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# SPDX-FileCopyrightText: : 2021 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
name: CI
# Caching method based on and described by:
# epassaro (2021): https://dev.to/epassaro/caching-anaconda-environments-in-github-actions-5hde
# and code in GitHub repo: https://github.com/epassaro/cache-conda-envs
on:
push:
branches:
- master
pull_request:
branches:
- master
schedule:
- cron: "0 5 * * TUE"
env:
CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
jobs:
build:
strategy:
matrix:
include:
# Matrix required to handle caching with Mambaforge
- os: ubuntu-latest
label: ubuntu-latest
prefix: /usr/share/miniconda3/envs/pypsa-eur
- os: macos-latest
label: macos-latest
prefix: /Users/runner/miniconda3/envs/pypsa-eur
- os: windows-latest
label: windows-latest
prefix: C:\Miniconda3\envs\pypsa-eur
name: ${{ matrix.label }}
runs-on: ${{ matrix.os }}
defaults:
run:
shell: bash -l {0}
steps:
- uses: actions/checkout@v2
- name: Setup secrets
run: |
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
- name: Add solver to environment
run: |
echo -e " - glpk\n - ipopt<3.13.3" >> envs/environment.yaml
- name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2
with:
miniforge-variant: Mambaforge
miniforge-version: latest
activate-environment: pypsa-eur
use-mamba: true
- name: Set cache date
run: echo "DATE=$(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- name: Update environment due to outdated or unavailable cache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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# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v2.4.0
hooks:
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
- id: check-merge-conflict
- id: check-added-large-files
args: ['--maxkb=2000']
- repo: https://github.com/fsfe/reuse-tool
rev: latest
hooks:
- id: reuse

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.readthedocs.yml
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version: 2
conda:
environment: envs/environment.docs.yaml
python:
version: 3.8
install:
- requirements: doc/requirements.txt
system_packages: true

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# SPDX-FileCopyrightText: : 2021 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
.snakemake
.git
.pytest_cache
.ipynb_checkpoints
.vscode
.DS_Store
__pycache__
*.pyc
*.pyo
*.ipynb
notebooks
doc
cutouts
data/bundle
*.nc

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

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.travis.yml
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# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
branches:
only:
- master
os:
- windows
- linux
- osx
language: bash
before_install:
# install conda
- wget https://raw.githubusercontent.com/trichter/conda4travis/latest/conda4travis.sh -O conda4travis.sh
- source conda4travis.sh
# install conda environment
- conda install -c conda-forge mamba
- mamba env create -f ./envs/environment.yaml
- conda activate pypsa-eur
# install open-source solver
- mamba install -c conda-forge glpk ipopt'<3.13.3'
# list packages for easier debugging
- conda list
script:
- cp ./test/config.test1.yaml ./config.yaml
- snakemake -j all solve_all_networks
- rm -rf resources/*.nc resources/*.geojson resources/*.h5 networks results
# could repeat for more configurations in future

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# SPDX-FileCopyrightText: : 2021 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
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
license: MIT
journal: Energy Strategy Reviews
doi: 10.1016/j.esr.2018.08.012
authors:
- family-names: Hörsch
given-names: Jonas
orcid: https://orcid.org/0000-0001-9438-767X
- family-names: Brown
given-names: Tom
orcid: https://orcid.org/0000-0001-5898-1911
- family-names: Hofmann
given-names: Fabian
orcid: https://orcid.org/0000-0002-6604-5450
- family-names: Neumann
given-names: Fabian
orcid: https://orcid.org/0000-0001-8551-1480
- family-names: Frysztacki
given-names: Martha
orcid: https://orcid.org/0000-0002-0788-1328
- family-names: Hampp
given-names: Johannes
orcid: https://orcid.org/0000-0002-1776-116X
- family-names: Schlachtberger
given-names: David
orcid: https://orcid.org/0000-0002-8167-8213

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LICENSES/GPL-3.0-or-later.txt
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use to the public, the best way to achieve this is to make it free software
which everyone can redistribute and change under these terms.
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version.
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ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with
this program. If not, see <https://www.gnu.org/licenses/>.
Also add information on how to contact you by electronic and paper mail.
If the program does terminal interaction, make it output a short notice like
this when it starts in an interactive mode:
<program> Copyright (C) <year> <name of author>
This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
This is free software, and you are welcome to redistribute it under certain
conditions; type `show c' for details.
The hypothetical commands `show w' and `show c' should show the appropriate
parts of the General Public License. Of course, your program's commands might
be different; for a GUI interface, you would use an "about box".
You should also get your employer (if you work as a programmer) or school,
if any, to sign a "copyright disclaimer" for the program, if necessary. For
more information on this, and how to apply and follow the GNU GPL, see <https://www.gnu.org/licenses/>.
The GNU General Public License does not permit incorporating your program
into proprietary programs. If your program is a subroutine library, you may
consider it more useful to permit linking proprietary applications with the
library. If this is what you want to do, use the GNU Lesser General Public
License instead of this License. But first, please read <https://www.gnu.org/
licenses /why-not-lgpl.html>.

20
LICENSES/MIT.txt Normal file
View File

@ -0,0 +1,20 @@
MIT License
Copyright 2017-2021 The PyPSA-Eur Authors
Permission is hereby granted, free of charge, to any person obtaining a copy of
this software and associated documentation files (the "Software"), to deal in
the Software without restriction, including without limitation the rights to
use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
the Software, and to permit persons to whom the Software is furnished to do so,
subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS
FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR
COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER
IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN
CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

4
README.md
View File

@ -4,7 +4,7 @@ SPDX-License-Identifier: CC-BY-4.0
-->
![GitHub release (latest by date including pre-releases)](https://img.shields.io/github/v/release/pypsa/pypsa-eur?include_prereleases)
[![Build Status](https://travis-ci.org/PyPSA/pypsa-eur.svg?branch=master)](https://travis-ci.org/PyPSA/pypsa-eur)
[![Build Status](https://github.com/pypsa/pypsa-eur/actions/workflows/ci.yaml/badge.svg)](https://github.com/PyPSA/pypsa-eur/actions)
[![Documentation](https://readthedocs.org/projects/pypsa-eur/badge/?version=latest)](https://pypsa-eur.readthedocs.io/en/latest/?badge=latest)
![Size](https://img.shields.io/github/repo-size/pypsa/pypsa-eur)
[![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.3520874.svg)](https://doi.org/10.5281/zenodo.3520874)
@ -58,7 +58,7 @@ The dataset consists of:
- Electrical demand time series from the
[OPSD project](https://open-power-system-data.org/).
- Renewable time series based on ERA5 and SARAH, assembled using the [atlite tool](https://github.com/FRESNA/atlite).
- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [vresutils library](https://github.com/FRESNA/vresutils) and the [glaes library](https://github.com/FZJ-IEK3-VSA/glaes).
- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [atlite library](https://github.com/PyPSA/atlite).
Already-built versions of the model can be found in the accompanying [Zenodo
repository](https://doi.org/10.5281/zenodo.3601881).

224
Snakefile
View File

@ -1,10 +1,13 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
from os.path import normpath, exists
from shutil import copyfile
from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
HTTP = HTTPRemoteProvider()
if not exists("config.yaml"):
copyfile("config.default.yaml", "config.yaml")
@ -23,19 +26,19 @@ wildcard_constraints:
rule cluster_all_networks:
input: expand("networks/elec{year}_s{simpl}_{clusters}.nc", **config['scenario'])
input: expand("networks/elec{weather_year}_s{simpl}_{clusters}.nc", **config['scenario'])
rule extra_components_all_networks:
input: expand("networks/elec{year}_s{simpl}_{clusters}_ec.nc", **config['scenario'])
input: expand("networks/elec{weather_year}_s{simpl}_{clusters}_ec.nc", **config['scenario'])
rule prepare_all_networks:
input: expand("networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", **config['scenario'])
input: expand("networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", **config['scenario'])
rule solve_all_networks:
input: expand("results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", **config['scenario'])
input: expand("results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc", **config['scenario'])
if config['enable'].get('prepare_links_p_nom', False):
@ -43,7 +46,7 @@ if config['enable'].get('prepare_links_p_nom', False):
output: 'data/links_p_nom.csv'
log: 'logs/prepare_links_p_nom.log'
threads: 1
resources: mem=500
resources: mem_mb=500
script: 'scripts/prepare_links_p_nom.py'
@ -66,7 +69,14 @@ if config['enable'].get('retrieve_databundle', True):
script: 'scripts/retrieve_databundle.py'
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"
shell: "mv {input} {output}"
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'
@ -79,7 +89,7 @@ rule build_powerplants:
output: "resources/powerplants.csv"
log: "logs/build_powerplants.log"
threads: 1
resources: mem=500
resources: mem_mb=500
script: "scripts/build_powerplants.py"
@ -100,7 +110,7 @@ rule base_network:
log: "logs/base_network.log"
benchmark: "benchmarks/base_network"
threads: 1
resources: mem=500
resources: mem_mb=500
script: "scripts/base_network.py"
@ -120,7 +130,7 @@ rule build_shapes:
nuts3_shapes='resources/nuts3_shapes.geojson'
log: "logs/build_shapes.log"
threads: 1
resources: mem=500
resources: mem_mb=500
script: "scripts/build_shapes.py"
@ -134,32 +144,34 @@ rule build_bus_regions:
regions_offshore="resources/regions_offshore.geojson"
log: "logs/build_bus_regions.log"
threads: 1
resources: mem=1000
resources: mem_mb=1000
script: "scripts/build_bus_regions.py"
if config['enable'].get('build_cutout', False):
rule build_cutout:
output: directory("cutouts/{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}"
threads: ATLITE_NPROCESSES
resources: mem=ATLITE_NPROCESSES * 1000
resources: mem_mb=ATLITE_NPROCESSES * 1000
script: "scripts/build_cutout.py"
if config['enable'].get('retrieve_cutout', True):
rule retrieve_cutout:
output: directory(expand("cutouts/{cutouts}", **config['atlite'])),
log: "logs/retrieve_cutout.log"
script: 'scripts/retrieve_cutout.py'
input: HTTP.remote("zenodo.org/record/4709858/files/{cutout}.nc", keep_local=True, static=True)
output: "cutouts/{cutout}.nc"
shell: "mv {input} {output}"
if config['enable'].get('build_natura_raster', False):
rule build_natura_raster:
input:
natura="data/bundle/natura/Natura2000_end2015.shp",
cutouts=expand("cutouts/{cutouts}", **config['atlite'])
cutouts=expand("cutouts/{cutouts}.nc", **config['atlite'])
output: "resources/natura.tiff"
log: "logs/build_natura_raster.log"
script: "scripts/build_natura_raster.py"
@ -167,9 +179,9 @@ if config['enable'].get('build_natura_raster', False):
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"
log: "logs/retrieve_natura_raster.log"
script: 'scripts/retrieve_natura_raster.py'
shell: "mv {input} {output}"
ruleorder: build_hydro_profile > build_renewable_profiles
@ -187,13 +199,13 @@ rule build_renewable_profiles:
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']
cutout=lambda w: "cutouts/" + config["renewable"][w.technology]['cutout'] + ".nc"
output:
profile="resources/profile{year}_{technology}.nc",
log: "logs/build_renewable_profile{year}_{technology}.log"
benchmark: "benchmarks/build_renewable_profiles{year}_{technology}"
profile="resources/profile{weather_year}_{technology}.nc",
log: "logs/build_renewable_profile{weather_year}_{technology}.log"
benchmark: "benchmarks/build_renewable_profiles{weather_year}_{technology}"
threads: ATLITE_NPROCESSES
resources: mem=ATLITE_NPROCESSES * 5000
resources: mem_mb=ATLITE_NPROCESSES * 5000
script: "scripts/build_renewable_profiles.py"
@ -202,9 +214,9 @@ if 'hydro' in config['renewable'].keys():
input:
country_shapes='resources/country_shapes.geojson',
eia_hydro_generation='data/bundle/EIA_hydro_generation_2000_2014.csv',
cutout="cutouts/" + config["renewable"]['hydro']['cutout']
output: 'resources/profile{year}_hydro.nc'
log: "logs/build_hydro_profile{year}.log"
cutout="cutouts/" + config["renewable"]['hydro']['cutout'] + ".nc"
output: 'resources/profile{weather_year}_hydro.nc'
log: "logs/build_hydro_profile{weather_year}.log"
resources: mem=5000
script: 'scripts/build_hydro_profile.py'
@ -219,73 +231,74 @@ rule add_electricity:
geth_hydro_capacities='data/geth2015_hydro_capacities.csv',
load='resources/load.csv',
nuts3_shapes='resources/nuts3_shapes.geojson',
**{f"profile_{tech}": "resources/profile{year}_" + f"{tech}.nc"
**{f"profile_{tech}": "resources/profile{weather_year}_" + f"{tech}.nc"
for tech in config['renewable']}
output: "networks/elec{year}.nc"
log: "logs/add_electricity{year}.log"
benchmark: "benchmarks/add_electricity{year}"
output: "networks/elec{weather_year}.nc"
log: "logs/add_electricity{weather_year}.log"
benchmark: "benchmarks/add_electricity{weather_year}"
threads: 1
resources: mem=3000
resources: mem_mb=5000
script: "scripts/add_electricity.py"
rule simplify_network:
input:
network='networks/elec{year}.nc',
network='networks/elec{weather_year}.nc',
tech_costs=COSTS,
regions_onshore="resources/regions_onshore.geojson",
regions_offshore="resources/regions_offshore.geojson"
output:
network='networks/elec{year}_s{simpl}.nc',
regions_onshore="resources/regions_onshore_elec{year}_s{simpl}.geojson",
regions_offshore="resources/regions_offshore_elec{year}_s{simpl}.geojson",
busmap='resources/busmap_elec{year}_s{simpl}.csv'
log: "logs/simplify_network/elec{year}_s{simpl}.log"
benchmark: "benchmarks/simplify_network/elec{year}_s{simpl}"
network='networks/elec{weather_year}_s{simpl}.nc',
regions_onshore="resources/regions_onshore_elec{weather_year}_s{simpl}.geojson",
regions_offshore="resources/regions_offshore_elec{weather_year}_s{simpl}.geojson",
busmap='resources/busmap_elec{weather_year}_s{simpl}.csv',
connection_costs='resources/connection_costs{weather_year}_s{simpl}.csv'
log: "logs/simplify_network/elec{weather_year}_s{simpl}.log"
benchmark: "benchmarks/simplify_network/elec{weather_year}_s{simpl}"
threads: 1
resources: mem=4000
resources: mem_mb=4000
script: "scripts/simplify_network.py"
rule cluster_network:
input:
network='networks/elec{year}_s{simpl}.nc',
regions_onshore="resources/regions_onshore_elec{year}_s{simpl}.geojson",
regions_offshore="resources/regions_offshore_elec{year}_s{simpl}.geojson",
busmap=ancient('resources/busmap_elec{year}_s{simpl}.csv'),
network='networks/elec{weather_year}_s{simpl}.nc',
regions_onshore="resources/regions_onshore_elec{weather_year}_s{simpl}.geojson",
regions_offshore="resources/regions_offshore_elec{weather_year}_s{simpl}.geojson",
busmap=ancient('resources/busmap_elec{weather_year}_s{simpl}.csv'),
tech_costs=COSTS
output:
network='networks/elec{year}_s{simpl}_{clusters}.nc',
regions_onshore="resources/regions_onshore_elec{year}_s{simpl}_{clusters}.geojson",
regions_offshore="resources/regions_offshore_elec{year}_s{simpl}_{clusters}.geojson",
busmap="resources/busmap_elec{year}_s{simpl}_{clusters}.csv",
linemap="resources/linemap_elec{year}_s{simpl}_{clusters}.csv"
log: "logs/cluster_network/elec{year}_s{simpl}_{clusters}.log"
benchmark: "benchmarks/cluster_network/elec{year}_s{simpl}_{clusters}"
network='networks/elec{weather_year}_s{simpl}_{clusters}.nc',
regions_onshore="resources/regions_onshore_elec{weather_year}_s{simpl}_{clusters}.geojson",
regions_offshore="resources/regions_offshore_elec{weather_year}_s{simpl}_{clusters}.geojson",
busmap="resources/busmap_elec{weather_year}_s{simpl}_{clusters}.csv",
linemap="resources/linemap_elec{weather_year}_s{simpl}_{clusters}.csv"
log: "logs/cluster_network/elec{weather_year}_s{simpl}_{clusters}.log"
benchmark: "benchmarks/cluster_network/elec{weather_year}_s{simpl}_{clusters}"
threads: 1
resources: mem=3000
resources: mem_mb=6000
script: "scripts/cluster_network.py"
rule add_extra_components:
input:
network='networks/elec{year}_s{simpl}_{clusters}.nc',
network='networks/elec{weather_year}_s{simpl}_{clusters}.nc',
tech_costs=COSTS,
output: 'networks/elec{year}_s{simpl}_{clusters}_ec.nc'
log: "logs/add_extra_components/elec{year}_s{simpl}_{clusters}.log"
benchmark: "benchmarks/add_extra_components/elec{year}_s{simpl}_{clusters}_ec"
output: 'networks/elec{weather_year}_s{simpl}_{clusters}_ec.nc'
log: "logs/add_extra_components/elec{weather_year}_s{simpl}_{clusters}.log"
benchmark: "benchmarks/add_extra_components/elec{weather_year}_s{simpl}_{clusters}_ec"
threads: 1
resources: mem=3000
resources: mem_mb=3000
script: "scripts/add_extra_components.py"
rule prepare_network:
input: 'networks/elec{year}_s{simpl}_{clusters}_ec.nc', tech_costs=COSTS
output: 'networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc'
log: "logs/prepare_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.log"
benchmark: "benchmarks/prepare_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}"
input: 'networks/elec{weather_year}_s{simpl}_{clusters}_ec.nc', tech_costs=COSTS
output: 'networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc'
log: "logs/prepare_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.log"
benchmark: "benchmarks/prepare_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}"
threads: 1
resources: mem=1000
resources: mem_mb=4000
script: "scripts/prepare_network.py"
@ -308,43 +321,43 @@ def memory(w):
rule solve_network:
input: "networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
input: "networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
log:
solver=normpath("logs/solve_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_solver.log"),
python="logs/solve_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_python.log",
memory="logs/solve_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_memory.log"
benchmark: "benchmarks/solve_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}"
solver=normpath("logs/solve_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_solver.log"),
python="logs/solve_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_python.log",
memory="logs/solve_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_memory.log"
benchmark: "benchmarks/solve_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}"
threads: 4
resources: mem=memory
shadow: "shallow"
resources: mem_mb=memory
shadow: "minimal"
script: "scripts/solve_network.py"
rule solve_operations_network:
input:
unprepared="networks/elec{year}_s{simpl}_{clusters}_ec.nc",
optimized="results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op.nc"
unprepared="networks/elec{weather_year}_s{simpl}_{clusters}_ec.nc",
optimized="results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op.nc"
log:
solver=normpath("logs/solve_operations_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_solver.log"),
python="logs/solve_operations_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_python.log",
memory="logs/solve_operations_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_memory.log"
benchmark: "benchmarks/solve_operations_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}"
solver=normpath("logs/solve_operations_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_solver.log"),
python="logs/solve_operations_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_python.log",
memory="logs/solve_operations_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op_memory.log"
benchmark: "benchmarks/solve_operations_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}"
threads: 4
resources: mem=(lambda w: 5000 + 372 * int(w.clusters))
shadow: "shallow"
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{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
network="results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
tech_costs=COSTS
output:
only_map="results/plots/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}.{ext}",
ext="results/plots/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_ext.{ext}"
log: "logs/plot_network/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_{ext}.log"
only_map="results/plots/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}.{ext}",
ext="results/plots/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_ext.{ext}"
log: "logs/plot_network/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{attr}_{ext}.log"
script: "scripts/plot_network.py"
@ -357,8 +370,7 @@ def input_make_summary(w):
else:
ll = w.ll
return ([COSTS] +
expand("results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc",
network=w.network,
expand("results/networks/elec{weather_year}_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"]}))
@ -366,53 +378,27 @@ def input_make_summary(w):
rule make_summary:
input: input_make_summary
output: directory("results/summaries/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}")
log: "logs/make_summary/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.log",
output: directory("results/summaries/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}")
log: "logs/make_summary/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.log",
script: "scripts/make_summary.py"
rule plot_summary:
input: "results/summaries/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}"
output: "results/plots/{year}/summary_{summary}_elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.{ext}"
log: "logs/plot_summary/{summary}_elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}_{ext}.log"
input: "results/summaries/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}"
output: "results/plots/{weather_year}/summary_{summary}_elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}.{ext}"
log: "logs/plot_summary/{summary}_elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_{country}_{ext}.log"
script: "scripts/plot_summary.py"
def input_plot_p_nom_max(w):
return [("networks/elec{year}_s{simpl}{maybe_cluster}.nc"
return [("networks/elec{weather_year}_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{year}_s{simpl}_cum_p_nom_max_{clusts}_{techs}_{country}.{ext}"
log: "logs/plot_p_nom_max/elec{year}_s{simpl}_{clusts}_{techs}_{country}_{ext}.log"
output: "results/plots/elec{weather_year}_s{simpl}_cum_p_nom_max_{clusts}_{techs}_{country}.{ext}"
log: "logs/plot_p_nom_max/elec{weather_year}_s{simpl}_{clusts}_{techs}_{country}_{ext}.log"
script: "scripts/plot_p_nom_max.py"
rule build_country_flh:
input:
base_network="networks/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',
pietzker="data/pietzker2014.xlsx",
regions=lambda w: ("resources/country_shapes.geojson"
if w.technology in ('onwind', 'solar')
else "resources/offshore_shapes.geojson"),
cutout=lambda w: "cutouts/" + config["renewable"][w.technology]['cutout']
output:
area="resources/country_flh_area_{technology}.csv",
aggregated="resources/country_flh_aggregated_{technology}.csv",
uncorrected="resources/country_flh_uncorrected_{technology}.csv",
plot="resources/country_flh_{technology}.pdf",
exclusion=directory("resources/country_exclusion_{technology}")
log: "logs/build_country_flh_{technology}.log"
resources: mem=10000
benchmark: "benchmarks/build_country_flh_{technology}"
script: "scripts/build_country_flh.py"

