': onshore_locs['x'],
'y': onshore_locs['y'],
'geometry': voronoi_partition_pts(onshore_locs.values, onshore_shape),
'country': country
}))
onshore_regions.append(
gpd.GeoDataFrame(
{
"name": onshore_locs.index,
"x": onshore_locs["x"],
"y": onshore_locs["y"],
"geometry": voronoi_partition_pts(
onshore_locs.values, onshore_shape
),
"country": country,
}
)
)
if country not in offshore_shapes.index: continue
if country not in offshore_shapes.index:
continue
offshore_shape = offshore_shapes[country]
offshore_locs = n.buses.loc[c_b & n.buses.substation_off, ["x", "y"]]
offshore_regions_c = gpd.GeoDataFrame({
'name': offshore_locs.index,
'x': offshore_locs['x'],
'y': offshore_locs['y'],
'geometry': voronoi_partition_pts(offshore_locs.values, offshore_shape),
'country': country
})
offshore_regions_c = gpd.GeoDataFrame(
{
"name": offshore_locs.index,
"x": offshore_locs["x"],
"y": offshore_locs["y"],
"geometry": voronoi_partition_pts(offshore_locs.values, offshore_shape),
"country": country,
}
)
offshore_regions_c = offshore_regions_c.loc[offshore_regions_c.area > 1e-2]
offshore_regions.append(offshore_regions_c)
save_to_geojson(pd.concat(onshore_regions, ignore_index=True), snakemake.output.regions_onshore)
save_to_geojson(pd.concat(offshore_regions, ignore_index=True), snakemake.output.regions_offshore)
pd.concat(onshore_regions, ignore_index=True).to_file(
snakemake.output.regions_onshore
)
if offshore_regions:
pd.concat(offshore_regions, ignore_index=True).to_file(
snakemake.output.regions_offshore
)
else:
offshore_shapes.to_frame().to_file(snakemake.output.regions_offshore)

34
scripts/build_cutout.py
View File

@ -1,4 +1,5 @@
# SPDX-FileCopyrightText: : 2017-2021 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -88,43 +89,42 @@ A **SARAH-2 cutout** can be used to amend the fields ``temperature``, ``influx_t
Description
-----------
"""
import logging
import atlite
import geopandas as gpd
import pandas as pd
from _helpers import configure_logging
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_cutout', cutout='europe-2013-era5')
snakemake = mock_snakemake("build_cutout", cutout="europe-2013-era5")
configure_logging(snakemake)
cutout_params = snakemake.config['atlite']['cutouts'][snakemake.wildcards.cutout]
cutout_params = snakemake.config["atlite"]["cutouts"][snakemake.wildcards.cutout]
snapshots = pd.date_range(freq='h', **snakemake.config['snapshots'])
snapshots = pd.date_range(freq="h", **snakemake.config["snapshots"])
time = [snapshots[0], snapshots[-1]]
cutout_params['time'] = slice(*cutout_params.get('time', time))
cutout_params["time"] = slice(*cutout_params.get("time", time))
if {'x', 'y', 'bounds'}.isdisjoint(cutout_params):
if {"x", "y", "bounds"}.isdisjoint(cutout_params):
# Determine the bounds from bus regions with a buffer of two grid cells
onshore = gpd.read_file(snakemake.input.regions_onshore)
offshore = gpd.read_file(snakemake.input.regions_offshore)
regions = onshore.append(offshore)
d = max(cutout_params.get('dx', 0.25), cutout_params.get('dy', 0.25))*2
cutout_params['bounds'] = regions.total_bounds + [-d, -d, d, d]
elif {'x', 'y'}.issubset(cutout_params):
cutout_params['x'] = slice(*cutout_params['x'])
cutout_params['y'] = slice(*cutout_params['y'])
regions = pd.concat([onshore, offshore])
d = max(cutout_params.get("dx", 0.25), cutout_params.get("dy", 0.25)) * 2
cutout_params["bounds"] = regions.total_bounds + [-d, -d, d, d]
elif {"x", "y"}.issubset(cutout_params):
cutout_params["x"] = slice(*cutout_params["x"])
cutout_params["y"] = slice(*cutout_params["y"])
logging.info(f"Preparing cutout with parameters {cutout_params}.")
features = cutout_params.pop('features', None)
features = cutout_params.pop("features", None)
cutout = atlite.Cutout(snakemake.output[0], **cutout_params)
cutout.prepare(features=features)

99
scripts/build_hydro_profile.py
View File

@ -1,6 +1,7 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -60,36 +61,98 @@ Description
"""
import logging
from _helpers import configure_logging
import atlite
import country_converter as coco
import geopandas as gpd
from vresutils import hydro as vhydro
import pandas as pd
from _helpers import configure_logging
cc = coco.CountryConverter()
def get_eia_annual_hydro_generation(fn, countries):
# in billion kWh/a = TWh/a
df = pd.read_csv(fn, skiprows=2, index_col=1, na_values=[" ", "--"]).iloc[1:, 1:]
df.index = df.index.str.strip()
former_countries = {
"Former Czechoslovakia": dict(
countries=["Czech Republic", "Slovakia"], start=1980, end=1992
),
"Former Serbia and Montenegro": dict(
countries=["Serbia", "Montenegro"], start=1992, end=2005
),
"Former Yugoslavia": dict(
countries=[
"Slovenia",
"Croatia",
"Bosnia and Herzegovina",
"Serbia",
"Montenegro",
"North Macedonia",
],
start=1980,
end=1991,
),
}
for k, v in former_countries.items():
period = [str(i) for i in range(v["start"], v["end"] + 1)]
ratio = df.loc[v["countries"]].T.dropna().sum()
ratio /= ratio.sum()
for country in v["countries"]:
df.loc[country, period] = df.loc[k, period] * ratio[country]
baltic_states = ["Latvia", "Estonia", "Lithuania"]
df.loc[baltic_states] = (
df.loc[baltic_states].T.fillna(df.loc[baltic_states].mean(axis=1)).T
)
df.loc["Germany"] = df.filter(like="Germany", axis=0).sum()
df.loc["Serbia"] += df.loc["Kosovo"].fillna(0.0)
df = df.loc[~df.index.str.contains("Former")]
df.drop(["Europe", "Germany, West", "Germany, East", "Kosovo"], inplace=True)
df.index = cc.convert(df.index, to="iso2")
df.index.name = "countries"
df = df.T[countries] * 1e6 # in MWh/a
return df
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_hydro_profile')
snakemake = mock_snakemake("build_hydro_profile")
configure_logging(snakemake)
config_hydro = snakemake.config['renewable']['hydro']
config_hydro = snakemake.config["renewable"]["hydro"]
cutout = atlite.Cutout(snakemake.input.cutout)
countries = snakemake.config['countries']
country_shapes = (gpd.read_file(snakemake.input.country_shapes)
.set_index('name')['geometry'].reindex(countries))
country_shapes.index.name = 'countries'
countries = snakemake.config["countries"]
country_shapes = (
gpd.read_file(snakemake.input.country_shapes)
.set_index("name")["geometry"]
.reindex(countries)
)
country_shapes.index.name = "countries"
eia_stats = vhydro.get_eia_annual_hydro_generation(
snakemake.input.eia_hydro_generation).reindex(columns=countries)
inflow = cutout.runoff(shapes=country_shapes,
smooth=True,
lower_threshold_quantile=True,
normalize_using_yearly=eia_stats)
fn = snakemake.input.eia_hydro_generation
eia_stats = get_eia_annual_hydro_generation(fn, countries)
if 'clip_min_inflow' in config_hydro:
inflow = inflow.where(inflow > config_hydro['clip_min_inflow'], 0)
inflow = cutout.runoff(
shapes=country_shapes,
smooth=True,
lower_threshold_quantile=True,
normalize_using_yearly=eia_stats,
)
if "clip_min_inflow" in config_hydro:
inflow = inflow.where(inflow > config_hydro["clip_min_inflow"], 0)
inflow.to_netcdf(snakemake.output[0])

267
scripts/build_load_data.py
View File

@ -1,10 +1,16 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2020 @JanFrederickUnnewehr, The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
This rule downloads the load data from `Open Power System Data Time series.
This rule downloads the load data from `Open Power System Data Time series <https://data.open-power-system-data.org/time_series/>`_. For all countries in the network, the per country load timeseries with suffix ``_load_actual_entsoe_transparency`` are extracted from the dataset. After filling small gaps linearly and large gaps by copying time-slice of a given period, the load data is exported to a ``.csv`` file.
<https://data.open-power-system-data.org/time_series/>`_. For all countries in
the network, the per country load timeseries with suffix
``_load_actual_entsoe_transparency`` are extracted from the dataset. After
filling small gaps linearly and large gaps by copying time-slice of a given
period, the load data is exported to a ``.csv`` file.
Relevant Settings
-----------------
@ -26,22 +32,21 @@ Relevant Settings
Inputs
------
- ``data/load_raw.csv``:
Outputs
-------
- ``resource/time_series_60min_singleindex_filtered.csv``:
- ``resources/load.csv``:
"""
import logging
logger = logging.getLogger(__name__)
from _helpers import configure_logging
import pandas as pd
import numpy as np
logger = logging.getLogger(__name__)
import dateutil
import numpy as np
import pandas as pd
from _helpers import configure_logging
from pandas import Timedelta as Delta
@ -70,24 +75,29 @@ def load_timeseries(fn, years, countries, powerstatistics=True):
"""
logger.info(f"Retrieving load data from '{fn}'.")
pattern = 'power_statistics' if powerstatistics else 'transparency'
pattern = f'_load_actual_entsoe_{pattern}'
rename = lambda s: s[:-len(pattern)]
pattern = "power_statistics" if powerstatistics else "transparency"
pattern = f"_load_actual_entsoe_{pattern}"
rename = lambda s: s[: -len(pattern)]
date_parser = lambda x: dateutil.parser.parse(x, ignoretz=True)
return (pd.read_csv(fn, index_col=0, parse_dates=[0], date_parser=date_parser)
.filter(like=pattern)
.rename(columns=rename)
.dropna(how="all", axis=0)
.rename(columns={'GB_UKM' : 'GB'})
.filter(items=countries)
.loc[years])
return (
pd.read_csv(fn, index_col=0, parse_dates=[0], date_parser=date_parser)
.filter(like=pattern)
.rename(columns=rename)
.dropna(how="all", axis=0)
.rename(columns={"GB_UKM": "GB"})
.filter(items=countries)
.loc[years]
)
def consecutive_nans(ds):
return (ds.isnull().astype(int)
.groupby(ds.notnull().astype(int).cumsum()[ds.isnull()])
.transform('sum').fillna(0))
return (
ds.isnull()
.astype(int)
.groupby(ds.notnull().astype(int).cumsum()[ds.isnull()])
.transform("sum")
.fillna(0)
)
def fill_large_gaps(ds, shift):
@ -97,94 +107,163 @@ def fill_large_gaps(ds, shift):
This function fills gaps ragning from 3 to 168 hours (one week).
"""
shift = Delta(shift)
nhours = shift / np.timedelta64(1, 'h')
nhours = shift / np.timedelta64(1, "h")
if (consecutive_nans(ds) > nhours).any():
logger.warning('There exist gaps larger then the time shift used for '
'copying time slices.')
logger.warning(
"There exist gaps larger then the time shift used for "
"copying time slices."
)
time_shift = pd.Series(ds.values, ds.index + shift)
return ds.where(ds.notnull(), time_shift.reindex_like(ds))
def nan_statistics(df):
def max_consecutive_nans(ds):
return (ds.isnull().astype(int)
.groupby(ds.notnull().astype(int).cumsum())
.sum().max())
return (
ds.isnull()
.astype(int)
.groupby(ds.notnull().astype(int).cumsum())
.sum()
.max()
)
consecutive = df.apply(max_consecutive_nans)
total = df.isnull().sum()
max_total_per_month = df.isnull().resample('m').sum().max()
return pd.concat([total, consecutive, max_total_per_month],
keys=['total', 'consecutive', 'max_total_per_month'], axis=1)
max_total_per_month = df.isnull().resample("m").sum().max()
return pd.concat(
[total, consecutive, max_total_per_month],
keys=["total", "consecutive", "max_total_per_month"],
axis=1,
)
def copy_timeslice(load, cntry, start, stop, delta):
def copy_timeslice(load, cntry, start, stop, delta, fn_load=None):
start = pd.Timestamp(start)
stop = pd.Timestamp(stop)
if start-delta in load.index and stop in load.index and cntry in load:
load.loc[start:stop, cntry] = load.loc[start-delta:stop-delta, cntry].values
if start in load.index and stop in load.index:
if start - delta in load.index and stop - delta in load.index and cntry in load:
load.loc[start:stop, cntry] = load.loc[
start - delta : stop - delta, cntry
].values
elif fn_load is not None:
duration = pd.date_range(freq="h", start=start - delta, end=stop - delta)
load_raw = load_timeseries(fn_load, duration, [cntry], powerstatistics)
load.loc[start:stop, cntry] = load_raw.loc[
start - delta : stop - delta, cntry
].values
def manual_adjustment(load, powerstatistics, countries):
def manual_adjustment(load, fn_load, powerstatistics, countries):
"""
Adjust gaps manual for load data from OPSD time-series package.
1. For the ENTSOE power statistics load data (if powerstatistics is True)
1. For the ENTSOE power statistics load data (if powerstatistics is True)
Kosovo (KV) and Albania (AL) do not exist in the data set. Kosovo gets the
same load curve as Serbia and Albania the same as Macdedonia, both scaled
by the corresponding ratio of total energy consumptions reported by
IEA Data browser [0] for the year 2013.
Kosovo (KV) and Albania (AL) do not exist in the data set. Kosovo gets the
same load curve as Serbia and Albania the same as Macdedonia, both scaled
by the corresponding ratio of total energy consumptions reported by
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
same load curve as Montenegro, scaled by the corresponding ratio of total energy
consumptions reported by IEA Data browser [0] for the year 2016.
Albania (AL) and Macedonia (MK) do not exist in the data set. Both get the
same load curve as Montenegro, scaled by the corresponding ratio of total energy
consumptions reported by IEA Data browser [0] for the year 2016.
[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
Parameters
----------
load : pd.DataFrame
Load time-series with UTC timestamps x ISO-2 countries
powerstatistics: bool
Whether argument load comprises the electricity consumption data of
the ENTSOE power statistics or of the ENTSOE transparency map
Parameters
----------
load : pd.DataFrame
Load time-series with UTC timestamps x ISO-2 countries
powerstatistics: bool
Whether argument load comprises the electricity consumption data of
the ENTSOE power statistics or of the ENTSOE transparency map
load_fn: str
File name or url location (file format .csv)
Returns
-------
load : pd.DataFrame
Manual adjusted and interpolated load time-series with UTC
timestamps x ISO-2 countries
Returns
-------
load : pd.DataFrame
Manual adjusted and interpolated load time-series with UTC
timestamps x ISO-2 countries
"""
if powerstatistics:
if 'MK' in load.columns:
if 'AL' not in load.columns or load.AL.isnull().values.all():
load['AL'] = load['MK'] * (4.1 / 7.4)
if 'RS' in load.columns:
if 'KV' not in load.columns or load.KV.isnull().values.all():
load['KV'] = load['RS'] * (4.8 / 27.)
if "MK" in load.columns:
if "AL" not in load.columns or load.AL.isnull().values.all():
load["AL"] = load["MK"] * (4.1 / 7.4)
if "RS" in load.columns:
if "KV" not in load.columns or load.KV.isnull().values.all():
load["KV"] = load["RS"] * (4.8 / 27.0)
copy_timeslice(load, 'GR', '2015-08-11 21:00', '2015-08-15 20:00', Delta(weeks=1))
copy_timeslice(load, 'AT', '2018-12-31 22:00', '2019-01-01 22:00', Delta(days=2))
copy_timeslice(load, 'CH', '2010-01-19 07:00', '2010-01-19 22:00', Delta(days=1))
copy_timeslice(load, 'CH', '2010-03-28 00:00', '2010-03-28 21:00', Delta(days=1))
copy_timeslice(
load, "GR", "2015-08-11 21:00", "2015-08-15 20:00", Delta(weeks=1)
)
copy_timeslice(
load, "AT", "2018-12-31 22:00", "2019-01-01 22:00", Delta(days=2)
)
copy_timeslice(
load, "CH", "2010-01-19 07:00", "2010-01-19 22:00", Delta(days=1)
)
copy_timeslice(
load, "CH", "2010-03-28 00:00", "2010-03-28 21:00", Delta(days=1)
)
# is a WE, so take WE before
copy_timeslice(load, 'CH', '2010-10-08 13:00', '2010-10-10 21:00', Delta(weeks=1))
copy_timeslice(load, 'CH', '2010-11-04 04:00', '2010-11-04 22:00', Delta(days=1))
copy_timeslice(load, 'NO', '2010-12-09 11:00', '2010-12-09 18:00', Delta(days=1))
copy_timeslice(
load, "CH", "2010-10-08 13:00", "2010-10-10 21:00", Delta(weeks=1)
)
copy_timeslice(
load, "CH", "2010-11-04 04:00", "2010-11-04 22:00", Delta(days=1)
)
copy_timeslice(
load, "NO", "2010-12-09 11:00", "2010-12-09 18:00", Delta(days=1)
)
# whole january missing
copy_timeslice(load, 'GB', '2009-12-31 23:00', '2010-01-31 23:00', Delta(days=-364))
copy_timeslice(
load,
"GB",
"2010-01-01 00:00",
"2010-01-31 23:00",
Delta(days=-365),
fn_load,
)
# 1.1. at midnight gets special treatment
copy_timeslice(
load,
"IE",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
copy_timeslice(
load,
"PT",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
copy_timeslice(
load,
"GB",
"2016-01-01 00:00",
"2016-01-01 01:00",
Delta(days=-366),
fn_load,
)
else:
if 'ME' in load:
if 'AL' not in load and 'AL' in countries:
load['AL'] = load.ME * (5.7/2.9)
if 'MK' not in load and 'MK' in countries:
load['MK'] = load.ME * (6.7/2.9)
copy_timeslice(load, 'BG', '2018-10-27 21:00', '2018-10-28 22:00', Delta(weeks=1))
if "ME" in load:
if "AL" not in load and "AL" in countries:
load["AL"] = load.ME * (5.7 / 2.9)
if "MK" not in load and "MK" in countries:
load["MK"] = load.ME * (6.7 / 2.9)
copy_timeslice(
load, "BG", "2018-10-27 21:00", "2018-10-28 22:00", Delta(weeks=1)
)
if 'UA' in countries:
copy_timeslice(load, 'UA', '2013-01-25 14:00', '2013-01-28 21:00', Delta(weeks=1))
@ -195,18 +274,19 @@ def manual_adjustment(load, powerstatistics, countries):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_load_data')
snakemake = mock_snakemake("build_load_data")
configure_logging(snakemake)
powerstatistics = snakemake.config['load']['power_statistics']
interpolate_limit = snakemake.config['load']['interpolate_limit']
countries = snakemake.config['countries']
snapshots = pd.date_range(freq='h', **snakemake.config['snapshots'])
powerstatistics = snakemake.config["load"]["power_statistics"]
interpolate_limit = snakemake.config["load"]["interpolate_limit"]
countries = snakemake.config["countries"]
snapshots = pd.date_range(freq="h", **snakemake.config["snapshots"])
years = slice(snapshots[0], snapshots[-1])
time_shift = snakemake.config['load']['time_shift_for_large_gaps']
time_shift = snakemake.config["load"]["time_shift_for_large_gaps"]
load = load_timeseries(snakemake.input[0], years, countries, powerstatistics)
@ -220,20 +300,21 @@ if __name__ == "__main__":
# https://www.iea.org/data-and-statistics/data-browser/?country=MOLDOVA&fuel=Energy%20consumption&indicator=TotElecCons
load['MD'] = 6.2e6*(load_ua/load_ua.sum())
if snakemake.config['load']['manual_adjustments']:
load = manual_adjustment(load, powerstatistics, countries)
if snakemake.config["load"]["manual_adjustments"]:
load = manual_adjustment(load, snakemake.input[0], powerstatistics, countries)
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)
logger.info("Filling larger gaps by copying time-slices of period "
f"'{time_shift}'.")
logger.info(
"Filling larger gaps by copying time-slices of period " f"'{time_shift}'."
)
load = load.apply(fill_large_gaps, shift=time_shift)
assert not load.isna().any().any(), (
'Load data contains nans. Adjust the parameters '
'`time_shift_for_large_gaps` or modify the `manual_adjustment` function '
'for implementing the needed load data modifications.')
"Load data contains nans. Adjust the parameters "
"`time_shift_for_large_gaps` or modify the `manual_adjustment` function "
"for implementing the needed load data modifications."
)
load.to_csv(snakemake.output[0])

44
scripts/build_natura_raster.py
View File

@ -1,9 +1,13 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Rasters the vector data of the `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas onto all cutout regions.
Rasters the vector data of the `Natura 2000.
<https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas onto all
cutout regions.
Relevant Settings
-----------------
@ -36,15 +40,14 @@ Outputs
Description
-----------
"""
import logging
from _helpers import configure_logging, retrieve_snakemake_keys
import atlite
import geopandas as gpd
import rasterio as rio
from _helpers import configure_logging
from rasterio.features import geometry_mask
from rasterio.warp import transform_bounds
@ -56,11 +59,11 @@ def determine_cutout_xXyY(cutout_name):
assert cutout.crs.to_epsg() == 4326
x, X, y, Y = cutout.extent
dx, dy = cutout.dx, cutout.dy
return [x - dx/2., X + dx/2., y - dy/2., Y + dy/2.]
return [x - dx / 2.0, X + dx / 2.0, y - dy / 2.0, Y + dy / 2.0]
def get_transform_and_shape(bounds, res):
left, bottom = [(b // res)* res for b in bounds[:2]]
left, bottom = [(b // res) * res for b in bounds[:2]]
right, top = [(b // res + 1) * res for b in bounds[2:]]
shape = int((top - bottom) // res), int((right - left) / res)
transform = rio.Affine(res, 0, left, 0, -res, top)
@ -68,25 +71,32 @@ def get_transform_and_shape(bounds, res):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_natura_raster')
snakemake = mock_snakemake("build_natura_raster")
configure_logging(snakemake)
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
cutouts = paths.cutouts
cutouts = snakemake.input.cutouts
xs, Xs, ys, Ys = zip(*(determine_cutout_xXyY(cutout) for cutout in cutouts))
bounds = transform_bounds(4326, 3035, min(xs), min(ys), max(Xs), max(Ys))
transform, out_shape = get_transform_and_shape(bounds, res=100)
# adjusted boundaries
shapes = gpd.read_file(paths.natura).to_crs(3035)
raster = ~geometry_mask(shapes.geometry, out_shape[::-1], transform)
shapes = gpd.read_file(snakemake.input.natura).to_crs(3035)
raster = ~geometry_mask(shapes.geometry, out_shape, transform)
raster = raster.astype(rio.uint8)
with rio.open(out[0], 'w', driver='GTiff', dtype=rio.uint8,
count=1, transform=transform, crs=3035, compress='lzw',
width=raster.shape[1], height=raster.shape[0]) as dst:
with rio.open(
snakemake.output[0],
"w",
driver="GTiff",
dtype=rio.uint8,
count=1,
transform=transform,
crs=3035,
compress="lzw",
width=raster.shape[1],
height=raster.shape[0],
) as dst:
dst.write(raster, indexes=1)

104
scripts/build_powerplants.py
View File

@ -1,10 +1,15 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Retrieves conventional powerplant capacities and locations from `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_, assigns these to buses and creates a ``.csv`` file. It is possible to amend the powerplant database with custom entries provided in ``data/custom_powerplants.csv``.
Retrieves conventional powerplant capacities and locations from
`powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_, assigns
these to buses and creates a ``.csv`` file. It is possible to amend the
powerplant database with custom entries provided in
``data/custom_powerplants.csv``.
Relevant Settings
-----------------
@ -68,18 +73,15 @@ The configuration options ``electricity: powerplants_filter`` and ``electricity:
powerplants_filter: Country not in ['Germany'] and YearCommissioned <= 2015
custom_powerplants: YearCommissioned <= 2015
"""
import logging
from _helpers import configure_logging
import pypsa
import powerplantmatching as pm
import pandas as pd
import numpy as np
from scipy.spatial import cKDTree as KDTree
import powerplantmatching as pm
import pypsa
from _helpers import configure_logging
from powerplantmatching.export import map_country_bus
logger = logging.getLogger(__name__)
@ -87,56 +89,78 @@ logger = logging.getLogger(__name__)
def add_custom_powerplants(ppl, custom_powerplants, custom_ppl_query=False):
if not custom_ppl_query:
return ppl
add_ppls = pd.read_csv(custom_powerplants, index_col=0,
dtype={'bus': 'str'})
add_ppls = pd.read_csv(custom_powerplants, index_col=0, dtype={"bus": "str"})
if isinstance(custom_ppl_query, str):
add_ppls.query(custom_ppl_query, inplace=True)
return pd.concat([ppl, add_ppls], sort=False, ignore_index=True, verify_integrity=True)
return pd.concat(
[ppl, add_ppls], sort=False, ignore_index=True, verify_integrity=True
)
def replace_natural_gas_technology(df):
mapping = {"Steam Turbine": "OCGT", "Combustion Engine": "OCGT"}
tech = df.Technology.replace(mapping).fillna("OCGT")
return df.Technology.where(df.Fueltype != "Natural Gas", tech)
def replace_natural_gas_fueltype(df):
return df.Fueltype.where(df.Fueltype != "Natural Gas", df.Technology)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_powerplants')
snakemake = mock_snakemake("build_powerplants")
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.base_network)
countries = n.buses.country.unique()
ppl = (pm.powerplants(from_url=True)
.powerplant.fill_missing_decommyears()
.powerplant.convert_country_to_alpha2()
.query('Fueltype not in ["Solar", "Wind"] and Country in @countries')
.replace({'Technology': {'Steam Turbine': 'OCGT'}})
.assign(Fueltype=lambda df: (
df.Fueltype
.where(df.Fueltype != 'Natural Gas',
df.Technology.replace('Steam Turbine',
'OCGT').fillna('OCGT')))))
ppl = (
pm.powerplants(from_url=True)
.powerplant.fill_missing_decommissioning_years()
.powerplant.convert_country_to_alpha2()
.query('Fueltype not in ["Solar", "Wind"] and Country in @countries')
.assign(Technology=replace_natural_gas_technology)
.assign(Fueltype=replace_natural_gas_fueltype)
)
ppl_query = snakemake.config['electricity']['powerplants_filter']
# Correct bioenergy for countries where possible
opsd = pm.data.OPSD_VRE().powerplant.convert_country_to_alpha2()
opsd = opsd.query('Country in @countries and Fueltype == "Bioenergy"')
opsd["Name"] = "Biomass"
available_countries = opsd.Country.unique()
ppl = ppl.query('not (Country in @available_countries and Fueltype == "Bioenergy")')
ppl = pd.concat([ppl, opsd])
ppl_query = snakemake.config["electricity"]["powerplants_filter"]
if isinstance(ppl_query, str):
ppl.query(ppl_query, inplace=True)
# add carriers from own powerplant files:
custom_ppl_query = snakemake.config['electricity']['custom_powerplants']
ppl = add_custom_powerplants(ppl, snakemake.input.custom_powerplants, custom_ppl_query)
custom_ppl_query = snakemake.config["electricity"]["custom_powerplants"]
ppl = add_custom_powerplants(
ppl, snakemake.input.custom_powerplants, custom_ppl_query
)
cntries_without_ppl = [c for c in countries if c not in ppl.Country.unique()]
countries_wo_ppl = set(countries) - set(ppl.Country.unique())
if countries_wo_ppl:
logging.warning(f"No powerplants known in: {', '.join(countries_wo_ppl)}")
for c in countries:
substation_i = n.buses.query('substation_lv and country == @c').index
kdtree = KDTree(n.buses.loc[substation_i, ['x','y']].values)
ppl_i = ppl.query('Country == @c').index
tree_i = kdtree.query(ppl.loc[ppl_i, ['lon','lat']].values)[1]
ppl.loc[ppl_i, 'bus'] = substation_i.append(pd.Index([np.nan]))[tree_i]
if cntries_without_ppl:
logging.warning(f"No powerplants known in: {', '.join(cntries_without_ppl)}")
substations = n.buses.query("substation_lv")
ppl = map_country_bus(ppl, substations)
bus_null_b = ppl["bus"].isnull()
if bus_null_b.any():
logging.warning(f"Couldn't find close bus for {bus_null_b.sum()} powerplants")
logging.warning(
f"Couldn't find close bus for {bus_null_b.sum()} powerplants. "
"Removing them from the powerplants list."
)
ppl = ppl[~bus_null_b]
ppl.to_csv(snakemake.output[0])
# TODO: This has to fixed in PPM, some powerplants are still duplicated
cumcount = ppl.groupby(["bus", "Fueltype"]).cumcount() + 1
ppl.Name = ppl.Name.where(cumcount == 1, ppl.Name + " " + cumcount.astype(str))
ppl.reset_index(drop=True).to_csv(snakemake.output[0])

199
scripts/build_renewable_profiles.py
View File

@ -1,15 +1,17 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""Calculates for each network node the
(i) installable capacity (based on land-use), (ii) the available generation time
series (based on weather data), and (iii) the average distance from the node for
onshore wind, AC-connected offshore wind, DC-connected offshore wind and solar
PV generators. In addition for offshore wind it calculates the fraction of the
grid connection which is under water.
"""
Calculates for each network node the (i) installable capacity (based on land-
use), (ii) the available generation time series (based on weather data), and
(iii) the average distance from the node for onshore wind, AC-connected
offshore wind, DC-connected offshore wind and solar PV generators. In addition
for offshore wind it calculates the fraction of the grid connection which is
under water.
.. note:: Hydroelectric profiles are built in script :mod:`build_hydro_profiles`.
@ -177,86 +179,109 @@ node (`p_nom_max`): ``simple`` and ``conservative``:
- ``conservative`` assertains the nodal limit by increasing capacities
proportional to the layout until the limit of an individual grid cell is
reached.
"""
import progressbar as pgb
import geopandas as gpd
import xarray as xr
import numpy as np
import functools
import atlite
import logging
from pypsa.geo import haversine
from shapely.geometry import LineString
import time
import atlite
import geopandas as gpd
import numpy as np
import progressbar as pgb
import xarray as xr
from _helpers import configure_logging
from dask.distributed import Client, LocalCluster
from pypsa.geo import haversine
from shapely.geometry import LineString
logger = logging.getLogger(__name__)
if __name__ == '__main__':
if 'snakemake' not in globals():
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_renewable_profiles', technology='solar')
snakemake = mock_snakemake("build_renewable_profiles", technology="solar")
configure_logging(snakemake)
pgb.streams.wrap_stderr()
nprocesses = snakemake.config['atlite'].get('nprocesses')
noprogress = not snakemake.config['atlite'].get('show_progress', True)
config = snakemake.config['renewable'][snakemake.wildcards.technology]
resource = config['resource'] # pv panel config / wind turbine config
correction_factor = config.get('correction_factor', 1.)
capacity_per_sqkm = config['capacity_per_sqkm']
p_nom_max_meth = config.get('potential', 'conservative')
nprocesses = int(snakemake.threads)
noprogress = not snakemake.config["atlite"].get("show_progress", False)
config = snakemake.config["renewable"][snakemake.wildcards.technology]
resource = config["resource"] # pv panel config / wind turbine config
correction_factor = config.get("correction_factor", 1.0)
capacity_per_sqkm = config["capacity_per_sqkm"]
p_nom_max_meth = config.get("potential", "conservative")
if isinstance(config.get("corine", {}), list):
config['corine'] = {'grid_codes': config['corine']}
config["corine"] = {"grid_codes": config["corine"]}
if correction_factor != 1.:
logger.info(f'correction_factor is set as {correction_factor}')
if correction_factor != 1.0:
logger.info(f"correction_factor is set as {correction_factor}")
cluster = LocalCluster(n_workers=nprocesses, threads_per_worker=1)
client = Client(cluster, asynchronous=True)
cutout = atlite.Cutout(snakemake.input['cutout'])
regions = gpd.read_file(snakemake.input.regions).set_index('name').rename_axis('bus')
cutout = atlite.Cutout(snakemake.input["cutout"])
regions = gpd.read_file(snakemake.input.regions)
assert not regions.empty, (
f"List of regions in {snakemake.input.regions} is empty, please "
"disable the corresponding renewable technology"
)
# do not pull up, set_index does not work if geo dataframe is empty
regions = regions.set_index("name").rename_axis("bus")
buses = regions.index
excluder = atlite.ExclusionContainer(crs=3035, res=100)
res = config.get("excluder_resolution", 100)
excluder = atlite.ExclusionContainer(crs=3035, res=res)
if config['natura']:
if config["natura"]:
excluder.add_raster(snakemake.input.natura, nodata=0, allow_no_overlap=True)
corine = config.get("corine", {})
if "grid_codes" in corine:
codes = corine["grid_codes"]
excluder.add_raster(snakemake.input.corine, codes=codes, invert=True, crs=3035)
if corine.get("distance", 0.) > 0.:
if corine.get("distance", 0.0) > 0.0:
codes = corine["distance_grid_codes"]
buffer = corine["distance"]
excluder.add_raster(snakemake.input.corine, codes=codes, buffer=buffer, crs=3035)
excluder.add_raster(
snakemake.input.corine, codes=codes, buffer=buffer, crs=3035
)
if "ship_threshold" in config:
shipping_threshold = (
config["ship_threshold"] * 8760 * 6
) # approximation because 6 years of data which is hourly collected
func = functools.partial(np.less, shipping_threshold)
excluder.add_raster(
snakemake.input.ship_density, codes=func, crs=4326, allow_no_overlap=True
)
if "max_depth" in config:
# lambda not supported for atlite + multiprocessing
# use named function np.greater with partially frozen argument instead
# and exclude areas where: -max_depth > grid cell depth
func = functools.partial(np.greater,-config['max_depth'])
excluder.add_raster(snakemake.input.gebco, codes=func, crs=4236, nodata=-1000)
func = functools.partial(np.greater, -config["max_depth"])
excluder.add_raster(snakemake.input.gebco, codes=func, crs=4326, nodata=-1000)
if 'min_shore_distance' in config:
buffer = config['min_shore_distance']
if "min_shore_distance" in config:
buffer = config["min_shore_distance"]
excluder.add_geometry(snakemake.input.country_shapes, buffer=buffer)
if 'max_shore_distance' in config:
buffer = config['max_shore_distance']
excluder.add_geometry(snakemake.input.country_shapes, buffer=buffer, invert=True)
if "max_shore_distance" in config:
buffer = config["max_shore_distance"]
excluder.add_geometry(
snakemake.input.country_shapes, buffer=buffer, invert=True
)
kwargs = dict(nprocesses=nprocesses, disable_progressbar=noprogress)
if noprogress:
logger.info('Calculate landuse availabilities...')
logger.info("Calculate landuse availabilities...")
start = time.time()
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
duration = time.time() - start
logger.info(f'Completed availability calculation ({duration:2.2f}s)')
logger.info(f"Completed availability calculation ({duration:2.2f}s)")
else:
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
@ -267,35 +292,41 @@ if __name__ == '__main__':
availability.loc[availability_MDUA.coords] = availability_MDUA
area = cutout.grid.to_crs(3035).area / 1e6
area = xr.DataArray(area.values.reshape(cutout.shape),
[cutout.coords['y'], cutout.coords['x']])
area = xr.DataArray(
area.values.reshape(cutout.shape), [cutout.coords["y"], cutout.coords["x"]]
)
potential = capacity_per_sqkm * availability.sum('bus') * area
func = getattr(cutout, resource.pop('method'))
resource['dask_kwargs'] = {'num_workers': nprocesses}
potential = capacity_per_sqkm * availability.sum("bus") * area
func = getattr(cutout, resource.pop("method"))
resource["dask_kwargs"] = {"scheduler": client}
capacity_factor = correction_factor * func(capacity_factor=True, **resource)
layout = capacity_factor * area * capacity_per_sqkm
profile, capacities = func(matrix=availability.stack(spatial=['y','x']),
layout=layout, index=buses,
per_unit=True, return_capacity=True, **resource)
profile, capacities = func(
matrix=availability.stack(spatial=["y", "x"]),
layout=layout,
index=buses,
per_unit=True,
return_capacity=True,
**resource,
)
logger.info(f"Calculating maximal capacity per bus (method '{p_nom_max_meth}')")
if p_nom_max_meth == 'simple':
if p_nom_max_meth == "simple":
p_nom_max = capacity_per_sqkm * availability @ area
elif p_nom_max_meth == 'conservative':
max_cap_factor = capacity_factor.where(availability!=0).max(['x', 'y'])
elif p_nom_max_meth == "conservative":
max_cap_factor = capacity_factor.where(availability != 0).max(["x", "y"])
p_nom_max = capacities / max_cap_factor
else:
raise AssertionError('Config key `potential` should be one of "simple" '
f'(default) or "conservative", not "{p_nom_max_meth}"')
raise AssertionError(
'Config key `potential` should be one of "simple" '
f'(default) or "conservative", not "{p_nom_max_meth}"'
)
logger.info("Calculate average distances.")
layoutmatrix = (layout * availability).stack(spatial=["y", "x"])
logger.info('Calculate average distances.')
layoutmatrix = (layout * availability).stack(spatial=['y','x'])
coords = cutout.grid[['x', 'y']]
bus_coords = regions[['x', 'y']]
coords = cutout.grid[["x", "y"]]
bus_coords = regions[["x", "y"]]
average_distance = []
centre_of_mass = []
@ -304,39 +335,45 @@ if __name__ == '__main__':
nz_b = row != 0
row = row[nz_b]
co = coords[nz_b]
distances = haversine(bus_coords.loc[bus], co)
distances = haversine(bus_coords.loc[bus], co)
average_distance.append((distances * (row / row.sum())).sum())
centre_of_mass.append(co.values.T @ (row / row.sum()))
average_distance = xr.DataArray(average_distance, [buses])
centre_of_mass = xr.DataArray(centre_of_mass, [buses, ('spatial', ['x', 'y'])])
ds = xr.merge([(correction_factor * profile).rename('profile'),
capacities.rename('weight'),
p_nom_max.rename('p_nom_max'),
potential.rename('potential'),
average_distance.rename('average_distance')])
centre_of_mass = xr.DataArray(centre_of_mass, [buses, ("spatial", ["x", "y"])])
ds = xr.merge(
[
(correction_factor * profile).rename("profile"),
capacities.rename("weight"),
p_nom_max.rename("p_nom_max"),
potential.rename("potential"),
average_distance.rename("average_distance"),
]
)
if snakemake.wildcards.technology.startswith("offwind"):
logger.info('Calculate underwater fraction of connections.')
offshore_shape = gpd.read_file(snakemake.input['offshore_shapes']).unary_union
logger.info("Calculate underwater fraction of connections.")
offshore_shape = gpd.read_file(snakemake.input["offshore_shapes"]).unary_union
underwater_fraction = []
for bus in buses:
p = centre_of_mass.sel(bus=bus).data
line = LineString([p, regions.loc[bus, ['x', 'y']]])
frac = line.intersection(offshore_shape).length/line.length
line = LineString([p, regions.loc[bus, ["x", "y"]]])
frac = line.intersection(offshore_shape).length / line.length
underwater_fraction.append(frac)
ds['underwater_fraction'] = xr.DataArray(underwater_fraction, [buses])
ds["underwater_fraction"] = xr.DataArray(underwater_fraction, [buses])
# select only buses with some capacity and minimal capacity factor
ds = ds.sel(bus=((ds['profile'].mean('time') > config.get('min_p_max_pu', 0.)) &
(ds['p_nom_max'] > config.get('min_p_nom_max', 0.))))
ds = ds.sel(
bus=(
(ds["profile"].mean("time") > config.get("min_p_max_pu", 0.0))
& (ds["p_nom_max"] > config.get("min_p_nom_max", 0.0))
)
)
if 'clip_p_max_pu' in config:
min_p_max_pu = config['clip_p_max_pu']
ds['profile'] = ds['profile'].where(ds['profile'] >= min_p_max_pu, 0)
if "clip_p_max_pu" in config:
min_p_max_pu = config["clip_p_max_pu"]
ds["profile"] = ds["profile"].where(ds["profile"] >= min_p_max_pu, 0)
ds.to_netcdf(snakemake.output.profile)

207
scripts/build_shapes.py
View File

@ -1,9 +1,12 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Creates GIS shape files of the countries, exclusive economic zones and `NUTS3 <https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics>`_ areas.
Creates GIS shape files of the countries, exclusive economic zones and `NUTS3 <
https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics>
`_ areas.
Relevant Settings
-----------------
@ -64,23 +67,20 @@ Outputs
Description
-----------
"""
import logging
from _helpers import configure_logging
import os
import numpy as np
from operator import attrgetter
from functools import reduce
from itertools import takewhile
from operator import attrgetter
import pandas as pd
import geopandas as gpd
import numpy as np
import pandas as pd
import pycountry as pyc
from _helpers import configure_logging
from shapely.geometry import MultiPolygon, Polygon
from shapely.ops import unary_union
import pycountry as pyc
logger = logging.getLogger(__name__)
@ -95,140 +95,187 @@ def _get_country(target, **keys):
def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
if isinstance(polys, MultiPolygon):
polys = sorted(polys.geoms, key=attrgetter('area'), reverse=True)
polys = sorted(polys.geoms, key=attrgetter("area"), reverse=True)
mainpoly = polys[0]
mainlength = np.sqrt(mainpoly.area/(2.*np.pi))
mainlength = np.sqrt(mainpoly.area / (2.0 * np.pi))
if mainpoly.area > minarea:
polys = MultiPolygon([p
for p in takewhile(lambda p: p.area > minarea, polys)
if not filterremote or (mainpoly.distance(p) < mainlength)])
polys = MultiPolygon(
[
p
for p in takewhile(lambda p: p.area > minarea, polys)
if not filterremote or (mainpoly.distance(p) < mainlength)
]
)
else:
polys = mainpoly
return polys.simplify(tolerance=tolerance)
def countries(naturalearth, country_list):
if 'RS' in country_list: country_list.append('KV')
if "RS" in country_list:
country_list.append("KV")
df = gpd.read_file(naturalearth)
# Names are a hassle in naturalearth, try several fields
fieldnames = (df[x].where(lambda s: s!='-99') for x in ('ISO_A2', 'WB_A2', 'ADM0_A3'))
df['name'] = reduce(lambda x,y: x.fillna(y), fieldnames, next(fieldnames)).str[0:2]
fieldnames = (
df[x].where(lambda s: s != "-99") for x in ("ISO_A2", "WB_A2", "ADM0_A3")
)
df["name"] = reduce(lambda x, y: x.fillna(y), fieldnames, next(fieldnames)).str[0:2]
df = df.loc[df.name.isin(country_list) & ((df['scalerank'] == 0) | (df['scalerank'] == 5))]
s = df.set_index('name')['geometry'].map(_simplify_polys)
if 'RS' in country_list: s['RS'] = s['RS'].union(s.pop('KV'))
df = df.loc[
df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5))
]
s = df.set_index("name")["geometry"].map(_simplify_polys)
if "RS" in country_list:
s["RS"] = s["RS"].union(s.pop("KV"))
# cleanup shape union
s["RS"] = Polygon(s["RS"].exterior.coords)
return s
def eez(country_shapes, eez, country_list):
df = gpd.read_file(eez)
df = df.loc[df['ISO_3digit'].isin([_get_country('alpha_3', alpha_2=c) for c in country_list])]
df['name'] = df['ISO_3digit'].map(lambda c: _get_country('alpha_2', alpha_3=c))
s = df.set_index('name').geometry.map(lambda s: _simplify_polys(s, filterremote=False))
s = gpd.GeoSeries({k:v for k,v in s.iteritems() if v.distance(country_shapes[k]) < 1e-3})
df = df.loc[
df["ISO_3digit"].isin(
[_get_country("alpha_3", alpha_2=c) for c in country_list]
)
]
df["name"] = df["ISO_3digit"].map(lambda c: _get_country("alpha_2", alpha_3=c))
s = df.set_index("name").geometry.map(
lambda s: _simplify_polys(s, filterremote=False)
)
s = gpd.GeoSeries(
{k: v for k, v in s.items() if v.distance(country_shapes[k]) < 1e-3}
)
s = s.to_frame("geometry")
s.index.name = "name"
return s
def country_cover(country_shapes, eez_shapes=None):
shapes = list(country_shapes)
shapes = country_shapes
if eez_shapes is not None:
shapes += list(eez_shapes)
shapes = pd.concat([shapes, eez_shapes])
europe_shape = unary_union(shapes)
if isinstance(europe_shape, MultiPolygon):
europe_shape = max(europe_shape, key=attrgetter('area'))
europe_shape = max(europe_shape, key=attrgetter("area"))
return Polygon(shell=europe_shape.exterior)
def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
df = gpd.read_file(nuts3)
df = df.loc[df['STAT_LEVL_'] == 3]
df['geometry'] = df['geometry'].map(_simplify_polys)
df = df.rename(columns={'NUTS_ID': 'id'})[['id', 'geometry']].set_index('id')
df = df.loc[df["STAT_LEVL_"] == 3]
df["geometry"] = df["geometry"].map(_simplify_polys)
df = df.rename(columns={"NUTS_ID": "id"})[["id", "geometry"]].set_index("id")
pop = pd.read_table(nuts3pop, na_values=[':'], delimiter=' ?\t', engine='python')
pop = (pop
.set_index(pd.MultiIndex.from_tuples(pop.pop('unit,geo\\time').str.split(','))).loc['THS']
.applymap(lambda x: pd.to_numeric(x, errors='coerce'))
.fillna(method='bfill', axis=1))['2014']
pop = pd.read_table(nuts3pop, na_values=[":"], delimiter=" ?\t", engine="python")
pop = (
pop.set_index(
pd.MultiIndex.from_tuples(pop.pop("unit,geo\\time").str.split(","))
)
.loc["THS"]
.applymap(lambda x: pd.to_numeric(x, errors="coerce"))
.fillna(method="bfill", axis=1)
)["2014"]
gdp = pd.read_table(nuts3gdp, na_values=[':'], delimiter=' ?\t', engine='python')
gdp = (gdp
.set_index(pd.MultiIndex.from_tuples(gdp.pop('unit,geo\\time').str.split(','))).loc['EUR_HAB']
.applymap(lambda x: pd.to_numeric(x, errors='coerce'))
.fillna(method='bfill', axis=1))['2014']
gdp = pd.read_table(nuts3gdp, na_values=[":"], delimiter=" ?\t", engine="python")
gdp = (
gdp.set_index(
pd.MultiIndex.from_tuples(gdp.pop("unit,geo\\time").str.split(","))
)
.loc["EUR_HAB"]
.applymap(lambda x: pd.to_numeric(x, errors="coerce"))
.fillna(method="bfill", axis=1)
)["2014"]
cantons = pd.read_csv(ch_cantons)
cantons = cantons.set_index(cantons['HASC'].str[3:])['NUTS']
cantons = cantons.str.pad(5, side='right', fillchar='0')
cantons = cantons.set_index(cantons["HASC"].str[3:])["NUTS"]
cantons = cantons.str.pad(5, side="right", fillchar="0")
swiss = pd.read_excel(ch_popgdp, skiprows=3, index_col=0)
swiss.columns = swiss.columns.to_series().map(cantons)
swiss_pop = pd.to_numeric(swiss.loc['Residents in 1000', 'CH040':])
swiss_pop = pd.to_numeric(swiss.loc["Residents in 1000", "CH040":])
pop = pd.concat([pop, swiss_pop])
swiss_gdp = pd.to_numeric(swiss.loc['Gross domestic product per capita in Swiss francs', 'CH040':])
swiss_gdp = pd.to_numeric(
swiss.loc["Gross domestic product per capita in Swiss francs", "CH040":]
)
gdp = pd.concat([gdp, swiss_gdp])
df = df.join(pd.DataFrame(dict(pop=pop, gdp=gdp)))
df['country'] = df.index.to_series().str[:2].replace(dict(UK='GB', EL='GR'))
df["country"] = df.index.to_series().str[:2].replace(dict(UK="GB", EL="GR"))
excludenuts = pd.Index(('FRA10', 'FRA20', 'FRA30', 'FRA40', 'FRA50',
'PT200', 'PT300',
'ES707', 'ES703', 'ES704','ES705', 'ES706', 'ES708', 'ES709',
'FI2', 'FR9'))
excludecountry = pd.Index(('MT', 'TR', 'LI', 'IS', 'CY', 'KV'))
excludenuts = pd.Index(
(
"FRA10",
"FRA20",
"FRA30",
"FRA40",
"FRA50",
"PT200",
"PT300",
"ES707",
"ES703",
"ES704",
"ES705",
"ES706",
"ES708",
"ES709",
"FI2",
"FR9",
)
)
excludecountry = pd.Index(("MT", "TR", "LI", "IS", "CY", "KV"))
df = df.loc[df.index.difference(excludenuts)]
df = df.loc[~df.country.isin(excludecountry)]
manual = gpd.GeoDataFrame(
[['BA1', 'BA', 3871.],
['RS1', 'RS', 7210.],
['AL1', 'AL', 2893.]],
columns=['NUTS_ID', 'country', 'pop']
).set_index('NUTS_ID')
manual['geometry'] = manual['country'].map(country_shapes)
[["BA1", "BA", 3871.0], ["RS1", "RS", 7210.0], ["AL1", "AL", 2893.0]],
columns=["NUTS_ID", "country", "pop"],
).set_index("NUTS_ID")
manual["geometry"] = manual["country"].map(country_shapes)
manual = manual.dropna()
df = pd.concat([df, manual], sort=False)
df.loc['ME000', 'pop'] = 650.
df.loc["ME000", "pop"] = 650.0
return df
def save_to_geojson(df, fn):
if os.path.exists(fn):
os.unlink(fn)
if not isinstance(df, gpd.GeoDataFrame):
df = gpd.GeoDataFrame(dict(geometry=df))
df = df.reset_index()
schema = {**gpd.io.file.infer_schema(df), 'geometry': 'Unknown'}
df.to_file(fn, driver='GeoJSON', schema=schema)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_shapes')
snakemake = mock_snakemake("build_shapes")
configure_logging(snakemake)
country_shapes = countries(snakemake.input.naturalearth, snakemake.config['countries'])
save_to_geojson(country_shapes, snakemake.output.country_shapes)
country_shapes = countries(
snakemake.input.naturalearth, snakemake.config["countries"]
)
country_shapes.reset_index().to_file(snakemake.output.country_shapes)
offshore_shapes = eez(country_shapes, snakemake.input.eez, snakemake.config['countries'])
save_to_geojson(offshore_shapes, snakemake.output.offshore_shapes)
offshore_shapes = eez(
country_shapes, snakemake.input.eez, snakemake.config["countries"]
)
offshore_shapes.reset_index().to_file(snakemake.output.offshore_shapes)
europe_shape = country_cover(country_shapes, offshore_shapes)
save_to_geojson(gpd.GeoSeries(europe_shape), snakemake.output.europe_shape)
europe_shape = gpd.GeoDataFrame(
geometry=[country_cover(country_shapes, offshore_shapes.geometry)]
)
europe_shape.reset_index().to_file(snakemake.output.europe_shape)
nuts3_shapes = nuts3(country_shapes, snakemake.input.nuts3, snakemake.input.nuts3pop,
snakemake.input.nuts3gdp, snakemake.input.ch_cantons, snakemake.input.ch_popgdp)
save_to_geojson(nuts3_shapes, snakemake.output.nuts3_shapes)
nuts3_shapes = nuts3(
country_shapes,
snakemake.input.nuts3,
snakemake.input.nuts3pop,
snakemake.input.nuts3gdp,
snakemake.input.ch_cantons,
snakemake.input.ch_popgdp,
)
nuts3_shapes.reset_index().to_file(snakemake.output.nuts3_shapes)