63
config.default.yaml
View File

@ -2,7 +2,7 @@
#
# SPDX-License-Identifier: CC0-1.0
version: 0.3.0
version: 0.4.0
tutorial: false
logging:
@ -20,6 +20,10 @@ 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', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK']
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"
@ -37,7 +41,7 @@ enable:
electricity:
voltages: [220., 300., 380.]
co2limit: 7.75e+7 # 0.05 * 3.1e9*0.5
co2base: 1.487e9
co2base: 1.487e+9
agg_p_nom_limits: data/agg_p_nom_minmax.csv
extendable_carriers:
@ -63,18 +67,28 @@ electricity:
atlite:
nprocesses: 4
cutouts:
# use 'base' to determine geographical bounds and time span from config
# base:
# module: era5
europe-2013-era5:
module: era5
xs: [-12., 35.]
ys: [72., 33.]
years: [2013, 2013]
module: era5 # in priority order
x: [-12., 35.]
y: [33., 72]
dx: 0.3
dy: 0.3
time: ['2013', '2013']
europe-2013-sarah:
module: sarah
resolution: 0.2
xs: [-12., 42.]
ys: [65., 33.]
years: [2013, 2013]
module: [sarah, era5] # in priority order
x: [-12., 45.]
y: [33., 65]
dx: 0.2
dy: 0.2
time: ['2013', '2013']
sarah_interpolate: false
sarah_dir:
features: [influx, temperature]
renewable:
onwind:
cutout: europe-2013-era5
@ -98,8 +112,11 @@ renewable:
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
capacity_per_sqkm: 3
# correction_factor: 0.93
capacity_per_sqkm: 2
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
max_depth: 50
@ -112,8 +129,11 @@ renewable:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
# ScholzPhd Tab 4.3.1: 10MW/km^2
capacity_per_sqkm: 3
# correction_factor: 0.93
capacity_per_sqkm: 2
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
max_depth: 50
@ -129,12 +149,14 @@ renewable:
slope: 35.
azimuth: 180.
capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2
# Determined by comparing uncorrected area-weighted full-load hours to those
# 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
# sector: The economic potential of photovoltaics and concentrating solar
# power." Applied Energy 135 (2014): 704-720.
correction_factor: 0.854337
# 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]
natura: true
@ -169,8 +191,7 @@ transformers:
type: ''
load:
url: https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv
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
@ -220,8 +241,8 @@ solving:
# threads: 4
# lpmethod: 4 # barrier
# solutiontype: 2 # non basic solution, ie no crossover
# barrier_convergetol: 1.e-5
# feasopt_tolerance: 1.e-6
# barrier.convergetol: 1.e-5
# feasopt.tolerance: 1.e-6
plotting:
map:

31
config.tutorial.yaml
View File

@ -2,7 +2,7 @@
#
# SPDX-License-Identifier: CC0-1.0
version: 0.3.0
version: 0.4.0
tutorial: true
logging:
@ -20,6 +20,10 @@ scenario:
countries: ['DE']
clustering:
simplify:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
snapshots:
start: "2013-03-01"
end: "2013-04-01"
@ -55,16 +59,15 @@ electricity:
atlite:
nprocesses: 4
cutouts:
europe-2013-era5:
europe-2013-era5-tutorial:
module: era5
xs: [4., 15.]
ys: [56., 46.]
months: [3, 3]
years: [2013, 2013]
x: [4., 15.]
y: [46., 56.]
time: ["2013-03", "2013-03"]
renewable:
onwind:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: wind
turbine: Vestas_V112_3MW
@ -81,7 +84,7 @@ renewable:
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-ac:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -93,7 +96,7 @@ renewable:
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-dc:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -106,7 +109,7 @@ renewable:
potential: simple # or conservative
clip_p_max_pu: 1.e-2
solar:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: pv
panel: CSi
@ -114,12 +117,13 @@ renewable:
slope: 35.
azimuth: 180.
capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2
# Determined by comparing uncorrected area-weighted full-load hours to those
# 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
# sector: The economic potential of photovoltaics and concentrating solar
# power." Applied Energy 135 (2014): 704-720.
correction_factor: 0.854337
# 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]
natura: true
@ -148,8 +152,7 @@ transformers:
type: ''
load:
url: https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv
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

2
doc/Makefile
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# Makefile for Sphinx documentation
#

2
doc/_static/theme_overrides.css vendored
View File

@ -1,5 +1,5 @@
/* SPDX-FileCopyrightText: 2017-2020 The PyPSA-Eur Authors
SPDX-License-Identifier: GPL-3.0-or-later
SPDX-License-Identifier: MIT
*/
.wy-side-nav-search {

1
doc/cloudcomputing.rst
View File

@ -108,6 +108,7 @@ Make sure that your instance is operating for the next steps.
- 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)
If you had struggle with the above steps, you could also try `this video <https://www.youtube.com/watch?v=lYx1oQkEF0E>`_.
.. note::

18
doc/conf.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: 20017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# -*- coding: utf-8 -*-
#
@ -76,7 +76,7 @@ author = u'Jonas Hoersch (KIT, FIAS), Fabian Hofmann (FIAS), David Schlachtberge
# The short X.Y version.
version = u'0.3'
# The full version, including alpha/beta/rc tags.
release = u'0.3.0'
release = u'0.4.0'
# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
@ -157,16 +157,12 @@ html_theme_options = {
# pixels large.
#html_favicon = None
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']
# These folders are copied to the documentation's HTML output
html_static_path = ["_static"]
html_context = {
'css_files': [
'_static/theme_overrides.css', # override wide tables in RTD theme
],
}
# These paths are either relative to html_static_path
# or fully qualified paths (eg. https://...)
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

11
doc/configtables/atlite.csv
View File

@ -1,8 +1,9 @@
,Unit,Values,Description
nprocesses,--,int,"Number of parallel processes in cutout preparation"
cutouts,,,
-- {name},--,"Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-era5``).","Directory to write cutout data to. The user may specify multiple cutouts under configuration ``atlite: cutouts:``. Reference is used in configuration ``renewable: {technology}: cutout:``"
-- -- module,--,"One of {'era5','sarah'}","Source of the reanalysis weather dataset (e.g. `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_)"
-- -- xs,°,"Float interval within [-180, 180]","Range of longitudes to download weather data for."
-- -- ys,°,"Float interval within [-90, 90]","Range of latitudes to download weather data for."
-- -- years,--,"Integer interval within [1979,2018]","Range of years to download weather data for."
-- {name},--,"Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-era5``).","Name of the cutout netcdf file. The user may specify multiple cutouts under configuration ``atlite: cutouts:``. Reference is used in configuration ``renewable: {technology}: cutout:``. The cutout ``base`` may be used to automatically calculate temporal and spatial bounds of the network."
-- -- module,--,"Subset of {'era5','sarah'}","Source of the reanalysis weather dataset (e.g. `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_)"
-- -- x,°,"Float interval within [-180, 180]","Range of longitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes."
-- -- y,°,"Float interval within [-90, 90]","Range of latitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes."
-- -- time,,"Time interval within ['1979', '2018'] (with valid pandas date time strings)","Time span to download weather data for. If not defined, it defaults to the time interval spanned by the snapshots."
-- -- features,,"String or list of strings with valid cutout features ('inlfux', 'wind').","When freshly building a cutout, retrieve data only for those features. If not defined, it defaults to all available features."

1 Unit Values Description
2 nprocesses -- int Number of parallel processes in cutout preparation
3 cutouts
4 -- {name} -- Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-era5``). Directory to write cutout data to. The user may specify multiple cutouts under configuration ``atlite: cutouts:``. Reference is used in configuration ``renewable: {technology}: cutout:`` Name of the cutout netcdf file. The user may specify multiple cutouts under configuration ``atlite: cutouts:``. Reference is used in configuration ``renewable: {technology}: cutout:``. The cutout ``base`` may be used to automatically calculate temporal and spatial bounds of the network.
5 -- -- module -- One of {'era5','sarah'} Subset of {'era5','sarah'} Source of the reanalysis weather dataset (e.g. `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_)
6 -- -- xs -- -- x ° Float interval within [-180, 180] Range of longitudes to download weather data for. Range of longitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes.
7 -- -- ys -- -- y ° Float interval within [-90, 90] Range of latitudes to download weather data for. Range of latitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes.
8 -- -- years -- -- time -- Integer interval within [1979,2018] Time interval within ['1979', '2018'] (with valid pandas date time strings) Range of years to download weather data for. Time span to download weather data for. If not defined, it defaults to the time interval spanned by the snapshots.
9 -- -- features String or list of strings with valid cutout features ('inlfux', 'wind'). When freshly building a cutout, retrieve data only for those features. If not defined, it defaults to all available features.

3
doc/configtables/clustering.csv Normal file
View File

@ -0,0 +1,3 @@
,Unit,Values,Description
simplify,,,
-- to_substations,bool,"{'true','false'}","Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones"
1 Unit Values Description
2 simplify
3 -- to_substations bool {'true','false'} Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones

24
doc/configtables/electricity.csv
View File

@ -1,19 +1,19 @@
,Unit,Values,Description,
voltages,kV,"Any subset of {220., 300., 380.}",Voltage levels to consider when,
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.,
,Unit,Values,Description
voltages,kV,"Any subset of {220., 300., 380.}",Voltage levels to consider when
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,,,,
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``.
max_hours,,,,
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>`_.
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,
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

Can't render this file because it has a wrong number of fields in line 5.

2
doc/configtables/offwind-dc.csv
View File

@ -2,7 +2,7 @@
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
resource,,,
-- method,--,"Must be 'wind'","A superordinate technology type."
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_","Specifies the turbine type and its characteristic power curve."
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__","Specifies the turbine type and its characteristic power curve."
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``."

1 Unit Values Description
2 cutout -- Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5. Specifies the directory where the relevant weather data ist stored.
3 resource
4 -- method -- Must be 'wind' A superordinate technology type.
5 -- turbine -- One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_ One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__ Specifies the turbine type and its characteristic power curve.
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``.

2
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@ -2,7 +2,7 @@
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
resource,,,
-- method,--,"Must be 'wind'","A superordinate technology type."
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_","Specifies the turbine type and its characteristic power curve."
-- turbine,--,"One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__","Specifies the turbine type and its characteristic power curve."
capacity_per_sqkm,:math:`MW/km^2`,float,"Allowable density of wind turbine placement."
corine,,,
-- grid_codes,--,"Any 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 wind turbine placement."

1 Unit Values Description
2 cutout -- Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5. Specifies the directory where the relevant weather data ist stored.
3 resource
4 -- method -- Must be 'wind' A superordinate technology type.
5 -- turbine -- One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`_ One of turbine types included in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/windturbine>`__ Specifies the turbine type and its characteristic power curve.
6 capacity_per_sqkm :math:`MW/km^2` float Allowable density of wind turbine placement.
7 corine
8 -- grid_codes -- Any 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 wind turbine placement.

10
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@ -1,11 +1,11 @@
Trigger, Description, Definition, Status
``nH``; i.e. ``2H``-``6H``, Resample the time-resolution by averaging over every ``n`` snapshots, ``prepare_network``: `average_every_nhours() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L110>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L146>`_), In active use
``nH``; i.e. ``2H``-``6H``, Resample the time-resolution by averaging over every ``n`` snapshots, ``prepare_network``: `average_every_nhours() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L110>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L146>`__), In active use
``nSEG``; e.g. ``4380SEG``, "Apply time series segmentation with `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package to ``n`` adjacent snapshots of varying lengths based on capacity factors of varying renewables, hydro inflow and load.", ``prepare_network``: apply_time_segmentation(), In active use
``Co2L``, Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``. If a float is appended an overall emission limit relative to the emission level given in ``electricity: co2base`` is added (e.g. ``Co2L0.05`` limits emissisions to 5% of what is given in ``electricity: co2base``), ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`_, In active use
``Ep``, Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well), ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`_, In active use
``Co2L``, Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``. If a float is appended an overall emission limit relative to the emission level given in ``electricity: co2base`` is added (e.g. ``Co2L0.05`` limits emissisions to 5% of what is given in ``electricity: co2base``), ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`__, In active use
``Ep``, Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well), ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`__, In active use
``CCL``, Add minimum and maximum levels of generator nominal capacity per carrier for individual countries. These can be specified in the file linked at ``electricity: agg_p_nom_limits`` in the configuration. File defaults to ``data/agg_p_nom_minmax.csv``., ``solve_network``, In active use
``EQ``, "Require each country or node to on average produce a minimal share of its total consumption itself. Example: ``EQ0.5c`` demands each country to produce on average at least 50% of its consumption; ``EQ0.5`` demands each node to produce on average at least 50% of its consumption.", ``solve_network``, In active use
``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
``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

1 Trigger Description Definition Status
2 ``nH``; i.e. ``2H``-``6H`` Resample the time-resolution by averaging over every ``n`` snapshots ``prepare_network``: `average_every_nhours() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L110>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L146>`_) ``prepare_network``: `average_every_nhours() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L110>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L146>`__) In active use
3 ``nSEG``; e.g. ``4380SEG`` Apply time series segmentation with `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package to ``n`` adjacent snapshots of varying lengths based on capacity factors of varying renewables, hydro inflow and load. ``prepare_network``: apply_time_segmentation() In active use
4 ``Co2L`` Add an overall absolute carbon-dioxide emissions limit configured in ``electricity: co2limit``. If a float is appended an overall emission limit relative to the emission level given in ``electricity: co2base`` is added (e.g. ``Co2L0.05`` limits emissisions to 5% of what is given in ``electricity: co2base``) ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`_ ``prepare_network``: `add_co2limit() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L19>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L154>`__ In active use
5 ``Ep`` Add cost for a carbon-dioxide price configured in ``costs: emission_prices: co2`` to ``marginal_cost`` of generators (other emission types listed in ``network.carriers`` possible as well) ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`_ ``prepare_network``: `add_emission_prices() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L24>`_ and its `caller <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/prepare_network.py#L158>`__ In active use
6 ``CCL`` Add minimum and maximum levels of generator nominal capacity per carrier for individual countries. These can be specified in the file linked at ``electricity: agg_p_nom_limits`` in the configuration. File defaults to ``data/agg_p_nom_minmax.csv``. ``solve_network`` In active use
7 ``EQ`` Require each country or node to on average produce a minimal share of its total consumption itself. Example: ``EQ0.5c`` demands each country to produce on average at least 50% of its consumption; ``EQ0.5`` demands each node to produce on average at least 50% of its consumption. ``solve_network`` In active use
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>`_ ``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>`_ ``solve_network`` `add_opts_constraints() <https://github.com/PyPSA/pypsa-eur/blob/6b964540ed39d44079cdabddee8333f486d0cd63/scripts/solve_network.py#L73>`__ Untested
11 ``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

2
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@ -2,7 +2,7 @@
cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module can be ERA5 or SARAH-2.","Specifies the directory where the relevant weather data ist stored that is specified at ``atlite/cutouts`` configuration. Both ``sarah`` and ``era5`` work."
resource,,,
-- method,--,"Must be 'pv'","A superordinate technology type."
-- panel,--,"One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`_","Specifies the solar panel technology and its characteristic attributes."
-- panel,--,"One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`__","Specifies the solar panel technology and its characteristic attributes."
-- orientation,,,
-- -- slope,°,"Realistically any angle in [0., 90.]","Specifies the tilt angle (or slope) of the solar panel. A slope of zero corresponds to the face of the panel aiming directly overhead. A positive tilt angle steers the panel towards the equator."
-- -- azimuth,°,"Any angle in [0., 360.]","Specifies the `azimuth <https://en.wikipedia.org/wiki/Azimuth>`_ orientation of the solar panel. South corresponds to 180.°."

1 Unit Values Description
2 cutout -- Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module can be ERA5 or SARAH-2. Specifies the directory where the relevant weather data ist stored that is specified at ``atlite/cutouts`` configuration. Both ``sarah`` and ``era5`` work.
3 resource
4 -- method -- Must be 'pv' A superordinate technology type.
5 -- panel -- One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`_ One of {'Csi', 'CdTe', 'KANENA'} as defined in `atlite <https://github.com/PyPSA/atlite/tree/master/atlite/resources/solarpanel>`__ Specifies the solar panel technology and its characteristic attributes.
6 -- orientation
7 -- -- slope ° Realistically any angle in [0., 90.] Specifies the tilt angle (or slope) of the solar panel. A slope of zero corresponds to the face of the panel aiming directly overhead. A positive tilt angle steers the panel towards the equator.
8 -- -- azimuth ° Any angle in [0., 360.] Specifies the `azimuth <https://en.wikipedia.org/wiki/Azimuth>`_ orientation of the solar panel. South corresponds to 180.°.

2
doc/configtables/solving-solver.csv
View File

@ -1,3 +1,3 @@
,Unit,Values,Description
name,--,"One of {'gurobi', 'cplex', 'cbc', 'glpk', 'ipopt'}; potentially more possible","Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow."
opts,--,"Parameter list for `Gurobi <https://www.gurobi.com/documentation/8.1/refman/parameters.html>`_ and `CPLEX <https://www.ibm.com/support/knowledgecenter/SSSA5P_12.5.1/ilog.odms.cplex.help/CPLEX/Parameters/topics/introListAlpha.html>`_","Solver specific parameter settings."
opts,--,"Parameter list for `Gurobi <https://www.gurobi.com/documentation/8.1/refman/parameters.html>`_ and `CPLEX <https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-topical-list-parameters>`_","Solver specific parameter settings."

1 Unit Values Description
2 name -- One of {'gurobi', 'cplex', 'cbc', 'glpk', 'ipopt'}; potentially more possible Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow.
3 opts -- Parameter list for `Gurobi <https://www.gurobi.com/documentation/8.1/refman/parameters.html>`_ and `CPLEX <https://www.ibm.com/support/knowledgecenter/SSSA5P_12.5.1/ilog.odms.cplex.help/CPLEX/Parameters/topics/introListAlpha.html>`_ Parameter list for `Gurobi <https://www.gurobi.com/documentation/8.1/refman/parameters.html>`_ and `CPLEX <https://www.ibm.com/docs/en/icos/20.1.0?topic=cplex-topical-list-parameters>`_ Solver specific parameter settings.

2
doc/configtables/toplevel.csv
View File

@ -14,4 +14,4 @@ enable,,,
-- retrieve_cutout,bool,"{true, false}","Switch to enable the retrieval of cutouts from zenodo with :mod:`retrieve_cutout`."
-- build_natura_raster,bool,"{true, false}","Switch to enable the creation of the raster ``natura.tiff`` via the rule :mod:`build_natura_raster`."
-- retrieve_natura_raster,bool,"{true, false}","Switch to enable the retrieval of ``natura.tiff`` from zenodo with :mod:`retrieve_natura_raster`."
-- custom_busmap,bool,"{true, false}","Switch to enable the use of custom busmaps in rule :mod:`cluster_network`. If activated the rule looks for provided busmaps at ``data/custom_busmap_elec{year}_s{simpl}_{clusters}.csv`` which should have the same format as ``resources/busmap_elec{year}_s{simpl}_{clusters}.csv``, i.e. the index should contain the buses of ``networks/elec_s{simpl}.nc``."
-- custom_busmap,bool,"{true, false}","Switch to enable the use of custom busmaps in rule :mod:`cluster_network`. If activated the rule looks for provided busmaps at ``data/custom_busmap_elec{weather_year}_s{simpl}_{clusters}.csv`` which should have the same format as ``resources/busmap_elec{weather_year}_s{simpl}_{clusters}.csv``, i.e. the index should contain the buses of ``networks/elec_s{simpl}.nc``."