68
scripts/build_ship_raster.py Normal file
View File

@ -0,0 +1,68 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Transforms the global ship density data from
https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-
Traffic-Density to the size of the considered cutout. The global ship density
raster is later used for the exclusion when calculating the offshore
potentials.
Relevant Settings
-----------------
.. code:: yaml
renewable:
{technology}:
cutout:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`renewable_cf`
Inputs
------
- ``data/bundle/shipdensity/shipdensity_global.zip``: `Global ship density from <https://datacatalog.worldbank.org/search/dataset/0037580/Global-Shipping-Traffic-Density>`.
Outputs
-------
- ``resources/europe_shipdensity_raster.nc``: Reduced version of `Global ship density from <https://datacatalog.worldbank.org/search/dataset/0037580/` to reduce computation time.
Description
-----------
"""
import logging
import os
import zipfile
import xarray as xr
from _helpers import configure_logging
from build_natura_raster import determine_cutout_xXyY
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_ship_raster")
configure_logging(snakemake)
cutouts = snakemake.input.cutouts
xs, Xs, ys, Ys = zip(*(determine_cutout_xXyY(cutout) for cutout in cutouts))
with zipfile.ZipFile(snakemake.input.ship_density) as zip_f:
zip_f.extract("shipdensity_global.tif")
with xr.open_rasterio("shipdensity_global.tif") as ship_density:
ship_density = ship_density.drop(["band"]).sel(
x=slice(min(xs), max(Xs)), y=slice(max(Ys), min(ys))
)
ship_density.to_netcdf(snakemake.output[0])
os.remove("shipdensity_global.tif")

453
scripts/cluster_network.py
View File

@ -1,21 +1,23 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Creates networks clustered to ``{cluster}`` number of zones with aggregated buses, generators and transmission corridors.
Creates networks clustered to ``{cluster}`` number of zones with aggregated
buses, generators and transmission corridors.
Relevant Settings
-----------------
.. code:: yaml
focus_weights:
clustering:
cluster_network:
aggregation_strategies:
renewable: (keys)
{technology}:
potential:
focus_weights:
solving:
solver:
@ -118,30 +120,28 @@ Exemplary unsolved network clustered to 37 nodes:
.. image:: ../img/elec_s_37.png
:scale: 40 %
:align: center
"""
import logging
from _helpers import configure_logging, update_p_nom_max
import pypsa
import os
import shapely
import pandas as pd
import numpy as np
import geopandas as gpd
import pyomo.environ as po
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
from functools import reduce
from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_spectral_clustering,
_make_consense, get_clustering_from_busmap)
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pyomo.environ as po
import pypsa
import seaborn as sns
from _helpers import configure_logging, get_aggregation_strategies, update_p_nom_max
from pypsa.networkclustering import (
busmap_by_greedy_modularity,
busmap_by_hac,
busmap_by_kmeans,
get_clustering_from_busmap,
)
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning)
warnings.filterwarnings(action="ignore", category=UserWarning)
from add_electricity import load_costs
@ -150,19 +150,21 @@ idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def normed(x): return (x/x.sum()).fillna(0.)
def normed(x):
return (x / x.sum()).fillna(0.0)
def weighting_for_country(n, x):
conv_carriers = {'OCGT','CCGT','PHS', 'hydro'}
gen = (n
.generators.loc[n.generators.carrier.isin(conv_carriers)]
.groupby('bus').p_nom.sum()
.reindex(n.buses.index, fill_value=0.) +
n
.storage_units.loc[n.storage_units.carrier.isin(conv_carriers)]
.groupby('bus').p_nom.sum()
.reindex(n.buses.index, fill_value=0.))
conv_carriers = {"OCGT", "CCGT", "PHS", "hydro"}
gen = n.generators.loc[n.generators.carrier.isin(conv_carriers)].groupby(
"bus"
).p_nom.sum().reindex(n.buses.index, fill_value=0.0) + n.storage_units.loc[
n.storage_units.carrier.isin(conv_carriers)
].groupby(
"bus"
).p_nom.sum().reindex(
n.buses.index, fill_value=0.0
)
load = n.loads_t.p_set.mean().groupby(n.loads.bus).sum()
b_i = x.index
@ -170,150 +172,278 @@ def weighting_for_country(n, x):
l = normed(load.reindex(b_i, fill_value=0))
w = g + l
return (w * (100. / w.max())).clip(lower=1.).astype(int)
return (w * (100.0 / w.max())).clip(lower=1.0).astype(int)
def get_feature_for_hac(n, buses_i=None, feature=None):
if buses_i is None:
buses_i = n.buses.index
if feature is None:
feature = "solar+onwind-time"
carriers = feature.split("-")[0].split("+")
if "offwind" in carriers:
carriers.remove("offwind")
carriers = np.append(
carriers, network.generators.carrier.filter(like="offwind").unique()
)
if feature.split("-")[1] == "cap":
feature_data = pd.DataFrame(index=buses_i, columns=carriers)
for carrier in carriers:
gen_i = n.generators.query("carrier == @carrier").index
attach = (
n.generators_t.p_max_pu[gen_i]
.mean()
.rename(index=n.generators.loc[gen_i].bus)
)
feature_data[carrier] = attach
if feature.split("-")[1] == "time":
feature_data = pd.DataFrame(columns=buses_i)
for carrier in carriers:
gen_i = n.generators.query("carrier == @carrier").index
attach = n.generators_t.p_max_pu[gen_i].rename(
columns=n.generators.loc[gen_i].bus
)
feature_data = pd.concat([feature_data, attach], axis=0)[buses_i]
feature_data = feature_data.T
# timestamp raises error in sklearn >= v1.2:
feature_data.columns = feature_data.columns.astype(str)
feature_data = feature_data.fillna(0)
return feature_data
def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="cbc"):
"""Determine the number of clusters per country"""
"""
Determine the number of clusters per country.
"""
L = (n.loads_t.p_set.mean()
.groupby(n.loads.bus).sum()
.groupby([n.buses.country, n.buses.sub_network]).sum()
.pipe(normed))
L = (
n.loads_t.p_set.mean()
.groupby(n.loads.bus)
.sum()
.groupby([n.buses.country, n.buses.sub_network])
.sum()
.pipe(normed)
)
N = n.buses.groupby(['country', 'sub_network']).size()
N = n.buses.groupby(["country", "sub_network"]).size()
assert n_clusters >= len(N) and n_clusters <= N.sum(), \
f"Number of clusters must be {len(N)} <= n_clusters <= {N.sum()} for this selection of countries."
assert (
n_clusters >= len(N) and n_clusters <= N.sum()
), f"Number of clusters must be {len(N)} <= n_clusters <= {N.sum()} for this selection of countries."
if focus_weights is not None:
total_focus = sum(list(focus_weights.values()))
assert total_focus <= 1.0, "The sum of focus weights must be less than or equal to 1."
assert (
total_focus <= 1.0
), "The sum of focus weights must be less than or equal to 1."
for country, weight in focus_weights.items():
L[country] = weight / len(L[country])
remainder = [c not in focus_weights.keys() for c in L.index.get_level_values('country')]
remainder = [
c not in focus_weights.keys() for c in L.index.get_level_values("country")
]
L[remainder] = L.loc[remainder].pipe(normed) * (1 - total_focus)
logger.warning('Using custom focus weights for determining number of clusters.')
logger.warning("Using custom focus weights for determining number of clusters.")
assert np.isclose(L.sum(), 1.0, rtol=1e-3), f"Country weights L must sum up to 1.0 when distributing clusters. Is {L.sum()}."
assert np.isclose(
L.sum(), 1.0, rtol=1e-3
), f"Country weights L must sum up to 1.0 when distributing clusters. Is {L.sum()}."
m = po.ConcreteModel()
def n_bounds(model, *n_id):
return (1, N[n_id])
m.n = po.Var(list(L.index), bounds=n_bounds, domain=po.Integers)
m.tot = po.Constraint(expr=(po.summation(m.n) == n_clusters))
m.objective = po.Objective(expr=sum((m.n[i] - L.loc[i]*n_clusters)**2 for i in L.index),
sense=po.minimize)
m.objective = po.Objective(
expr=sum((m.n[i] - L.loc[i] * n_clusters) ** 2 for i in L.index),
sense=po.minimize,
)
opt = po.SolverFactory(solver_name)
if not opt.has_capability('quadratic_objective'):
logger.warning(f'The configured solver `{solver_name}` does not support quadratic objectives. Falling back to `ipopt`.')
opt = po.SolverFactory('ipopt')
if not opt.has_capability("quadratic_objective"):
logger.warning(
f"The configured solver `{solver_name}` does not support quadratic objectives. Falling back to `ipopt`."
)
opt = po.SolverFactory("ipopt")
results = opt.solve(m)
assert results['Solver'][0]['Status'] == 'ok', f"Solver returned non-optimally: {results}"
assert (
results["Solver"][0]["Status"] == "ok"
), f"Solver returned non-optimally: {results}"
return pd.Series(m.n.get_values(), index=L.index).round().astype(int)
def busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights=None, algorithm="kmeans", **algorithm_kwds):
def busmap_for_n_clusters(
n,
n_clusters,
solver_name,
focus_weights=None,
algorithm="kmeans",
feature=None,
**algorithm_kwds,
):
if algorithm == "kmeans":
algorithm_kwds.setdefault('n_init', 1000)
algorithm_kwds.setdefault('max_iter', 30000)
algorithm_kwds.setdefault('tol', 1e-6)
algorithm_kwds.setdefault('random_state', 0)
algorithm_kwds.setdefault("n_init", 1000)
algorithm_kwds.setdefault("max_iter", 30000)
algorithm_kwds.setdefault("tol", 1e-6)
algorithm_kwds.setdefault("random_state", 0)
def fix_country_assignment_for_hac(n):
from scipy.sparse import csgraph
# overwrite country of nodes that are disconnected from their country-topology
for country in n.buses.country.unique():
m = n[n.buses.country == country].copy()
_, labels = csgraph.connected_components(
m.adjacency_matrix(), directed=False
)
component = pd.Series(labels, index=m.buses.index)
component_sizes = component.value_counts()
if len(component_sizes) > 1:
disconnected_bus = component[
component == component_sizes.index[-1]
].index[0]
neighbor_bus = n.lines.query(
"bus0 == @disconnected_bus or bus1 == @disconnected_bus"
).iloc[0][["bus0", "bus1"]]
new_country = list(
set(n.buses.loc[neighbor_bus].country) - set([country])
)[0]
logger.info(
f"overwriting country `{country}` of bus `{disconnected_bus}` "
f"to new country `{new_country}`, because it is disconnected "
"from its initial inter-country transmission grid."
)
n.buses.at[disconnected_bus, "country"] = new_country
return n
if algorithm == "hac":
feature = get_feature_for_hac(n, buses_i=n.buses.index, feature=feature)
n = fix_country_assignment_for_hac(n)
if (algorithm != "hac") and (feature is not None):
logger.warning(
f"Keyword argument feature is only valid for algorithm `hac`. "
f"Given feature `{feature}` will be ignored."
)
n.determine_network_topology()
n_clusters = distribute_clusters(n, n_clusters, focus_weights=focus_weights, solver_name=solver_name)
def reduce_network(n, buses):
nr = pypsa.Network()
nr.import_components_from_dataframe(buses, "Bus")
nr.import_components_from_dataframe(n.lines.loc[n.lines.bus0.isin(buses.index) & n.lines.bus1.isin(buses.index)], "Line")
return nr
n_clusters = distribute_clusters(
n, n_clusters, focus_weights=focus_weights, solver_name=solver_name
)
def busmap_for_country(x):
prefix = x.name[0] + x.name[1] + ' '
prefix = x.name[0] + x.name[1] + " "
logger.debug(f"Determining busmap for country {prefix[:-1]}")
if len(x) == 1:
return pd.Series(prefix + '0', index=x.index)
return pd.Series(prefix + "0", index=x.index)
weight = weighting_for_country(n, x)
if algorithm == "kmeans":
return prefix + busmap_by_kmeans(n, weight, n_clusters[x.name], buses_i=x.index, **algorithm_kwds)
elif algorithm == "spectral":
return prefix + busmap_by_spectral_clustering(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
elif algorithm == "louvain":
return prefix + busmap_by_louvain(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
return prefix + busmap_by_kmeans(
n, weight, n_clusters[x.name], buses_i=x.index, **algorithm_kwds
)
elif algorithm == "hac":
return prefix + busmap_by_hac(
n, n_clusters[x.name], buses_i=x.index, feature=feature.loc[x.index]
)
elif algorithm == "modularity":
return prefix + busmap_by_greedy_modularity(
n, n_clusters[x.name], buses_i=x.index
)
else:
raise ValueError(f"`algorithm` must be one of 'kmeans', 'spectral' or 'louvain'. Is {algorithm}.")
raise ValueError(
f"`algorithm` must be one of 'kmeans' or 'hac'. Is {algorithm}."
)
return (n.buses.groupby(['country', 'sub_network'], group_keys=False)
.apply(busmap_for_country).squeeze().rename('busmap'))
return (
n.buses.groupby(["country", "sub_network"], group_keys=False)
.apply(busmap_for_country)
.squeeze()
.rename("busmap")
)
def clustering_for_n_clusters(n, n_clusters, custom_busmap=False, aggregate_carriers=None,
line_length_factor=1.25, potential_mode='simple', solver_name="cbc",
algorithm="kmeans", extended_link_costs=0, focus_weights=None):
def clustering_for_n_clusters(
n,
n_clusters,
custom_busmap=False,
aggregate_carriers=None,
line_length_factor=1.25,
aggregation_strategies=dict(),
solver_name="cbc",
algorithm="hac",
feature=None,
extended_link_costs=0,
focus_weights=None,
):
if potential_mode == 'simple':
p_nom_max_strategy = pd.Series.sum
elif potential_mode == 'conservative':
p_nom_max_strategy = pd.Series.min
else:
raise AttributeError(f"potential_mode should be one of 'simple' or 'conservative' but is '{potential_mode}'")
bus_strategies, generator_strategies = get_aggregation_strategies(
aggregation_strategies
)
if not isinstance(custom_busmap, pd.Series):
busmap = busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights, algorithm)
busmap = busmap_for_n_clusters(
n, n_clusters, solver_name, focus_weights, algorithm, feature
)
else:
busmap = custom_busmap
clustering = get_clustering_from_busmap(
n, busmap,
bus_strategies=dict(country=_make_consense("Bus", "country")),
n,
busmap,
bus_strategies=bus_strategies,
aggregate_generators_weighted=True,
aggregate_generators_carriers=aggregate_carriers,
aggregate_one_ports=["Load", "StorageUnit"],
line_length_factor=line_length_factor,
generator_strategies={'p_nom_max': p_nom_max_strategy, 'p_nom_min': pd.Series.sum},
scale_link_capital_costs=False)
generator_strategies=generator_strategies,
scale_link_capital_costs=False,
)
if not n.links.empty:
nc = clustering.network
nc.links['underwater_fraction'] = (n.links.eval('underwater_fraction * length')
.div(nc.links.length).dropna())
nc.links['capital_cost'] = (nc.links['capital_cost']
.add((nc.links.length - n.links.length)
.clip(lower=0).mul(extended_link_costs),
fill_value=0))
nc.links["underwater_fraction"] = (
n.links.eval("underwater_fraction * length").div(nc.links.length).dropna()
)
nc.links["capital_cost"] = nc.links["capital_cost"].add(
(nc.links.length - n.links.length).clip(lower=0).mul(extended_link_costs),
fill_value=0,
)
return clustering
def save_to_geojson(s, fn):
if os.path.exists(fn):
os.unlink(fn)
df = s.reset_index()
schema = {**gpd.io.file.infer_schema(df), 'geometry': 'Unknown'}
df.to_file(fn, driver='GeoJSON', schema=schema)
def cluster_regions(busmaps, input=None, output=None):
busmap = reduce(lambda x, y: x.map(y), busmaps[1:], busmaps[0])
for which in ('regions_onshore', 'regions_offshore'):
regions = gpd.read_file(getattr(input, which)).set_index('name')
geom_c = regions.geometry.groupby(busmap).apply(shapely.ops.unary_union)
regions_c = gpd.GeoDataFrame(dict(geometry=geom_c))
regions_c.index.name = 'name'
save_to_geojson(regions_c, getattr(output, which))
for which in ("regions_onshore", "regions_offshore"):
regions = gpd.read_file(getattr(input, which))
regions = regions.reindex(columns=["name", "geometry"]).set_index("name")
regions_c = regions.dissolve(busmap)
regions_c.index.name = "name"
regions_c = regions_c.reset_index()
regions_c.to_file(getattr(output, which))
def plot_busmap_for_n_clusters(n, n_clusters, fn=None):
@ -322,69 +452,112 @@ def plot_busmap_for_n_clusters(n, n_clusters, fn=None):
cr = sns.color_palette("hls", len(cs))
n.plot(bus_colors=busmap.map(dict(zip(cs, cr))))
if fn is not None:
plt.savefig(fn, bbox_inches='tight')
plt.savefig(fn, bbox_inches="tight")
del cs, cr
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('cluster_network', network='elec', simpl='', clusters='5')
snakemake = mock_snakemake("cluster_network", simpl="", clusters="5")
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
focus_weights = snakemake.config.get('focus_weights', None)
focus_weights = snakemake.config.get("focus_weights", None)
renewable_carriers = pd.Index([tech
for tech in n.generators.carrier.unique()
if tech in snakemake.config['renewable']])
renewable_carriers = pd.Index(
[
tech
for tech in n.generators.carrier.unique()
if tech in snakemake.config["renewable"]
]
)
if snakemake.wildcards.clusters.endswith('m'):
exclude_carriers = snakemake.config["clustering"]["cluster_network"].get(
"exclude_carriers", []
)
aggregate_carriers = set(n.generators.carrier) - set(exclude_carriers)
if snakemake.wildcards.clusters.endswith("m"):
n_clusters = int(snakemake.wildcards.clusters[:-1])
aggregate_carriers = pd.Index(n.generators.carrier.unique()).difference(renewable_carriers)
elif snakemake.wildcards.clusters == 'all':
aggregate_carriers = snakemake.config["electricity"].get(
"conventional_carriers"
)
elif snakemake.wildcards.clusters == "all":
n_clusters = len(n.buses)
aggregate_carriers = None # All
else:
n_clusters = int(snakemake.wildcards.clusters)
aggregate_carriers = None # All
if n_clusters == len(n.buses):
# Fast-path if no clustering is necessary
busmap = n.buses.index.to_series()
linemap = n.lines.index.to_series()
clustering = pypsa.networkclustering.Clustering(n, busmap, linemap, linemap, pd.Series(dtype='O'))
clustering = pypsa.networkclustering.Clustering(
n, busmap, linemap, linemap, pd.Series(dtype="O")
)
else:
line_length_factor = snakemake.config['lines']['length_factor']
Nyears = n.snapshot_weightings.objective.sum()/8760
line_length_factor = snakemake.config["lines"]["length_factor"]
Nyears = n.snapshot_weightings.objective.sum() / 8760
hvac_overhead_cost = (load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
.at['HVAC overhead', 'capital_cost'])
hvac_overhead_cost = load_costs(
snakemake.input.tech_costs,
snakemake.config["costs"],
snakemake.config["electricity"],
Nyears,
).at["HVAC overhead", "capital_cost"]
def consense(x):
v = x.iat[0]
assert ((x == v).all() or x.isnull().all()), (
"The `potential` configuration option must agree for all renewable carriers, for now!"
)
assert (
x == v
).all() or x.isnull().all(), "The `potential` configuration option must agree for all renewable carriers, for now!"
return v
potential_mode = consense(pd.Series([snakemake.config['renewable'][tech]['potential']
for tech in renewable_carriers]))
aggregation_strategies = snakemake.config["clustering"].get(
"aggregation_strategies", {}
)
# translate str entries of aggregation_strategies to pd.Series functions:
aggregation_strategies = {
p: {k: getattr(pd.Series, v) for k, v in aggregation_strategies[p].items()}
for p in aggregation_strategies.keys()
}
custom_busmap = snakemake.config["enable"].get("custom_busmap", False)
if custom_busmap:
custom_busmap = pd.read_csv(snakemake.input.custom_busmap, index_col=0, squeeze=True)
custom_busmap = pd.read_csv(
snakemake.input.custom_busmap, index_col=0, squeeze=True
)
custom_busmap.index = custom_busmap.index.astype(str)
logger.info(f"Imported custom busmap from {snakemake.input.custom_busmap}")
clustering = clustering_for_n_clusters(n, n_clusters, custom_busmap, aggregate_carriers,
line_length_factor, potential_mode,
snakemake.config['solving']['solver']['name'],
"kmeans", hvac_overhead_cost, focus_weights)
cluster_config = snakemake.config.get("clustering", {}).get(
"cluster_network", {}
)
clustering = clustering_for_n_clusters(
n,
n_clusters,
custom_busmap,
aggregate_carriers,
line_length_factor,
aggregation_strategies,
snakemake.config["solving"]["solver"]["name"],
cluster_config.get("algorithm", "hac"),
cluster_config.get("feature", "solar+onwind-time"),
hvac_overhead_cost,
focus_weights,
)
update_p_nom_max(n)
update_p_nom_max(clustering.network)
clustering.network.meta = dict(
snakemake.config, **dict(wildcards=dict(snakemake.wildcards))
)
clustering.network.export_to_netcdf(snakemake.output.network)
for attr in ('busmap', 'linemap'): #also available: linemap_positive, linemap_negative
for attr in (
"busmap",
"linemap",
): # also available: linemap_positive, linemap_negative
getattr(clustering, attr).to_csv(snakemake.output[attr])
cluster_regions((clustering.busmap,), snakemake.input, snakemake.output)

421
scripts/make_summary.py
View File

@ -1,4 +1,5 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -11,8 +12,9 @@ Relevant Settings
.. code:: yaml
costs:
USD2013_to_EUR2013:
discountrate:
year:
version:
fill_values:
marginal_cost:
capital_cost:
@ -32,9 +34,9 @@ Outputs
Description
-----------
The following rule can be used to summarize the results in seperate .csv files:
The following rule can be used to summarize the results in separate .csv files:
.. code::
.. code:: bash
snakemake results/summaries/elec_s_all_lall_Co2L-3H_all
clusters
@ -45,28 +47,26 @@ The following rule can be used to summarize the results in seperate .csv files:
the line volume/cost cap field can be set to one of the following:
* ``lv1.25`` for a particular line volume extension by 25%
* ``lc1.25`` for a line cost extension by 25 %
* ``lall`` for all evalutated caps
* ``lall`` for all evaluated caps
* ``lvall`` for all line volume caps
* ``lcall`` for all line cost caps
Replacing '/summaries/' with '/plots/' creates nice colored maps of the results.
"""
import logging
from _helpers import configure_logging
import os
import pypsa
import pandas as pd
import pandas as pd
import pypsa
from _helpers import configure_logging
from add_electricity import load_costs, update_transmission_costs
idx = pd.IndexSlice
logger = logging.getLogger(__name__)
opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"}
opt_name = {"Store": "e", "Line": "s", "Transformer": "s"}
def _add_indexed_rows(df, raw_index):
@ -78,102 +78,149 @@ def _add_indexed_rows(df, raw_index):
def assign_carriers(n):
if "carrier" not in n.loads:
n.loads["carrier"] = "electricity"
for carrier in ["transport","heat","urban heat"]:
n.loads.loc[n.loads.index.str.contains(carrier),"carrier"] = carrier
for carrier in ["transport", "heat", "urban heat"]:
n.loads.loc[n.loads.index.str.contains(carrier), "carrier"] = carrier
n.storage_units['carrier'].replace({'hydro': 'hydro+PHS', 'PHS': 'hydro+PHS'}, inplace=True)
n.storage_units["carrier"].replace(
{"hydro": "hydro+PHS", "PHS": "hydro+PHS"}, inplace=True
)
if "carrier" not in n.lines:
n.lines["carrier"] = "AC"
n.lines["carrier"].replace({"AC": "lines"}, inplace=True)
if n.links.empty: n.links["carrier"] = pd.Series(dtype=str)
if n.links.empty:
n.links["carrier"] = pd.Series(dtype=str)
n.links["carrier"].replace({"DC": "lines"}, inplace=True)
if "EU gas store" in n.stores.index and n.stores.loc["EU gas Store","carrier"] == "":
n.stores.loc["EU gas Store","carrier"] = "gas Store"
if (
"EU gas store" in n.stores.index
and n.stores.loc["EU gas Store", "carrier"] == ""
):
n.stores.loc["EU gas Store", "carrier"] = "gas Store"
def calculate_costs(n, label, costs):
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
capital_costs = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
for c in n.iterate_components(
n.branch_components | n.controllable_one_port_components ^ {"Load"}
):
capital_costs = c.df.capital_cost * c.df[opt_name.get(c.name, "p") + "_nom_opt"]
capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum()
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[c.list_name],["capital"],list(capital_costs_grouped.index)])
raw_index = tuple(
[[c.list_name], ["capital"], list(capital_costs_grouped.index)]
)
costs = _add_indexed_rows(costs, raw_index)
costs.loc[idx[raw_index],label] = capital_costs_grouped.values
costs.loc[idx[raw_index], label] = capital_costs_grouped.values
if c.name == "Link":
p = c.pnl.p0.multiply(n.snapshot_weightings.generators,axis=0).sum()
p = c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
elif c.name == "Line":
continue
elif c.name == "StorageUnit":
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators,axis=0)
p_all[p_all < 0.] = 0.
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
p_all[p_all < 0.0] = 0.0
p = p_all.sum()
else:
p = c.pnl.p.multiply(n.snapshot_weightings.generators,axis=0).sum()
p = c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0).sum()
marginal_costs = p*c.df.marginal_cost
marginal_costs = p * c.df.marginal_cost
marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
costs = costs.reindex(costs.index.union(pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index])))
costs = costs.reindex(
costs.index.union(
pd.MultiIndex.from_product(
[[c.list_name], ["marginal"], marginal_costs_grouped.index]
)
)
)
costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values
costs.loc[
idx[c.list_name, "marginal", list(marginal_costs_grouped.index)], label
] = marginal_costs_grouped.values
return costs
def calculate_curtailment(n, label, curtailment):
avail = n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt).sum().groupby(n.generators.carrier).sum()
def calculate_curtailment(n, label, curtailment):
avail = (
n.generators_t.p_max_pu.multiply(n.generators.p_nom_opt)
.sum()
.groupby(n.generators.carrier)
.sum()
)
used = n.generators_t.p.sum().groupby(n.generators.carrier).sum()
curtailment[label] = (((avail - used)/avail)*100).round(3)
curtailment[label] = (((avail - used) / avail) * 100).round(3)
return curtailment
def calculate_energy(n, label, energy):
for c in n.iterate_components(n.one_port_components | n.branch_components):
for c in n.iterate_components(n.one_port_components|n.branch_components):
if c.name in {'Generator', 'Load', 'ShuntImpedance'}:
c_energies = c.pnl.p.multiply(n.snapshot_weightings.generators,axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
elif c.name in {'StorageUnit', 'Store'}:
c_energies = c.pnl.p.multiply(n.snapshot_weightings.stores,axis=0).sum().multiply(c.df.sign).groupby(c.df.carrier).sum()
if c.name in {"Generator", "Load", "ShuntImpedance"}:
c_energies = (
c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
.sum()
.multiply(c.df.sign)
.groupby(c.df.carrier)
.sum()
)
elif c.name in {"StorageUnit", "Store"}:
c_energies = (
c.pnl.p.multiply(n.snapshot_weightings.stores, axis=0)
.sum()
.multiply(c.df.sign)
.groupby(c.df.carrier)
.sum()
)
else:
c_energies = (-c.pnl.p1.multiply(n.snapshot_weightings.generators,axis=0).sum() - c.pnl.p0.multiply(n.snapshot_weightings.generators,axis=0).sum()).groupby(c.df.carrier).sum()
c_energies = (
(
-c.pnl.p1.multiply(n.snapshot_weightings.generators, axis=0).sum()
- c.pnl.p0.multiply(n.snapshot_weightings.generators, axis=0).sum()
)
.groupby(c.df.carrier)
.sum()
)
energy = include_in_summary(energy, [c.list_name], label, c_energies)
return energy
def include_in_summary(summary, multiindexprefix, label, item):
def include_in_summary(summary, multiindexprefix, label, item):
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([multiindexprefix,list(item.index)])
raw_index = tuple([multiindexprefix, list(item.index)])
summary = _add_indexed_rows(summary, raw_index)
summary.loc[idx[raw_index], label] = item.values
return summary
def calculate_capacity(n,label,capacity):
def calculate_capacity(n, label, capacity):
for c in n.iterate_components(n.one_port_components):
if 'p_nom_opt' in c.df.columns:
c_capacities = abs(c.df.p_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
if "p_nom_opt" in c.df.columns:
c_capacities = (
abs(c.df.p_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
)
capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
elif "e_nom_opt" in c.df.columns:
c_capacities = (
abs(c.df.e_nom_opt.multiply(c.df.sign)).groupby(c.df.carrier).sum()
)
capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
for c in n.iterate_components(n.passive_branch_components):
c_capacities = c.df['s_nom_opt'].groupby(c.df.carrier).sum()
c_capacities = c.df["s_nom_opt"].groupby(c.df.carrier).sum()
capacity = include_in_summary(capacity, [c.list_name], label, c_capacities)
for c in n.iterate_components(n.controllable_branch_components):
@ -182,16 +229,20 @@ def calculate_capacity(n,label,capacity):
return capacity
def calculate_supply(n, label, supply):
"""calculate the max dispatch of each component at the buses where the loads are attached"""
load_types = n.loads.carrier.value_counts().index
def calculate_supply(n, label, supply):
"""
calculate the max dispatch of each component at the buses where the loads
are attached.
"""
load_types = n.buses.carrier.unique()
for i in load_types:
buses = n.loads.bus[n.loads.carrier == i].values
buses = n.buses.query("carrier == @i").index
bus_map = pd.Series(False,index=n.buses.index)
bus_map = pd.Series(False, index=n.buses.index)
bus_map.loc[buses] = True
@ -202,43 +253,57 @@ def calculate_supply(n, label, supply):
if len(items) == 0 or c.pnl.p.empty:
continue
s = c.pnl.p[items].max().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
s = (
c.pnl.p[items]
.max()
.multiply(c.df.loc[items, "sign"])
.groupby(c.df.loc[items, "carrier"])
.sum()
)
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[i],[c.list_name],list(s.index)])
raw_index = tuple([[i], [c.list_name], list(s.index)])
supply = _add_indexed_rows(supply, raw_index)
supply.loc[idx[raw_index],label] = s.values
supply.loc[idx[raw_index], label] = s.values
for c in n.iterate_components(n.branch_components):
for end in ["0","1"]:
for end in ["0", "1"]:
items = c.df.index[c.df["bus" + end].map(bus_map)]
if len(items) == 0 or c.pnl["p"+end].empty:
if len(items) == 0 or c.pnl["p" + end].empty:
continue
#lots of sign compensation for direction and to do maximums
s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items,'carrier']).sum()
# lots of sign compensation for direction and to do maximums
s = (-1) ** (1 - int(end)) * (
(-1) ** int(end) * c.pnl["p" + end][items]
).max().groupby(c.df.loc[items, "carrier"]).sum()
supply = supply.reindex(supply.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values
supply = supply.reindex(
supply.index.union(
pd.MultiIndex.from_product([[i], [c.list_name], s.index])
)
)
supply.loc[idx[i, c.list_name, list(s.index)], label] = s.values
return supply
def calculate_supply_energy(n, label, supply_energy):
"""calculate the total dispatch of each component at the buses where the loads are attached"""
"""
calculate the total dispatch of each component at the buses where the loads
are attached.
"""
load_types = n.loads.carrier.value_counts().index
load_types = n.buses.carrier.unique()
for i in load_types:
buses = n.loads.bus[n.loads.carrier == i].values
buses = n.buses.query("carrier == @i").index
bus_map = pd.Series(False,index=n.buses.index)
bus_map = pd.Series(False, index=n.buses.index)
bus_map.loc[buses] = True
@ -249,55 +314,83 @@ def calculate_supply_energy(n, label, supply_energy):
if len(items) == 0 or c.pnl.p.empty:
continue
s = c.pnl.p[items].sum().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
s = (
c.pnl.p[items]
.sum()
.multiply(c.df.loc[items, "sign"])
.groupby(c.df.loc[items, "carrier"])
.sum()
)
# Index tuple(s) indicating the newly to-be-added row(s)
raw_index = tuple([[i],[c.list_name],list(s.index)])
raw_index = tuple([[i], [c.list_name], list(s.index)])
supply_energy = _add_indexed_rows(supply_energy, raw_index)
supply_energy.loc[idx[raw_index],label] = s.values
supply_energy.loc[idx[raw_index], label] = s.values
for c in n.iterate_components(n.branch_components):
for end in ["0","1"]:
for end in ["0", "1"]:
items = c.df.index[c.df["bus" + end].map(bus_map)]
if len(items) == 0 or c.pnl['p' + end].empty:
if len(items) == 0 or c.pnl["p" + end].empty:
continue
s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'carrier']).sum()
s = (-1) * c.pnl["p" + end][items].sum().groupby(
c.df.loc[items, "carrier"]
).sum()
supply_energy = supply_energy.reindex(supply_energy.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values
supply_energy = supply_energy.reindex(
supply_energy.index.union(
pd.MultiIndex.from_product([[i], [c.list_name], s.index])
)
)
supply_energy.loc[idx[i, c.list_name, list(s.index)], label] = s.values
return supply_energy
def calculate_metrics(n,label,metrics):
def calculate_metrics(n, label, metrics):
metrics = metrics.reindex(
metrics.index.union(
pd.Index(
[
"line_volume",
"line_volume_limit",
"line_volume_AC",
"line_volume_DC",
"line_volume_shadow",
"co2_shadow",
]
)
)
)
metrics = metrics.reindex(metrics.index.union(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])))
metrics.at["line_volume_DC", label] = (n.links.length * n.links.p_nom_opt)[
n.links.carrier == "DC"
].sum()
metrics.at["line_volume_AC", label] = (n.lines.length * n.lines.s_nom_opt).sum()
metrics.at["line_volume", label] = metrics.loc[
["line_volume_AC", "line_volume_DC"], label
].sum()
metrics.at["line_volume_DC",label] = (n.links.length*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
metrics.at["line_volume_AC",label] = (n.lines.length*n.lines.s_nom_opt).sum()
metrics.at["line_volume",label] = metrics.loc[["line_volume_AC","line_volume_DC"],label].sum()
if hasattr(n, "line_volume_limit"):
metrics.at["line_volume_limit", label] = n.line_volume_limit
if hasattr(n,"line_volume_limit"):
metrics.at["line_volume_limit",label] = n.line_volume_limit
if hasattr(n,"line_volume_limit_dual"):
metrics.at["line_volume_shadow",label] = n.line_volume_limit_dual
if hasattr(n, "line_volume_limit_dual"):
metrics.at["line_volume_shadow", label] = n.line_volume_limit_dual
if "CO2Limit" in n.global_constraints.index:
metrics.at["co2_shadow",label] = n.global_constraints.at["CO2Limit","mu"]
metrics.at["co2_shadow", label] = n.global_constraints.at["CO2Limit", "mu"]
return metrics
def calculate_prices(n,label,prices):
bus_type = pd.Series(n.buses.index.str[3:],n.buses.index).replace("","electricity")
def calculate_prices(n, label, prices):
bus_type = pd.Series(n.buses.index.str[3:], n.buses.index).replace(
"", "electricity"
)
prices = prices.reindex(prices.index.union(bus_type.value_counts().index))
@ -307,19 +400,37 @@ def calculate_prices(n,label,prices):
return prices
def calculate_weighted_prices(n,label,weighted_prices):
def calculate_weighted_prices(n, label, weighted_prices):
logger.warning("Weighted prices don't include storage units as loads")
weighted_prices = weighted_prices.reindex(pd.Index(["electricity","heat","space heat","urban heat","space urban heat","gas","H2"]))
weighted_prices = weighted_prices.reindex(
pd.Index(
[
"electricity",
"heat",
"space heat",
"urban heat",
"space urban heat",
"gas",
"H2",
]
)
)
link_loads = {"electricity" : ["heat pump", "resistive heater", "battery charger", "H2 Electrolysis"],
"heat" : ["water tanks charger"],
"urban heat" : ["water tanks charger"],
"space heat" : [],
"space urban heat" : [],
"gas" : ["OCGT","gas boiler","CHP electric","CHP heat"],
"H2" : ["Sabatier", "H2 Fuel Cell"]}
link_loads = {
"electricity": [
"heat pump",
"resistive heater",
"battery charger",
"H2 Electrolysis",
],
"heat": ["water tanks charger"],
"urban heat": ["water tanks charger"],
"space heat": [],
"space urban heat": [],
"gas": ["OCGT", "gas boiler", "CHP electric", "CHP heat"],
"H2": ["Sabatier", "H2 Fuel Cell"],
}
for carrier in link_loads:
@ -328,64 +439,77 @@ def calculate_weighted_prices(n,label,weighted_prices):
elif carrier[:5] == "space":
suffix = carrier[5:]
else:
suffix = " " + carrier
suffix = " " + carrier
buses = n.buses.index[n.buses.index.str[2:] == suffix]
if buses.empty:
continue
if carrier in ["H2","gas"]:
load = pd.DataFrame(index=n.snapshots,columns=buses,data=0.)
if carrier in ["H2", "gas"]:
load = pd.DataFrame(index=n.snapshots, columns=buses, data=0.0)
elif carrier[:5] == "space":
load = heat_demand_df[buses.str[:2]].rename(columns=lambda i: str(i)+suffix)
load = heat_demand_df[buses.str[:2]].rename(
columns=lambda i: str(i) + suffix
)
else:
load = n.loads_t.p_set[buses]
for tech in link_loads[carrier]:
names = n.links.index[n.links.index.to_series().str[-len(tech):] == tech]
names = n.links.index[n.links.index.to_series().str[-len(tech) :] == tech]
if names.empty:
continue
load += n.links_t.p0[names].groupby(n.links.loc[names,"bus0"],axis=1).sum(axis=1)
load += (
n.links_t.p0[names]
.groupby(n.links.loc[names, "bus0"], axis=1)
.sum(axis=1)
)
# Add H2 Store when charging
if carrier == "H2":
stores = n.stores_t.p[buses+ " Store"].groupby(n.stores.loc[buses+ " Store","bus"],axis=1).sum(axis=1)
stores[stores > 0.] = 0.
stores = (
n.stores_t.p[buses + " Store"]
.groupby(n.stores.loc[buses + " Store", "bus"], axis=1)
.sum(axis=1)
)
stores[stores > 0.0] = 0.0
load += -stores
weighted_prices.loc[carrier,label] = (load*n.buses_t.marginal_price[buses]).sum().sum()/load.sum().sum()
weighted_prices.loc[carrier, label] = (
load * n.buses_t.marginal_price[buses]
).sum().sum() / load.sum().sum()
if carrier[:5] == "space":
print(load*n.buses_t.marginal_price[buses])
print(load * n.buses_t.marginal_price[buses])
return weighted_prices
outputs = ["costs",
"curtailment",
"energy",
"capacity",
"supply",
"supply_energy",
"prices",
"weighted_prices",
"metrics",
]
outputs = [
"costs",
"curtailment",
"energy",
"capacity",
"supply",
"supply_energy",
"prices",
"weighted_prices",
"metrics",
]
def make_summaries(networks_dict, paths, config, country='all'):
columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["simpl","clusters","ll","opts"])
def make_summaries(networks_dict, paths, config, country="all"):
columns = pd.MultiIndex.from_tuples(
networks_dict.keys(), names=["simpl", "clusters", "ll", "opts"]
)
dfs = {}
for output in outputs:
dfs[output] = pd.DataFrame(columns=columns,dtype=float)
dfs[output] = pd.DataFrame(columns=columns, dtype=float)
for label, filename in networks_dict.items():
print(label, filename)
@ -399,12 +523,12 @@ def make_summaries(networks_dict, paths, config, country='all'):
logger.warning("Skipping {filename}".format(filename=filename))
continue
if country != 'all':
if country != "all":
n = n[n.buses.country == country]
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(paths[0], config['costs'], config['electricity'], Nyears)
update_transmission_costs(n, costs, simple_hvdc_costs=False)
Nyears = n.snapshot_weightings.objective.sum() / 8760.0
costs = load_costs(paths[0], config["costs"], config["electricity"], Nyears)
update_transmission_costs(n, costs)
assign_carriers(n)
@ -421,13 +545,24 @@ def to_csv(dfs, dir):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('make_summary', network='elec', simpl='',
clusters='5', ll='copt', opts='Co2L-24H', country='all')
network_dir = os.path.join('..', 'results', 'networks')
snakemake = mock_snakemake(
"make_summary",
simpl="",
clusters="5",
ll="copt",
opts="Co2L-24H",
country="all",
)
network_dir = os.path.join(
"..", "results", "networks", snakemake.config["run"]["name"]
)
else:
network_dir = os.path.join('results', 'networks')
network_dir = os.path.join(
"results", "networks", snakemake.config["run"]["name"]
)
configure_logging(snakemake)
config = snakemake.config
@ -444,14 +579,18 @@ if __name__ == "__main__":
else:
ll = [wildcards.ll]
networks_dict = {(simpl,clusters,l,opts) :
os.path.join(network_dir, f'elec_s{simpl}_'
f'{clusters}_ec_l{l}_{opts}.nc')
for simpl in expand_from_wildcard("simpl", config)
for clusters in expand_from_wildcard("clusters", config)
for l in ll
for opts in expand_from_wildcard("opts", config)}
networks_dict = {
(simpl, clusters, l, opts): os.path.join(
network_dir, f"elec_s{simpl}_" f"{clusters}_ec_l{l}_{opts}.nc"
)
for simpl in expand_from_wildcard("simpl", config)
for clusters in expand_from_wildcard("clusters", config)
for l in ll
for opts in expand_from_wildcard("opts", config)
}
dfs = make_summaries(networks_dict, snakemake.input, config, country=wildcards.country)
dfs = make_summaries(
networks_dict, snakemake.input, config, country=wildcards.country
)
to_csv(dfs, snakemake.output[0])