1 Unit Values Description
14 -- retrieve_cutout bool {true, false} Switch to enable the retrieval of cutouts from zenodo with :mod:`retrieve_cutout`.
15 -- build_natura_raster bool {true, false} Switch to enable the creation of the raster ``natura.tiff`` via the rule :mod:`build_natura_raster`.
16 -- retrieve_natura_raster bool {true, false} Switch to enable the retrieval of ``natura.tiff`` from zenodo with :mod:`retrieve_natura_raster`.
17 -- custom_busmap bool {true, false} Switch to enable the use of custom busmaps in rule :mod:`cluster_network`. If activated the rule looks for provided busmaps at ``data/custom_busmap_elec{year}_s{simpl}_{clusters}.csv`` which should have the same format as ``resources/busmap_elec{year}_s{simpl}_{clusters}.csv``, i.e. the index should contain the buses of ``networks/elec_s{simpl}.nc``. Switch to enable the use of custom busmaps in rule :mod:`cluster_network`. If activated the rule looks for provided busmaps at ``data/custom_busmap_elec{weather_year}_s{simpl}_{clusters}.csv`` which should have the same format as ``resources/busmap_elec{weather_year}_s{simpl}_{clusters}.csv``, i.e. the index should contain the buses of ``networks/elec_s{simpl}.nc``.

59
doc/configuration.rst
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@ -18,7 +18,8 @@ Top-level configuration
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 5-12,20,27-34
:lines: 5-12,20,31-38
.. csv-table::
:header-rows: 1
@ -42,7 +43,7 @@ facilitate running multiple scenarios through a single command
snakemake -j 1 solve_all_networks
For each wildcard, a **list of values** is provided. The rule ``solve_all_networks`` will trigger the rules for creating ``results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc`` for **all combinations** of the provided wildcard values as defined by Python's `itertools.product(...) <https://docs.python.org/2/library/itertools.html#itertools.product>`_ function that snakemake's `expand(...) function <https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#targets>`_ uses.
For each wildcard, a **list of values** is provided. The rule ``solve_all_networks`` will trigger the rules for creating ``results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc`` for **all combinations** of the provided wildcard values as defined by Python's `itertools.product(...) <https://docs.python.org/2/library/itertools.html#itertools.product>`_ function that snakemake's `expand(...) function <https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#targets>`_ uses.
An exemplary dependency graph (starting from the simplification rules) then looks like this:
@ -50,7 +51,8 @@ An exemplary dependency graph (starting from the simplification rules) then look
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 14-18
:start-at: scenario:
:end-before: countries:
.. csv-table::
:header-rows: 1
@ -66,7 +68,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 22-25
:start-at: snapshots:
:end-before: enable:
.. csv-table::
:header-rows: 1
@ -80,7 +83,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 36-60
:start-at: electricity:
:end-before: atlite:
.. csv-table::
:header-rows: 1
@ -93,11 +97,14 @@ Specifies the temporal range to build an energy system model for as arguments to
.. _atlite_cf:
``atlite``
=============
==========
Define and specify the ``atlite.Cutout`` used for calculating renewable potentials and time-series. All options except for ``features`` are directly used as `cutout parameters <https://atlite.readthedocs.io/en/latest/ref_api.html#cutout>`_.
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 62-75
:start-at: atlite:
:end-before: renewable:
.. csv-table::
:header-rows: 1
@ -114,7 +121,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 77-94
:start-at: renewable:
:end-before: offwind-ac:
.. csv-table::
:header-rows: 1
@ -126,7 +134,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 77,95-107
:start-at: offwind-ac:
:end-before: offwind-dc:
.. csv-table::
:header-rows: 1
@ -138,7 +147,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 77,108-121
:start-at: offwind-dc:
:end-before: solar:
.. csv-table::
:header-rows: 1
@ -150,7 +160,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 77,122-141
:start-at: solar:
:end-before: hydro:
.. csv-table::
:header-rows: 1
@ -162,7 +173,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 77,142-147
:start-at: hydro:
:end-before: lines:
.. csv-table::
:header-rows: 1
@ -176,7 +188,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 149-157
:start-at: lines:
:end-before: links:
.. csv-table::
:header-rows: 1
@ -190,7 +203,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 159-163
:start-at: links:
:end-before: transformers:
.. csv-table::
:header-rows: 1
@ -204,7 +218,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 165-168
:start-at: transformers:
:end-before: load:
.. csv-table::
:header-rows: 1
@ -218,7 +233,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 170-176
:start-at: load:
:end-before: costs:
.. csv-table::
:header-rows: 1
@ -232,7 +248,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 178-190
:start-after: scaling_factor:
:end-before: solving:
.. csv-table::
:header-rows: 1
@ -253,7 +270,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 192-202
:start-at: solving:
:end-before: solver:
.. csv-table::
:header-rows: 1
@ -265,7 +283,8 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 192,203-219
:start-at: solver:
:end-before: plotting:
.. csv-table::
:header-rows: 1
@ -279,7 +298,7 @@ Specifies the temporal range to build an energy system model for as arguments to
.. literalinclude:: ../config.default.yaml
:language: yaml
:lines: 221-299
:start-at: plotting:
.. csv-table::
:header-rows: 1

21
doc/index.rst
View File

@ -9,8 +9,8 @@ PyPSA-Eur: An Open Optimisation Model of the European Transmission System
.. image:: https://img.shields.io/github/v/release/pypsa/pypsa-eur?include_prereleases
:alt: GitHub release (latest by date including pre-releases)
.. image:: https://travis-ci.org/PyPSA/pypsa-eur.svg?branch=master
:target: https://travis-ci.org/PyPSA/pypsa-eur
.. image:: https://github.com/pypsa/pypsa-eur/actions/workflows/ci.yaml/badge.svg
:target: https://github.com/PyPSA/pypsa-eur/actions
.. image:: https://readthedocs.org/projects/pypsa-eur/badge/?version=latest
:target: https://pypsa-eur.readthedocs.io/en/latest/?badge=latest
@ -49,7 +49,18 @@ The restriction to freely available and open data encourages the open exchange o
PyPSA-Eur is designed to be imported into the open toolbox `PyPSA <https://www.pypsa.org>`_ for which `documentation <https://pypsa.org/doc>`_ is available as well.
This project is maintained by the `Energy System Modelling group <https://www.iai.kit.edu/english/2338.php>`_ at the `Institute for Automation and Applied Informatics <https://www.iai.kit.edu/english/index.php>`_ at the `Karlsruhe Institute of Technology <http://www.kit.edu/english/index.php>`_. The group is funded by the `Helmholtz Association <https://www.helmholtz.de/en/>`_ until 2024. Previous versions were developed by the `Renewable Energy Group <https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/>`_ at `FIAS <https://fias.uni-frankfurt.de/>`_ to carry out simulations for the `CoNDyNet project <http://condynet.de/>`_, financed by the `German Federal Ministry for Education and Research (BMBF) <https://www.bmbf.de/en/index.html>`_ as part of the `Stromnetze Research Initiative <http://forschung-stromnetze.info/projekte/grundlagen-und-konzepte-fuer-effiziente-dezentrale-stromnetze/>`_.
This project is currently maintained by the `Department of Digital
Transformation in Energy Systems <https:/www.ensys.tu-berlin.de>`_ at the
`Technische Universität Berlin <https://www.tu.berlin>`_. Previous versions were
developed within the `IAI <http://www.iai.kit.edu>`_ at the `Karlsruhe Institute of
Technology (KIT) <http://www.kit.edu/english/index.php>`_ and by the `Renewable
Energy Group
<https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/>`_
at `FIAS <https://fias.uni-frankfurt.de/>`_ to carry out simulations for the
`CoNDyNet project <http://condynet.de/>`_, financed by the `German Federal
Ministry for Education and Research (BMBF) <https://www.bmbf.de/en/index.html>`_
as part of the `Stromnetze Research Initiative
<http://forschung-stromnetze.info/projekte/grundlagen-und-konzepte-fuer-effiziente-dezentrale-stromnetze/>`_.
A version of the model that adds building heating, transport and industry sectors to the model,
as well as gas networks, is currently being developed in the `PyPSA-Eur-Sec repository <https://github.com/pypsa/pypsa-eur-sec>`_.
@ -141,7 +152,7 @@ If you are (relatively) new to energy system modelling and optimisation
and plan to use PyPSA-Eur, the following resources are *one way* to get started
in addition to reading this documentation.
- Documentation of `PyPSA <https://pypsa.readthedocs.io>`_, the package for
- Documentation of `PyPSA <https://pypsa.readthedocs.io>`__, the package for
simulating and optimising modern power systems which PyPSA-Eur uses under the hood.
- Course on `Energy System Modelling <https://nworbmot.org/courses/esm-2019/>`_,
Karlsruhe Institute of Technology (KIT), `Dr. Tom Brown <https://nworbmot.org>`_
@ -196,7 +207,7 @@ Licence
PyPSA-Eur work is released under multiple licenses:
* All original source code is licensed as free software under `GPL-3.0-or-later <LICENSES/GPL-3.0-or-later.txt>`_.
* All original source code is licensed as free software under `MIT <LICENSES/MIT.txt>`_.
* The documentation is licensed under `CC-BY-4.0 <LICENSES/CC-BY-4.0.txt>`_.
* Configuration files are mostly licensed under `CC0-1.0 <LICENSES/CC0-1.0.txt>`_.
* Data files are licensed under `CC-BY-4.0 <LICENSES/CC-BY-4.0.txt>`_.

2
doc/make.bat
View File

@ -1,5 +1,5 @@
REM SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
REM SPDX-License-Identifier: GPL-3.0-or-later
REM SPDX-License-Identifier: MIT
@ECHO OFF

48
doc/plotting.rst
View File

@ -9,50 +9,6 @@ Plotting and Summary
.. warning:: The corresponding code is currently under revision and has only minimal documentation.
.. _flh:
Rule ``build_country_flh``
=============================
.. graphviz::
:align: center
digraph snakemake_dag {
graph [bgcolor=white,
margin=0,
size="8,5"
];
node [fontname=sans,
fontsize=10,
penwidth=2,
shape=box,
style=rounded
];
edge [color=grey,
penwidth=2
];
0 [color="0.31 0.6 0.85",
fillcolor=gray,
label=build_country_flh,
style=filled];
1 [color="0.06 0.6 0.85",
label=base_network];
1 -> 0;
2 [color="0.42 0.6 0.85",
label=build_natura_raster];
2 -> 0;
3 [color="0.58 0.6 0.85",
label=build_shapes];
3 -> 0;
4 [color="0.14 0.6 0.85",
label=build_cutout];
4 -> 0;
}
|
.. automodule:: build_country_flh
.. _plot_potentials:
Rule ``plot_p_nom_max``
@ -128,8 +84,8 @@ Rule ``make_summary``
Rule ``plot_summary``
========================
.. graphviz::
:align: center
.. .. graphviz::
.. :align: center

1
doc/preparation.rst
View File

@ -45,7 +45,6 @@ together into a detailed PyPSA network stored in ``networks/elec.nc``.
preparation/prepare_links_p_nom
preparation/base_network
preparation/build_bus_regions
preparation/build_natura_raster
preparation/build_powerplants
preparation/build_renewable_profiles
preparation/build_hydro_profile

54
doc/preparation/retrieve.rst
View File

@ -21,9 +21,59 @@ Rule ``retrieve_databundle``
Rule ``retrieve_cutout``
------------------------
.. automodule:: retrieve_cutout
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3517949.svg
:target: https://doi.org/10.5281/zenodo.3517949
Cutouts are spatio-temporal subsets of the European weather data from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ reanalysis dataset and the `CMSAF SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_ solar surface radiation dataset for the year 2013.
They have been prepared by and are for use with the `atlite <https://github.com/PyPSA/atlite>`_ tool. You can either generate them yourself using the ``build_cutouts`` rule or retrieve them directly from `zenodo <https://doi.org/10.5281/zenodo.3517949>`__ through the rule ``retrieve_cutout``.
The :ref:`tutorial` uses a smaller cutout than required for the full model (30 MB), which is also automatically downloaded.
.. 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**
.. code:: yaml
tutorial:
enable:
build_cutout:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`toplevel_cf`
**Outputs**
- ``cutouts/{cutout}``: weather data from either the `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ reanalysis weather dataset or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewProduktSearch>`_ satellite-based historic weather data.
.. seealso::
For details see :mod:`build_cutout` and read the `atlite documentation <https://atlite.readthedocs.io>`_.
Rule ``retrieve_natura_raster``
-------------------------------
.. automodule:: retrieve_natura_raster
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4706686.svg
:target: https://doi.org/10.5281/zenodo.4706686
This rule, as a substitute for :mod:`build_natura_raster`, downloads an already rasterized version (`natura.tiff <https://zenodo.org/record/4706686/files/natura.tiff>`_) of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times. The file is placed into the ``resources`` sub-directory.
**Relevant Settings**
.. code:: yaml
enable:
build_natura_raster:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`toplevel_cf`
**Outputs**
- ``resources/natura.tiff``: Rasterized version of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times.
.. seealso::
For details see :mod:`build_natura_raster`.

138
doc/release_notes.rst
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@ -1,5 +1,5 @@
..
SPDX-FileCopyrightText: 2019-2020 The PyPSA-Eur Authors
SPDX-FileCopyrightText: 2019-2021 The PyPSA-Eur Authors
SPDX-License-Identifier: CC-BY-4.0
@ -11,13 +11,140 @@ Release Notes
Upcoming Release
================
* Fix: 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. The old value was ~2x too high and used when the emissions wildcard in ``{opts}`` was used.
* 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>`_].
* Add option to include marginal costs of links representing fuel cells, electrolysis, and battery inverters
* 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.
PyPSA-Eur 0.4.0 (22th September 2021)
=====================================
**New Features and Changes**
* With this release, we change the license from copyleft GPLv3 to the more
liberal MIT license with the consent of all contributors
[`#276 <https://github.com/PyPSA/pypsa-eur/pull/276>`_].
* Switch to the new major ``atlite`` release v0.2. The version upgrade comes
along with significant speed up for the rule ``build_renewable_profiles.py``
(~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>`_].
* Implemented changes to ``n.snapshot_weightings`` in new PyPSA version v0.18
(cf. `PyPSA/PyPSA/#227 <https://github.com/PyPSA/PyPSA/pull/227>`_)
[`#259 <https://github.com/PyPSA/pypsa-eur/pull/259>`_].
* Add option to pre-aggregate nodes without power injections (positive or
negative, i.e. generation or demand) to electrically closest nodes or neighbors
in ``simplify_network``. Defaults to ``False``. This affects nodes that are no
substations or have no offshore connection.
* In :mod:`simplify_network`, bus columns with no longer correct entries are
removed (symbol, tags, under_construction, substation_lv, substation_off)
[`#219 <https://github.com/PyPSA/pypsa-eur/pull/219>`_]
* Add option to include marginal costs of links representing fuel cells,
electrolysis, and battery inverters
[`#232 <https://github.com/PyPSA/pypsa-eur/pull/232>`_].
* The rule and script ``build_country_flh`` are removed as they are no longer
used or maintained.
* The connection cost of generators in :mod:`simplify_network` are now reported
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 side-by-side.
* The flag ``keep_all_available_areas`` in the configuration for renewable
potentials was deprecated and now defaults to ``True``.
* 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
[`#252 <https://github.com/PyPSA/pypsa-eur/pull/252>`_].
* Documentation on readthedocs.io is now built with ``pip`` only and no longer
requires ``conda`` [`#267 <https://github.com/PyPSA/pypsa-eur/pull/267>`_].
* Use ``Citation.cff`` [`#273 <https://github.com/PyPSA/pypsa-eur/pull/273>`_].
**Bugs and Compatibility**
* Support for PyPSA v0.18 [`#268 <https://github.com/PyPSA/pypsa-eur/pull/268>`_].
* Minimum Python version set to ``3.8``.
* Removed ``six`` dependency [`#245 <https://github.com/PyPSA/pypsa-eur/pull/245>`_].
* Update :mod:`plot_network` and :mod:`make_summary` rules to latest PyPSA
versions [`#270 <https://github.com/PyPSA/pypsa-eur/pull/270>`_].
* Keep converter links to store components when using the ``ATK``
wildcard and only remove DC links [`#214 <https://github.com/PyPSA/pypsa-eur/pull/214>`_].
* 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
[`#233 <https://github.com/PyPSA/pypsa-eur/pull/233>`_].
* Add escape in :mod:`base_network` if all TYNDP links are already
contained in the network
[`#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
[`#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
[`#241 <https://github.com/PyPSA/pypsa-eur/pull/241>`_].
* in :mod:`build_renewable_profile` where offshore wind profiles could
no longer be created [`#249 <https://github.com/PyPSA/pypsa-eur/pull/249>`_].
* Lower expansion limit of extendable carriers is now set to the
existing capacity, i.e. ``p_nom_min = p_nom`` (0 before). Simultaneously, the
upper limit (``p_nom_max``) is now the maximum of the installed capacity
(``p_nom``) and the previous estimate based on land availability (``p_nom_max``)
[`#260 <https://github.com/PyPSA/pypsa-eur/pull/260>`_].
* Solving an operations network now includes optimized store capacities
as well. Before only lines, links, generators and storage units were considered
[`#269 <https://github.com/PyPSA/pypsa-eur/pull/269>`_].
* With ``load_shedding: true`` in the solving options of ``config.yaml``
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
[`#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)
==================================
===================================
**New Features**
@ -40,6 +167,7 @@ Using the ``{opts}`` wildcard for scenarios:
uses the `tsam <https://tsam.readthedocs.io/en/latest/index.html>`_ package
[`#186 <https://github.com/PyPSA/pypsa-eur/pull/186>`_].
More OPSD integration:
* Add renewable power plants from `OPSD <https://data.open-power-system-data.org/renewable_power_plants/2020-08-25>`_ to the network for specified technologies.
@ -197,7 +325,7 @@ Release Process
* Tag a release on Github via ``git tag v0.x.x``, ``git push``, ``git push --tags``. Include release notes in the tag message.
* Upload code to `zenodo code repository <https://doi.org/10.5281/zenodo.3520874>`_ with `GNU GPL 3.0 <https://www.gnu.org/licenses/gpl-3.0.en.html>`_ license.
* Upload code to `zenodo code repository <https://doi.org/10.5281/zenodo.3520874>`_ with `MIT license <https://opensource.org/licenses/MIT>`_.
* Create pre-built networks for ``config.default.yaml`` by running ``snakemake -j 1 extra_components_all_networks``.