401
scripts/plot_network.py
View File

@ -1,4 +1,5 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -16,21 +17,24 @@ Outputs
Description
-----------
"""
import logging
from _helpers import (retrieve_snakemake_keys, load_network_for_plots,
aggregate_p, aggregate_costs, configure_logging)
import pandas as pd
import numpy as np
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.patches import Circle, Ellipse
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from _helpers import (
aggregate_costs,
aggregate_p,
configure_logging,
load_network_for_plots,
)
from matplotlib.legend_handler import HandlerPatch
from matplotlib.patches import Circle, Ellipse
to_rgba = mpl.colors.colorConverter.to_rgba
logger = logging.getLogger(__name__)
@ -38,253 +42,368 @@ logger = logging.getLogger(__name__)
def make_handler_map_to_scale_circles_as_in(ax, dont_resize_actively=False):
fig = ax.get_figure()
def axes2pt():
return np.diff(ax.transData.transform([(0,0), (1,1)]), axis=0)[0] * (72./fig.dpi)
return np.diff(ax.transData.transform([(0, 0), (1, 1)]), axis=0)[0] * (
72.0 / fig.dpi
)
ellipses = []
if not dont_resize_actively:
def update_width_height(event):
dist = axes2pt()
for e, radius in ellipses: e.width, e.height = 2. * radius * dist
fig.canvas.mpl_connect('resize_event', update_width_height)
ax.callbacks.connect('xlim_changed', update_width_height)
ax.callbacks.connect('ylim_changed', update_width_height)
for e, radius in ellipses:
e.width, e.height = 2.0 * radius * dist
def legend_circle_handler(legend, orig_handle, xdescent, ydescent,
width, height, fontsize):
w, h = 2. * orig_handle.get_radius() * axes2pt()
e = Ellipse(xy=(0.5*width-0.5*xdescent, 0.5*height-0.5*ydescent), width=w, height=w)
fig.canvas.mpl_connect("resize_event", update_width_height)
ax.callbacks.connect("xlim_changed", update_width_height)
ax.callbacks.connect("ylim_changed", update_width_height)
def legend_circle_handler(
legend, orig_handle, xdescent, ydescent, width, height, fontsize
):
w, h = 2.0 * orig_handle.get_radius() * axes2pt()
e = Ellipse(
xy=(0.5 * width - 0.5 * xdescent, 0.5 * height - 0.5 * ydescent),
width=w,
height=w,
)
ellipses.append((e, orig_handle.get_radius()))
return e
return {Circle: HandlerPatch(patch_func=legend_circle_handler)}
def make_legend_circles_for(sizes, scale=1.0, **kw):
return [Circle((0,0), radius=(s/scale)**0.5, **kw) for s in sizes]
return [Circle((0, 0), radius=(s / scale) ** 0.5, **kw) for s in sizes]
def set_plot_style():
plt.style.use(['classic', 'seaborn-white',
{'axes.grid': False, 'grid.linestyle': '--', 'grid.color': u'0.6',
'hatch.color': 'white',
'patch.linewidth': 0.5,
'font.size': 12,
'legend.fontsize': 'medium',
'lines.linewidth': 1.5,
'pdf.fonttype': 42,
}])
plt.style.use(
[
"classic",
"seaborn-white",
{
"axes.grid": False,
"grid.linestyle": "--",
"grid.color": "0.6",
"hatch.color": "white",
"patch.linewidth": 0.5,
"font.size": 12,
"legend.fontsize": "medium",
"lines.linewidth": 1.5,
"pdf.fonttype": 42,
},
]
)
def plot_map(n, ax=None, attribute='p_nom', opts={}):
def plot_map(n, opts, ax=None, attribute="p_nom"):
if ax is None:
ax = plt.gca()
## DATA
line_colors = {'cur': "purple",
'exp': mpl.colors.rgb2hex(to_rgba("red", 0.7), True)}
tech_colors = opts['tech_colors']
line_colors = {
"cur": "purple",
"exp": mpl.colors.rgb2hex(to_rgba("red", 0.7), True),
}
tech_colors = opts["tech_colors"]
if attribute == 'p_nom':
if attribute == "p_nom":
# bus_sizes = n.generators_t.p.sum().loc[n.generators.carrier == "load"].groupby(n.generators.bus).sum()
bus_sizes = pd.concat((n.generators.query('carrier != "load"').groupby(['bus', 'carrier']).p_nom_opt.sum(),
n.storage_units.groupby(['bus', 'carrier']).p_nom_opt.sum()))
bus_sizes = pd.concat(
(
n.generators.query('carrier != "load"')
.groupby(["bus", "carrier"])
.p_nom_opt.sum(),
n.storage_units.groupby(["bus", "carrier"]).p_nom_opt.sum(),
)
)
line_widths_exp = n.lines.s_nom_opt
line_widths_cur = n.lines.s_nom_min
link_widths_exp = n.links.p_nom_opt
link_widths_cur = n.links.p_nom_min
else:
raise 'plotting of {} has not been implemented yet'.format(attribute)
raise "plotting of {} has not been implemented yet".format(attribute)
line_colors_with_alpha = \
((line_widths_cur / n.lines.s_nom > 1e-3)
.map({True: line_colors['cur'], False: to_rgba(line_colors['cur'], 0.)}))
link_colors_with_alpha = \
((link_widths_cur / n.links.p_nom > 1e-3)
.map({True: line_colors['cur'], False: to_rgba(line_colors['cur'], 0.)}))
line_colors_with_alpha = (line_widths_cur / n.lines.s_nom > 1e-3).map(
{True: line_colors["cur"], False: to_rgba(line_colors["cur"], 0.0)}
)
link_colors_with_alpha = (link_widths_cur / n.links.p_nom > 1e-3).map(
{True: line_colors["cur"], False: to_rgba(line_colors["cur"], 0.0)}
)
## FORMAT
linewidth_factor = opts['map'][attribute]['linewidth_factor']
bus_size_factor = opts['map'][attribute]['bus_size_factor']
linewidth_factor = opts["map"][attribute]["linewidth_factor"]
bus_size_factor = opts["map"][attribute]["bus_size_factor"]
## PLOT
n.plot(line_widths=line_widths_exp/linewidth_factor,
link_widths=link_widths_exp/linewidth_factor,
line_colors=line_colors['exp'],
link_colors=line_colors['exp'],
bus_sizes=bus_sizes/bus_size_factor,
bus_colors=tech_colors,
boundaries=map_boundaries,
color_geomap=True, geomap=True,
ax=ax)
n.plot(line_widths=line_widths_cur/linewidth_factor,
link_widths=link_widths_cur/linewidth_factor,
line_colors=line_colors_with_alpha,
link_colors=link_colors_with_alpha,
bus_sizes=0,
boundaries=map_boundaries,
color_geomap=True, geomap=False,
ax=ax)
ax.set_aspect('equal')
ax.axis('off')
n.plot(
line_widths=line_widths_exp / linewidth_factor,
link_widths=link_widths_exp / linewidth_factor,
line_colors=line_colors["exp"],
link_colors=line_colors["exp"],
bus_sizes=bus_sizes / bus_size_factor,
bus_colors=tech_colors,
boundaries=map_boundaries,
color_geomap=True,
geomap=True,
ax=ax,
)
n.plot(
line_widths=line_widths_cur / linewidth_factor,
link_widths=link_widths_cur / linewidth_factor,
line_colors=line_colors_with_alpha,
link_colors=link_colors_with_alpha,
bus_sizes=0,
boundaries=map_boundaries,
color_geomap=True,
geomap=True,
ax=ax,
)
ax.set_aspect("equal")
ax.axis("off")
# Rasterize basemap
# TODO : Check if this also works with cartopy
for c in ax.collections[:2]: c.set_rasterized(True)
for c in ax.collections[:2]:
c.set_rasterized(True)
# LEGEND
handles = []
labels = []
for s in (10, 1):
handles.append(plt.Line2D([0],[0],color=line_colors['exp'],
linewidth=s*1e3/linewidth_factor))
handles.append(
plt.Line2D(
[0], [0], color=line_colors["exp"], linewidth=s * 1e3 / linewidth_factor
)
)
labels.append("{} GW".format(s))
l1_1 = ax.legend(handles, labels,
loc="upper left", bbox_to_anchor=(0.24, 1.01),
frameon=False,
labelspacing=0.8, handletextpad=1.5,
title='Transmission Exp./Exist. ')
l1_1 = ax.legend(
handles,
labels,
loc="upper left",
bbox_to_anchor=(0.24, 1.01),
frameon=False,
labelspacing=0.8,
handletextpad=1.5,
title="Transmission Exp./Exist. ",
)
ax.add_artist(l1_1)
handles = []
labels = []
for s in (10, 5):
handles.append(plt.Line2D([0],[0],color=line_colors['cur'],
linewidth=s*1e3/linewidth_factor))
handles.append(
plt.Line2D(
[0], [0], color=line_colors["cur"], linewidth=s * 1e3 / linewidth_factor
)
)
labels.append("/")
l1_2 = ax.legend(handles, labels,
loc="upper left", bbox_to_anchor=(0.26, 1.01),
frameon=False,
labelspacing=0.8, handletextpad=0.5,
title=' ')
l1_2 = ax.legend(
handles,
labels,
loc="upper left",
bbox_to_anchor=(0.26, 1.01),
frameon=False,
labelspacing=0.8,
handletextpad=0.5,
title=" ",
)
ax.add_artist(l1_2)
handles = make_legend_circles_for([10e3, 5e3, 1e3], scale=bus_size_factor, facecolor="w")
handles = make_legend_circles_for(
[10e3, 5e3, 1e3], scale=bus_size_factor, facecolor="w"
)
labels = ["{} GW".format(s) for s in (10, 5, 3)]
l2 = ax.legend(handles, labels,
loc="upper left", bbox_to_anchor=(0.01, 1.01),
frameon=False, labelspacing=1.0,
title='Generation',
handler_map=make_handler_map_to_scale_circles_as_in(ax))
l2 = ax.legend(
handles,
labels,
loc="upper left",
bbox_to_anchor=(0.01, 1.01),
frameon=False,
labelspacing=1.0,
title="Generation",
handler_map=make_handler_map_to_scale_circles_as_in(ax),
)
ax.add_artist(l2)
techs = (bus_sizes.index.levels[1]).intersection(pd.Index(opts['vre_techs'] + opts['conv_techs'] + opts['storage_techs']))
techs = (bus_sizes.index.levels[1]).intersection(
pd.Index(opts["vre_techs"] + opts["conv_techs"] + opts["storage_techs"])
)
handles = []
labels = []
for t in techs:
handles.append(plt.Line2D([0], [0], color=tech_colors[t], marker='o', markersize=8, linewidth=0))
labels.append(opts['nice_names'].get(t, t))
l3 = ax.legend(handles, labels, loc="upper center", bbox_to_anchor=(0.5, -0.), # bbox_to_anchor=(0.72, -0.05),
handletextpad=0., columnspacing=0.5, ncol=4, title='Technology')
handles.append(
plt.Line2D(
[0], [0], color=tech_colors[t], marker="o", markersize=8, linewidth=0
)
)
labels.append(opts["nice_names"].get(t, t))
l3 = ax.legend(
handles,
labels,
loc="upper center",
bbox_to_anchor=(0.5, -0.0), # bbox_to_anchor=(0.72, -0.05),
handletextpad=0.0,
columnspacing=0.5,
ncol=4,
title="Technology",
)
return fig
def plot_total_energy_pie(n, ax=None):
if ax is None: ax = plt.gca()
def plot_total_energy_pie(n, opts, ax=None):
if ax is None:
ax = plt.gca()
ax.set_title('Energy per technology', fontdict=dict(fontsize="medium"))
ax.set_title("Energy per technology", fontdict=dict(fontsize="medium"))
e_primary = aggregate_p(n).drop('load', errors='ignore').loc[lambda s: s>0]
e_primary = aggregate_p(n).drop("load", errors="ignore").loc[lambda s: s > 0]
patches, texts, autotexts = ax.pie(e_primary,
patches, texts, autotexts = ax.pie(
e_primary,
startangle=90,
labels = e_primary.rename(opts['nice_names']).index,
autopct='%.0f%%',
labels=e_primary.rename(opts["nice_names"]).index,
autopct="%.0f%%",
shadow=False,
colors = [opts['tech_colors'][tech] for tech in e_primary.index])
colors=[opts["tech_colors"][tech] for tech in e_primary.index],
)
for t1, t2, i in zip(texts, autotexts, e_primary.index):
if e_primary.at[i] < 0.04 * e_primary.sum():
t1.remove()
t2.remove()
def plot_total_cost_bar(n, ax=None):
if ax is None: ax = plt.gca()
def plot_total_cost_bar(n, opts, ax=None):
if ax is None:
ax = plt.gca()
total_load = (n.snapshot_weightings.generators * n.loads_t.p.sum(axis=1)).sum()
tech_colors = opts['tech_colors']
tech_colors = opts["tech_colors"]
def split_costs(n):
costs = aggregate_costs(n).reset_index(level=0, drop=True)
costs_ex = aggregate_costs(n, existing_only=True).reset_index(level=0, drop=True)
return (costs['capital'].add(costs['marginal'], fill_value=0.),
costs_ex['capital'], costs['capital'] - costs_ex['capital'], costs['marginal'])
costs_ex = aggregate_costs(n, existing_only=True).reset_index(
level=0, drop=True
)
return (
costs["capital"].add(costs["marginal"], fill_value=0.0),
costs_ex["capital"],
costs["capital"] - costs_ex["capital"],
costs["marginal"],
)
costs, costs_cap_ex, costs_cap_new, costs_marg = split_costs(n)
costs_graph = pd.DataFrame(dict(a=costs.drop('load', errors='ignore')),
index=['AC-AC', 'AC line', 'onwind', 'offwind-ac',
'offwind-dc', 'solar', 'OCGT','CCGT', 'battery', 'H2']).dropna()
bottom = np.array([0., 0.])
costs_graph = pd.DataFrame(
dict(a=costs.drop("load", errors="ignore")),
index=[
"AC-AC",
"AC line",
"onwind",
"offwind-ac",
"offwind-dc",
"solar",
"OCGT",
"CCGT",
"battery",
"H2",
],
).dropna()
bottom = np.array([0.0, 0.0])
texts = []
for i,ind in enumerate(costs_graph.index):
data = np.asarray(costs_graph.loc[ind])/total_load
ax.bar([0.5], data, bottom=bottom, color=tech_colors[ind],
width=0.7, zorder=-1)
for i, ind in enumerate(costs_graph.index):
data = np.asarray(costs_graph.loc[ind]) / total_load
ax.bar([0.5], data, bottom=bottom, color=tech_colors[ind], width=0.7, zorder=-1)
bottom_sub = bottom
bottom = bottom+data
bottom = bottom + data
if ind in opts['conv_techs'] + ['AC line']:
if ind in opts["conv_techs"] + ["AC line"]:
for c in [costs_cap_ex, costs_marg]:
if ind in c:
data_sub = np.asarray([c.loc[ind]])/total_load
ax.bar([0.5], data_sub, linewidth=0,
bottom=bottom_sub, color=tech_colors[ind],
width=0.7, zorder=-1, alpha=0.8)
data_sub = np.asarray([c.loc[ind]]) / total_load
ax.bar(
[0.5],
data_sub,
linewidth=0,
bottom=bottom_sub,
color=tech_colors[ind],
width=0.7,
zorder=-1,
alpha=0.8,
)
bottom_sub += data_sub
if abs(data[-1]) < 5:
continue
text = ax.text(1.1,(bottom-0.5*data)[-1]-3,opts['nice_names'].get(ind,ind))
text = ax.text(
1.1, (bottom - 0.5 * data)[-1] - 3, opts["nice_names"].get(ind, ind)
)
texts.append(text)
ax.set_ylabel("Average system cost [Eur/MWh]")
ax.set_ylim([0, opts.get('costs_max', 80)])
ax.set_ylim([0, opts.get("costs_max", 80)])
ax.set_xlim([0, 1])
ax.set_xticklabels([])
ax.grid(True, axis="y", color='k', linestyle='dotted')
ax.grid(True, axis="y", color="k", linestyle="dotted")
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('plot_network', network='elec', simpl='',
clusters='5', ll='copt', opts='Co2L-24H',
attr='p_nom', ext="pdf")
snakemake = mock_snakemake(
"plot_network",
simpl="",
clusters="5",
ll="copt",
opts="Co2L-24H",
attr="p_nom",
ext="pdf",
)
configure_logging(snakemake)
set_plot_style()
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
config, wildcards = snakemake.config, snakemake.wildcards
map_figsize = config['map']['figsize']
map_boundaries = config['map']['boundaries']
map_figsize = config["plotting"]["map"]["figsize"]
map_boundaries = config["plotting"]["map"]["boundaries"]
n = load_network_for_plots(paths.network, paths.tech_costs, config)
n = load_network_for_plots(
snakemake.input.network, snakemake.input.tech_costs, config
)
scenario_opts = wildcards.opts.split('-')
scenario_opts = wildcards.opts.split("-")
fig, ax = plt.subplots(figsize=map_figsize, subplot_kw={"projection": ccrs.PlateCarree()})
plot_map(n, ax, wildcards.attr, config)
fig, ax = plt.subplots(
figsize=map_figsize, subplot_kw={"projection": ccrs.PlateCarree()}
)
plot_map(n, config["plotting"], ax=ax, attribute=wildcards.attr)
fig.savefig(out.only_map, dpi=150, bbox_inches='tight')
fig.savefig(snakemake.output.only_map, dpi=150, bbox_inches="tight")
ax1 = fig.add_axes([-0.115, 0.625, 0.2, 0.2])
plot_total_energy_pie(n, ax1)
plot_total_energy_pie(n, config["plotting"], ax=ax1)
ax2 = fig.add_axes([-0.075, 0.1, 0.1, 0.45])
plot_total_cost_bar(n, ax2)
plot_total_cost_bar(n, config["plotting"], ax=ax2)
ll = wildcards.ll
ll_type = ll[0]
ll_factor = ll[1:]
lbl = dict(c='line cost', v='line volume')[ll_type]
amnt = '{ll} x today\'s'.format(ll=ll_factor) if ll_factor != 'opt' else 'optimal'
fig.suptitle('Expansion to {amount} {label} at {clusters} clusters'
.format(amount=amnt, label=lbl, clusters=wildcards.clusters))
lbl = dict(c="line cost", v="line volume")[ll_type]
amnt = "{ll} x today's".format(ll=ll_factor) if ll_factor != "opt" else "optimal"
fig.suptitle(
"Expansion to {amount} {label} at {clusters} clusters".format(
amount=amnt, label=lbl, clusters=wildcards.clusters
)
)
fig.savefig(out.ext, transparent=True, bbox_inches='tight')
fig.savefig(snakemake.output.ext, transparent=True, bbox_inches="tight")

57
scripts/plot_p_nom_max.py
View File

@ -1,4 +1,5 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -16,14 +17,13 @@ Outputs
Description
-----------
"""
import logging
from _helpers import configure_logging, retrieve_snakemake_keys
import pypsa
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
import pypsa
from _helpers import configure_logging
logger = logging.getLogger(__name__)
@ -31,11 +31,13 @@ logger = logging.getLogger(__name__)
def cum_p_nom_max(net, tech, country=None):
carrier_b = net.generators.carrier == tech
generators = pd.DataFrame(dict(
p_nom_max=net.generators.loc[carrier_b, 'p_nom_max'],
p_max_pu=net.generators_t.p_max_pu.loc[:,carrier_b].mean(),
country=net.generators.loc[carrier_b, 'bus'].map(net.buses.country)
)).sort_values("p_max_pu", ascending=False)
generators = pd.DataFrame(
dict(
p_nom_max=net.generators.loc[carrier_b, "p_nom_max"],
p_max_pu=net.generators_t.p_max_pu.loc[:, carrier_b].mean(),
country=net.generators.loc[carrier_b, "bus"].map(net.buses.country),
)
).sort_values("p_max_pu", ascending=False)
if country is not None:
generators = generators.loc[generators.country == country]
@ -46,33 +48,38 @@ def cum_p_nom_max(net, tech, country=None):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('plot_p_nom_max', network='elec', simpl='',
techs='solar,onwind,offwind-dc', ext='png',
clusts= '5,full', country= 'all')
configure_logging(snakemake)
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
snakemake = mock_snakemake(
"plot_p_nom_max",
simpl="",
techs="solar,onwind,offwind-dc",
ext="png",
clusts="5,full",
country="all",
)
configure_logging(snakemake)
plot_kwds = dict(drawstyle="steps-post")
clusters = wildcards.clusts.split(',')
techs = wildcards.techs.split(',')
country = wildcards.country
if country == 'all':
clusters = snakemake.wildcards.clusts.split(",")
techs = snakemake.wildcards.techs.split(",")
country = snakemake.wildcards.country
if country == "all":
country = None
else:
plot_kwds['marker'] = 'x'
plot_kwds["marker"] = "x"
fig, axes = plt.subplots(1, len(techs))
for j, cluster in enumerate(clusters):
net = pypsa.Network(paths[j])
net = pypsa.Network(snakemake.input[j])
for i, tech in enumerate(techs):
cum_p_nom_max(net, tech, country).plot(x="p_max_pu", y="cum_p_nom_max",
label=cluster, ax=axes[i], **plot_kwds)
cum_p_nom_max(net, tech, country).plot(
x="p_max_pu", y="cum_p_nom_max", label=cluster, ax=axes[i], **plot_kwds
)
for i, tech in enumerate(techs):
ax = axes[i]
@ -81,4 +88,4 @@ if __name__ == "__main__":
plt.legend(title="Cluster level")
fig.savefig(out[0], transparent=True, bbox_inches='tight')
fig.savefig(snakemake.output[0], transparent=True, bbox_inches="tight")

113
scripts/plot_summary.py
View File

@ -1,4 +1,5 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -16,15 +17,14 @@ Outputs
Description
-----------
"""
import os
import logging
from _helpers import configure_logging, retrieve_snakemake_keys
import os
import pandas as pd
import matplotlib.pyplot as plt
import pandas as pd
from _helpers import configure_logging
logger = logging.getLogger(__name__)
@ -52,22 +52,37 @@ def rename_techs(label):
return label
preferred_order = pd.Index(["transmission lines","hydroelectricity","hydro reservoir","run of river","pumped hydro storage","onshore wind","offshore wind ac", "offshore wind dc","solar PV","solar thermal","OCGT","hydrogen storage","battery storage"])
preferred_order = pd.Index(
[
"transmission lines",
"hydroelectricity",
"hydro reservoir",
"run of river",
"pumped hydro storage",
"onshore wind",
"offshore wind ac",
"offshore wind dc",
"solar PV",
"solar thermal",
"OCGT",
"hydrogen storage",
"battery storage",
]
)
def plot_costs(infn, config, fn=None):
## For now ignore the simpl header
cost_df = pd.read_csv(infn,index_col=list(range(3)),header=[1,2,3])
cost_df = pd.read_csv(infn, index_col=list(range(3)), header=[1, 2, 3])
df = cost_df.groupby(cost_df.index.get_level_values(2)).sum()
#convert to billions
df = df/1e9
# convert to billions
df = df / 1e9
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[df.max(axis=1) < config['plotting']['costs_threshold']]
to_drop = df.index[df.max(axis=1) < config["plotting"]["costs_threshold"]]
print("dropping")
@ -77,22 +92,28 @@ def plot_costs(infn, config, fn=None):
print(df.sum())
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
new_index = (preferred_order.intersection(df.index)).append(
df.index.difference(preferred_order)
)
new_columns = df.sum().sort_values().index
fig, ax = plt.subplots()
fig.set_size_inches((12,8))
fig.set_size_inches((12, 8))
df.loc[new_index,new_columns].T.plot(kind="bar",ax=ax,stacked=True,color=[config['plotting']['tech_colors'][i] for i in new_index])
df.loc[new_index, new_columns].T.plot(
kind="bar",
ax=ax,
stacked=True,
color=[config["plotting"]["tech_colors"][i] for i in new_index],
)
handles,labels = ax.get_legend_handles_labels()
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
ax.set_ylim([0,config['plotting']['costs_max']])
ax.set_ylim([0, config["plotting"]["costs_max"]])
ax.set_ylabel("System Cost [EUR billion per year]")
@ -100,8 +121,7 @@ def plot_costs(infn, config, fn=None):
ax.grid(axis="y")
ax.legend(handles,labels,ncol=4,loc="upper left")
ax.legend(handles, labels, ncol=4, loc="upper left")
fig.tight_layout()
@ -110,17 +130,16 @@ def plot_costs(infn, config, fn=None):
def plot_energy(infn, config, fn=None):
energy_df = pd.read_csv(infn, index_col=list(range(2)),header=[1,2,3])
energy_df = pd.read_csv(infn, index_col=list(range(2)), header=[1, 2, 3])
df = energy_df.groupby(energy_df.index.get_level_values(1)).sum()
#convert MWh to TWh
df = df/1e6
# convert MWh to TWh
df = df / 1e6
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[df.abs().max(axis=1) < config['plotting']['energy_threshold']]
to_drop = df.index[df.abs().max(axis=1) < config["plotting"]["energy_threshold"]]
print("dropping")
@ -130,22 +149,28 @@ def plot_energy(infn, config, fn=None):
print(df.sum())
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
new_index = (preferred_order.intersection(df.index)).append(
df.index.difference(preferred_order)
)
new_columns = df.columns.sort_values()
fig, ax = plt.subplots()
fig.set_size_inches((12,8))
fig.set_size_inches((12, 8))
df.loc[new_index,new_columns].T.plot(kind="bar",ax=ax,stacked=True,color=[config['plotting']['tech_colors'][i] for i in new_index])
df.loc[new_index, new_columns].T.plot(
kind="bar",
ax=ax,
stacked=True,
color=[config["plotting"]["tech_colors"][i] for i in new_index],
)
handles,labels = ax.get_legend_handles_labels()
handles, labels = ax.get_legend_handles_labels()
handles.reverse()
labels.reverse()
ax.set_ylim([config['plotting']['energy_min'], config['plotting']['energy_max']])
ax.set_ylim([config["plotting"]["energy_min"], config["plotting"]["energy_max"]])
ax.set_ylabel("Energy [TWh/a]")
@ -153,8 +178,7 @@ def plot_energy(infn, config, fn=None):
ax.grid(axis="y")
ax.legend(handles,labels,ncol=4,loc="upper left")
ax.legend(handles, labels, ncol=4, loc="upper left")
fig.tight_layout()
@ -163,19 +187,30 @@ def plot_energy(infn, config, fn=None):
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('plot_summary', summary='energy', network='elec',
simpl='', clusters=5, ll='copt', opts='Co2L-24H',
attr='', ext='png', country='all')
snakemake = mock_snakemake(
"plot_summary",
summary="energy",
simpl="",
clusters=5,
ll="copt",
opts="Co2L-24H",
attr="",
ext="png",
country="all",
)
configure_logging(snakemake)
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
config = snakemake.config
summary = wildcards.summary
summary = snakemake.wildcards.summary
try:
func = globals()[f"plot_{summary}"]
except KeyError:
raise RuntimeError(f"plotting function for {summary} has not been defined")
func(os.path.join(paths[0], f"{summary}.csv"), config, out[0])
func(
os.path.join(snakemake.input[0], f"{summary}.csv"), config, snakemake.output[0]
)