21
doc/requirements.txt Normal file
View File

@ -0,0 +1,21 @@
# SPDX-FileCopyrightText: : 2019-2021 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
sphinx
sphinx_rtd_theme
pypsa
vresutils>=0.3.1
powerplantmatching>=0.4.8
atlite>=0.2.2
dask<=2021.3.1
# cartopy
scikit-learn
pycountry
pyyaml
seaborn
memory_profiler
tables
descartes

18
doc/tutorial.rst
View File

@ -14,7 +14,7 @@ Tutorial
<iframe width="832" height="468" src="https://www.youtube.com/embed/mAwhQnNRIvs" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
Before getting started with **PyPSA-Eur** it makes sense to be familiar
with its general modelling framework `PyPSA <https://pypsa.readthedocs.io>`_.
with its general modelling framework `PyPSA <https://pypsa.readthedocs.io>`__.
Running the tutorial requires limited computational resources compared to the full model,
which allows the user to explore most of its functionalities on a local machine.
@ -53,41 +53,41 @@ Likewise, the example's temporal scope can be restricted (e.g. to a single month
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 22-25
:lines: 24-27
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: 36,38
:lines: 38,40
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:
.. literalinclude:: ../config.tutorial.yaml
:language: yaml
:lines: 36,52
:lines: 38,54
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: 54-62
:lines: 56-63
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: 64,107-108
:lines: 65,108-109
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: 170,180-181
:lines: 171,181-182
.. note::
@ -119,8 +119,8 @@ orders ``snakemake`` to run the script ``solve_network`` that produces the solve
.. code::
rule solve_network:
input: "networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
input: "networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
output: "results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc"
[...]
script: "scripts/solve_network.py"

5
doc/wildcards.rst
View File

@ -126,12 +126,11 @@ The ``{technology}`` wildcard
The ``{technology}`` wildcard specifies for which renewable energy technology to produce availablity 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.
(since hydroelectric plant profiles are created by a different rule).
The wildcard can moreover be used to create technology specific figures and summaries.
For instance ``{technology}`` can be used to plot regionally disaggregated potentials
with the rule :mod:`plot_p_nom_max` or to summarize a particular technology's
full load hours in various countries with the rule :mod:`build_country_flh`.
with the rule :mod:`plot_p_nom_max`.
.. _attr:

45
envs/environment.docs.yaml
View File

@ -1,45 +0,0 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
name: pypsa-eur-docs
channels:
- conda-forge
dependencies:
- python<=3.7
- pip
- pypsa>=0.17.1
- atlite=0.0.3
- pre-commit
# Dependencies of the workflow itself
- scikit-learn
- pycountry
- seaborn
- memory_profiler
- yaml
- pytables
- powerplantmatching>=0.4.3
# Second order dependencies which should really be deps of atlite
- xarray
- progressbar2
- pyyaml>=5.1.0
# GIS dependencies have to come all from conda-forge
- cartopy
- fiona
- proj
- pyshp
- geopandas
- rasterio
- shapely
- libgdal
- pip:
- vresutils==0.3.1
- git+https://github.com/PyPSA/glaes.git#egg=glaes
- git+https://github.com/PyPSA/geokit.git#egg=geokit
- cdsapi
- sphinx
- sphinx_rtd_theme

447
envs/environment.fixed.yaml
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: CC0-1.0
name: pypsa-eur
channels:
@ -11,231 +11,300 @@ channels:
dependencies:
- _libgcc_mutex=0.1
- affine=2.3.0
- appdirs=1.4.3
- atlite=0.0.3
- attrs=19.3.0
- backcall=0.1.0
- beautifulsoup4=4.9.1
- blas=1.0
- blosc=1.16.3
- bokeh=2.0.2
- 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
- bzip2=1.0.8
- ca-certificates=2020.1.1
- cairo=1.14.12
- cartopy=0.17.0
- certifi=2020.4.5.1
- cffi=1.14.0
- 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=1.1.2
- chardet=3.0.4
- cftime=1.5.0
- chardet=4.0.0
- charset-normalizer=2.0.0
- click=7.1.2
- click-plugins=1.1.1
- cligj=0.5.0
- cloudpickle=1.4.1
- cligj=0.7.2
- cloudpickle=2.0.0
- coincbc=2.10.5
- configargparse=1.1
- cryptography=2.9.2
- curl=7.67.0
- 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=0.10.1
- dask=2.17.2
- dask-core=2.17.2
- cytoolz=0.11.0
- dask=2021.3.1
- dask-core=2021.3.1
- datrie=0.8.2
- dbus=1.13.14
- dbus=1.13.6
- decorator=4.4.2
- distributed=2.17.0
- docutils=0.16
- entsoe-py=0.2.10
- expat=2.2.6
- fiona=1.8.11
- fontconfig=2.13.0
- freetype=2.9.1
- freexl=1.0.5
- fsspec=0.7.4
- gdal=3.0.2
- geographiclib=1.50
- geopandas=0.6.1
- geopy=1.22.0
- geos=3.8.0
- geotiff=1.5.1
- giflib=5.1.4
- gitdb=4.0.2
- gitpython=3.1.1
- glib=2.63.1
- gst-plugins-base=1.14.0
- gstreamer=1.14.0
- gurobi=9.0.2
- hdf4=4.2.13
- hdf5=1.10.4
- 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=58.2
- idna=2.9
- importlib-metadata=1.6.0
- importlib_metadata=1.6.0
- intel-openmp=2020.1
- ipopt=3.13.2
- ipython=7.13.0
- icu=68.1
- idna=3.1
- importlib-metadata=4.8.1
- iniconfig=1.1.1
- ipython=7.27.0
- ipython_genutils=0.2.0
- jedi=0.17.0
- jinja2=2.11.2
- joblib=0.15.1
- jpeg=9b
- json-c=0.13.1
- 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=4.6.3
- kealib=1.4.7
- kiwisolver=1.2.0
- krb5=1.16.4
- ld_impl_linux-64=2.33.1
- libblas=3.8.0
- libboost=1.67.0
- libcblas=3.8.0
- libcurl=7.67.0
- libdap4=3.19.1
- libedit=3.1.20181209
- libffi=3.3
- libgcc-ng=9.1.0
- libgdal=3.0.2
- libgfortran-ng=7.3.0
- 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=3.8.0
- libnetcdf=4.6.1
- 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=11.5
- libpq=13.3
- librttopo=1.1.0
- libsolv=0.7.19
- libspatialindex=1.9.3
- libspatialite=4.3.0a
- libssh2=1.9.0
- libstdcxx-ng=9.1.0
- libtiff=4.1.0
- libuuid=1.0.3
- 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
- libxml2=2.9.9
- libxkbcommon=1.0.3
- libxml2=2.9.12
- libxslt=1.1.33
- locket=0.2.0
- lxml=4.5.0
- lz4-c=1.8.1.2
- lxml=4.6.3
- lz4-c=1.9.3
- lzo=2.10
- markupsafe=1.1.1
- matplotlib=3.1.3
- matplotlib-base=3.1.3
- memory_profiler=0.55.0
- metis=5.1.0
- mkl=2020.1
- mkl-service=2.3.0
- mkl_fft=1.0.15
- mkl_random=1.1.1
- mock=4.0.2
- more-itertools=8.3.0
- msgpack-python=1.0.0
- 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
- nbformat=5.0.6
- mysql-common=8.0.25
- mysql-libs=8.0.25
- nbformat=5.1.3
- ncurses=6.2
- netcdf4=1.4.2
- networkx=2.4
- nose=1.3.7
- numexpr=2.7.1
- numpy=1.18.1
- numpy-base=1.18.1
- 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
- openjpeg=2.3.0
- openssl=1.1.1g
- owslib=0.19.2
- packaging=20.3
- pandas=1.0.3
- parso=0.7.0
- partd=1.1.0
- pcre=8.43
- 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=7.1.2
- pip=20.0.2
- pixman=0.38.0
- pluggy=0.13.1
- pillow=8.2.0
- pip=21.2.4
- pixman=0.40.0
- pluggy=1.0.0
- ply=3.11
- poppler=0.65.0
- poppler-data=0.4.9
- postgresql=11.5
- powerplantmatching=0.4.5
- progressbar2=3.37.1
- proj=6.2.1
- prompt-toolkit=3.0.5
- prompt_toolkit=3.0.5
- psutil=5.7.0
- ptyprocess=0.6.0
- py=1.8.1
- pycountry=19.8.18
- 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
- pyepsg=0.4.0
- pygments=2.6.1
- pykdtree=1.3.1
- pyomo=5.6.9
- pyopenssl=19.1.0
- pygments=2.10.0
- pyomo=6.1.2
- pyopenssl=20.0.1
- pyparsing=2.4.7
- pyproj=2.6.1.post1
- pypsa=0.17.0
- pyqt=5.9.2
- pyrsistent=0.16.0
- pyshp=2.1.0
- 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=5.4.2
- pytest-runner=5.2
- python=3.7.7
- python-dateutil=2.8.1
- python-utils=2.3.0
- python_abi=3.7
- pytz=2020.1
- pyutilib=5.8.0
- pyyaml=5.3.1
- qt=5.9.7
- rasterio=1.1.0
- 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=8.0
- requests=2.23.0
- rtree=0.9.4
- scikit-learn=0.22.1
- scipy=1.4.1
- seaborn=0.10.1
- setuptools=47.1.1
- shapely=1.7.0
- sip=4.19.8
- six=1.15.0
- smmap=3.0.2
- snakemake-minimal=5.19.2
- snappy=1.1.7
- 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=2.1.0
- sortedcontainers=2.4.0
- soupsieve=2.0.1
- sqlite=3.31.1
- tbb=2018.0.5
- tblib=1.6.0
- tiledb=1.6.3
- tk=8.6.8
- toolz=0.10.0
- toposort=1.5
- tornado=6.0.4
- traitlets=4.3.3
- typing_extensions=3.7.4.1
- tzcode=2020a
- urllib3=1.25.8
- wcwidth=0.1.9
- wheel=0.34.2
- 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=0.15.1
- xerces-c=3.2.2
- xlrd=1.2.0
- 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=0.1.7
- zict=2.0.0
- zipp=3.1.0
- zipp=3.5.0
- zlib=1.2.11
- zstd=1.3.7
- zstd=1.4.9
- pip:
- cdsapi==0.2.7
- countrycode==0.2
- descartes==1.1.0
- geokit==1.1.2
- glaes==1.1.2
- tqdm==4.46.1
- sklearn==0.0
- tsam==1.1.1
- vresutils==0.3.1

49
envs/environment.yaml
View File

@ -1,25 +1,22 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
name: pypsa-eur
channels:
- conda-forge
- bioconda
- http://conda.anaconda.org/gurobi
dependencies:
- python
- python>=3.8
- pip
- pre-commit
- mamba # esp for windows build
- pypsa>=0.17.1
- atlite=0.0.3
- pypsa>=0.18.1
- atlite>=0.2.5
- dask
# Dependencies of the workflow itself
- xlrd
- openpyxl
- scikit-learn
- pycountry
- seaborn
- snakemake-minimal
@ -28,34 +25,34 @@ dependencies:
- pytables
- lxml
- powerplantmatching>=0.4.8
- numpy<=1.19.0 # otherwise macos fails
# Second order dependencies which should really be deps of atlite
- numpy
- pandas<1.3
- geopandas
- xarray
- netcdf4
- bottleneck
- toolz
- dask
- networkx
- scipy
- shapely
- progressbar2
- pyyaml>=5.1.0
- pyomo
- matplotlib
- proj
# Keep in conda environment when calling ipython
- ipython
# GIS dependencies:
- cartopy
- fiona
- proj
- pyshp
- geopandas
- rasterio
- shapely
- libgdal<=3.0.4
- descartes
- rasterio
# PyPSA-Eur-Sec Dependencies
- geopy
- tqdm
- pytz
- country_converter
- tabula-py
- pip:
- vresutils==0.3.1
- vresutils>=0.3.1
- tsam>=1.1.0
- git+https://github.com/PyPSA/glaes.git#egg=glaes
- git+https://github.com/PyPSA/geokit.git#egg=geokit
- cdsapi

19
scripts/_helpers.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
import pandas as pd
from pathlib import Path
@ -119,12 +119,20 @@ def load_network_for_plots(fn, tech_costs, config, combine_hydro_ps=True):
# bus_carrier = n.storage_units.bus.map(n.buses.carrier)
# n.storage_units.loc[bus_carrier == "heat","carrier"] = "water tanks"
Nyears = n.snapshot_weightings.sum() / 8760.
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(Nyears, tech_costs, config['costs'], config['electricity'])
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)
def aggregate_p_nom(n):
return pd.concat([
n.generators.groupby("carrier").p_nom_opt.sum(),
@ -156,7 +164,6 @@ def aggregate_p_curtailed(n):
])
def aggregate_costs(n, flatten=False, opts=None, existing_only=False):
from six import iterkeys, itervalues
components = dict(Link=("p_nom", "p0"),
Generator=("p_nom", "p"),
@ -167,8 +174,8 @@ def aggregate_costs(n, flatten=False, opts=None, existing_only=False):
costs = {}
for c, (p_nom, p_attr) in zip(
n.iterate_components(iterkeys(components), skip_empty=False),
itervalues(components)
n.iterate_components(components.keys(), skip_empty=False),
components.values()
):
if c.df.empty: continue
if not existing_only: p_nom += "_opt"
@ -233,7 +240,7 @@ def mock_snakemake(rulename, **wildcards):
if os.path.exists(p):
snakefile = p
break
workflow = sm.Workflow(snakefile)
workflow = sm.Workflow(snakefile, overwrite_configfiles=[])
workflow.include(snakefile)
workflow.global_resources = {}
rule = workflow.get_rule(rulename)

141
scripts/add_electricity.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
@ -84,7 +84,7 @@ It further adds extendable ``generators`` with **zero** capacity for
"""
import logging
from _helpers import configure_logging
from _helpers import configure_logging, update_p_nom_max
import pypsa
import pandas as pd
@ -96,7 +96,6 @@ import powerplantmatching as pm
from powerplantmatching.export import map_country_bus
from vresutils.costdata import annuity
from vresutils.load import timeseries_opsd
from vresutils import transfer as vtransfer
idx = pd.IndexSlice
@ -119,12 +118,7 @@ def _add_missing_carriers_from_costs(n, costs, carriers):
n.import_components_from_dataframe(emissions, 'Carrier')
def load_costs(Nyears=1., tech_costs=None, config=None, elec_config=None):
if tech_costs is None:
tech_costs = snakemake.input.tech_costs
if config is None:
config = snakemake.config['costs']
def load_costs(tech_costs, config, elec_config, Nyears=1.):
# set all asset costs and other parameters
costs = pd.read_csv(tech_costs, index_col=list(range(3))).sort_index()
@ -170,8 +164,6 @@ def load_costs(Nyears=1., tech_costs=None, config=None, elec_config=None):
marginal_cost=0.,
co2_emissions=0.))
if elec_config is None:
elec_config = snakemake.config['electricity']
max_hours = elec_config['max_hours']
costs.loc["battery"] = \
costs_for_storage(costs.loc["battery storage"], costs.loc["battery inverter"],
@ -189,9 +181,7 @@ def load_costs(Nyears=1., tech_costs=None, config=None, elec_config=None):
return costs
def load_powerplants(ppl_fn=None):
if ppl_fn is None:
ppl_fn = snakemake.input.powerplants
def load_powerplants(ppl_fn):
carrier_dict = {'ocgt': 'OCGT', 'ccgt': 'CCGT', 'bioenergy': 'biomass',
'ccgt, thermal': 'CCGT', 'hard coal': 'coal'}
return (pd.read_csv(ppl_fn, index_col=0, dtype={'bus': 'str'})
@ -200,9 +190,10 @@ def load_powerplants(ppl_fn=None):
.replace({'carrier': carrier_dict}))
def attach_load(n):
def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.):
substation_lv_i = n.buses.index[n.buses['substation_lv']]
regions = (gpd.read_file(snakemake.input.regions).set_index('name')
regions = (gpd.read_file(regions).set_index('name')
.reindex(substation_lv_i))
# ------------- TO MERGE --------------
@ -230,15 +221,14 @@ def attach_load(n):
# Convert to naive UTC (has to be explicit since pandas 0.24)
opsd_load.index = opsd_load.index.tz_localize(None)
opsd_load = (pd.read_csv(snakemake.input.load, index_col=0, parse_dates=True)
.filter(items=snakemake.config['countries']))
opsd_load = (pd.read_csv(load, index_col=0, parse_dates=True)
.filter(items=countries))
scaling = snakemake.config.get('load', {}).get('scaling_factor', 1.0)
logger.info(f"Load data scaled with scalling factor {scaling}.")
opsd_load *= scaling
# ------------- TO MERGE --------------
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
nuts3 = gpd.read_file(nuts3_shapes).set_index('index')
def upsample(cntry, group):
l = opsd_load[cntry]
@ -255,7 +245,6 @@ def attach_load(n):
# relative factors 0.6 and 0.4 have been determined from a linear
# regression on the country to continent load data
# (refer to vresutils.load._upsampling_weights)
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
return pd.DataFrame(factors.values * l.values[:,np.newaxis],
index=l.index, columns=factors.index)
@ -267,6 +256,9 @@ def attach_load(n):
def update_transmission_costs(n, costs, length_factor=1.0, simple_hvdc_costs=False):
# TODO: line length factor of lines is applied to lines and links.
# Separate the function to distinguish.
n.lines['capital_cost'] = (n.lines['length'] * length_factor *
costs.at['HVAC overhead', 'capital_cost'])
@ -291,18 +283,20 @@ def update_transmission_costs(n, costs, length_factor=1.0, simple_hvdc_costs=Fal
n.links.loc[dc_b, 'capital_cost'] = costs
def attach_wind_and_solar(n, costs):
for tech in snakemake.config['renewable']:
def attach_wind_and_solar(n, costs, input_profiles, technologies, line_length_factor=1):
# TODO: rename tech -> carrier, technologies -> carriers
for tech in technologies:
if tech == 'hydro': continue
n.add("Carrier", name=tech)
with xr.open_dataset(getattr(snakemake.input, 'profile_' + tech)) as ds:
with xr.open_dataset(getattr(input_profiles, 'profile_' + tech)) as ds:
if ds.indexes['bus'].empty: continue
suptech = tech.split('-', 2)[0]
if suptech == 'offwind':
underwater_fraction = ds['underwater_fraction'].to_pandas()
connection_cost = (snakemake.config['lines']['length_factor'] *
connection_cost = (line_length_factor *
ds['average_distance'].to_pandas() *
(underwater_fraction *
costs.at[tech + '-connection-submarine', 'capital_cost'] +
@ -328,8 +322,7 @@ def attach_wind_and_solar(n, costs):
p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())
def attach_conventional_generators(n, costs, ppl):
carriers = snakemake.config['electricity']['conventional_carriers']
def attach_conventional_generators(n, costs, ppl, carriers):
_add_missing_carriers_from_costs(n, costs, carriers)
@ -350,10 +343,7 @@ def attach_conventional_generators(n, costs, ppl):
logger.warning(f'Capital costs for conventional generators put to 0 EUR/MW.')
def attach_hydro(n, costs, ppl):
if 'hydro' not in snakemake.config['renewable']: return
c = snakemake.config['renewable']['hydro']
carriers = c.get('carriers', ['ror', 'PHS', 'hydro'])
def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities, carriers, **config):
_add_missing_carriers_from_costs(n, costs, carriers)
@ -369,11 +359,11 @@ def attach_hydro(n, costs, ppl):
if not inflow_idx.empty:
dist_key = ppl.loc[inflow_idx, 'p_nom'].groupby(country).transform(normed)
with xr.open_dataarray(snakemake.input.profile_hydro) as inflow:
with xr.open_dataarray(profile_hydro) as inflow:
inflow_countries = pd.Index(country[inflow_idx])
missing_c = (inflow_countries.unique()
.difference(inflow.indexes['countries']))
assert missing_c.empty, (f"'{snakemake.input.profile_hydro}' is missing "
assert missing_c.empty, (f"'{profile_hydro}' is missing "
f"inflow time-series for at least one country: {', '.join(missing_c)}")
inflow_t = (inflow.sel(countries=inflow_countries)
@ -398,7 +388,8 @@ def attach_hydro(n, costs, ppl):
if 'PHS' in carriers and not phs.empty:
# fill missing max hours to config value and
# assume no natural inflow due to lack of data
phs = phs.replace({'max_hours': {0: c['PHS_max_hours']}})
max_hours = config.get('PHS_max_hours', 6)
phs = phs.replace({'max_hours': {0: max_hours}})
n.madd('StorageUnit', phs.index,
carrier='PHS',
bus=phs['bus'],
@ -410,8 +401,11 @@ def attach_hydro(n, costs, ppl):
cyclic_state_of_charge=True)
if 'hydro' in carriers and not hydro.empty:
hydro_max_hours = c.get('hydro_max_hours')
hydro_stats = pd.read_csv(snakemake.input.hydro_capacities,
hydro_max_hours = config.get('hydro_max_hours')
assert hydro_max_hours is not None, "No path for hydro capacities given."
hydro_stats = pd.read_csv(hydro_capacities,
comment="#", na_values='-', index_col=0)
e_target = hydro_stats["E_store[TWh]"].clip(lower=0.2) * 1e6
e_installed = hydro.eval('p_nom * max_hours').groupby(hydro.country).sum()
@ -439,8 +433,7 @@ def attach_hydro(n, costs, ppl):
bus=hydro['bus'],
p_nom=hydro['p_nom'],
max_hours=hydro_max_hours,
capital_cost=(costs.at['hydro', 'capital_cost']
if c.get('hydro_capital_cost') else 0.),
capital_cost=costs.at['hydro', 'capital_cost'],
marginal_cost=costs.at['hydro', 'marginal_cost'],
p_max_pu=1., # dispatch
p_min_pu=0., # store
@ -450,9 +443,7 @@ def attach_hydro(n, costs, ppl):
inflow=inflow_t.loc[:, hydro.index])
def attach_extendable_generators(n, costs, ppl):
elec_opts = snakemake.config['electricity']
carriers = pd.Index(elec_opts['extendable_carriers']['Generator'])
def attach_extendable_generators(n, costs, ppl, carriers):
_add_missing_carriers_from_costs(n, costs, carriers)
@ -500,12 +491,11 @@ def attach_extendable_generators(n, costs, ppl):
def attach_OPSD_renewables(n):
def attach_OPSD_renewables(n, techs):
available = ['DE', 'FR', 'PL', 'CH', 'DK', 'CZ', 'SE', 'GB']
tech_map = {'Onshore': 'onwind', 'Offshore': 'offwind', 'Solar': 'solar'}
countries = set(available) & set(n.buses.country)
techs = snakemake.config['electricity'].get('renewable_capacities_from_OPSD', [])
tech_map = {k: v for k, v in tech_map.items() if v in techs}
if not tech_map:
@ -529,13 +519,11 @@ def attach_OPSD_renewables(n):
caps = caps / gens_per_bus.reindex(caps.index, fill_value=1)
n.generators.p_nom.update(gens.bus.map(caps).dropna())
n.generators.p_nom_min.update(gens.bus.map(caps).dropna())
def estimate_renewable_capacities(n, tech_map=None):
if tech_map is None:
tech_map = (snakemake.config['electricity']
.get('estimate_renewable_capacities_from_capacity_stats', {}))
def estimate_renewable_capacities(n, tech_map):
if len(tech_map) == 0: return
@ -564,10 +552,10 @@ def estimate_renewable_capacities(n, tech_map=None):
.groupby(n.generators.bus.map(n.buses.country))
.transform(lambda s: normed(s) * tech_capacities.at[s.name])
.where(lambda s: s>0.1, 0.)) # only capacities above 100kW
n.generators.loc[tech_i, 'p_nom_min'] = n.generators.loc[tech_i, 'p_nom']
def add_nice_carrier_names(n, config=None):
if config is None: config = snakemake.config
def add_nice_carrier_names(n, config):
carrier_i = n.carriers.index
nice_names = (pd.Series(config['plotting']['nice_names'])
.reindex(carrier_i).fillna(carrier_i.to_series().str.title()))
@ -575,11 +563,9 @@ def add_nice_carrier_names(n, config=None):
colors = pd.Series(config['plotting']['tech_colors']).reindex(carrier_i)
if colors.isna().any():
missing_i = list(colors.index[colors.isna()])
logger.warning(f'tech_colors for carriers {missing_i} not defined '
'in config.')
logger.warning(f'tech_colors for carriers {missing_i} not defined in config.')
n.carriers['color'] = colors
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
@ -588,27 +574,48 @@ if __name__ == "__main__":
n = pypsa.Network(snakemake.input.base_network)
year = snakemake.wildcards.year
snapshots = dict(start=year, end=str(int(year)+1), closed="left") if year else snakemake.config['snapshots']
weather_year = snakemake.wildcards.weather_year
if weather_year:
snapshots = dict(
start=weather_year,
end=str(int(weather_year)+1),
closed="left"
)
else:
snakemake.config['snapshots']
n.set_snapshots(pd.date_range(freq='h', **snapshots))
n.snapshot_weightings[:] *= 8760. / n.snapshot_weightings.sum()
Nyears = n.snapshot_weightings.sum() / 8760.
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(Nyears)
ppl = load_powerplants()
costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
ppl = load_powerplants(snakemake.input.powerplants)
attach_load(n)
attach_load(n, snakemake.input.regions, snakemake.input.load, snakemake.input.nuts3_shapes,
snakemake.config['countries'], snakemake.config['load']['scaling_factor'])
update_transmission_costs(n, costs)
update_transmission_costs(n, costs, snakemake.config['lines']['length_factor'])
attach_conventional_generators(n, costs, ppl)
attach_wind_and_solar(n, costs)
attach_hydro(n, costs, ppl)
attach_extendable_generators(n, costs, ppl)
carriers = snakemake.config['electricity']['conventional_carriers']
attach_conventional_generators(n, costs, ppl, carriers)
estimate_renewable_capacities(n)
attach_OPSD_renewables(n)
carriers = snakemake.config['renewable']
attach_wind_and_solar(n, costs, snakemake.input, carriers, snakemake.config['lines']['length_factor'])
add_nice_carrier_names(n)
if 'hydro' in snakemake.config['renewable']:
carriers = snakemake.config['renewable']['hydro'].pop('carriers', [])
attach_hydro(n, costs, ppl, snakemake.input.profile_hydro, snakemake.input.hydro_capacities,
carriers, **snakemake.config['renewable']['hydro'])
carriers = snakemake.config['electricity']['extendable_carriers']['Generator']
attach_extendable_generators(n, costs, ppl, carriers)
tech_map = snakemake.config['electricity'].get('estimate_renewable_capacities_from_capacity_stats', {})
estimate_renewable_capacities(n, tech_map)
techs = snakemake.config['electricity'].get('renewable_capacities_from_OPSD', [])
attach_OPSD_renewables(n, techs)
update_p_nom_max(n)
add_nice_carrier_names(n, snakemake.config)
n.export_to_netcdf(snakemake.output[0])

33
scripts/add_extra_components.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
@ -37,13 +37,13 @@ Inputs
Outputs
-------
- ``networks/elec{year}_s{simpl}_{clusters}_ec.nc``:
- ``networks/elec{weather_year}_s{simpl}_{clusters}_ec.nc``:
Description
-----------
The rule :mod:`add_extra_components` attaches additional extendable components to the clustered and simplified network. These can be configured in the ``config.yaml`` at ``electricity: extendable_carriers: ``. It processes ``networks/elec{year}_s{simpl}_{clusters}.nc`` to build ``networks/elec{year}_s{simpl}_{clusters}_ec.nc``, which in contrast to the former (depending on the configuration) contain with **zero** initial capacity
The rule :mod:`add_extra_components` attaches additional extendable components to the clustered and simplified network. These can be configured in the ``config.yaml`` at ``electricity: extendable_carriers: ``. It processes ``networks/elec{weather_year}_s{simpl}_{clusters}.nc`` to build ``networks/elec{weather_year}_s{simpl}_{clusters}_ec.nc``, which in contrast to the former (depending on the configuration) contain with **zero** initial capacity
- ``StorageUnits`` of carrier 'H2' and/or 'battery'. If this option is chosen, every bus is given an extendable ``StorageUnit`` of the corresponding carrier. The energy and power capacities are linked through a parameter that specifies the energy capacity as maximum hours at full dispatch power and is configured in ``electricity: max_hours:``. This linkage leads to one investment variable per storage unit. The default ``max_hours`` lead to long-term hydrogen and short-term battery storage units.
@ -64,8 +64,7 @@ idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def attach_storageunits(n, costs):
elec_opts = snakemake.config['electricity']
def attach_storageunits(n, costs, elec_opts):
carriers = elec_opts['extendable_carriers']['StorageUnit']
max_hours = elec_opts['max_hours']
@ -89,8 +88,7 @@ def attach_storageunits(n, costs):
cyclic_state_of_charge=True)
def attach_stores(n, costs):
elec_opts = snakemake.config['electricity']
def attach_stores(n, costs, elec_opts):
carriers = elec_opts['extendable_carriers']['Store']
_add_missing_carriers_from_costs(n, costs, carriers)
@ -152,14 +150,11 @@ def attach_stores(n, costs):
bus1=buses_i,
carrier='battery discharger',
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"])
def attach_hydrogen_pipelines(n, costs):
elec_opts = snakemake.config['electricity']
def attach_hydrogen_pipelines(n, costs, elec_opts):
ext_carriers = elec_opts['extendable_carriers']
as_stores = ext_carriers.get('Store', [])
@ -199,15 +194,15 @@ if __name__ == "__main__":
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
Nyears = n.snapshot_weightings.sum() / 8760.
costs = load_costs(Nyears, tech_costs=snakemake.input.tech_costs,
config=snakemake.config['costs'],
elec_config=snakemake.config['electricity'])
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)
attach_storageunits(n, costs)
attach_stores(n, costs)
attach_hydrogen_pipelines(n, costs)
attach_storageunits(n, costs, elec_config)
attach_stores(n, costs, elec_config)
attach_hydrogen_pipelines(n, costs, elec_config)
add_nice_carrier_names(n, config=snakemake.config)
add_nice_carrier_names(n, snakemake.config)
n.export_to_netcdf(snakemake.output[0])

146
scripts/base_network.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
@ -68,11 +68,10 @@ import yaml
import pandas as pd
import geopandas as gpd
import numpy as np
import scipy as sp
import networkx as nx
from scipy import spatial
from scipy.sparse import csgraph
from six import iteritems
from itertools import product
from shapely.geometry import Point, LineString
@ -96,11 +95,11 @@ 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))[[0, -1]].flatten()
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 = sp.spatial.KDTree(treecoords)
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)
@ -111,9 +110,9 @@ def _find_closest_links(links, new_links, distance_upper_bound=1.5):
.sort_index()['i']
def _load_buses_from_eg():
buses = (pd.read_csv(snakemake.input.eg_buses, quotechar="'",
true_values='t', false_values='f',
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)
@ -123,19 +122,19 @@ def _load_buses_from_eg():
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(snakemake.input.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_with_v_nom_to_keep_b = buses.v_nom.isin(snakemake.config['electricity']['voltages']) | buses.v_nom.isnull()
logger.info("Removing buses with voltages {}".format(pd.Index(buses.v_nom.unique()).dropna().difference(snakemake.config['electricity']['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):
transformers = (pd.read_csv(snakemake.input.eg_transformers, quotechar="'",
true_values='t', false_values='f',
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'))
@ -144,9 +143,9 @@ def _load_transformers_from_eg(buses):
return transformers
def _load_converters_from_eg(buses):
converters = (pd.read_csv(snakemake.input.eg_converters, quotechar="'",
true_values='t', false_values='f',
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'))
@ -157,8 +156,8 @@ def _load_converters_from_eg(buses):
return converters
def _load_links_from_eg(buses):
links = (pd.read_csv(snakemake.input.eg_links, quotechar="'", true_values='t', false_values='f',
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'))
@ -175,11 +174,11 @@ def _load_links_from_eg(buses):
return links
def _add_links_from_tyndp(buses, links):
links_tyndp = pd.read_csv(snakemake.input.links_tyndp)
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(snakemake.input.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)
@ -211,8 +210,9 @@ def _add_links_from_tyndp(buses, links):
if links_tyndp["j"].notnull().any():
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
tree = sp.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]]
@ -243,11 +243,13 @@ def _add_links_from_tyndp(buses, links):
links_tyndp.index = "T" + links_tyndp.index.astype(str)
return buses, links.append(links_tyndp, sort=True)
links = pd.concat([links, links_tyndp], sort=True)
return buses, links
def _load_lines_from_eg(buses):
lines = (pd.read_csv(snakemake.input.eg_lines, quotechar="'", true_values='t', false_values='f',
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')
@ -260,19 +262,19 @@ def _load_lines_from_eg(buses):
return lines
def _apply_parameter_corrections(n):
with open(snakemake.input.parameter_corrections) as f:
def _apply_parameter_corrections(n, parameter_corrections):
with open(parameter_corrections) as f:
corrections = yaml.safe_load(f)
if corrections is None: return
for component, attrs in iteritems(corrections):
for component, attrs in corrections.items():
df = n.df(component)
oid = _get_oid(df)
if attrs is None: continue
for attr, repls in iteritems(attrs):
for i, r in iteritems(repls):
for attr, repls in attrs.items():
for i, r in repls.items():
if i == 'oid':
r = oid.map(repls["oid"]).dropna()
elif i == 'index':
@ -283,14 +285,14 @@ def _apply_parameter_corrections(n):
df.loc[inds, attr] = r[inds].astype(df[attr].dtype)
def _set_electrical_parameters_lines(lines):
v_noms = snakemake.config['electricity']['voltages']
linetypes = snakemake.config['lines']['types']
def _set_electrical_parameters_lines(lines, config):
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['s_max_pu'] = snakemake.config['lines']['s_max_pu']
lines['s_max_pu'] = config['lines']['s_max_pu']
return lines
@ -302,14 +304,14 @@ def _set_lines_s_nom_from_linetypes(n):
)
def _set_electrical_parameters_links(links):
def _set_electrical_parameters_links(links, config, links_p_nom):
if links.empty: return links
p_max_pu = snakemake.config['links'].get('p_max_pu', 1.)
p_max_pu = config['links'].get('p_max_pu', 1.)
links['p_max_pu'] = p_max_pu
links['p_min_pu'] = -p_max_pu
links_p_nom = pd.read_csv(snakemake.input.links_p_nom)
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)
@ -329,8 +331,8 @@ def _set_electrical_parameters_links(links):
return links
def _set_electrical_parameters_converters(converters):
p_max_pu = snakemake.config['links'].get('p_max_pu', 1.)
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
@ -343,8 +345,8 @@ def _set_electrical_parameters_converters(converters):
return converters
def _set_electrical_parameters_transformers(transformers):
config = snakemake.config['transformers']
def _set_electrical_parameters_transformers(transformers, config):
config = config['transformers']
## Add transformer parameters
transformers["x"] = config.get('x', 0.1)
@ -371,7 +373,7 @@ def _remove_unconnected_components(network):
return network[component == component_sizes.index[0]]
def _set_countries_and_substations(n):
def _set_countries_and_substations(n, config, country_shapes, offshore_shapes):
buses = n.buses
@ -384,9 +386,9 @@ def _set_countries_and_substations(n):
index=buses.index
)
countries = snakemake.config['countries']
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']
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)
def prefer_voltage(x, which):
@ -496,19 +498,19 @@ def _replace_b2b_converter_at_country_border_by_link(n):
.format(i, b0, line, linkcntry.at[i], buscntry.at[b1]))
def _set_links_underwater_fraction(n):
def _set_links_underwater_fraction(n, offshore_shapes):
if n.links.empty: return
if not hasattr(n.links, 'geometry'):
n.links['underwater_fraction'] = 0.
else:
offshore_shape = gpd.read_file(snakemake.input.offshore_shapes).unary_union
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
def _adjust_capacities_of_under_construction_branches(n):
lines_mode = snakemake.config['lines'].get('under_construction', 'undef')
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.
@ -517,7 +519,7 @@ def _adjust_capacities_of_under_construction_branches(n):
elif lines_mode != 'keep':
logger.warning("Unrecognized configuration for `lines: under_construction` = `{}`. Keeping under construction lines.")
links_mode = snakemake.config['links'].get('under_construction', 'undef')
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':
@ -532,28 +534,30 @@ def _adjust_capacities_of_under_construction_branches(n):
return n
def base_network():
buses = _load_buses_from_eg()
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):
links = _load_links_from_eg(buses)
if snakemake.config['links'].get('include_tyndp'):
buses, links = _add_links_from_tyndp(buses, links)
buses = _load_buses_from_eg(eg_buses, europe_shape, config['electricity'])
converters = _load_converters_from_eg(buses)
links = _load_links_from_eg(buses, eg_links)
if config['links'].get('include_tyndp'):
buses, links = _add_links_from_tyndp(buses, links, links_tyndp, europe_shape)
lines = _load_lines_from_eg(buses)
transformers = _load_transformers_from_eg(buses)
converters = _load_converters_from_eg(buses, eg_converters)
lines = _set_electrical_parameters_lines(lines)
transformers = _set_electrical_parameters_transformers(transformers)
links = _set_electrical_parameters_links(links)
converters = _set_electrical_parameters_converters(converters)
lines = _load_lines_from_eg(buses, eg_lines)
transformers = _load_transformers_from_eg(buses, eg_transformers)
lines = _set_electrical_parameters_lines(lines, config)
transformers = _set_electrical_parameters_transformers(transformers, config)
links = _set_electrical_parameters_links(links, config, links_p_nom)
converters = _set_electrical_parameters_converters(converters, config)
n = pypsa.Network()
n.name = 'PyPSA-Eur'
n.set_snapshots(pd.date_range(freq='h', **snakemake.config['snapshots']))
n.snapshot_weightings[:] *= 8760. / n.snapshot_weightings.sum()
n.set_snapshots(pd.date_range(freq='h', **config['snapshots']))
n.import_components_from_dataframe(buses, "Bus")
n.import_components_from_dataframe(lines, "Line")
@ -563,17 +567,17 @@ def base_network():
_set_lines_s_nom_from_linetypes(n)
_apply_parameter_corrections(n)
_apply_parameter_corrections(n, parameter_corrections)
n = _remove_unconnected_components(n)
_set_countries_and_substations(n)
_set_countries_and_substations(n, config, country_shapes, offshore_shapes)
_set_links_underwater_fraction(n)
_set_links_underwater_fraction(n, offshore_shapes)
_replace_b2b_converter_at_country_border_by_link(n)
n = _adjust_capacities_of_under_construction_branches(n)
n = _adjust_capacities_of_under_construction_branches(n, config)
return n
@ -583,6 +587,8 @@ if __name__ == "__main__":
snakemake = mock_snakemake('base_network')
configure_logging(snakemake)
n = base_network()
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.export_to_netcdf(snakemake.output[0])

2
scripts/build_bus_regions.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Creates Voronoi shapes for each bus representing both onshore and offshore regions.

35
scripts/build_cutout.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
# SPDX-FileCopyrightText: : 2017-2021 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Create cutouts with `atlite <https://atlite.readthedocs.io/en/latest/>`_.
@ -92,10 +92,11 @@ Description
"""
import logging
import atlite
import geopandas as gpd
import pandas as pd
from _helpers import configure_logging
import os
import atlite
logger = logging.getLogger(__name__)
@ -107,14 +108,24 @@ if __name__ == "__main__":
# TODO make it accept year independent config
cutout_params = snakemake.config['atlite']['cutouts'][snakemake.wildcards.cutout]
for p in ('xs', 'ys', 'years', 'months'):
if p in cutout_params:
cutout_params[p] = slice(*cutout_params[p])
cutout = atlite.Cutout(snakemake.wildcards.cutout,
cutout_dir=os.path.dirname(snakemake.output[0]),
**cutout_params)
snapshots = pd.date_range(freq='h', **snakemake.config['snapshots'])
time = [snapshots[0], snapshots[-1]]
cutout_params['time'] = slice(*cutout_params.get('time', time))
nprocesses = snakemake.config['atlite'].get('nprocesses', 4)
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'])
cutout.prepare(nprocesses=nprocesses)
logging.info(f"Preparing cutout with parameters {cutout_params}.")
features = cutout_params.pop('features', None)
cutout = atlite.Cutout(snakemake.output[0], **cutout_params)
cutout.prepare(features=features)

19
scripts/build_hydro_profile.py
View File

@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Build hydroelectric inflow time-series for each country.
@ -62,7 +62,6 @@ Description
import logging
from _helpers import configure_logging
import os
import atlite
import geopandas as gpd
from vresutils import hydro as vhydro
@ -75,16 +74,12 @@ if __name__ == "__main__":
snakemake = mock_snakemake('build_hydro_profile')
configure_logging(snakemake)
year = snakemake.wildcards.year
config = snakemake.config['renewable']['hydro']
cutout_dir = os.path.dirname(snakemake.input.cutout)
cutout_config = config['cutout']
if year: cutout_config = cutout_config.format(year=year)
cutout = atlite.Cutout(cutout_config, cutout_dir=cutout_dir)
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 = (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)
@ -97,7 +92,7 @@ if __name__ == "__main__":
lower_threshold_quantile=True,
normalize_using_yearly=eia_stats)
if 'clip_min_inflow' in config:
inflow.values[inflow.values < config['clip_min_inflow']] = 0.
if 'clip_min_inflow' in config_hydro:
inflow = inflow.where(inflow > config_hydro['clip_min_inflow'], 0)
inflow.to_netcdf(snakemake.output[0])

21
scripts/build_load_data.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2020 @JanFrederickUnnewehr, The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
@ -14,7 +14,6 @@ Relevant Settings
snapshots:
load:
url:
interpolate_limit:
time_shift_for_large_gaps:
manual_adjustments:
@ -71,7 +70,7 @@ def load_timeseries(fn, years, countries, powerstatistics=True):
"""
logger.info(f"Retrieving load data from '{fn}'.")
pattern = 'power_statistics' if powerstatistics else '_transparency'
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)
@ -197,18 +196,16 @@ if __name__ == "__main__":
configure_logging(snakemake)
config = snakemake.config
powerstatistics = config['load']['power_statistics']
url = config['load']['url']
interpolate_limit = config['load']['interpolate_limit']
countries = config['countries']
snapshots = pd.date_range(freq='h', **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 = config['load']['time_shift_for_large_gaps']
time_shift = snakemake.config['load']['time_shift_for_large_gaps']
load = load_timeseries(url, years, countries, powerstatistics)
load = load_timeseries(snakemake.input[0], years, countries, powerstatistics)
if config['load']['manual_adjustments']:
if snakemake.config['load']['manual_adjustments']:
load = manual_adjustment(load, powerstatistics)
logger.info(f"Linearly interpolate gaps of size {interpolate_limit} and less.")