56
scripts/prepare_links_p_nom.py
View File

@ -1,11 +1,14 @@
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Extracts capacities of HVDC links from `Wikipedia <https://en.wikipedia.org/wiki/List_of_HVDC_projects>`_.
Extracts capacities of HVDC links from `Wikipedia.
<https://en.wikipedia.org/wiki/List_of_HVDC_projects>`_.
Relevant Settings
-----------------
@ -33,13 +36,12 @@ Description
-----------
*None*
"""
import logging
from _helpers import configure_logging, retrieve_snakemake_keys
import pandas as pd
from _helpers import configure_logging
logger = logging.getLogger(__name__)
@ -49,31 +51,45 @@ def multiply(s):
def extract_coordinates(s):
regex = (r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(N|S) "
r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(E|W)")
regex = (
r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(N|S) " r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(E|W)"
)
e = s.str.extract(regex, expand=True)
lat = (e[0].astype(float) + (e[1].astype(float) + e[2].astype(float)/60.)/60.)*e[3].map({'N': +1., 'S': -1.})
lon = (e[4].astype(float) + (e[5].astype(float) + e[6].astype(float)/60.)/60.)*e[7].map({'E': +1., 'W': -1.})
lat = (
e[0].astype(float) + (e[1].astype(float) + e[2].astype(float) / 60.0) / 60.0
) * e[3].map({"N": +1.0, "S": -1.0})
lon = (
e[4].astype(float) + (e[5].astype(float) + e[6].astype(float) / 60.0) / 60.0
) * e[7].map({"E": +1.0, "W": -1.0})
return lon, lat
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake #rule must be enabled in config
snakemake = mock_snakemake('prepare_links_p_nom', simpl='', network='elec')
if "snakemake" not in globals():
from _helpers import mock_snakemake # rule must be enabled in config
snakemake = mock_snakemake("prepare_links_p_nom", simpl="")
configure_logging(snakemake)
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
links_p_nom = pd.read_html('https://en.wikipedia.org/wiki/List_of_HVDC_projects', header=0, match="SwePol")[0]
links_p_nom = pd.read_html(
"https://en.wikipedia.org/wiki/List_of_HVDC_projects", header=0, match="SwePol"
)[0]
mw = "Power (MW)"
m_b = links_p_nom[mw].str.contains('x').fillna(False)
m_b = links_p_nom[mw].str.contains("x").fillna(False)
links_p_nom.loc[m_b, mw] = links_p_nom.loc[m_b, mw].str.split('x').pipe(multiply)
links_p_nom[mw] = links_p_nom[mw].str.extract("[-/]?([\d.]+)", expand=False).astype(float)
links_p_nom.loc[m_b, mw] = links_p_nom.loc[m_b, mw].str.split("x").pipe(multiply)
links_p_nom[mw] = (
links_p_nom[mw].str.extract("[-/]?([\d.]+)", expand=False).astype(float)
)
links_p_nom['x1'], links_p_nom['y1'] = extract_coordinates(links_p_nom['Converterstation 1'])
links_p_nom['x2'], links_p_nom['y2'] = extract_coordinates(links_p_nom['Converterstation 2'])
links_p_nom["x1"], links_p_nom["y1"] = extract_coordinates(
links_p_nom["Converterstation 1"]
)
links_p_nom["x2"], links_p_nom["y2"] = extract_coordinates(
links_p_nom["Converterstation 2"]
)
links_p_nom.dropna(subset=['x1', 'y1', 'x2', 'y2']).to_csv(out[0], index=False)
links_p_nom.dropna(subset=["x1", "y1", "x2", "y2"]).to_csv(
snakemake.output[0], index=False
)

224
scripts/prepare_network.py
View File

@ -1,10 +1,12 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Prepare PyPSA network for solving according to :ref:`opts` and :ref:`ll`, such as
Prepare PyPSA network for solving according to :ref:`opts` and :ref:`ll`, such
as.
- adding an annual **limit** of carbon-dioxide emissions,
- adding an exogenous **price** per tonne emissions of carbon-dioxide (or other kinds),
@ -20,9 +22,10 @@ Relevant Settings
.. code:: yaml
costs:
year:
version:
fill_values:
emission_prices:
USD2013_to_EUR2013:
discountrate:
marginal_cost:
capital_cost:
@ -37,7 +40,7 @@ Relevant Settings
Inputs
------
- ``data/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``resources/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``networks/elec_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
Outputs
@ -52,17 +55,15 @@ Description
The rule :mod:`prepare_all_networks` runs
for all ``scenario`` s in the configuration file
the rule :mod:`prepare_network`.
"""
import logging
from _helpers import configure_logging
import re
import pypsa
import numpy as np
import pandas as pd
import pypsa
from _helpers import configure_logging
from add_electricity import load_costs, update_transmission_costs
idx = pd.IndexSlice
@ -70,55 +71,84 @@ idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def add_co2limit(n, co2limit, Nyears=1.):
n.add("GlobalConstraint", "CO2Limit",
carrier_attribute="co2_emissions", sense="<=",
constant=co2limit * Nyears)
def add_co2limit(n, co2limit, Nyears=1.0):
n.add(
"GlobalConstraint",
"CO2Limit",
carrier_attribute="co2_emissions",
sense="<=",
constant=co2limit * Nyears,
)
def add_emission_prices(n, emission_prices={'co2': 0.}, exclude_co2=False):
if exclude_co2: emission_prices.pop('co2')
ep = (pd.Series(emission_prices).rename(lambda x: x+'_emissions') *
n.carriers.filter(like='_emissions')).sum(axis=1)
def add_gaslimit(n, gaslimit, Nyears=1.0):
sel = n.carriers.index.intersection(["OCGT", "CCGT", "CHP"])
n.carriers.loc[sel, "gas_usage"] = 1.0
n.add(
"GlobalConstraint",
"GasLimit",
carrier_attribute="gas_usage",
sense="<=",
constant=gaslimit * Nyears,
)
def add_emission_prices(n, emission_prices={"co2": 0.0}, exclude_co2=False):
if exclude_co2:
emission_prices.pop("co2")
ep = (
pd.Series(emission_prices).rename(lambda x: x + "_emissions")
* n.carriers.filter(like="_emissions")
).sum(axis=1)
gen_ep = n.generators.carrier.map(ep) / n.generators.efficiency
n.generators['marginal_cost'] += gen_ep
n.generators["marginal_cost"] += gen_ep
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 set_line_s_max_pu(n, s_max_pu = 0.7):
n.lines['s_max_pu'] = s_max_pu
def set_line_s_max_pu(n, s_max_pu=0.7):
n.lines["s_max_pu"] = s_max_pu
logger.info(f"N-1 security margin of lines set to {s_max_pu}")
def set_transmission_limit(n, ll_type, factor, costs, Nyears=1):
links_dc_b = n.links.carrier == 'DC' if not n.links.empty else pd.Series()
links_dc_b = n.links.carrier == "DC" if not n.links.empty else pd.Series()
_lines_s_nom = (np.sqrt(3) * n.lines.type.map(n.line_types.i_nom) *
n.lines.num_parallel * n.lines.bus0.map(n.buses.v_nom))
lines_s_nom = n.lines.s_nom.where(n.lines.type == '', _lines_s_nom)
_lines_s_nom = (
np.sqrt(3)
* n.lines.type.map(n.line_types.i_nom)
* n.lines.num_parallel
* n.lines.bus0.map(n.buses.v_nom)
)
lines_s_nom = n.lines.s_nom.where(n.lines.type == "", _lines_s_nom)
col = "capital_cost" if ll_type == "c" else "length"
ref = (
lines_s_nom @ n.lines[col]
+ n.links.loc[links_dc_b, "p_nom"] @ n.links.loc[links_dc_b, col]
)
col = 'capital_cost' if ll_type == 'c' else 'length'
ref = (lines_s_nom @ n.lines[col] +
n.links.loc[links_dc_b, "p_nom"] @ n.links.loc[links_dc_b, col])
update_transmission_costs(n, costs)
update_transmission_costs(n, costs, simple_hvdc_costs=False)
if factor == "opt" or float(factor) > 1.0:
n.lines["s_nom_min"] = lines_s_nom
n.lines["s_nom_extendable"] = True
if factor == 'opt' or float(factor) > 1.0:
n.lines['s_nom_min'] = lines_s_nom
n.lines['s_nom_extendable'] = True
n.links.loc[links_dc_b, "p_nom_min"] = n.links.loc[links_dc_b, "p_nom"]
n.links.loc[links_dc_b, "p_nom_extendable"] = True
n.links.loc[links_dc_b, 'p_nom_min'] = n.links.loc[links_dc_b, 'p_nom']
n.links.loc[links_dc_b, 'p_nom_extendable'] = True
if factor != 'opt':
con_type = 'expansion_cost' if ll_type == 'c' else 'volume_expansion'
if factor != "opt":
con_type = "expansion_cost" if ll_type == "c" else "volume_expansion"
rhs = float(factor) * ref
n.add('GlobalConstraint', f'l{ll_type}_limit',
type=f'transmission_{con_type}_limit',
sense='<=', constant=rhs, carrier_attribute='AC, DC')
n.add(
"GlobalConstraint",
f"l{ll_type}_limit",
type=f"transmission_{con_type}_limit",
sense="<=",
constant=rhs,
carrier_attribute="AC, DC",
)
return n
@ -132,7 +162,7 @@ def average_every_nhours(n, offset):
m.snapshot_weightings = snapshot_weightings
for c in n.iterate_components():
pnl = getattr(m, c.list_name+"_t")
pnl = getattr(m, c.list_name + "_t")
for k, df in c.pnl.items():
if not df.empty:
pnl[k] = df.resample(offset).mean()
@ -145,23 +175,29 @@ def apply_time_segmentation(n, segments, solver_name="cbc"):
try:
import tsam.timeseriesaggregation as tsam
except:
raise ModuleNotFoundError("Optional dependency 'tsam' not found."
"Install via 'pip install tsam'")
raise ModuleNotFoundError(
"Optional dependency 'tsam' not found." "Install via 'pip install tsam'"
)
p_max_pu_norm = n.generators_t.p_max_pu.max()
p_max_pu = n.generators_t.p_max_pu / p_max_pu_norm
load_norm = n.loads_t.p_set.max()
load = n.loads_t.p_set / load_norm
inflow_norm = n.storage_units_t.inflow.max()
inflow = n.storage_units_t.inflow / inflow_norm
raw = pd.concat([p_max_pu, load, inflow], axis=1, sort=False)
agg = tsam.TimeSeriesAggregation(raw, hoursPerPeriod=len(raw),
noTypicalPeriods=1, noSegments=int(segments),
segmentation=True, solver=solver_name)
agg = tsam.TimeSeriesAggregation(
raw,
hoursPerPeriod=len(raw),
noTypicalPeriods=1,
noSegments=int(segments),
segmentation=True,
solver=solver_name,
)
segmented = agg.createTypicalPeriods()
@ -169,9 +205,11 @@ def apply_time_segmentation(n, segments, solver_name="cbc"):
offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
snapshots = [n.snapshots[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
n.set_snapshots(pd.DatetimeIndex(snapshots, name='name'))
n.snapshot_weightings = pd.Series(weightings, index=snapshots, name="weightings", dtype="float64")
n.set_snapshots(pd.DatetimeIndex(snapshots, name="name"))
n.snapshot_weightings = pd.Series(
weightings, index=snapshots, name="weightings", dtype="float64"
)
segmented.index = snapshots
n.generators_t.p_max_pu = segmented[n.generators_t.p_max_pu.columns] * p_max_pu_norm
n.loads_t.p_set = segmented[n.loads_t.p_set.columns] * load_norm
@ -179,49 +217,57 @@ def apply_time_segmentation(n, segments, solver_name="cbc"):
return n
def enforce_autarky(n, only_crossborder=False):
if only_crossborder:
lines_rm = n.lines.loc[
n.lines.bus0.map(n.buses.country) !=
n.lines.bus1.map(n.buses.country)
].index
n.lines.bus0.map(n.buses.country) != n.lines.bus1.map(n.buses.country)
].index
links_rm = n.links.loc[
n.links.bus0.map(n.buses.country) !=
n.links.bus1.map(n.buses.country)
].index
n.links.bus0.map(n.buses.country) != n.links.bus1.map(n.buses.country)
].index
else:
lines_rm = n.lines.index
links_rm = n.links.loc[n.links.carrier=="DC"].index
links_rm = n.links.loc[n.links.carrier == "DC"].index
n.mremove("Line", lines_rm)
n.mremove("Link", links_rm)
def set_line_nom_max(n, s_nom_max_set=np.inf, p_nom_max_set=np.inf):
n.lines.s_nom_max.clip(upper=s_nom_max_set, inplace=True)
n.links.p_nom_max.clip(upper=p_nom_max_set, inplace=True)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('prepare_network', network='elec', simpl='',
clusters='40', ll='v0.3', opts='Co2L-24H')
snakemake = mock_snakemake(
"prepare_network", simpl="", clusters="40", ll="v0.3", opts="Co2L-24H"
)
configure_logging(snakemake)
opts = snakemake.wildcards.opts.split('-')
opts = snakemake.wildcards.opts.split("-")
n = pypsa.Network(snakemake.input[0])
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
Nyears = n.snapshot_weightings.objective.sum() / 8760.0
costs = load_costs(
snakemake.input.tech_costs,
snakemake.config["costs"],
snakemake.config["electricity"],
Nyears,
)
set_line_s_max_pu(n, snakemake.config['lines']['s_max_pu'])
set_line_s_max_pu(n, snakemake.config["lines"]["s_max_pu"])
for o in opts:
m = re.match(r'^\d+h$', o, re.IGNORECASE)
m = re.match(r"^\d+h$", o, re.IGNORECASE)
if m is not None:
n = average_every_nhours(n, m.group(0))
break
for o in opts:
m = re.match(r'^\d+seg$', o, re.IGNORECASE)
m = re.match(r"^\d+seg$", o, re.IGNORECASE)
if m is not None:
solver_name = snakemake.config["solving"]["solver"]["name"]
n = apply_time_segmentation(n, m.group(0)[:-3], solver_name)
@ -231,10 +277,24 @@ if __name__ == "__main__":
if "Co2L" in o:
m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0:
co2limit = float(m[0]) * snakemake.config['electricity']['co2base']
co2limit = float(m[0]) * snakemake.config["electricity"]["co2base"]
add_co2limit(n, co2limit, Nyears)
logger.info("Setting CO2 limit according to wildcard value.")
else:
add_co2limit(n, snakemake.config['electricity']['co2limit'], Nyears)
add_co2limit(n, snakemake.config["electricity"]["co2limit"], Nyears)
logger.info("Setting CO2 limit according to config value.")
break
for o in opts:
if "CH4L" in o:
m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0:
limit = float(m[0]) * 1e6
add_gaslimit(n, limit, Nyears)
logger.info("Setting gas usage limit according to wildcard value.")
else:
add_gaslimit(n, snakemake.config["electricity"].get("gaslimit"), Nyears)
logger.info("Setting gas usage limit according to config value.")
break
for o in opts:
@ -243,7 +303,7 @@ if __name__ == "__main__":
if oo[0].startswith(tuple(suptechs)):
carrier = oo[0]
# handles only p_nom_max as stores and lines have no potentials
attr_lookup = {"p": "p_nom_max", "c": "capital_cost"}
attr_lookup = {"p": "p_nom_max", "c": "capital_cost", "m": "marginal_cost"}
attr = attr_lookup[oo[1][0]]
factor = float(oo[1][1:])
if carrier == "AC": # lines do not have carrier
@ -252,20 +312,32 @@ if __name__ == "__main__":
comps = {"Generator", "Link", "StorageUnit", "Store"}
for c in n.iterate_components(comps):
sel = c.df.carrier.str.contains(carrier)
c.df.loc[sel,attr] *= factor
c.df.loc[sel, attr] *= factor
if 'Ep' in opts:
add_emission_prices(n, snakemake.config['costs']['emission_prices'])
for o in opts:
if "Ep" in o:
m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0:
logger.info("Setting emission prices according to wildcard value.")
add_emission_prices(n, dict(co2=float(m[0])))
else:
logger.info("Setting emission prices according to config value.")
add_emission_prices(n, snakemake.config["costs"]["emission_prices"])
break
ll_type, factor = snakemake.wildcards.ll[0], snakemake.wildcards.ll[1:]
set_transmission_limit(n, ll_type, factor, costs, Nyears)
set_line_nom_max(n, s_nom_max_set=snakemake.config["lines"].get("s_nom_max,", np.inf),
p_nom_max_set=snakemake.config["links"].get("p_nom_max,", np.inf))
set_line_nom_max(
n,
s_nom_max_set=snakemake.config["lines"].get("s_nom_max,", np.inf),
p_nom_max_set=snakemake.config["links"].get("p_nom_max,", np.inf),
)
if "ATK" in opts:
enforce_autarky(n)
elif "ATKc" in opts:
enforce_autarky(n, only_crossborder=True)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])

30
scripts/retrieve_databundle.py
View File

@ -1,5 +1,6 @@
# Copyright 2019-2020 Fabian Hofmann (FIAS)
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# Copyright 2019-2022 Fabian Hofmann (TUB, FIAS)
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
@ -11,7 +12,7 @@ The data bundle (1.4 GB) contains common GIS datasets like NUTS3 shapes, EEZ sha
This rule downloads the data bundle from `zenodo <https://doi.org/10.5281/zenodo.3517935>`_ and extracts it in the ``data`` sub-directory, such that all files of the bundle are stored in the ``data/bundle`` subdirectory.
The :ref:`tutorial` uses a smaller `data bundle <https://zenodo.org/record/3517921/files/pypsa-eur-tutorial-data-bundle.tar.xz>`_ than required for the full model (19 MB)
The :ref:`tutorial` uses a smaller `data bundle <https://zenodo.org/record/3517921/files/pypsa-eur-tutorial-data-bundle.tar.xz>`_ than required for the full model (188 MB)
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3517921.svg
:target: https://doi.org/10.5281/zenodo.3517921
@ -28,29 +29,32 @@ The :ref:`tutorial` uses a smaller `data bundle <https://zenodo.org/record/35179
**Outputs**
- ``cutouts/bundle``: input data collected from various sources
- ``data/bundle``: input data collected from various sources
"""
import logging
from _helpers import progress_retrieve, configure_logging
import tarfile
from pathlib import Path
from _helpers import configure_logging, progress_retrieve
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('retrieve_databundle')
rootpath = '..'
else:
rootpath = '.'
configure_logging(snakemake) # TODO Make logging compatible with progressbar (see PR #102)
if snakemake.config['tutorial']:
snakemake = mock_snakemake("retrieve_databundle")
rootpath = ".."
else:
rootpath = "."
configure_logging(
snakemake
) # TODO Make logging compatible with progressbar (see PR #102)
if snakemake.config["tutorial"]:
url = "https://zenodo.org/record/3517921/files/pypsa-eur-tutorial-data-bundle.tar.xz"
else:
url = "https://zenodo.org/record/3517935/files/pypsa-eur-data-bundle.tar.xz"