48
scripts/build_natura_raster.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# 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.
@ -40,33 +40,53 @@ Description
"""
import logging
from _helpers import configure_logging
from _helpers import configure_logging, retrieve_snakemake_keys
import atlite
import geokit as gk
from pathlib import Path
import geopandas as gpd
import rasterio as rio
from rasterio.features import geometry_mask
from rasterio.warp import transform_bounds
logger = logging.getLogger(__name__)
def determine_cutout_xXyY(cutout_name):
cutout = atlite.Cutout(cutout_name, cutout_dir=cutout_dir)
cutout = atlite.Cutout(cutout_name)
assert cutout.crs.to_epsg() == 4326
x, X, y, Y = cutout.extent
dx = (X - x) / (cutout.shape[1] - 1)
dy = (Y - y) / (cutout.shape[0] - 1)
dx, dy = cutout.dx, cutout.dy
return [x - dx/2., X + dx/2., y - dy/2., Y + dy/2.]
def get_transform_and_shape(bounds, res):
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)
return transform, shape
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_natura_raster')
configure_logging(snakemake)
cutout_dir = Path(snakemake.input.cutouts[0]).parent.resolve()
cutout_names = {res['cutout'] for res in snakemake.config['renewable'].values()}
xs, Xs, ys, Ys = zip(*(determine_cutout_xXyY(cutout) for cutout in cutout_names))
xXyY = min(xs), max(Xs), min(ys), max(Ys)
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
cutouts = paths.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)
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:
dst.write(raster, indexes=1)
natura = gk.vector.loadVector(snakemake.input.natura)
extent = gk.Extent.from_xXyY(xXyY).castTo(3035).fit(100)
extent.rasterize(natura, pixelWidth=100, pixelHeight=100, output=snakemake.output[0])

13
scripts/build_powerplants.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
@ -84,15 +84,14 @@ from scipy.spatial import cKDTree as KDTree
logger = logging.getLogger(__name__)
def add_custom_powerplants(ppl):
custom_ppl_query = snakemake.config['electricity']['custom_powerplants']
def add_custom_powerplants(ppl, custom_powerplants, custom_ppl_query=False):
if not custom_ppl_query:
return ppl
add_ppls = pd.read_csv(snakemake.input.custom_powerplants, index_col=0,
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 ppl.append(add_ppls, sort=False, ignore_index=True, verify_integrity=True)
return pd.concat([ppl, add_ppls], sort=False, ignore_index=True, verify_integrity=True)
if __name__ == "__main__":
@ -119,7 +118,9 @@ if __name__ == "__main__":
if isinstance(ppl_query, str):
ppl.query(ppl_query, inplace=True)
ppl = add_custom_powerplants(ppl) # add carriers from own powerplant files
# 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)
cntries_without_ppl = [c for c in countries if c not in ppl.Country.unique()]

303
scripts/build_renewable_profiles.py
View File

@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""Calculates for each network node the
(i) installable capacity (based on land-use), (ii) the available generation time
@ -60,7 +60,6 @@ Inputs
**Source:** `GEBCO <https://www.gebco.net/data_and_products/images/gebco_2019_grid_image.jpg>`_
- ``resources/natura.tiff``: confer :ref:`natura`
- ``resources/country_shapes.geojson``: confer :ref:`shapes`
- ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
- ``resources/regions_onshore.geojson``: (if not offshore wind), confer :ref:`busregions`
- ``resources/regions_offshore.geojson``: (if offshore wind), :ref:`busregions`
@ -180,224 +179,158 @@ node (`p_nom_max`): ``simple`` and ``conservative``:
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
from _helpers import configure_logging
import os
import atlite
import numpy as np
import xarray as xr
import pandas as pd
import multiprocessing as mp
import matplotlib.pyplot as plt
import progressbar as pgb
from scipy.sparse import csr_matrix, vstack
from pypsa.geo import haversine
from vresutils import landuse as vlanduse
from vresutils.array import spdiag
logger = logging.getLogger(__name__)
bounds = dx = dy = config = paths = gebco = clc = natura = None
def init_globals(bounds_xXyY, n_dx, n_dy, n_config, n_paths):
# Late import so that the GDAL Context is only created in the new processes
global gl, gk, gdal
import glaes as gl
import geokit as gk
from osgeo import gdal as gdal
# global in each process of the multiprocessing.Pool
global bounds, dx, dy, config, paths, gebco, clc, natura
bounds = gk.Extent.from_xXyY(bounds_xXyY)
dx = n_dx
dy = n_dy
config = n_config
paths = n_paths
if "max_depth" in config:
gebco = gk.raster.loadRaster(paths["gebco"])
gebco.SetProjection(gk.srs.loadSRS(4326).ExportToWkt())
clc = gk.raster.loadRaster(paths["corine"])
clc.SetProjection(gk.srs.loadSRS(3035).ExportToWkt())
natura = gk.raster.loadRaster(paths["natura"])
def downsample_to_coarse_grid(bounds, dx, dy, mask, data):
# The GDAL warp function with the 'average' resample algorithm needs a band of zero values of at least
# the size of one coarse cell around the original raster or it produces erroneous results
orig = mask.createRaster(data=data)
padded_extent = mask.extent.castTo(bounds.srs).pad(max(dx, dy)).castTo(mask.srs)
padded = padded_extent.fit((mask.pixelWidth, mask.pixelHeight)).warp(orig, mask.pixelWidth, mask.pixelHeight)
orig = None # free original raster
average = bounds.createRaster(dx, dy, dtype=gdal.GDT_Float32)
assert gdal.Warp(average, padded, resampleAlg='average') == 1, "gdal warp failed: %s" % gdal.GetLastErrorMsg()
return average
def calculate_potential(gid, save_map=None):
feature = gk.vector.extractFeature(paths["regions"], where=gid)
ec = gl.ExclusionCalculator(feature.geom)
corine = config.get("corine", {})
if isinstance(corine, list):
corine = {'grid_codes': corine}
if "grid_codes" in corine:
ec.excludeRasterType(clc, value=corine["grid_codes"], invert=True)
if corine.get("distance", 0.) > 0.:
ec.excludeRasterType(clc, value=corine["distance_grid_codes"], buffer=corine["distance"])
if config.get("natura", False):
ec.excludeRasterType(natura, value=1)
if "max_depth" in config:
ec.excludeRasterType(gebco, (None, -config["max_depth"]))
# TODO compute a distance field as a raster beforehand
if 'max_shore_distance' in config:
ec.excludeVectorType(paths["country_shapes"], buffer=config['max_shore_distance'], invert=True)
if 'min_shore_distance' in config:
ec.excludeVectorType(paths["country_shapes"], buffer=config['min_shore_distance'])
if save_map is not None:
ec.draw()
plt.savefig(save_map, transparent=True)
plt.close()
availability = downsample_to_coarse_grid(bounds, dx, dy, ec.region, np.where(ec.region.mask, ec._availability, 0))
return csr_matrix(gk.raster.extractMatrix(availability).flatten() / 100.)
if __name__ == '__main__':
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('build_renewable_profiles', technology='solar')
configure_logging(snakemake)
pgb.streams.wrap_stderr()
year = snakemake.wildcards.year
nprocesses = snakemake.config['atlite'].get('nprocesses')
noprogress = not snakemake.config['atlite'].get('show_progress', True)
config = snakemake.config['renewable'][snakemake.wildcards.technology]
cutout_name = config['cutout']
if year: cutout_name = cutout_name.format(year=year)
snapshots = dict(start=year, end=str(int(year)+1), closed="left") if year else snakemake.config['snapshots']
time = pd.date_range(freq='m', **snapshots)
params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]]))
cutout = atlite.Cutout(cutout_name,
cutout_dir=os.path.dirname(snakemake.input.cutout),
**params)
minx, maxx, miny, maxy = cutout.extent
dx = (maxx - minx) / (cutout.shape[1] - 1)
dy = (maxy - miny) / (cutout.shape[0] - 1)
bounds_xXyY = (minx - dx/2., maxx + dx/2., miny - dy/2., maxy + dy/2.)
# Use GLAES to compute available potentials and the transition matrix
paths = dict(snakemake.input)
# Use the following for testing the default windows method on linux
# mp.set_start_method('spawn')
with mp.Pool(initializer=init_globals, initargs=(bounds_xXyY, dx, dy, config, paths),
maxtasksperchild=20, processes=snakemake.config['atlite'].get('nprocesses', 2)) as pool:
# The GDAL library creates a GDAL context on module import, which may not be shared over multiple
# processes or the PROJ4 library has a hickup, so we import only after forking.
import geokit as gk
regions = gk.vector.extractFeatures(paths["regions"], onlyAttr=True)
buses = pd.Index(regions['name'], name="bus")
widgets = [
pgb.widgets.Percentage(),
' ', pgb.widgets.SimpleProgress(format='(%s)' % pgb.widgets.SimpleProgress.DEFAULT_FORMAT),
' ', pgb.widgets.Bar(),
' ', pgb.widgets.Timer(),
' ', pgb.widgets.ETA()
]
progressbar = pgb.ProgressBar(prefix='Compute GIS potentials: ', widgets=widgets, max_value=len(regions))
matrix = vstack(list(progressbar(pool.imap(calculate_potential, regions.index))))
potentials = config['capacity_per_sqkm'] * vlanduse._cutout_cell_areas(cutout)
potmatrix = matrix * spdiag(potentials.ravel())
if not config.get('keep_all_available_areas', False):
potmatrix.data[potmatrix.data < 1.] = 0 # ignore weather cells where only less than 1 MW can be installed
potmatrix.eliminate_zeros()
resource = config['resource']
func = getattr(cutout, resource.pop('method'))
resource = config['resource'] # pv panel config / wind turbine config
correction_factor = config.get('correction_factor', 1.)
if correction_factor != 1.:
logger.warning('correction_factor is set as {}'.format(correction_factor))
capacity_factor = correction_factor * func(capacity_factor=True, show_progress='Compute capacity factors: ', **resource).stack(spatial=('y', 'x')).values
layoutmatrix = potmatrix * spdiag(capacity_factor)
profile, capacities = func(matrix=layoutmatrix, index=buses, per_unit=True,
return_capacity=True, show_progress='Compute profiles: ',
**resource)
capacity_per_sqkm = config['capacity_per_sqkm']
p_nom_max_meth = config.get('potential', 'conservative')
if p_nom_max_meth == 'simple':
p_nom_max = xr.DataArray(np.asarray(potmatrix.sum(axis=1)).squeeze(), [buses])
elif p_nom_max_meth == 'conservative':
# p_nom_max has to be calculated for each bus and is the minimal ratio
# (min over all weather grid cells of the bus region) between the available
# potential (potmatrix) and the used normalised layout (layoutmatrix /
# capacities), so we would like to calculate i.e. potmatrix / (layoutmatrix /
# capacities). Since layoutmatrix = potmatrix * capacity_factor, this
# corresponds to capacities/max(capacity factor in the voronoi cell)
p_nom_max = xr.DataArray([1./np.max(capacity_factor[inds]) if len(inds) else 0.
for inds in np.split(potmatrix.indices, potmatrix.indptr[1:-1])], [buses]) * capacities
if isinstance(config.get("corine", {}), list):
config['corine'] = {'grid_codes': config['corine']}
if correction_factor != 1.:
logger.info(f'correction_factor is set as {correction_factor}')
cutout = atlite.Cutout(snakemake.input['cutout'])
regions = gpd.read_file(snakemake.input.regions).set_index('name').rename_axis('bus')
buses = regions.index
excluder = atlite.ExclusionContainer(crs=3035, res=100)
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.:
codes = corine["distance_grid_codes"]
buffer = corine["distance"]
excluder.add_raster(snakemake.input.corine, codes=codes, buffer=buffer, crs=3035)
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)
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)
kwargs = dict(nprocesses=nprocesses, disable_progressbar=noprogress)
if noprogress:
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)')
else:
raise AssertionError('Config key `potential` should be one of "simple" (default) or "conservative",'
' not "{}"'.format(p_nom_max_meth))
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
layout = xr.DataArray(np.asarray(potmatrix.sum(axis=0)).reshape(cutout.shape),
[cutout.meta.indexes[ax] for ax in ['y', 'x']])
area = cutout.grid.to_crs(3035).area / 1e6
area = xr.DataArray(area.values.reshape(cutout.shape),
[cutout.coords['y'], cutout.coords['x']])
# Determine weighted average distance from substation
cell_coords = cutout.grid_coordinates()
potential = capacity_per_sqkm * availability.sum('bus') * area
func = getattr(cutout, resource.pop('method'))
resource['dask_kwargs'] = {'num_workers': nprocesses}
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)
logger.info(f"Calculating maximal capacity per bus (method '{p_nom_max_meth}')")
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'])
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}"')
logger.info('Calculate average distances.')
layoutmatrix = (layout * availability).stack(spatial=['y','x'])
coords = cutout.grid[['x', 'y']]
bus_coords = regions[['x', 'y']]
average_distance = []
for i in regions.index:
row = layoutmatrix[i]
distances = haversine(regions.loc[i, ['x', 'y']], cell_coords[row.indices])[0]
average_distance.append((distances * (row.data / row.data.sum())).sum())
centre_of_mass = []
for bus in buses:
row = layoutmatrix.sel(bus=bus).data
nz_b = row != 0
row = row[nz_b]
co = coords[nz_b]
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'),
layout.rename('potential'),
average_distance.rename('average_distance')])
capacities.rename('weight'),
p_nom_max.rename('p_nom_max'),
potential.rename('potential'),
average_distance.rename('average_distance')])
if snakemake.wildcards.technology.startswith("offwind"):
import geopandas as gpd
from shapely.geometry import LineString
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 i in regions.index:
row = layoutmatrix[i]
centre_of_mass = (cell_coords[row.indices] * (row.data / row.data.sum())[:,np.newaxis]).sum(axis=0)
line = LineString([centre_of_mass, regions.loc[i, ['x', 'y']]])
underwater_fraction.append(line.intersection(offshore_shape).length / line.length)
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
underwater_fraction.append(frac)
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['p_nom_max'] > config.get('min_p_nom_max', 0.))))
if 'clip_p_max_pu' in config:
ds['profile'].values[ds['profile'].values < config['clip_p_max_pu']] = 0.
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)

69
scripts/build_shapes.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# 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.
@ -34,8 +34,8 @@ Inputs
.. image:: ../img/nuts3.png
:scale: 33 %
- ``data/bundle/nama_10r_3popgdp.tsv.gz``: Average annual population by NUTS3 region (`eurostat <http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en>`_)
- ``data/bundle/nama_10r_3gdp.tsv.gz``: Gross domestic product (GDP) by NUTS 3 regions (`eurostat <http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=en>`_)
- ``data/bundle/nama_10r_3popgdp.tsv.gz``: Average annual population by NUTS3 region (`eurostat <http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en>`__)
- ``data/bundle/nama_10r_3gdp.tsv.gz``: Gross domestic product (GDP) by NUTS 3 regions (`eurostat <http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=en>`__)
- ``data/bundle/ch_cantons.csv``: Mapping between Swiss Cantons and NUTS3 regions
- ``data/bundle/je-e-21.03.02.xls``: Population and GDP data per Canton (`BFS - Swiss Federal Statistical Office <https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html>`_ )
@ -73,13 +73,13 @@ from _helpers import configure_logging
import os
import numpy as np
from operator import attrgetter
from six.moves import reduce
from functools import reduce
from itertools import takewhile
import pandas as pd
import geopandas as gpd
from shapely.geometry import MultiPolygon, Polygon
from shapely.ops import cascaded_union
from shapely.ops import unary_union
import pycountry as pyc
logger = logging.getLogger(__name__)
@ -95,7 +95,7 @@ def _get_country(target, **keys):
def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
if isinstance(polys, MultiPolygon):
polys = sorted(polys, 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))
if mainpoly.area > minarea:
@ -107,26 +107,25 @@ def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
return polys.simplify(tolerance=tolerance)
def countries():
cntries = snakemake.config['countries']
if 'RS' in cntries: cntries.append('KV')
def countries(naturalearth, country_list):
if 'RS' in country_list: country_list.append('KV')
df = gpd.read_file(snakemake.input.naturalearth)
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]
df = df.loc[df.name.isin(cntries) & ((df['scalerank'] == 0) | (df['scalerank'] == 5))]
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 cntries: s['RS'] = s['RS'].union(s.pop('KV'))
if 'RS' in country_list: s['RS'] = s['RS'].union(s.pop('KV'))
return s
def eez(country_shapes):
df = gpd.read_file(snakemake.input.eez)
df = df.loc[df['ISO_3digit'].isin([_get_country('alpha_3', alpha_2=c) for c in snakemake.config['countries']])]
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})
@ -139,39 +138,41 @@ def country_cover(country_shapes, eez_shapes=None):
if eez_shapes is not None:
shapes += list(eez_shapes)
europe_shape = cascaded_union(shapes)
europe_shape = unary_union(shapes)
if isinstance(europe_shape, MultiPolygon):
europe_shape = max(europe_shape, key=attrgetter('area'))
return Polygon(shell=europe_shape.exterior)
def nuts3(country_shapes):
df = gpd.read_file(snakemake.input.nuts3)
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')
pop = pd.read_table(snakemake.input.nuts3pop, na_values=[':'], delimiter=' ?\t', engine='python')
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(snakemake.input.nuts3gdp, na_values=[':'], delimiter=' ?\t', engine='python')
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(snakemake.input.ch_cantons)
cantons = pd.read_csv(ch_cantons)
cantons = cantons.set_index(cantons['HASC'].str[3:])['NUTS']
cantons = cantons.str.pad(5, side='right', fillchar='0')
swiss = pd.read_excel(snakemake.input.ch_popgdp, skiprows=3, index_col=0)
swiss = pd.read_excel(ch_popgdp, skiprows=3, index_col=0)
swiss.columns = swiss.columns.to_series().map(cantons)
pop = pop.append(pd.to_numeric(swiss.loc['Residents in 1000', 'CH040':]))
gdp = gdp.append(pd.to_numeric(swiss.loc['Gross domestic product per capita in Swiss francs', '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':])
gdp = pd.concat([gdp, swiss_gdp])
df = df.join(pd.DataFrame(dict(pop=pop, gdp=gdp)))
@ -195,7 +196,7 @@ def nuts3(country_shapes):
manual['geometry'] = manual['country'].map(country_shapes)
manual = manual.dropna()
df = df.append(manual, sort=False)
df = pd.concat([df, manual], sort=False)
df.loc['ME000', 'pop'] = 650.
@ -218,16 +219,16 @@ if __name__ == "__main__":
snakemake = mock_snakemake('build_shapes')
configure_logging(snakemake)
out = snakemake.output
country_shapes = countries(snakemake.input.naturalearth, snakemake.config['countries'])
save_to_geojson(country_shapes, snakemake.output.country_shapes)
country_shapes = countries()
save_to_geojson(country_shapes, out.country_shapes)
offshore_shapes = eez(country_shapes)
save_to_geojson(offshore_shapes, out.offshore_shapes)
offshore_shapes = eez(country_shapes, snakemake.input.eez, snakemake.config['countries'])
save_to_geojson(offshore_shapes, snakemake.output.offshore_shapes)
europe_shape = country_cover(country_shapes, offshore_shapes)
save_to_geojson(gpd.GeoSeries(europe_shape), out.europe_shape)
save_to_geojson(gpd.GeoSeries(europe_shape), snakemake.output.europe_shape)
nuts3_shapes = nuts3(country_shapes)
save_to_geojson(nuts3_shapes, out.nuts3_shapes)
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)

77
scripts/cluster_network.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
@ -31,28 +31,28 @@ Relevant Settings
Inputs
------
- ``resources/regions_onshore_elec{year}_s{simpl}.geojson``: confer :ref:`simplify`
- ``resources/regions_offshore_elec{year}_s{simpl}.geojson``: confer :ref:`simplify`
- ``networks/elec{year}_s{simpl}.nc``: confer :ref:`simplify`
- ``resources/busmap_elec{year}_s{simpl}.csv``: confer :ref:`simplify`
- ``data/custom_busmap_elec{year}_s{simpl}_{clusters}.csv``: optional input
- ``resources/regions_onshore_elec{weather_year}_s{simpl}.geojson``: confer :ref:`simplify`
- ``resources/regions_offshore_elec{weather_year}_s{simpl}.geojson``: confer :ref:`simplify`
- ``networks/elec{weather_year}_s{simpl}.nc``: confer :ref:`simplify`
- ``resources/busmap_elec{weather_year}_s{simpl}.csv``: confer :ref:`simplify`
- ``data/custom_busmap_elec{weather_year}_s{simpl}_{clusters}.csv``: optional input
Outputs
-------
- ``resources/regions_onshore_elec{year}_s{simpl}_{clusters}.geojson``:
- ``resources/regions_onshore_elec{weather_year}_s{simpl}_{clusters}.geojson``:
.. image:: ../img/regions_onshore_elec_s_X.png
:scale: 33 %
- ``resources/regions_offshore_elec{year}_s{simpl}_{clusters}.geojson``:
- ``resources/regions_offshore_elec{weather_year}_s{simpl}_{clusters}.geojson``:
.. image:: ../img/regions_offshore_elec_s_X.png
:scale: 33 %
- ``resources/busmap_elec{year}_s{simpl}_{clusters}.csv``: Mapping of buses from ``networks/elec_s{simpl}.nc`` to ``networks/elec_s{simpl}_{clusters}.nc``;
- ``resources/linemap{year}_elec_s{simpl}_{clusters}.csv``: Mapping of lines from ``networks/elec_s{simpl}.nc`` to ``networks/elec_s{simpl}_{clusters}.nc``;
- ``networks/elec{year}_s{simpl}_{clusters}.nc``:
- ``resources/busmap_elec{weather_year}_s{simpl}_{clusters}.csv``: Mapping of buses from ``networks/elec_s{simpl}.nc`` to ``networks/elec_s{simpl}_{clusters}.nc``;
- ``resources/linemap{weather_year}_elec_s{simpl}_{clusters}.csv``: Mapping of lines from ``networks/elec_s{simpl}.nc`` to ``networks/elec_s{simpl}_{clusters}.nc``;
- ``networks/elec{weather_year}_s{simpl}_{clusters}.nc``:
.. image:: ../img/elec_s_X.png
:scale: 40 %
@ -122,7 +122,7 @@ Exemplary unsolved network clustered to 37 nodes:
"""
import logging
from _helpers import configure_logging
from _helpers import configure_logging, update_p_nom_max
import pypsa
import os
@ -135,11 +135,14 @@ import pyomo.environ as po
import matplotlib.pyplot as plt
import seaborn as sns
from six.moves import reduce
from functools import reduce
from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_spectral_clustering,
_make_consense, get_clustering_from_busmap)
import warnings
warnings.filterwarnings(action='ignore', category=UserWarning)
from add_electricity import load_costs
idx = pd.IndexSlice
@ -170,12 +173,9 @@ def weighting_for_country(n, x):
return (w * (100. / w.max())).clip(lower=1.).astype(int)
def distribute_clusters(n, n_clusters, focus_weights=None, solver_name=None):
def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="cbc"):
"""Determine the number of clusters per country"""
if solver_name is None:
solver_name = snakemake.config['solving']['solver']['name']
L = (n.loads_t.p_set.mean()
.groupby(n.loads.bus).sum()
.groupby([n.buses.country, n.buses.sub_network]).sum()
@ -218,7 +218,7 @@ def distribute_clusters(n, n_clusters, focus_weights=None, solver_name=None):
results = opt.solve(m)
assert results['Solver'][0]['Status'] == 'ok', f"Solver returned non-optimally: {results}"
return pd.Series(m.n.get_values(), index=L.index).astype(int)
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):
@ -226,6 +226,7 @@ def busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights=None, algori
algorithm_kwds.setdefault('n_init', 1000)
algorithm_kwds.setdefault('max_iter', 30000)
algorithm_kwds.setdefault('tol', 1e-6)
algorithm_kwds.setdefault('random_state', 0)
n.determine_network_topology()
@ -268,12 +269,10 @@ def clustering_for_n_clusters(n, n_clusters, custom_busmap=False, aggregate_carr
else:
raise AttributeError(f"potential_mode should be one of 'simple' or 'conservative' but is '{potential_mode}'")
if custom_busmap:
busmap = pd.read_csv(snakemake.input.custom_busmap, index_col=0, squeeze=True)
busmap.index = busmap.index.astype(str)
logger.info(f"Imported custom busmap from {snakemake.input.custom_busmap}")
else:
if not custom_busmap:
busmap = busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights, algorithm)
else:
busmap = custom_busmap
clustering = get_clustering_from_busmap(
n, busmap,
@ -282,7 +281,7 @@ def clustering_for_n_clusters(n, n_clusters, custom_busmap=False, aggregate_carr
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},
generator_strategies={'p_nom_max': p_nom_max_strategy, 'p_nom_min': np.sum},
scale_link_capital_costs=False)
if not n.links.empty:
@ -306,14 +305,12 @@ def save_to_geojson(s, fn):
def cluster_regions(busmaps, input=None, output=None):
if input is None: input = snakemake.input
if output is None: output = snakemake.output
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.cascaded_union)
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))
@ -357,10 +354,9 @@ if __name__ == "__main__":
clustering = pypsa.networkclustering.Clustering(n, busmap, linemap, linemap, pd.Series(dtype='O'))
else:
line_length_factor = snakemake.config['lines']['length_factor']
hvac_overhead_cost = (load_costs(n.snapshot_weightings.sum()/8760,
tech_costs=snakemake.input.tech_costs,
config=snakemake.config['costs'],
elec_config=snakemake.config['electricity'])
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'])
def consense(x):
@ -372,15 +368,20 @@ if __name__ == "__main__":
potential_mode = consense(pd.Series([snakemake.config['renewable'][tech]['potential']
for tech in renewable_carriers]))
custom_busmap = snakemake.config["enable"].get("custom_busmap", False)
clustering = clustering_for_n_clusters(n, n_clusters, custom_busmap, aggregate_carriers,
line_length_factor=line_length_factor,
potential_mode=potential_mode,
solver_name=snakemake.config['solving']['solver']['name'],
extended_link_costs=hvac_overhead_cost,
focus_weights=focus_weights)
if custom_busmap:
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)
update_p_nom_max(n)
clustering.network.export_to_netcdf(snakemake.output.network)
for attr in ('busmap', 'linemap'): #also available: linemap_positive, linemap_negative
getattr(clustering, attr).to_csv(snakemake.output[attr])
cluster_regions((clustering.busmap,))
cluster_regions((clustering.busmap,), snakemake.input, snakemake.output)

63
scripts/make_summary.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Creates summaries of aggregated energy and costs as ``.csv`` files.
@ -54,13 +54,12 @@ Replacing '/summaries/' with '/plots/' creates nice colored maps of the results.
"""
import logging
from _helpers import configure_logging
from _helpers import configure_logging, retrieve_snakemake_keys
import os
import pypsa
import pandas as pd
from six import iteritems
from add_electricity import load_costs, update_transmission_costs
idx = pd.IndexSlice
@ -112,15 +111,15 @@ def calculate_costs(n, label, costs):
costs.loc[idx[raw_index],label] = capital_costs_grouped.values
if c.name == "Link":
p = c.pnl.p0.multiply(n.snapshot_weightings,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,axis=0)
p_all = c.pnl.p.multiply(n.snapshot_weightings.generators,axis=0)
p_all[p_all < 0.] = 0.
p = p_all.sum()
else:
p = c.pnl.p.multiply(n.snapshot_weightings,axis=0).sum()
p = c.pnl.p.multiply(n.snapshot_weightings.generators,axis=0).sum()
marginal_costs = p*c.df.marginal_cost
@ -145,10 +144,12 @@ def calculate_energy(n, label, energy):
for c in n.iterate_components(n.one_port_components|n.branch_components):
if c.name in n.one_port_components:
c_energies = c.pnl.p.multiply(n.snapshot_weightings,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,axis=0).sum() - c.pnl.p0.multiply(n.snapshot_weightings,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)
@ -377,7 +378,7 @@ outputs = ["costs",
]
def make_summaries(networks_dict, country='all'):
def make_summaries(networks_dict, paths, config, country='all'):
columns = pd.MultiIndex.from_tuples(networks_dict.keys(),names=["simpl","clusters","ll","opts"])
@ -386,7 +387,7 @@ def make_summaries(networks_dict, country='all'):
for output in outputs:
dfs[output] = pd.DataFrame(columns=columns,dtype=float)
for label, filename in iteritems(networks_dict):
for label, filename in networks_dict.items():
print(label, filename)
if not os.path.exists(filename):
print("does not exist!!")
@ -401,9 +402,8 @@ def make_summaries(networks_dict, country='all'):
if country != 'all':
n = n[n.buses.country == country]
Nyears = n.snapshot_weightings.sum() / 8760.
costs = load_costs(Nyears, snakemake.input[0],
snakemake.config['costs'], snakemake.config['electricity'])
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)
assign_carriers(n)
@ -414,10 +414,9 @@ def make_summaries(networks_dict, country='all'):
return dfs
def to_csv(dfs):
dir = snakemake.output[0]
def to_csv(dfs, dir):
os.makedirs(dir, exist_ok=True)
for key, df in iteritems(dfs):
for key, df in dfs.items():
df.to_csv(os.path.join(dir, f"{key}.csv"))
@ -431,25 +430,27 @@ if __name__ == "__main__":
network_dir = os.path.join('results', 'networks')
configure_logging(snakemake)
def expand_from_wildcard(key):
w = getattr(snakemake.wildcards, key)
return snakemake.config["scenario"][key] if w == "all" else [w]
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
if snakemake.wildcards.ll.endswith("all"):
ll = snakemake.config["scenario"]["ll"]
if len(snakemake.wildcards.ll) == 4:
ll = [l for l in ll if l[0] == snakemake.wildcards.ll[0]]
def expand_from_wildcard(key, config):
w = getattr(wildcards, key)
return config["scenario"][key] if w == "all" else [w]
if wildcards.ll.endswith("all"):
ll = config["scenario"]["ll"]
if len(wildcards.ll) == 4:
ll = [l for l in ll if l[0] == wildcards.ll[0]]
else:
ll = [snakemake.wildcards.ll]
ll = [wildcards.ll]
networks_dict = {(simpl,clusters,l,opts) :
os.path.join(network_dir, f'{snakemake.wildcards.network}_s{simpl}_'
os.path.join(network_dir, f'elec_s{simpl}_'
f'{clusters}_ec_l{l}_{opts}.nc')
for simpl in expand_from_wildcard("simpl")
for clusters in expand_from_wildcard("clusters")
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")}
for opts in expand_from_wildcard("opts", config)}
dfs = make_summaries(networks_dict, country=snakemake.wildcards.country)
dfs = make_summaries(networks_dict, paths, config, country=wildcards.country)
to_csv(dfs)
to_csv(dfs, out[0])

63
scripts/plot_network.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Plots map with pie charts and cost box bar charts.
@ -20,12 +20,11 @@ Description
"""
import logging
from _helpers import (load_network_for_plots, aggregate_p, aggregate_costs,
configure_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
from six.moves import zip
import cartopy.crs as ccrs
import matplotlib.pyplot as plt
@ -89,36 +88,43 @@ def plot_map(n, ax=None, attribute='p_nom', opts={}):
# 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()))
line_widths_exp = dict(Line=n.lines.s_nom_opt, Link=n.links.p_nom_opt)
line_widths_cur = dict(Line=n.lines.s_nom_min, Link=n.links.p_nom_min)
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)
line_colors_with_alpha = \
dict(Line=(line_widths_cur['Line'] / n.lines.s_nom > 1e-3)
.map({True: line_colors['cur'], False: to_rgba(line_colors['cur'], 0.)}),
Link=(line_widths_cur['Link'] / n.links.p_nom > 1e-3)
((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.)}))
## FORMAT
linewidth_factor = opts['map'][attribute]['linewidth_factor']
bus_size_factor = opts['map'][attribute]['bus_size_factor']
## PLOT
n.plot(line_widths=pd.concat(line_widths_exp)/linewidth_factor,
line_colors=dict(Line=line_colors['exp'], Link=line_colors['exp']),
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,
geomap=True,
color_geomap=True, geomap=True,
ax=ax)
n.plot(line_widths=pd.concat(line_widths_cur)/linewidth_factor,
line_colors=pd.concat(line_colors_with_alpha),
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,
bus_colors=tech_colors,
boundaries=map_boundaries,
geomap=False,
color_geomap=True, geomap=False,
ax=ax)
ax.set_aspect('equal')
ax.axis('off')
@ -139,7 +145,7 @@ def plot_map(n, ax=None, attribute='p_nom', opts={}):
loc="upper left", bbox_to_anchor=(0.24, 1.01),
frameon=False,
labelspacing=0.8, handletextpad=1.5,
title='Transmission Exist./Exp. ')
title='Transmission Exp./Exist. ')
ax.add_artist(l1_1)
handles = []
@ -197,7 +203,7 @@ def plot_total_energy_pie(n, ax=None):
def plot_total_cost_bar(n, ax=None):
if ax is None: ax = plt.gca()
total_load = (n.snapshot_weightings * n.loads_t.p.sum(axis=1)).sum()
total_load = (n.snapshot_weightings.generators * n.loads_t.p.sum(axis=1)).sum()
tech_colors = opts['tech_colors']
def split_costs(n):
@ -253,18 +259,19 @@ if __name__ == "__main__":
set_plot_style()
opts = snakemake.config['plotting']
map_figsize = opts['map']['figsize']
map_boundaries = opts['map']['boundaries']
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
n = load_network_for_plots(snakemake.input.network, snakemake.input.tech_costs, snakemake.config)
map_figsize = config['map']['figsize']
map_boundaries = config['map']['boundaries']
scenario_opts = snakemake.wildcards.opts.split('-')
n = load_network_for_plots(paths.network, paths.tech_costs, config)
scenario_opts = wildcards.opts.split('-')
fig, ax = plt.subplots(figsize=map_figsize, subplot_kw={"projection": ccrs.PlateCarree()})
plot_map(n, ax, snakemake.wildcards.attr, opts)
plot_map(n, ax, wildcards.attr, config)
fig.savefig(snakemake.output.only_map, dpi=150, bbox_inches='tight')
fig.savefig(out.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)
@ -272,12 +279,12 @@ if __name__ == "__main__":
ax2 = fig.add_axes([-0.075, 0.1, 0.1, 0.45])
plot_total_cost_bar(n, ax2)
ll = snakemake.wildcards.ll
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=snakemake.wildcards.clusters))
.format(amount=amnt, label=lbl, clusters=wildcards.clusters))
fig.savefig(snakemake.output.ext, transparent=True, bbox_inches='tight')
fig.savefig(out.ext, transparent=True, bbox_inches='tight')

16
scripts/plot_p_nom_max.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Plots renewable installation potentials per capacity factor.
@ -19,7 +19,7 @@ Description
"""
import logging
from _helpers import configure_logging
from _helpers import configure_logging, retrieve_snakemake_keys
import pypsa
import pandas as pd
@ -53,11 +53,13 @@ if __name__ == "__main__":
clusts= '5,full', country= 'all')
configure_logging(snakemake)
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
plot_kwds = dict(drawstyle="steps-post")
clusters = snakemake.wildcards.clusts.split(',')
techs = snakemake.wildcards.techs.split(',')
country = snakemake.wildcards.country
clusters = wildcards.clusts.split(',')
techs = wildcards.techs.split(',')
country = wildcards.country
if country == 'all':
country = None
else:
@ -66,7 +68,7 @@ if __name__ == "__main__":
fig, axes = plt.subplots(1, len(techs))
for j, cluster in enumerate(clusters):
net = pypsa.Network(snakemake.input[j])
net = pypsa.Network(paths[j])
for i, tech in enumerate(techs):
cum_p_nom_max(net, tech, country).plot(x="p_max_pu", y="cum_p_nom_max",
@ -79,4 +81,4 @@ if __name__ == "__main__":
plt.legend(title="Cluster level")
fig.savefig(snakemake.output[0], transparent=True, bbox_inches='tight')
fig.savefig(out[0], transparent=True, bbox_inches='tight')

26
scripts/plot_summary.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Plots energy and cost summaries for solved networks.
@ -21,7 +21,7 @@ Description
import os
import logging
from _helpers import configure_logging
from _helpers import configure_logging, retrieve_snakemake_keys
import pandas as pd
import matplotlib.pyplot as plt
@ -55,7 +55,7 @@ def rename_techs(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"])
def plot_costs(infn, fn=None):
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])
@ -67,7 +67,7 @@ def plot_costs(infn, fn=None):
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[df.max(axis=1) < snakemake.config['plotting']['costs_threshold']]
to_drop = df.index[df.max(axis=1) < config['plotting']['costs_threshold']]
print("dropping")
@ -84,7 +84,7 @@ def plot_costs(infn, fn=None):
fig, ax = plt.subplots()
fig.set_size_inches((12,8))
df.loc[new_index,new_columns].T.plot(kind="bar",ax=ax,stacked=True,color=[snakemake.config['plotting']['tech_colors'][i] for i in new_index])
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()
@ -92,7 +92,7 @@ def plot_costs(infn, fn=None):
handles.reverse()
labels.reverse()
ax.set_ylim([0,snakemake.config['plotting']['costs_max']])
ax.set_ylim([0,config['plotting']['costs_max']])
ax.set_ylabel("System Cost [EUR billion per year]")
@ -109,7 +109,7 @@ def plot_costs(infn, fn=None):
fig.savefig(fn, transparent=True)
def plot_energy(infn, fn=None):
def plot_energy(infn, config, fn=None):
energy_df = pd.read_csv(infn, index_col=list(range(2)),header=[1,2,3])
@ -120,7 +120,7 @@ def plot_energy(infn, fn=None):
df = df.groupby(df.index.map(rename_techs)).sum()
to_drop = df.index[df.abs().max(axis=1) < snakemake.config['plotting']['energy_threshold']]
to_drop = df.index[df.abs().max(axis=1) < config['plotting']['energy_threshold']]
print("dropping")
@ -137,7 +137,7 @@ def plot_energy(infn, fn=None):
fig, ax = plt.subplots()
fig.set_size_inches((12,8))
df.loc[new_index,new_columns].T.plot(kind="bar",ax=ax,stacked=True,color=[snakemake.config['plotting']['tech_colors'][i] for i in new_index])
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()
@ -145,7 +145,7 @@ def plot_energy(infn, fn=None):
handles.reverse()
labels.reverse()
ax.set_ylim([snakemake.config['plotting']['energy_min'],snakemake.config['plotting']['energy_max']])
ax.set_ylim([config['plotting']['energy_min'], config['plotting']['energy_max']])
ax.set_ylabel("Energy [TWh/a]")
@ -170,10 +170,12 @@ if __name__ == "__main__":
attr='', ext='png', country='all')
configure_logging(snakemake)
summary = snakemake.wildcards.summary
paths, config, wildcards, logs, out = retrieve_snakemake_keys(snakemake)
summary = 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(snakemake.input[0], f"{summary}.csv"), snakemake.output[0])
func(os.path.join(paths[0], f"{summary}.csv"), config, out[0])

8
scripts/prepare_links_p_nom.py
View File

@ -2,7 +2,7 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Extracts capacities of HVDC links from `Wikipedia <https://en.wikipedia.org/wiki/List_of_HVDC_projects>`_.
@ -37,7 +37,7 @@ Description
"""
import logging
from _helpers import configure_logging
from _helpers import configure_logging, retrieve_snakemake_keys
import pandas as pd
@ -63,6 +63,8 @@ if __name__ == "__main__":
snakemake = mock_snakemake('prepare_links_p_nom', simpl='', network='elec')
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]
mw = "Power (MW)"
@ -74,4 +76,4 @@ if __name__ == "__main__":
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(snakemake.output[0], index=False)
links_p_nom.dropna(subset=['x1', 'y1', 'x2', 'y2']).to_csv(out[0], index=False)

59
scripts/prepare_network.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
@ -38,12 +38,12 @@ 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.
- ``networks/elec{year}_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
- ``networks/elec{weather_year}_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
Outputs
-------
- ``networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Complete PyPSA network that will be handed to the ``solve_network`` rule.
- ``networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Complete PyPSA network that will be handed to the ``solve_network`` rule.
Description
-----------
@ -62,7 +62,6 @@ import re
import pypsa
import numpy as np
import pandas as pd
from six import iteritems
from add_electricity import load_costs, update_transmission_costs
@ -71,21 +70,14 @@ idx = pd.IndexSlice
logger = logging.getLogger(__name__)
def add_co2limit(n, Nyears=1., factor=None):
if factor is not None:
annual_emissions = factor*snakemake.config['electricity']['co2base']
else:
annual_emissions = snakemake.config['electricity']['co2limit']
def add_co2limit(n, co2limit, Nyears=1.):
n.add("GlobalConstraint", "CO2Limit",
carrier_attribute="co2_emissions", sense="<=",
constant=annual_emissions * Nyears)
constant=co2limit * Nyears)
def add_emission_prices(n, emission_prices=None, exclude_co2=False):
if emission_prices is None:
emission_prices = snakemake.config['costs']['emission_prices']
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)
@ -95,13 +87,12 @@ def add_emission_prices(n, emission_prices=None, exclude_co2=False):
n.storage_units['marginal_cost'] += su_ep
def set_line_s_max_pu(n):
s_max_pu = snakemake.config['lines']['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, Nyears=1):
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()
_lines_s_nom = (np.sqrt(3) * n.lines.type.map(n.line_types.i_nom) *
@ -113,9 +104,6 @@ def set_transmission_limit(n, ll_type, factor, Nyears=1):
ref = (lines_s_nom @ n.lines[col] +
n.links.loc[links_dc_b, "p_nom"] @ n.links.loc[links_dc_b, col])
costs = load_costs(Nyears, snakemake.input.tech_costs,
snakemake.config['costs'],
snakemake.config['electricity'])
update_transmission_costs(n, costs, simple_hvdc_costs=False)
if factor == 'opt' or float(factor) > 1.0:
@ -145,13 +133,14 @@ def average_every_nhours(n, offset):
for c in n.iterate_components():
pnl = getattr(m, c.list_name+"_t")
for k, df in iteritems(c.pnl):
for k, df in c.pnl.items():
if not df.empty:
pnl[k] = df.resample(offset).mean()
return m
def apply_time_segmentation(n, segments):
def apply_time_segmentation(n, segments, solver_name="cbc"):
logger.info(f"Aggregating time series to {segments} segments.")
try:
import tsam.timeseriesaggregation as tsam
@ -170,8 +159,6 @@ def apply_time_segmentation(n, segments):
raw = pd.concat([p_max_pu, load, inflow], axis=1, sort=False)
solver_name = snakemake.config["solving"]["solver"]["name"]
agg = tsam.TimeSeriesAggregation(raw, hoursPerPeriod=len(raw),
noTypicalPeriods=1, noSegments=int(segments),
segmentation=True, solver=solver_name)
@ -208,9 +195,7 @@ def enforce_autarky(n, only_crossborder=False):
n.mremove("Line", lines_rm)
n.mremove("Link", links_rm)
def 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)
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)
@ -224,9 +209,10 @@ if __name__ == "__main__":
opts = snakemake.wildcards.opts.split('-')
n = pypsa.Network(snakemake.input[0])
Nyears = n.snapshot_weightings.sum() / 8760.
Nyears = n.snapshot_weightings.objective.sum() / 8760.
costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
set_line_s_max_pu(n)
set_line_s_max_pu(n, snakemake.config['lines']['s_max_pu'])
for o in opts:
m = re.match(r'^\d+h$', o, re.IGNORECASE)
@ -237,16 +223,18 @@ if __name__ == "__main__":
for o in opts:
m = re.match(r'^\d+seg$', o, re.IGNORECASE)
if m is not None:
n = apply_time_segmentation(n, m.group(0)[:-3])
solver_name = snakemake.config["solving"]["solver"]["name"]
n = apply_time_segmentation(n, m.group(0)[:-3], solver_name)
break
for o in opts:
if "Co2L" in o:
m = re.findall("[0-9]*\.?[0-9]+$", o)
if len(m) > 0:
add_co2limit(n, Nyears, float(m[0]))
co2limit = float(m[0]) * snakemake.config['electricity']['co2base']
add_co2limit(n, co2limit, Nyears)
else:
add_co2limit(n, Nyears)
add_co2limit(n, snakemake.config['electricity']['co2limit'], Nyears)
break
for o in opts:
@ -267,12 +255,13 @@ if __name__ == "__main__":
c.df.loc[sel,attr] *= factor
if 'Ep' in opts:
add_emission_prices(n)
add_emission_prices(n, snakemake.config['costs']['emission_prices'])
ll_type, factor = snakemake.wildcards.ll[0], snakemake.wildcards.ll[1:]
set_transmission_limit(n, ll_type, factor, Nyears)
set_transmission_limit(n, ll_type, factor, costs, Nyears)
set_line_nom_max(n)
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)