454
scripts/simplify_network.py
View File

@ -1,31 +1,34 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Lifts electrical transmission network to a single 380 kV voltage layer,
removes dead-ends of the network,
and reduces multi-hop HVDC connections to a single link.
Lifts electrical transmission network to a single 380 kV voltage layer, removes
dead-ends of the network, and reduces multi-hop HVDC connections to a single
link.
Relevant Settings
-----------------
.. code:: yaml
clustering:
simplify_network:
cluster_network:
aggregation_strategies:
costs:
USD2013_to_EUR2013:
discountrate:
year:
version:
fill_values:
marginal_cost:
capital_cost:
electricity:
max_hours:
renewables: (keys)
{technology}:
potential:
lines:
length_factor:
@ -44,7 +47,7 @@ Relevant Settings
Inputs
------
- ``data/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``resources/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``resources/regions_onshore.geojson``: confer :ref:`busregions`
- ``resources/regions_offshore.geojson``: confer :ref:`busregions`
- ``networks/elec.nc``: confer :ref:`electricity`
@ -75,7 +78,7 @@ The rule :mod:`simplify_network` does up to four things:
1. Create an equivalent transmission network in which all voltage levels are mapped to the 380 kV level by the function ``simplify_network(...)``.
2. DC only sub-networks that are connected at only two buses to the AC network are reduced to a single representative link in the function ``simplify_links(...)``. The components attached to buses in between are moved to the nearest endpoint. The grid connection cost of offshore wind generators are added to the captial costs of the generator.
2. DC only sub-networks that are connected at only two buses to the AC network are reduced to a single representative link in the function ``simplify_links(...)``. The components attached to buses in between are moved to the nearest endpoint. The grid connection cost of offshore wind generators are added to the capital costs of the generator.
3. Stub lines and links, i.e. dead-ends of the network, are sequentially removed from the network in the function ``remove_stubs(...)``. Components are moved along.
@ -83,54 +86,60 @@ The rule :mod:`simplify_network` does up to four things:
"""
import logging
from _helpers import configure_logging, update_p_nom_max
from cluster_network import clustering_for_n_clusters, cluster_regions
from add_electricity import load_costs
import pandas as pd
import numpy as np
import scipy as sp
from scipy.sparse.csgraph import connected_components, dijkstra
from functools import reduce
import numpy as np
import pandas as pd
import pypsa
import scipy as sp
from _helpers import configure_logging, get_aggregation_strategies, update_p_nom_max
from add_electricity import load_costs
from cluster_network import cluster_regions, clustering_for_n_clusters
from pypsa.io import import_components_from_dataframe, import_series_from_dataframe
from pypsa.networkclustering import busmap_by_stubs, aggregategenerators, aggregateoneport, get_clustering_from_busmap, _make_consense
from pypsa.networkclustering import (
aggregategenerators,
aggregateoneport,
busmap_by_stubs,
get_clustering_from_busmap,
)
from scipy.sparse.csgraph import connected_components, dijkstra
logger = logging.getLogger(__name__)
def simplify_network_to_380(n):
## All goes to v_nom == 380
"""
Fix all lines to a voltage level of 380 kV and remove all transformers.
The function preserves the transmission capacity for each line while updating
its voltage level, line type and number of parallel bundles (num_parallel).
Transformers are removed and connected components are moved from their
starting bus to their ending bus. The corresponding starting buses are
removed as well.
"""
logger.info("Mapping all network lines onto a single 380kV layer")
n.buses['v_nom'] = 380.
n.buses["v_nom"] = 380.0
linetype_380, = n.lines.loc[n.lines.v_nom == 380., 'type'].unique()
lines_v_nom_b = n.lines.v_nom != 380.
n.lines.loc[lines_v_nom_b, 'num_parallel'] *= (n.lines.loc[lines_v_nom_b, 'v_nom'] / 380.)**2
n.lines.loc[lines_v_nom_b, 'v_nom'] = 380.
n.lines.loc[lines_v_nom_b, 'type'] = linetype_380
n.lines.loc[lines_v_nom_b, 's_nom'] = (
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom) * n.lines.num_parallel
)
(linetype_380,) = n.lines.loc[n.lines.v_nom == 380.0, "type"].unique()
n.lines["type"] = linetype_380
n.lines["v_nom"] = 380
n.lines["i_nom"] = n.line_types.i_nom[linetype_380]
n.lines["num_parallel"] = n.lines.eval("s_nom / (sqrt(3) * v_nom * i_nom)")
# Replace transformers by lines
trafo_map = pd.Series(n.transformers.bus1.values, index=n.transformers.bus0.values)
trafo_map = trafo_map[~trafo_map.index.duplicated(keep='first')]
trafo_map = pd.Series(n.transformers.bus1.values, n.transformers.bus0.values)
trafo_map = trafo_map[~trafo_map.index.duplicated(keep="first")]
several_trafo_b = trafo_map.isin(trafo_map.index)
trafo_map.loc[several_trafo_b] = trafo_map.loc[several_trafo_b].map(trafo_map)
trafo_map[several_trafo_b] = trafo_map[several_trafo_b].map(trafo_map)
missing_buses_i = n.buses.index.difference(trafo_map.index)
missing = pd.Series(missing_buses_i, missing_buses_i)
trafo_map = pd.concat([trafo_map, missing])
for c in n.one_port_components|n.branch_components:
for c in n.one_port_components | n.branch_components:
df = n.df(c)
for col in df.columns:
if col.startswith('bus'):
if col.startswith("bus"):
df[col] = df[col].map(trafo_map)
n.mremove("Transformer", n.transformers.index)
@ -140,22 +149,30 @@ def simplify_network_to_380(n):
def _prepare_connection_costs_per_link(n, costs, config):
if n.links.empty: return {}
if n.links.empty:
return {}
connection_costs_per_link = {}
for tech in config['renewable']:
if tech.startswith('offwind'):
for tech in config["renewable"]:
if tech.startswith("offwind"):
connection_costs_per_link[tech] = (
n.links.length * config['lines']['length_factor'] *
(n.links.underwater_fraction * costs.at[tech + '-connection-submarine', 'capital_cost'] +
(1. - n.links.underwater_fraction) * costs.at[tech + '-connection-underground', 'capital_cost'])
n.links.length
* config["lines"]["length_factor"]
* (
n.links.underwater_fraction
* costs.at[tech + "-connection-submarine", "capital_cost"]
+ (1.0 - n.links.underwater_fraction)
* costs.at[tech + "-connection-underground", "capital_cost"]
)
)
return connection_costs_per_link
def _compute_connection_costs_to_bus(n, busmap, costs, config, connection_costs_per_link=None, buses=None):
def _compute_connection_costs_to_bus(
n, busmap, costs, config, connection_costs_per_link=None, buses=None
):
if connection_costs_per_link is None:
connection_costs_per_link = _prepare_connection_costs_per_link(n, costs, config)
@ -165,12 +182,21 @@ def _compute_connection_costs_to_bus(n, busmap, costs, config, connection_costs_
connection_costs_to_bus = pd.DataFrame(index=buses)
for tech in connection_costs_per_link:
adj = n.adjacency_matrix(weights=pd.concat(dict(Link=connection_costs_per_link[tech].reindex(n.links.index),
Line=pd.Series(0., n.lines.index))))
adj = n.adjacency_matrix(
weights=pd.concat(
dict(
Link=connection_costs_per_link[tech].reindex(n.links.index),
Line=pd.Series(0.0, n.lines.index),
)
)
)
costs_between_buses = dijkstra(adj, directed=False, indices=n.buses.index.get_indexer(buses))
connection_costs_to_bus[tech] = costs_between_buses[np.arange(len(buses)),
n.buses.index.get_indexer(busmap.loc[buses])]
costs_between_buses = dijkstra(
adj, directed=False, indices=n.buses.index.get_indexer(buses)
)
connection_costs_to_bus[tech] = costs_between_buses[
np.arange(len(buses)), n.buses.index.get_indexer(busmap.loc[buses])
]
return connection_costs_to_bus
@ -179,17 +205,35 @@ def _adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, out
connection_costs = {}
for tech in connection_costs_to_bus:
tech_b = n.generators.carrier == tech
costs = n.generators.loc[tech_b, "bus"].map(connection_costs_to_bus[tech]).loc[lambda s: s>0]
costs = (
n.generators.loc[tech_b, "bus"]
.map(connection_costs_to_bus[tech])
.loc[lambda s: s > 0]
)
if not costs.empty:
n.generators.loc[costs.index, "capital_cost"] += costs
logger.info("Displacing {} generator(s) and adding connection costs to capital_costs: {} "
.format(tech, ", ".join("{:.0f} Eur/MW/a for `{}`".format(d, b) for b, d in costs.iteritems())))
logger.info(
"Displacing {} generator(s) and adding connection costs to capital_costs: {} ".format(
tech,
", ".join(
"{:.0f} Eur/MW/a for `{}`".format(d, b)
for b, d in costs.items()
),
)
)
connection_costs[tech] = costs
pd.DataFrame(connection_costs).to_csv(output.connection_costs)
pd.DataFrame(connection_costs).to_csv(output.connection_costs)
def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, output, aggregate_one_ports={"Load", "StorageUnit"}):
def _aggregate_and_move_components(
n,
busmap,
connection_costs_to_bus,
output,
aggregate_one_ports={"Load", "StorageUnit"},
aggregation_strategies=dict(),
exclude_carriers=None,
):
def replace_components(n, c, df, pnl):
n.mremove(c, n.df(c).index)
@ -200,7 +244,13 @@ def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, output, a
_adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, output)
generators, generators_pnl = aggregategenerators(n, busmap, custom_strategies={'p_nom_min': np.sum})
_, generator_strategies = get_aggregation_strategies(aggregation_strategies)
carriers = set(n.generators.carrier) - set(exclude_carriers)
generators, generators_pnl = aggregategenerators(
n, busmap, carriers=carriers, custom_strategies=generator_strategies
)
replace_components(n, "Generator", generators, generators_pnl)
for one_port in aggregate_one_ports:
@ -214,7 +264,7 @@ def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, output, a
n.mremove(c, df.index[df.bus0.isin(buses_to_del) | df.bus1.isin(buses_to_del)])
def simplify_links(n, costs, config, output):
def simplify_links(n, costs, config, output, aggregation_strategies=dict()):
## Complex multi-node links are folded into end-points
logger.info("Simplifying connected link components")
@ -222,8 +272,10 @@ def simplify_links(n, costs, config, output):
return n, n.buses.index.to_series()
# Determine connected link components, ignore all links but DC
adjacency_matrix = n.adjacency_matrix(branch_components=['Link'],
weights=dict(Link=(n.links.carrier == 'DC').astype(float)))
adjacency_matrix = n.adjacency_matrix(
branch_components=["Link"],
weights=dict(Link=(n.links.carrier == "DC").astype(float)),
)
_, labels = connected_components(adjacency_matrix, directed=False)
labels = pd.Series(labels, n.buses.index)
@ -234,22 +286,23 @@ def simplify_links(n, costs, config, output):
nodes = frozenset(nodes)
seen = set()
supernodes = {m for m in nodes
if len(G.adj[m]) > 2 or (set(G.adj[m]) - nodes)}
supernodes = {m for m in nodes if len(G.adj[m]) > 2 or (set(G.adj[m]) - nodes)}
for u in supernodes:
for m, ls in G.adj[u].items():
if m not in nodes or m in seen: continue
if m not in nodes or m in seen:
continue
buses = [u, m]
links = [list(ls)] #[name for name in ls]]
links = [list(ls)] # [name for name in ls]]
while m not in (supernodes | seen):
seen.add(m)
for m2, ls in G.adj[m].items():
if m2 in seen or m2 == u: continue
if m2 in seen or m2 == u:
continue
buses.append(m2)
links.append(list(ls)) # [name for name in ls])
links.append(list(ls)) # [name for name in ls])
break
else:
# stub
@ -262,81 +315,135 @@ def simplify_links(n, costs, config, output):
busmap = n.buses.index.to_series()
connection_costs_per_link = _prepare_connection_costs_per_link(n, costs, config)
connection_costs_to_bus = pd.DataFrame(0., index=n.buses.index, columns=list(connection_costs_per_link))
connection_costs_to_bus = pd.DataFrame(
0.0, index=n.buses.index, columns=list(connection_costs_per_link)
)
for lbl in labels.value_counts().loc[lambda s: s > 2].index:
for b, buses, links in split_links(labels.index[labels == lbl]):
if len(buses) <= 2: continue
if len(buses) <= 2:
continue
logger.debug('nodes = {}'.format(labels.index[labels == lbl]))
logger.debug('b = {}\nbuses = {}\nlinks = {}'.format(b, buses, links))
logger.debug("nodes = {}".format(labels.index[labels == lbl]))
logger.debug("b = {}\nbuses = {}\nlinks = {}".format(b, buses, links))
m = sp.spatial.distance_matrix(n.buses.loc[b, ['x', 'y']],
n.buses.loc[buses[1:-1], ['x', 'y']])
m = sp.spatial.distance_matrix(
n.buses.loc[b, ["x", "y"]], n.buses.loc[buses[1:-1], ["x", "y"]]
)
busmap.loc[buses] = b[np.r_[0, m.argmin(axis=0), 1]]
connection_costs_to_bus.loc[buses] += _compute_connection_costs_to_bus(n, busmap, costs, config, connection_costs_per_link, buses)
connection_costs_to_bus.loc[buses] += _compute_connection_costs_to_bus(
n, busmap, costs, config, connection_costs_per_link, buses
)
all_links = [i for _, i in sum(links, [])]
p_max_pu = config['links'].get('p_max_pu', 1.)
lengths = n.links.loc[all_links, 'length']
name = lengths.idxmax() + '+{}'.format(len(links) - 1)
p_max_pu = config["links"].get("p_max_pu", 1.0)
lengths = n.links.loc[all_links, "length"]
name = lengths.idxmax() + "+{}".format(len(links) - 1)
params = dict(
carrier='DC',
bus0=b[0], bus1=b[1],
length=sum(n.links.loc[[i for _, i in l], 'length'].mean() for l in links),
p_nom=min(n.links.loc[[i for _, i in l], 'p_nom'].sum() for l in links),
underwater_fraction=sum(lengths/lengths.sum() * n.links.loc[all_links, 'underwater_fraction']),
carrier="DC",
bus0=b[0],
bus1=b[1],
length=sum(
n.links.loc[[i for _, i in l], "length"].mean() for l in links
),
p_nom=min(n.links.loc[[i for _, i in l], "p_nom"].sum() for l in links),
underwater_fraction=sum(
lengths
/ lengths.sum()
* n.links.loc[all_links, "underwater_fraction"]
),
p_max_pu=p_max_pu,
p_min_pu=-p_max_pu,
underground=False,
under_construction=False
under_construction=False,
)
logger.info("Joining the links {} connecting the buses {} to simple link {}".format(", ".join(all_links), ", ".join(buses), name))
logger.info(
"Joining the links {} connecting the buses {} to simple link {}".format(
", ".join(all_links), ", ".join(buses), name
)
)
n.mremove("Link", all_links)
static_attrs = n.components["Link"]["attrs"].loc[lambda df: df.static]
for attr, default in static_attrs.default.iteritems(): params.setdefault(attr, default)
for attr, default in static_attrs.default.items():
params.setdefault(attr, default)
n.links.loc[name] = pd.Series(params)
# n.add("Link", **params)
logger.debug("Collecting all components using the busmap")
_aggregate_and_move_components(n, busmap, connection_costs_to_bus, output)
exclude_carriers = config["clustering"]["simplify_network"].get(
"exclude_carriers", []
)
_aggregate_and_move_components(
n,
busmap,
connection_costs_to_bus,
output,
aggregation_strategies=aggregation_strategies,
exclude_carriers=exclude_carriers,
)
return n, busmap
def remove_stubs(n, costs, config, output):
def remove_stubs(n, costs, config, output, aggregation_strategies=dict()):
logger.info("Removing stubs")
busmap = busmap_by_stubs(n) # ['country'])
across_borders = config["clustering"]["simplify_network"].get("remove_stubs_across_borders", True)
matching_attrs = [] if across_borders else ['country']
busmap = busmap_by_stubs(n, matching_attrs)
connection_costs_to_bus = _compute_connection_costs_to_bus(n, busmap, costs, config)
_aggregate_and_move_components(n, busmap, connection_costs_to_bus, output)
exclude_carriers = config["clustering"]["simplify_network"].get(
"exclude_carriers", []
)
_aggregate_and_move_components(
n,
busmap,
connection_costs_to_bus,
output,
aggregation_strategies=aggregation_strategies,
exclude_carriers=exclude_carriers,
)
return n, busmap
def aggregate_to_substations(n, buses_i=None):
def aggregate_to_substations(n, aggregation_strategies=dict(), buses_i=None):
# can be used to aggregate a selection of buses to electrically closest neighbors
# if no buses are given, nodes that are no substations or without offshore connection are aggregated
if buses_i is None:
logger.info("Aggregating buses that are no substations or have no valid offshore connection")
buses_i = list(set(n.buses.index)-set(n.generators.bus)-set(n.loads.bus))
logger.info(
"Aggregating buses that are no substations or have no valid offshore connection"
)
buses_i = list(set(n.buses.index) - set(n.generators.bus) - set(n.loads.bus))
weight = pd.concat({'Line': n.lines.length/n.lines.s_nom.clip(1e-3),
'Link': n.links.length/n.links.p_nom.clip(1e-3)})
weight = pd.concat(
{
"Line": n.lines.length / n.lines.s_nom.clip(1e-3),
"Link": n.links.length / n.links.p_nom.clip(1e-3),
}
)
adj = n.adjacency_matrix(branch_components=['Line', 'Link'], weights=weight)
adj = n.adjacency_matrix(branch_components=["Line", "Link"], weights=weight)
bus_indexer = n.buses.index.get_indexer(buses_i)
dist = pd.DataFrame(dijkstra(adj, directed=False, indices=bus_indexer), buses_i, n.buses.index)
dist = pd.DataFrame(
dijkstra(adj, directed=False, indices=bus_indexer), buses_i, n.buses.index
)
dist[buses_i] = np.inf # bus in buses_i should not be assigned to different bus in buses_i
dist[
buses_i
] = np.inf # bus in buses_i should not be assigned to different bus in buses_i
for c in n.buses.country.unique():
incountry_b = n.buses.country == c
@ -345,77 +452,150 @@ def aggregate_to_substations(n, buses_i=None):
busmap = n.buses.index.to_series()
busmap.loc[buses_i] = dist.idxmin(1)
clustering = get_clustering_from_busmap(n, busmap,
bus_strategies=dict(country=_make_consense("Bus", "country")),
aggregate_generators_weighted=True,
aggregate_generators_carriers=None,
aggregate_one_ports=["Load", "StorageUnit"],
line_length_factor=1.0,
generator_strategies={'p_nom_max': 'sum'},
scale_link_capital_costs=False)
bus_strategies, generator_strategies = get_aggregation_strategies(
aggregation_strategies
)
clustering = get_clustering_from_busmap(
n,
busmap,
bus_strategies=bus_strategies,
aggregate_generators_weighted=True,
aggregate_generators_carriers=None,
aggregate_one_ports=["Load", "StorageUnit"],
line_length_factor=1.0,
generator_strategies=generator_strategies,
scale_link_capital_costs=False,
)
return clustering.network, busmap
def cluster(n, n_clusters, config):
def cluster(
n, n_clusters, config, algorithm="hac", feature=None, aggregation_strategies=dict()
):
logger.info(f"Clustering to {n_clusters} buses")
focus_weights = config.get('focus_weights', None)
renewable_carriers = pd.Index([tech
for tech in n.generators.carrier.unique()
if tech.split('-', 2)[0] in config['renewable']])
def consense(x):
v = x.iat[0]
assert ((x == v).all() or x.isnull().all()), (
"The `potential` configuration option must agree for all renewable carriers, for now!"
)
return v
potential_mode = (consense(pd.Series([config['renewable'][tech]['potential']
for tech in renewable_carriers]))
if len(renewable_carriers) > 0 else 'conservative')
clustering = clustering_for_n_clusters(n, n_clusters, custom_busmap=False, potential_mode=potential_mode,
solver_name=config['solving']['solver']['name'],
focus_weights=focus_weights)
focus_weights = config.get("focus_weights", None)
renewable_carriers = pd.Index(
[
tech
for tech in n.generators.carrier.unique()
if tech.split("-", 2)[0] in config["renewable"]
]
)
clustering = clustering_for_n_clusters(
n,
n_clusters,
custom_busmap=False,
aggregation_strategies=aggregation_strategies,
solver_name=config["solving"]["solver"]["name"],
algorithm=algorithm,
feature=feature,
focus_weights=focus_weights,
)
return clustering.network, clustering.busmap
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('simplify_network', simpl='', network='elec')
snakemake = mock_snakemake("simplify_network", simpl="")
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
aggregation_strategies = snakemake.config["clustering"].get(
"aggregation_strategies", {}
)
# translate str entries of aggregation_strategies to pd.Series functions:
aggregation_strategies = {
p: {k: getattr(pd.Series, v) for k, v in aggregation_strategies[p].items()}
for p in aggregation_strategies.keys()
}
n, trafo_map = simplify_network_to_380(n)
Nyears = n.snapshot_weightings.objective.sum() / 8760
technology_costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
technology_costs = load_costs(
snakemake.input.tech_costs,
snakemake.config["costs"],
snakemake.config["electricity"],
Nyears,
)
n, simplify_links_map = simplify_links(n, technology_costs, snakemake.config, snakemake.output)
n, simplify_links_map = simplify_links(
n, technology_costs, snakemake.config, snakemake.output, aggregation_strategies
)
n, stub_map = remove_stubs(n, technology_costs, snakemake.config, snakemake.output)
busmaps = [trafo_map, simplify_links_map]
busmaps = [trafo_map, simplify_links_map, stub_map]
cluster_config = snakemake.config["clustering"]["simplify_network"]
if cluster_config.get("remove_stubs", True):
n, stub_map = remove_stubs(
n,
technology_costs,
snakemake.config,
snakemake.output,
aggregation_strategies=aggregation_strategies,
)
busmaps.append(stub_map)
if snakemake.config.get('clustering', {}).get('simplify', {}).get('to_substations', False):
n, substation_map = aggregate_to_substations(n)
if cluster_config.get("to_substations", False):
n, substation_map = aggregate_to_substations(n, aggregation_strategies)
busmaps.append(substation_map)
# treatment of outliers (nodes without a profile for considered carrier):
# all nodes that have no profile of the given carrier are being aggregated to closest neighbor
if (
snakemake.config.get("clustering", {})
.get("cluster_network", {})
.get("algorithm", "hac")
== "hac"
or cluster_config.get("algorithm", "hac") == "hac"
):
carriers = (
cluster_config.get("feature", "solar+onwind-time").split("-")[0].split("+")
)
for carrier in carriers:
buses_i = list(
set(n.buses.index) - set(n.generators.query("carrier == @carrier").bus)
)
logger.info(
f"clustering preparaton (hac): aggregating {len(buses_i)} buses of type {carrier}."
)
n, busmap_hac = aggregate_to_substations(n, aggregation_strategies, buses_i)
busmaps.append(busmap_hac)
if snakemake.wildcards.simpl:
n, cluster_map = cluster(n, int(snakemake.wildcards.simpl), snakemake.config)
n, cluster_map = cluster(
n,
int(snakemake.wildcards.simpl),
snakemake.config,
cluster_config.get("algorithm", "hac"),
cluster_config.get("feature", None),
aggregation_strategies,
)
busmaps.append(cluster_map)
# some entries in n.buses are not updated in previous functions, therefore can be wrong. as they are not needed
# and are lost when clustering (for example with the simpl wildcard), we remove them for consistency:
buses_c = {'symbol', 'tags', 'under_construction', 'substation_lv', 'substation_off'}.intersection(n.buses.columns)
buses_c = {
"symbol",
"tags",
"under_construction",
"substation_lv",
"substation_off",
}.intersection(n.buses.columns)
n.buses = n.buses.drop(buses_c, axis=1)
update_p_nom_max(n)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output.network)
busmap_s = reduce(lambda x, y: x.map(y), busmaps[1:], busmaps[0])

381
scripts/solve_network.py
View File

@ -1,9 +1,11 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Solves linear optimal power flow for a network iteratively while updating reactances.
Solves linear optimal power flow for a network iteratively while updating
reactances.
Relevant Settings
-----------------
@ -73,101 +75,123 @@ Details (and errors made through this heuristic) are discussed in the paper
The rule :mod:`solve_all_networks` runs
for all ``scenario`` s in the configuration file
the rule :mod:`solve_network`.
"""
import logging
from _helpers import configure_logging
import re
from pathlib import Path
import numpy as np
import pandas as pd
import re
import pypsa
from pypsa.linopf import (get_var, define_constraints, linexpr, join_exprs,
network_lopf, ilopf)
from pathlib import Path
from _helpers import configure_logging
from pypsa.descriptors import get_switchable_as_dense as get_as_dense
from pypsa.linopf import (
define_constraints,
define_variables,
get_var,
ilopf,
join_exprs,
linexpr,
network_lopf,
)
from vresutils.benchmark import memory_logger
logger = logging.getLogger(__name__)
def prepare_network(n, solve_opts):
if 'clip_p_max_pu' in solve_opts:
if "clip_p_max_pu" in solve_opts:
for df in (n.generators_t.p_max_pu, n.storage_units_t.inflow):
df.where(df>solve_opts['clip_p_max_pu'], other=0., inplace=True)
df.where(df > solve_opts["clip_p_max_pu"], other=0.0, inplace=True)
if solve_opts.get('load_shedding'):
n.add("Carrier", "Load")
load_shedding = solve_opts.get("load_shedding")
if load_shedding:
n.add("Carrier", "load", color="#dd2e23", nice_name="Load shedding")
buses_i = n.buses.query("carrier == 'AC'").index
n.madd("Generator", buses_i, " load",
bus=buses_i,
carrier='load',
sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
marginal_cost=1e2, # Eur/kWh
# intersect between macroeconomic and surveybased
# willingness to pay
# http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full
p_nom=1e9 # kW
)
if not np.isscalar(load_shedding):
load_shedding = 1e2 # Eur/kWh
# intersect between macroeconomic and surveybased
# willingness to pay
# http://journal.frontiersin.org/article/10.3389/fenrg.2015.00055/full)
n.madd(
"Generator",
buses_i,
" load",
bus=buses_i,
carrier="load",
sign=1e-3, # Adjust sign to measure p and p_nom in kW instead of MW
marginal_cost=load_shedding,
p_nom=1e9, # kW
)
if solve_opts.get('noisy_costs'):
if solve_opts.get("noisy_costs"):
for t in n.iterate_components(n.one_port_components):
#if 'capital_cost' in t.df:
# if 'capital_cost' in t.df:
# t.df['capital_cost'] += 1e1 + 2.*(np.random.random(len(t.df)) - 0.5)
if 'marginal_cost' in t.df:
t.df['marginal_cost'] += (1e-2 + 2e-3 *
(np.random.random(len(t.df)) - 0.5))
if "marginal_cost" in t.df:
t.df["marginal_cost"] += 1e-2 + 2e-3 * (
np.random.random(len(t.df)) - 0.5
)
for t in n.iterate_components(['Line', 'Link']):
t.df['capital_cost'] += (1e-1 +
2e-2*(np.random.random(len(t.df)) - 0.5)) * t.df['length']
for t in n.iterate_components(["Line", "Link"]):
t.df["capital_cost"] += (
1e-1 + 2e-2 * (np.random.random(len(t.df)) - 0.5)
) * t.df["length"]
if solve_opts.get('nhours'):
nhours = solve_opts['nhours']
if solve_opts.get("nhours"):
nhours = solve_opts["nhours"]
n.set_snapshots(n.snapshots[:nhours])
n.snapshot_weightings[:] = 8760. / nhours
n.snapshot_weightings[:] = 8760.0 / nhours
return n
def add_CCL_constraints(n, config):
agg_p_nom_limits = config['electricity'].get('agg_p_nom_limits')
agg_p_nom_limits = config["electricity"].get("agg_p_nom_limits")
try:
agg_p_nom_minmax = pd.read_csv(agg_p_nom_limits,
index_col=list(range(2)))
agg_p_nom_minmax = pd.read_csv(agg_p_nom_limits, index_col=list(range(2)))
except IOError:
logger.exception("Need to specify the path to a .csv file containing "
"aggregate capacity limits per country in "
"config['electricity']['agg_p_nom_limit'].")
logger.info("Adding per carrier generation capacity constraints for "
"individual countries")
logger.exception(
"Need to specify the path to a .csv file containing "
"aggregate capacity limits per country in "
"config['electricity']['agg_p_nom_limit']."
)
logger.info(
"Adding per carrier generation capacity constraints for " "individual countries"
)
gen_country = n.generators.bus.map(n.buses.country)
# cc means country and carrier
p_nom_per_cc = (pd.DataFrame(
{'p_nom': linexpr((1, get_var(n, 'Generator', 'p_nom'))),
'country': gen_country, 'carrier': n.generators.carrier})
.dropna(subset=['p_nom'])
.groupby(['country', 'carrier']).p_nom
.apply(join_exprs))
minimum = agg_p_nom_minmax['min'].dropna()
p_nom_per_cc = (
pd.DataFrame(
{
"p_nom": linexpr((1, get_var(n, "Generator", "p_nom"))),
"country": gen_country,
"carrier": n.generators.carrier,
}
)
.dropna(subset=["p_nom"])
.groupby(["country", "carrier"])
.p_nom.apply(join_exprs)
)
minimum = agg_p_nom_minmax["min"].dropna()
if not minimum.empty:
minconstraint = define_constraints(n, p_nom_per_cc[minimum.index],
'>=', minimum, 'agg_p_nom', 'min')
maximum = agg_p_nom_minmax['max'].dropna()
minconstraint = define_constraints(
n, p_nom_per_cc[minimum.index], ">=", minimum, "agg_p_nom", "min"
)
maximum = agg_p_nom_minmax["max"].dropna()
if not maximum.empty:
maxconstraint = define_constraints(n, p_nom_per_cc[maximum.index],
'<=', maximum, 'agg_p_nom', 'max')
maxconstraint = define_constraints(
n, p_nom_per_cc[maximum.index], "<=", maximum, "agg_p_nom", "max"
)
def add_EQ_constraints(n, o, scaling=1e-1):
float_regex = "[0-9]*\.?[0-9]+"
level = float(re.findall(float_regex, o)[0])
if o[-1] == 'c':
if o[-1] == "c":
ggrouper = n.generators.bus.map(n.buses.country)
lgrouper = n.loads.bus.map(n.buses.country)
sgrouper = n.storage_units.bus.map(n.buses.country)
@ -175,116 +199,239 @@ def add_EQ_constraints(n, o, scaling=1e-1):
ggrouper = n.generators.bus
lgrouper = n.loads.bus
sgrouper = n.storage_units.bus
load = n.snapshot_weightings.generators @ \
n.loads_t.p_set.groupby(lgrouper, axis=1).sum()
inflow = n.snapshot_weightings.stores @ \
n.storage_units_t.inflow.groupby(sgrouper, axis=1).sum()
inflow = inflow.reindex(load.index).fillna(0.)
rhs = scaling * ( level * load - inflow )
lhs_gen = linexpr((n.snapshot_weightings.generators * scaling,
get_var(n, "Generator", "p").T)
).T.groupby(ggrouper, axis=1).apply(join_exprs)
lhs_spill = linexpr((-n.snapshot_weightings.stores * scaling,
get_var(n, "StorageUnit", "spill").T)
).T.groupby(sgrouper, axis=1).apply(join_exprs)
load = (
n.snapshot_weightings.generators
@ n.loads_t.p_set.groupby(lgrouper, axis=1).sum()
)
inflow = (
n.snapshot_weightings.stores
@ n.storage_units_t.inflow.groupby(sgrouper, axis=1).sum()
)
inflow = inflow.reindex(load.index).fillna(0.0)
rhs = scaling * (level * load - inflow)
lhs_gen = (
linexpr(
(n.snapshot_weightings.generators * scaling, get_var(n, "Generator", "p").T)
)
.T.groupby(ggrouper, axis=1)
.apply(join_exprs)
)
lhs_spill = (
linexpr(
(
-n.snapshot_weightings.stores * scaling,
get_var(n, "StorageUnit", "spill").T,
)
)
.T.groupby(sgrouper, axis=1)
.apply(join_exprs)
)
lhs_spill = lhs_spill.reindex(lhs_gen.index).fillna("")
lhs = lhs_gen + lhs_spill
define_constraints(n, lhs, ">=", rhs, "equity", "min")
def add_BAU_constraints(n, config):
mincaps = pd.Series(config['electricity']['BAU_mincapacities'])
lhs = (linexpr((1, get_var(n, 'Generator', 'p_nom')))
.groupby(n.generators.carrier).apply(join_exprs))
define_constraints(n, lhs, '>=', mincaps[lhs.index], 'Carrier', 'bau_mincaps')
mincaps = pd.Series(config["electricity"]["BAU_mincapacities"])
lhs = (
linexpr((1, get_var(n, "Generator", "p_nom")))
.groupby(n.generators.carrier)
.apply(join_exprs)
)
define_constraints(n, lhs, ">=", mincaps[lhs.index], "Carrier", "bau_mincaps")
def add_SAFE_constraints(n, config):
peakdemand = (1. + config['electricity']['SAFE_reservemargin']) *\
n.loads_t.p_set.sum(axis=1).max()
conv_techs = config['plotting']['conv_techs']
exist_conv_caps = n.generators.query('~p_nom_extendable & carrier in @conv_techs')\
.p_nom.sum()
ext_gens_i = n.generators.query('carrier in @conv_techs & p_nom_extendable').index
lhs = linexpr((1, get_var(n, 'Generator', 'p_nom')[ext_gens_i])).sum()
peakdemand = (
1.0 + config["electricity"]["SAFE_reservemargin"]
) * n.loads_t.p_set.sum(axis=1).max()
conv_techs = config["plotting"]["conv_techs"]
exist_conv_caps = n.generators.query(
"~p_nom_extendable & carrier in @conv_techs"
).p_nom.sum()
ext_gens_i = n.generators.query("carrier in @conv_techs & p_nom_extendable").index
lhs = linexpr((1, get_var(n, "Generator", "p_nom")[ext_gens_i])).sum()
rhs = peakdemand - exist_conv_caps
define_constraints(n, lhs, '>=', rhs, 'Safe', 'mintotalcap')
define_constraints(n, lhs, ">=", rhs, "Safe", "mintotalcap")
def add_operational_reserve_margin_constraint(n, config):
reserve_config = config["electricity"]["operational_reserve"]
EPSILON_LOAD = reserve_config["epsilon_load"]
EPSILON_VRES = reserve_config["epsilon_vres"]
CONTINGENCY = reserve_config["contingency"]
# Reserve Variables
reserve = get_var(n, "Generator", "r")
lhs = linexpr((1, reserve)).sum(1)
# Share of extendable renewable capacities
ext_i = n.generators.query("p_nom_extendable").index
vres_i = n.generators_t.p_max_pu.columns
if not ext_i.empty and not vres_i.empty:
capacity_factor = n.generators_t.p_max_pu[vres_i.intersection(ext_i)]
renewable_capacity_variables = get_var(n, "Generator", "p_nom")[
vres_i.intersection(ext_i)
]
lhs += linexpr(
(-EPSILON_VRES * capacity_factor, renewable_capacity_variables)
).sum(1)
# Total demand at t
demand = n.loads_t.p_set.sum(1)
# VRES potential of non extendable generators
capacity_factor = n.generators_t.p_max_pu[vres_i.difference(ext_i)]
renewable_capacity = n.generators.p_nom[vres_i.difference(ext_i)]
potential = (capacity_factor * renewable_capacity).sum(1)
# Right-hand-side
rhs = EPSILON_LOAD * demand + EPSILON_VRES * potential + CONTINGENCY
define_constraints(n, lhs, ">=", rhs, "Reserve margin")
def update_capacity_constraint(n):
gen_i = n.generators.index
ext_i = n.generators.query("p_nom_extendable").index
fix_i = n.generators.query("not p_nom_extendable").index
dispatch = get_var(n, "Generator", "p")
reserve = get_var(n, "Generator", "r")
capacity_fixed = n.generators.p_nom[fix_i]
p_max_pu = get_as_dense(n, "Generator", "p_max_pu")
lhs = linexpr((1, dispatch), (1, reserve))
if not ext_i.empty:
capacity_variable = get_var(n, "Generator", "p_nom")
lhs += linexpr((-p_max_pu[ext_i], capacity_variable)).reindex(
columns=gen_i, fill_value=""
)
rhs = (p_max_pu[fix_i] * capacity_fixed).reindex(columns=gen_i, fill_value=0)
define_constraints(n, lhs, "<=", rhs, "Generators", "updated_capacity_constraint")
def add_operational_reserve_margin(n, sns, config):
"""
Build reserve margin constraints based on the formulation given in
https://genxproject.github.io/GenX/dev/core/#Reserves.
"""
define_variables(n, 0, np.inf, "Generator", "r", axes=[sns, n.generators.index])
add_operational_reserve_margin_constraint(n, config)
update_capacity_constraint(n)
def add_battery_constraints(n):
nodes = n.buses.index[n.buses.carrier == "battery"]
if nodes.empty or ('Link', 'p_nom') not in n.variables.index:
if nodes.empty or ("Link", "p_nom") not in n.variables.index:
return
link_p_nom = get_var(n, "Link", "p_nom")
lhs = linexpr((1,link_p_nom[nodes + " charger"]),
(-n.links.loc[nodes + " discharger", "efficiency"].values,
link_p_nom[nodes + " discharger"].values))
define_constraints(n, lhs, "=", 0, 'Link', 'charger_ratio')
lhs = linexpr(
(1, link_p_nom[nodes + " charger"]),
(
-n.links.loc[nodes + " discharger", "efficiency"].values,
link_p_nom[nodes + " discharger"].values,
),
)
define_constraints(n, lhs, "=", 0, "Link", "charger_ratio")
def extra_functionality(n, snapshots):
"""
Collects supplementary constraints which will be passed to ``pypsa.linopf.network_lopf``.
If you want to enforce additional custom constraints, this is a good location to add them.
The arguments ``opts`` and ``snakemake.config`` are expected to be attached to the network.
Collects supplementary constraints which will be passed to
``pypsa.linopf.network_lopf``.
If you want to enforce additional custom constraints, this is a good
location to add them. The arguments ``opts`` and
``snakemake.config`` are expected to be attached to the network.
"""
opts = n.opts
config = n.config
if 'BAU' in opts and n.generators.p_nom_extendable.any():
if "BAU" in opts and n.generators.p_nom_extendable.any():
add_BAU_constraints(n, config)
if 'SAFE' in opts and n.generators.p_nom_extendable.any():
if "SAFE" in opts and n.generators.p_nom_extendable.any():
add_SAFE_constraints(n, config)
if 'CCL' in opts and n.generators.p_nom_extendable.any():
if "CCL" in opts and n.generators.p_nom_extendable.any():
add_CCL_constraints(n, config)
reserve = config["electricity"].get("operational_reserve", {})
if reserve.get("activate"):
add_operational_reserve_margin(n, snapshots, config)
for o in opts:
if "EQ" in o:
add_EQ_constraints(n, o)
add_battery_constraints(n)
def solve_network(n, config, opts='', **kwargs):
solver_options = config['solving']['solver'].copy()
solver_name = solver_options.pop('name')
cf_solving = config['solving']['options']
track_iterations = cf_solving.get('track_iterations', False)
min_iterations = cf_solving.get('min_iterations', 4)
max_iterations = cf_solving.get('max_iterations', 6)
def solve_network(n, config, opts="", **kwargs):
solver_options = config["solving"]["solver"].copy()
solver_name = solver_options.pop("name")
cf_solving = config["solving"]["options"]
track_iterations = cf_solving.get("track_iterations", False)
min_iterations = cf_solving.get("min_iterations", 4)
max_iterations = cf_solving.get("max_iterations", 6)
# add to network for extra_functionality
n.config = config
n.opts = opts
if cf_solving.get('skip_iterations', False):
network_lopf(n, solver_name=solver_name, solver_options=solver_options,
extra_functionality=extra_functionality, **kwargs)
skip_iterations = cf_solving.get("skip_iterations", False)
if not n.lines.s_nom_extendable.any():
skip_iterations = True
logger.info("No expandable lines found. Skipping iterative solving.")
if skip_iterations:
network_lopf(
n, solver_name=solver_name, solver_options=solver_options, **kwargs
)
else:
ilopf(n, solver_name=solver_name, solver_options=solver_options,
track_iterations=track_iterations,
min_iterations=min_iterations,
max_iterations=max_iterations,
extra_functionality=extra_functionality, **kwargs)
ilopf(
n,
solver_name=solver_name,
solver_options=solver_options,
track_iterations=track_iterations,
min_iterations=min_iterations,
max_iterations=max_iterations,
**kwargs
)
return n
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('solve_network', network='elec', simpl='',
clusters='5', ll='copt', opts='Co2L-BAU-CCL-24H')
snakemake = mock_snakemake(
"solve_network", simpl="", clusters="5", ll="copt", opts="Co2L-BAU-CCL-24H"
)
configure_logging(snakemake)
tmpdir = snakemake.config['solving'].get('tmpdir')
tmpdir = snakemake.config["solving"].get("tmpdir")
if tmpdir is not None:
Path(tmpdir).mkdir(parents=True, exist_ok=True)
opts = snakemake.wildcards.opts.split('-')
solve_opts = snakemake.config['solving']['options']
opts = snakemake.wildcards.opts.split("-")
solve_opts = snakemake.config["solving"]["options"]
fn = getattr(snakemake.log, 'memory', None)
with memory_logger(filename=fn, interval=30.) as mem:
fn = getattr(snakemake.log, "memory", None)
with memory_logger(filename=fn, interval=30.0) as mem:
n = pypsa.Network(snakemake.input[0])
n = prepare_network(n, solve_opts)
n = solve_network(n, snakemake.config, opts, solver_dir=tmpdir,
solver_logfile=snakemake.log.solver)
n = solve_network(
n,
snakemake.config,
opts,
extra_functionality=extra_functionality,
solver_dir=tmpdir,
solver_logfile=snakemake.log.solver,
)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])
logger.info("Maximum memory usage: {}".format(mem.mem_usage))

112
scripts/solve_operations_network.py
View File

@ -1,10 +1,11 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Solves linear optimal dispatch in hourly resolution
using the capacities of previous capacity expansion in rule :mod:`solve_network`.
Solves linear optimal dispatch in hourly resolution using the capacities of
previous capacity expansion in rule :mod:`solve_network`.
Relevant Settings
-----------------
@ -42,65 +43,80 @@ Outputs
Description
-----------
"""
import logging
from _helpers import configure_logging
import pypsa
import numpy as np
from pathlib import Path
import numpy as np
import pypsa
from _helpers import configure_logging
from solve_network import prepare_network, solve_network
from vresutils.benchmark import memory_logger
from solve_network import solve_network, prepare_network
logger = logging.getLogger(__name__)
def set_parameters_from_optimized(n, n_optim):
lines_typed_i = n.lines.index[n.lines.type != '']
n.lines.loc[lines_typed_i, 'num_parallel'] = \
n_optim.lines['num_parallel'].reindex(lines_typed_i, fill_value=0.)
n.lines.loc[lines_typed_i, 's_nom'] = (
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom) * n.lines.num_parallel)
lines_untyped_i = n.lines.index[n.lines.type == '']
for attr in ('s_nom', 'r', 'x'):
n.lines.loc[lines_untyped_i, attr] = \
n_optim.lines[attr].reindex(lines_untyped_i, fill_value=0.)
n.lines['s_nom_extendable'] = False
def set_parameters_from_optimized(n, n_optim):
lines_typed_i = n.lines.index[n.lines.type != ""]
n.lines.loc[lines_typed_i, "num_parallel"] = n_optim.lines["num_parallel"].reindex(
lines_typed_i, fill_value=0.0
)
n.lines.loc[lines_typed_i, "s_nom"] = (
np.sqrt(3)
* n.lines["type"].map(n.line_types.i_nom)
* n.lines.bus0.map(n.buses.v_nom)
* n.lines.num_parallel
)
lines_untyped_i = n.lines.index[n.lines.type == ""]
for attr in ("s_nom", "r", "x"):
n.lines.loc[lines_untyped_i, attr] = n_optim.lines[attr].reindex(
lines_untyped_i, fill_value=0.0
)
n.lines["s_nom_extendable"] = False
links_dc_i = n.links.index[n.links.p_nom_extendable]
n.links.loc[links_dc_i, 'p_nom'] = \
n_optim.links['p_nom_opt'].reindex(links_dc_i, fill_value=0.)
n.links.loc[links_dc_i, 'p_nom_extendable'] = False
n.links.loc[links_dc_i, "p_nom"] = n_optim.links["p_nom_opt"].reindex(
links_dc_i, fill_value=0.0
)
n.links.loc[links_dc_i, "p_nom_extendable"] = False
gen_extend_i = n.generators.index[n.generators.p_nom_extendable]
n.generators.loc[gen_extend_i, 'p_nom'] = \
n_optim.generators['p_nom_opt'].reindex(gen_extend_i, fill_value=0.)
n.generators.loc[gen_extend_i, 'p_nom_extendable'] = False
n.generators.loc[gen_extend_i, "p_nom"] = n_optim.generators["p_nom_opt"].reindex(
gen_extend_i, fill_value=0.0
)
n.generators.loc[gen_extend_i, "p_nom_extendable"] = False
stor_units_extend_i = n.storage_units.index[n.storage_units.p_nom_extendable]
n.storage_units.loc[stor_units_extend_i, 'p_nom'] = \
n_optim.storage_units['p_nom_opt'].reindex(stor_units_extend_i, fill_value=0.)
n.storage_units.loc[stor_units_extend_i, 'p_nom_extendable'] = False
n.storage_units.loc[stor_units_extend_i, "p_nom"] = n_optim.storage_units[
"p_nom_opt"
].reindex(stor_units_extend_i, fill_value=0.0)
n.storage_units.loc[stor_units_extend_i, "p_nom_extendable"] = False
stor_extend_i = n.stores.index[n.stores.e_nom_extendable]
n.stores.loc[stor_extend_i, 'e_nom'] = \
n_optim.stores['e_nom_opt'].reindex(stor_extend_i, fill_value=0.)
n.stores.loc[stor_extend_i, 'e_nom_extendable'] = False
n.stores.loc[stor_extend_i, "e_nom"] = n_optim.stores["e_nom_opt"].reindex(
stor_extend_i, fill_value=0.0
)
n.stores.loc[stor_extend_i, "e_nom_extendable"] = False
return n
if __name__ == "__main__":
if 'snakemake' not in globals():
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('solve_operations_network', network='elec',
simpl='', clusters='5', ll='copt', opts='Co2L-BAU-24H')
snakemake = mock_snakemake(
"solve_operations_network",
simpl="",
clusters="5",
ll="copt",
opts="Co2L-BAU-24H",
)
configure_logging(snakemake)
tmpdir = snakemake.config['solving'].get('tmpdir')
tmpdir = snakemake.config["solving"].get("tmpdir")
if tmpdir is not None:
Path(tmpdir).mkdir(parents=True, exist_ok=True)
@ -109,14 +125,20 @@ if __name__ == "__main__":
n = set_parameters_from_optimized(n, n_optim)
del n_optim
opts = snakemake.wildcards.opts.split('-')
snakemake.config['solving']['options']['skip_iterations'] = False
opts = snakemake.wildcards.opts.split("-")
snakemake.config["solving"]["options"]["skip_iterations"] = False
fn = getattr(snakemake.log, 'memory', None)
with memory_logger(filename=fn, interval=30.) as mem:
n = prepare_network(n, snakemake.config['solving']['options'])
n = solve_network(n, snakemake.config, opts, solver_dir=tmpdir,
solver_logfile=snakemake.log.solver)
fn = getattr(snakemake.log, "memory", None)
with memory_logger(filename=fn, interval=30.0) as mem:
n = prepare_network(n, snakemake.config["solving"]["options"])
n = solve_network(
n,
snakemake.config,
opts,
solver_dir=tmpdir,
solver_logfile=snakemake.log.solver,
)
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
n.export_to_netcdf(snakemake.output[0])
logger.info("Maximum memory usage: {}".format(mem.mem_usage))

162
test/config.test1.yaml
View File

@ -1,14 +1,15 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
version: 0.4.0
version: 0.6.1
tutorial: true
logging:
level: INFO
format: '%(levelname)s:%(name)s:%(message)s'
summary_dir: results
run:
name: ""
scenario:
simpl: ['']
@ -16,11 +17,7 @@ scenario:
clusters: [5]
opts: [Co2L-24H]
countries: ['DE']
clustering:
simplify:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
countries: ['BE']
snapshots:
start: "2013-03-01"
@ -30,6 +27,7 @@ snapshots:
enable:
prepare_links_p_nom: false
retrieve_databundle: true
retrieve_cost_data: true
build_cutout: false
retrieve_cutout: true
build_natura_raster: false
@ -56,8 +54,9 @@ electricity:
atlite:
nprocesses: 4
show_progress: false # false saves time
cutouts:
europe-2013-era5-tutorial:
be-03-2013-era5:
module: era5
x: [4., 15.]
y: [46., 56.]
@ -65,7 +64,7 @@ atlite:
renewable:
onwind:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: wind
turbine: Vestas_V112_3MW
@ -74,15 +73,15 @@ renewable:
corine:
# Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
# development of the REMix model and application for Europe. ( p.42 / p.28)
grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
24, 25, 26, 27, 28, 29, 31, 32]
grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32]
distance: 1000
distance_grid_codes: [1, 2, 3, 4, 5, 6]
natura: true
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-ac:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -90,11 +89,13 @@ renewable:
# correction_factor: 0.93
corine: [44, 255]
natura: true
ship_threshold: 400
max_shore_distance: 30000
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-dc:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -103,11 +104,13 @@ renewable:
# correction_factor: 0.93
corine: [44, 255]
natura: true
ship_threshold: 400
min_shore_distance: 30000
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
solar:
cutout: europe-2013-era5-tutorial
cutout: be-03-2013-era5
resource:
method: pv
panel: CSi
@ -121,9 +124,9 @@ renewable:
# sector: The economic potential of photovoltaics and concentrating solar
# power." Applied Energy 135 (2014): 704-720.
correction_factor: 0.854337
corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
natura: true
excluder_resolution: 200
potential: simple # or conservative
clip_p_max_pu: 1.e-2
@ -149,7 +152,7 @@ transformers:
type: ''
load:
power_statistics: True # only for files from <2019; set false in order to get ENTSOE transparency data
power_statistics: true # only for files from <2019; set false in order to get ENTSOE transparency data
interpolate_limit: 3 # data gaps up until this size are interpolated linearly
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
manual_adjustments: true # false
@ -157,8 +160,17 @@ load:
costs:
year: 2030
discountrate: 0.07 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
version: v0.4.0
rooftop_share: 0.14
fill_values:
FOM: 0
VOM: 0
efficiency: 1
fuel: 0
investment: 0
lifetime: 25
"CO2 intensity": 0
"discount rate": 0.07
marginal_cost:
solar: 0.01
onwind: 0.015
@ -168,6 +180,26 @@ costs:
emission_prices: # only used with the option Ep
co2: 0.
clustering:
simplify_network:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
algorithm: kmeans # choose from: [hac, kmeans]
feature: solar+onwind-time # only for hac. choose from: [solar+onwind-time, solar+onwind-cap, solar-time, solar-cap, solar+offwind-cap] etc.
cluster_network:
algorithm: kmeans
feature: solar+onwind-time
exclude_carriers: ["OCGT", "offwind-ac", "coal"]
aggregation_strategies:
generators:
p_nom_max: sum # use "min" for more conservative assumptions
p_nom_min: sum
p_min_pu: mean
marginal_cost: mean
committable: any
ramp_limit_up: max
ramp_limit_down: max
efficiency: mean
solving:
options:
formulation: kirchhoff
@ -200,7 +232,7 @@ solving:
plotting:
map:
figsize: [7, 7]
boundaries: [-10.2, 29, 35, 72]
boundaries: [-10.2, 29, 35, 72]
p_nom:
bus_size_factor: 5.e+4
linewidth_factor: 3.e+3
@ -219,50 +251,50 @@ plotting:
AC_carriers: ["AC line", "AC transformer"]
link_carriers: ["DC line", "Converter AC-DC"]
tech_colors:
"onwind" : "#235ebc"
"onshore wind" : "#235ebc"
'offwind' : "#6895dd"
'offwind-ac' : "#6895dd"
'offshore wind' : "#6895dd"
'offshore wind ac' : "#6895dd"
'offwind-dc' : "#74c6f2"
'offshore wind dc' : "#74c6f2"
"hydro" : "#08ad97"
"hydro+PHS" : "#08ad97"
"PHS" : "#08ad97"
"hydro reservoir" : "#08ad97"
'hydroelectricity' : '#08ad97'
"ror" : "#4adbc8"
"run of river" : "#4adbc8"
'solar' : "#f9d002"
'solar PV' : "#f9d002"
'solar thermal' : '#ffef60'
'biomass' : '#0c6013'
'solid biomass' : '#06540d'
'biogas' : '#23932d'
'waste' : '#68896b'
'geothermal' : '#ba91b1'
"OCGT" : "#d35050"
"gas" : "#d35050"
"natural gas" : "#d35050"
"CCGT" : "#b20101"
"nuclear" : "#ff9000"
"coal" : "#707070"
"lignite" : "#9e5a01"
"oil" : "#262626"
"H2" : "#ea048a"
"hydrogen storage" : "#ea048a"
"battery" : "#b8ea04"
"Electric load" : "#f9d002"
"electricity" : "#f9d002"
"lines" : "#70af1d"
"transmission lines" : "#70af1d"
"AC-AC" : "#70af1d"
"AC line" : "#70af1d"
"links" : "#8a1caf"
"HVDC links" : "#8a1caf"
"DC-DC" : "#8a1caf"
"DC link" : "#8a1caf"
"onwind": "#235ebc"
"onshore wind": "#235ebc"
'offwind': "#6895dd"
'offwind-ac': "#6895dd"
'offshore wind': "#6895dd"
'offshore wind ac': "#6895dd"
'offwind-dc': "#74c6f2"
'offshore wind dc': "#74c6f2"
"hydro": "#08ad97"
"hydro+PHS": "#08ad97"
"PHS": "#08ad97"
"hydro reservoir": "#08ad97"
'hydroelectricity': '#08ad97'
"ror": "#4adbc8"
"run of river": "#4adbc8"
'solar': "#f9d002"
'solar PV': "#f9d002"
'solar thermal': '#ffef60'
'biomass': '#0c6013'
'solid biomass': '#06540d'
'biogas': '#23932d'
'waste': '#68896b'
'geothermal': '#ba91b1'
"OCGT": "#d35050"
"gas": "#d35050"
"natural gas": "#d35050"
"CCGT": "#b20101"
"nuclear": "#ff9000"
"coal": "#707070"
"lignite": "#9e5a01"
"oil": "#262626"
"H2": "#ea048a"
"hydrogen storage": "#ea048a"
"battery": "#b8ea04"
"Electric load": "#f9d002"
"electricity": "#f9d002"
"lines": "#70af1d"
"transmission lines": "#70af1d"
"AC-AC": "#70af1d"
"AC line": "#70af1d"
"links": "#8a1caf"
"HVDC links": "#8a1caf"
"DC-DC": "#8a1caf"
"DC link": "#8a1caf"
nice_names:
OCGT: "Open-Cycle Gas"
CCGT: "Combined-Cycle Gas"