75
scripts/retrieve_cutout.py
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@ -1,75 +0,0 @@
# SPDX-FileCopyrightText: 2019-2020 Fabian Hofmann (FIAS)
#
# SPDX-License-Identifier: GPL-3.0-or-later
"""
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3517949.svg
:target: https://doi.org/10.5281/zenodo.3517949
Cutouts are spatiotemporal subsets of the European weather data from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ reanalysis dataset and the `CMSAF SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_ solar surface radiation dataset for the year 2013 (3.9 GB).
They have been prepared by and are for use with the `atlite <https://github.com/PyPSA/atlite>`_ tool. You can either generate them yourself using the ``build_cutouts`` rule or retrieve them directly from `zenodo <https://doi.org/10.5281/zenodo.3517949>`_ through the rule ``retrieve_cutout`` described here.
.. 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>`_.
The :ref:`tutorial` uses smaller `cutouts <https://zenodo.org/record/3518020/files/pypsa-eur-tutorial-cutouts.tar.xz>`_ than required for the full model (19 MB)
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3518020.svg
:target: https://doi.org/10.5281/zenodo.3518020
**Relevant Settings**
.. code:: yaml
tutorial:
enable:
build_cutout:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`toplevel_cf`
**Outputs**
- ``cutouts/{cutout}``: weather data from either the `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ reanalysis weather dataset or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewProduktSearch>`_ satellite-based historic weather data.
.. seealso::
For details see :mod:`build_cutout` and read the `atlite documentation <https://atlite.readthedocs.io>`_.
"""
import logging
logger = logging.getLogger(__name__)
from pathlib import Path
import tarfile
from _helpers import progress_retrieve, configure_logging
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('retrieve_cutout')
rootpath = '..'
else:
rootpath = '.'
configure_logging(snakemake) # TODO Make logging compatible with progressbar (see PR #102)
if snakemake.config['tutorial']:
url = "https://zenodo.org/record/3518020/files/pypsa-eur-tutorial-cutouts.tar.xz"
else:
url = "https://zenodo.org/record/3517949/files/pypsa-eur-cutouts.tar.xz"
# Save location
tarball_fn = Path(f"{rootpath}/cutouts.tar.xz")
logger.info(f"Downloading cutouts from '{url}'.")
progress_retrieve(url, tarball_fn)
logger.info(f"Extracting cutouts.")
tarfile.open(tarball_fn).extractall(path=rootpath)
tarball_fn.unlink()
logger.info(f"Cutouts available in '{Path(tarball_fn.stem).stem}'.")

2
scripts/retrieve_databundle.py
View File

@ -1,7 +1,7 @@
# Copyright 2019-2020 Fabian Hofmann (FIAS)
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3517935.svg

49
scripts/retrieve_natura_raster.py
View File

@ -1,49 +0,0 @@
# Copyright 2019-2020 Fabian Hofmann (FIAS)
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
"""
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3518215.svg
:target: https://doi.org/10.5281/zenodo.3518215
This rule, as a substitute for :mod:`build_natura_raster`, downloads an already rasterized version (`natura.tiff <https://zenodo.org/record/3518215/files/natura.tiff>`_) of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times. The file is placed into the ``resources`` sub-directory.
**Relevant Settings**
.. code:: yaml
enable:
build_natura_raster:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`toplevel_cf`
**Outputs**
- ``resources/natura.tiff``: Rasterized version of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times.
.. seealso::
For details see :mod:`build_natura_raster`.
"""
import logging
from _helpers import progress_retrieve, configure_logging
logger = logging.getLogger(__name__)
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('retrieve_natura_raster')
configure_logging(snakemake) # TODO Make logging compatible with progressbar (see PR #102)
url = "https://zenodo.org/record/3518215/files/natura.tiff"
logger.info(f"Downloading natura raster from '{url}'.")
progress_retrieve(url, snakemake.output[0])
logger.info(f"Natura raster available as '{snakemake.output[0]}'.")

136
scripts/simplify_network.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
@ -52,18 +52,18 @@ Inputs
Outputs
-------
- ``resources/regions_onshore_elec{year}_s{simpl}.geojson``:
- ``resources/regions_onshore_elec{weather_year}_s{simpl}.geojson``:
.. image:: ../img/regions_onshore_elec_s.png
:scale: 33 %
- ``resources/regions_offshore_elec{year}_s{simpl}.geojson``:
- ``resources/regions_offshore_elec{weather_year}_s{simpl}.geojson``:
.. image:: ../img/regions_offshore_elec_s .png
:scale: 33 %
- ``resources/busmap_elec{year}_s{simpl}.h5``: Mapping of buses from ``networks/elec.nc`` to ``networks/elec{year}_s{simpl}.nc``; has keys ['/busmap_s']
- ``networks/elec{year}_s{simpl}.nc``:
- ``resources/busmap_elec{weather_year}_s{simpl}.h5``: Mapping of buses from ``networks/elec.nc`` to ``networks/elec{weather_year}_s{simpl}.nc``; has keys ['/busmap_s']
- ``networks/elec{weather_year}_s{simpl}.nc``:
.. image:: ../img/elec_s.png
:scale: 33 %
@ -83,7 +83,7 @@ The rule :mod:`simplify_network` does up to four things:
"""
import logging
from _helpers import configure_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
@ -93,12 +93,11 @@ import numpy as np
import scipy as sp
from scipy.sparse.csgraph import connected_components, dijkstra
from six import iteritems
from six.moves import reduce
from functools import reduce
import pypsa
from pypsa.io import import_components_from_dataframe, import_series_from_dataframe
from pypsa.networkclustering import busmap_by_stubs, aggregategenerators, aggregateoneport
from pypsa.networkclustering import busmap_by_stubs, aggregategenerators, aggregateoneport, get_clustering_from_busmap, _make_consense
logger = logging.getLogger(__name__)
@ -125,7 +124,8 @@ def simplify_network_to_380(n):
several_trafo_b = trafo_map.isin(trafo_map.index)
trafo_map.loc[several_trafo_b] = trafo_map.loc[several_trafo_b].map(trafo_map)
missing_buses_i = n.buses.index.difference(trafo_map.index)
trafo_map = trafo_map.append(pd.Series(missing_buses_i, missing_buses_i))
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:
df = n.df(c)
@ -139,18 +139,15 @@ def simplify_network_to_380(n):
return n, trafo_map
def _prepare_connection_costs_per_link(n):
def _prepare_connection_costs_per_link(n, costs, config):
if n.links.empty: return {}
costs = load_costs(n.snapshot_weightings.sum() / 8760, snakemake.input.tech_costs,
snakemake.config['costs'], snakemake.config['electricity'])
connection_costs_per_link = {}
for tech in snakemake.config['renewable']:
for tech in config['renewable']:
if tech.startswith('offwind'):
connection_costs_per_link[tech] = (
n.links.length * snakemake.config['lines']['length_factor'] *
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'])
)
@ -158,9 +155,9 @@ def _prepare_connection_costs_per_link(n):
return connection_costs_per_link
def _compute_connection_costs_to_bus(n, busmap, 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)
connection_costs_per_link = _prepare_connection_costs_per_link(n, costs, config)
if buses is None:
buses = busmap.index[busmap.index != busmap.values]
@ -178,7 +175,8 @@ def _compute_connection_costs_to_bus(n, busmap, connection_costs_per_link=None,
return connection_costs_to_bus
def _adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus):
def _adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, output):
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]
@ -186,20 +184,23 @@ def _adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus):
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())))
connection_costs[tech] = costs
pd.DataFrame(connection_costs).to_csv(output.connection_costs)
def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, aggregate_one_ports={"Load", "StorageUnit"}):
def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, output, aggregate_one_ports={"Load", "StorageUnit"}):
def replace_components(n, c, df, pnl):
n.mremove(c, n.df(c).index)
import_components_from_dataframe(n, df, c)
for attr, df in iteritems(pnl):
for attr, df in pnl.items():
if not df.empty:
import_series_from_dataframe(n, df, c, attr)
_adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus)
_adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, output)
generators, generators_pnl = aggregategenerators(n, busmap)
generators, generators_pnl = aggregategenerators(n, busmap, custom_strategies={'p_nom_min': np.sum})
replace_components(n, "Generator", generators, generators_pnl)
for one_port in aggregate_one_ports:
@ -213,7 +214,7 @@ def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, aggregate
n.mremove(c, df.index[df.bus0.isin(buses_to_del) | df.bus1.isin(buses_to_del)])
def simplify_links(n):
def simplify_links(n, costs, config, output):
## Complex multi-node links are folded into end-points
logger.info("Simplifying connected link components")
@ -237,7 +238,7 @@ def simplify_links(n):
if len(G.adj[m]) > 2 or (set(G.adj[m]) - nodes)}
for u in supernodes:
for m, ls in iteritems(G.adj[u]):
for m, ls in G.adj[u].items():
if m not in nodes or m in seen: continue
buses = [u, m]
@ -245,7 +246,7 @@ def simplify_links(n):
while m not in (supernodes | seen):
seen.add(m)
for m2, ls in iteritems(G.adj[m]):
for m2, ls in G.adj[m].items():
if m2 in seen or m2 == u: continue
buses.append(m2)
links.append(list(ls)) # [name for name in ls])
@ -260,7 +261,7 @@ def simplify_links(n):
busmap = n.buses.index.to_series()
connection_costs_per_link = _prepare_connection_costs_per_link(n)
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))
for lbl in labels.value_counts().loc[lambda s: s > 2].index:
@ -274,11 +275,11 @@ def simplify_links(n):
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, 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 = snakemake.config['links'].get('p_max_pu', 1.)
p_max_pu = config['links'].get('p_max_pu', 1.)
lengths = n.links.loc[all_links, 'length']
name = lengths.idxmax() + '+{}'.format(len(links) - 1)
params = dict(
@ -305,39 +306,77 @@ def simplify_links(n):
logger.debug("Collecting all components using the busmap")
_aggregate_and_move_components(n, busmap, connection_costs_to_bus)
_aggregate_and_move_components(n, busmap, connection_costs_to_bus, output)
return n, busmap
def remove_stubs(n):
def remove_stubs(n, costs, config, output):
logger.info("Removing stubs")
busmap = busmap_by_stubs(n) # ['country'])
connection_costs_to_bus = _compute_connection_costs_to_bus(n, busmap)
connection_costs_to_bus = _compute_connection_costs_to_bus(n, busmap, costs, config)
_aggregate_and_move_components(n, busmap, connection_costs_to_bus)
_aggregate_and_move_components(n, busmap, connection_costs_to_bus, output)
return n, busmap
def aggregate_to_substations(n, 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))
def cluster(n, n_clusters):
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)
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[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
dist.loc[incountry_b, ~incountry_b] = np.inf
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)
return clustering.network, busmap
def cluster(n, n_clusters, config):
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 snakemake.config['renewable']])
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([snakemake.config['renewable'][tech]['potential']
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=snakemake.config['solving']['solver']['name'])
solver_name=config['solving']['solver']['name'],
focus_weights=focus_weights)
return clustering.network, clustering.busmap
@ -352,16 +391,31 @@ if __name__ == "__main__":
n, trafo_map = simplify_network_to_380(n)
n, simplify_links_map = simplify_links(n)
Nyears = n.snapshot_weightings.objective.sum() / 8760
n, stub_map = remove_stubs(n)
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, stub_map = remove_stubs(n, technology_costs, snakemake.config, snakemake.output)
busmaps = [trafo_map, simplify_links_map, stub_map]
if snakemake.config.get('clustering', {}).get('simplify', {}).get('to_substations', False):
n, substation_map = aggregate_to_substations(n)
busmaps.append(substation_map)
if snakemake.wildcards.simpl:
n, cluster_map = cluster(n, int(snakemake.wildcards.simpl))
n, cluster_map = cluster(n, int(snakemake.wildcards.simpl), snakemake.config)
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)
n.buses = n.buses.drop(buses_c, axis=1)
update_p_nom_max(n)
n.export_to_netcdf(snakemake.output.network)
busmap_s = reduce(lambda x, y: x.map(y), busmaps[1:], busmaps[0])

27
scripts/solve_network.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Solves linear optimal power flow for a network iteratively while updating reactances.
@ -32,12 +32,12 @@ Relevant Settings
Inputs
------
- ``networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: confer :ref:`prepare`
- ``networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: confer :ref:`prepare`
Outputs
-------
- ``results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Solved PyPSA network including optimisation results
- ``results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: Solved PyPSA network including optimisation results
.. image:: ../img/results.png
:scale: 40 %
@ -101,8 +101,9 @@ def prepare_network(n, solve_opts):
if solve_opts.get('load_shedding'):
n.add("Carrier", "Load")
n.madd("Generator", n.buses.index, " load",
bus=n.buses.index,
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
@ -127,7 +128,7 @@ def prepare_network(n, solve_opts):
if solve_opts.get('nhours'):
nhours = solve_opts['nhours']
n.set_snapshots(n.snapshots[:nhours])
n.snapshot_weightings[:] = 8760./nhours
n.snapshot_weightings[:] = 8760. / nhours
return n
@ -174,16 +175,16 @@ 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 @ \
load = n.snapshot_weightings.generators @ \
n.loads_t.p_set.groupby(lgrouper, axis=1).sum()
inflow = n.snapshot_weightings @ \
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 * scaling,
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 * scaling,
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("")
@ -241,7 +242,7 @@ def extra_functionality(n, snapshots):
add_battery_constraints(n)
def solve_network(n, config, solver_log=None, opts='', **kwargs):
def solve_network(n, config, opts='', **kwargs):
solver_options = config['solving']['solver'].copy()
solver_name = solver_options.pop('name')
cf_solving = config['solving']['options']
@ -282,8 +283,8 @@ if __name__ == "__main__":
with memory_logger(filename=fn, interval=30.) as mem:
n = pypsa.Network(snakemake.input[0])
n = prepare_network(n, solve_opts)
n = solve_network(n, config=snakemake.config, solver_dir=tmpdir,
solver_log=snakemake.log.solver, opts=opts)
n = solve_network(n, snakemake.config, opts, solver_dir=tmpdir,
solver_logfile=snakemake.log.solver)
n.export_to_netcdf(snakemake.output[0])
logger.info("Maximum memory usage: {}".format(mem.mem_usage))

32
scripts/solve_operations_network.py
View File

@ -1,6 +1,6 @@
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: GPL-3.0-or-later
# SPDX-License-Identifier: MIT
"""
Solves linear optimal dispatch in hourly resolution
@ -32,13 +32,13 @@ Relevant Settings
Inputs
------
- ``networks/elec{year}_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
- ``results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: confer :ref:`solve`
- ``networks/elec{weather_year}_s{simpl}_{clusters}.nc``: confer :ref:`cluster`
- ``results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc``: confer :ref:`solve`
Outputs
-------
- ``results/networks/elec{year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op.nc``: Solved PyPSA network for optimal dispatch including optimisation results
- ``results/networks/elec{weather_year}_s{simpl}_{clusters}_ec_l{ll}_{opts}_op.nc``: Solved PyPSA network for optimal dispatch including optimisation results
Description
-----------
@ -71,7 +71,7 @@ def set_parameters_from_optimized(n, n_optim):
n_optim.lines[attr].reindex(lines_untyped_i, fill_value=0.)
n.lines['s_nom_extendable'] = False
links_dc_i = n.links.index[n.links.carrier == 'DC']
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
@ -81,10 +81,15 @@ def set_parameters_from_optimized(n, n_optim):
n_optim.generators['p_nom_opt'].reindex(gen_extend_i, fill_value=0.)
n.generators.loc[gen_extend_i, 'p_nom_extendable'] = False
stor_extend_i = n.storage_units.index[n.storage_units.p_nom_extendable]
n.storage_units.loc[stor_extend_i, 'p_nom'] = \
n_optim.storage_units['p_nom_opt'].reindex(stor_extend_i, fill_value=0.)
n.storage_units.loc[stor_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
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
return n
@ -104,15 +109,14 @@ if __name__ == "__main__":
n = set_parameters_from_optimized(n, n_optim)
del n_optim
config = snakemake.config
opts = snakemake.wildcards.opts.split('-')
config['solving']['options']['skip_iterations'] = False
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, solve_opts=snakemake.config['solving']['options'])
n = solve_network(n, config, solver_dir=tmpdir,
solver_log=snakemake.log.solver, opts=opts)
n = prepare_network(n, snakemake.config['solving']['options'])
n = solve_network(n, snakemake.config, opts, solver_dir=tmpdir,
solver_logfile=snakemake.log.solver)
n.export_to_netcdf(snakemake.output[0])
logger.info("Maximum memory usage: {}".format(mem.mem_usage))

30
test/config.test1.yaml
View File

@ -2,7 +2,7 @@
#
# SPDX-License-Identifier: CC0-1.0
version: 0.3.0
version: 0.4.0
tutorial: true
logging:
level: INFO
@ -19,9 +19,13 @@ scenario:
countries: ['DE']
clustering:
simplify:
to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
snapshots:
start: "2013-03-01"
end: "2014-04-01"
end: "2013-03-08"
closed: 'left' # end is not inclusive
enable:
@ -54,16 +58,15 @@ electricity:
atlite:
nprocesses: 4
cutouts:
europe-2013-era5:
europe-2013-era5-tutorial:
module: era5
xs: [4., 15.]
ys: [56., 46.]
months: [3, 3]
years: [2013, 2013]
x: [4., 15.]
y: [46., 56.]
time: ["2013-03-01", "2013-03-08"]
renewable:
onwind:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: wind
turbine: Vestas_V112_3MW
@ -80,7 +83,7 @@ renewable:
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-ac:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -92,7 +95,7 @@ renewable:
potential: simple # or conservative
clip_p_max_pu: 1.e-2
offwind-dc:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore
@ -105,7 +108,7 @@ renewable:
potential: simple # or conservative
clip_p_max_pu: 1.e-2
solar:
cutout: europe-2013-era5
cutout: europe-2013-era5-tutorial
resource:
method: pv
panel: CSi
@ -147,7 +150,6 @@ transformers:
type: ''
load:
url: https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv
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
@ -194,8 +196,8 @@ solving:
# threads: 4
# lpmethod: 4 # barrier
# solutiontype: 2 # non basic solution, ie no crossover
# barrier_convergetol: 1.e-5
# feasopt_tolerance: 1.e-6
# barrier.convergetol: 1.e-5
# feasopt.tolerance: 1.e-6
plotting:
map: