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Merge branch 'master' into becc_energy_penalty

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lisazeyen 2024-08-02 14:35:01 +02:00 committed by GitHub
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# #
# SPDX-License-Identifier: CC0-1.0 # SPDX-License-Identifier: CC0-1.0
# Exclude pre-commit applications # Note
5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0 # Needs to be setup via
92080b1cd2ca5f123158571481722767b99c2b27 # git config blame.ignoreRevsFile .git-blame-ignore-revs
13769f90af4500948b0376d57df4cceaa13e78b5
# Custom commits
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36003c96270f622e67288ed8179e04cbbaa42d84
b134a395b44d1023f1c9f5cf63e2feffa1faf9c0
64745e7ec2ef7008d128d9ef2234f5d78a2243b8
03db47ed5e4b1e02a784dbf0274627ecb0714b73
9f87099dbbb0d2519235cc19327dbb198776e040
cb94e5974eddb705f5391cee0fea489e5f573fbb
e6ecbc95d71a47e913680db14f3861daad4dae11
8bdba5653a51e42ac1651b8def36f1b4a3a0999a
0cf6d47afbac9d273f27e37f84592c5fb4b430db
3c099095dc50ce7d27d080686b005b49248cc216
acc6ee6bfe25114e9527c3f27de1307ff098490a
94e5f160b0f46764c4c95eed6a7de90ef3d65717
dcd16e32a88385fb1fe8366da4de9807ce33baf3
85d01bceb0dd87fb0d02648cd70d753450175f62
92080b1cd2ca5f123158571481722767b99c2b27
5d1ef8a64055a039aa4a0834d2d26fe7752fe9a0

39
.github/workflows/update-fixed-env.yaml vendored Normal file
View File

@ -0,0 +1,39 @@
name: Fixed Environment YAML Monitor
on:
push:
branches:
- master
paths:
- 'env/environment.yaml'
jobs:
update_environment_fixed:
runs-on: ubuntu-latest
steps:
- name: Checkout Repository
uses: actions/checkout@v4
- name: Setup micromamba
uses: mamba-org/setup-micromamba@v1
with:
micromamba-version: latest
environment-file: envs/environment.yaml
log-level: debug
init-shell: bash
cache-environment: true
cache-downloads: true
- name: Update environment.fixed.yaml
run: |
mamba env export --file envs/environment.fixed.yaml --no-builds
- name: Create Pull Request
uses: peter-evans/create-pull-request@v6
with:
token: ${{ secrets.GITHUB_TOKEN }}
branch: update-environment-fixed
title: Update fixed environment
body: Automatically generated PR to update environment.fixed.yaml
labels: automated

4
.pre-commit-config.yaml
View File

@ -67,7 +67,7 @@ repos:
# Do YAML formatting (before the linter checks it for misses) # Do YAML formatting (before the linter checks it for misses)
- repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks - repo: https://github.com/macisamuele/language-formatters-pre-commit-hooks
rev: v2.13.0 rev: v2.14.0
hooks: hooks:
- id: pretty-format-yaml - id: pretty-format-yaml
args: [--autofix, --indent, "2", --preserve-quotes] args: [--autofix, --indent, "2", --preserve-quotes]
@ -87,6 +87,6 @@ repos:
# Check for FSFE REUSE compliance (licensing) # Check for FSFE REUSE compliance (licensing)
- repo: https://github.com/fsfe/reuse-tool - repo: https://github.com/fsfe/reuse-tool
rev: v3.1.0a1 rev: v4.0.3
hooks: hooks:
- id: reuse - id: reuse

4
README.md
View File

@ -65,10 +65,10 @@ The dataset consists of:
(alternating current lines at and above 220kV voltage level and all high (alternating current lines at and above 220kV voltage level and all high
voltage direct current lines) and 3803 substations. voltage direct current lines) and 3803 substations.
- The open power plant database - The open power plant database
[powerplantmatching](https://github.com/FRESNA/powerplantmatching). [powerplantmatching](https://github.com/PyPSA/powerplantmatching).
- Electrical demand time series from the - Electrical demand time series from the
[OPSD project](https://open-power-system-data.org/). [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). - Renewable time series based on ERA5 and SARAH, assembled using the [atlite tool](https://github.com/PyPSA/atlite).
- Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [atlite library](https://github.com/PyPSA/atlite). - Geographical potentials for wind and solar generators based on land use (CORINE) and excluding nature reserves (Natura2000) are computed with the [atlite library](https://github.com/PyPSA/atlite).
A sector-coupled extension adds demand A sector-coupled extension adds demand

63
config/config.default.yaml
View File

@ -134,35 +134,25 @@ electricity:
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#atlite # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#atlite
atlite: atlite:
default_cutout: europe-2013-era5 default_cutout: europe-2013-sarah3-era5
nprocesses: 4 nprocesses: 4
show_progress: false show_progress: false
cutouts: cutouts:
# use 'base' to determine geographical bounds and time span from config # use 'base' to determine geographical bounds and time span from config
# base: # base:
# module: era5 # module: era5
europe-2013-era5: europe-2013-sarah3-era5:
module: era5 # in priority order module: [sarah, era5] # in priority order
x: [-12., 42.] x: [-12., 42.]
y: [33., 72] y: [33., 72.]
dx: 0.3 dx: 0.3
dy: 0.3 dy: 0.3
time: ['2013', '2013'] time: ['2013', '2013']
europe-2013-sarah:
module: [sarah, era5] # in priority order
x: [-12., 42.]
y: [33., 65]
dx: 0.2
dy: 0.2
time: ['2013', '2013']
sarah_interpolate: false
sarah_dir:
features: [influx, temperature]
# docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#renewable # docs in https://pypsa-eur.readthedocs.io/en/latest/configuration.html#renewable
renewable: renewable:
onwind: onwind:
cutout: europe-2013-era5 cutout: europe-2013-sarah3-era5
resource: resource:
method: wind method: wind
turbine: Vestas_V112_3MW turbine: Vestas_V112_3MW
@ -181,7 +171,7 @@ renewable:
excluder_resolution: 100 excluder_resolution: 100
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
offwind-ac: offwind-ac:
cutout: europe-2013-era5 cutout: europe-2013-sarah3-era5
resource: resource:
method: wind method: wind
turbine: NREL_ReferenceTurbine_2020ATB_5.5MW turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
@ -197,7 +187,7 @@ renewable:
excluder_resolution: 200 excluder_resolution: 200
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
offwind-dc: offwind-dc:
cutout: europe-2013-era5 cutout: europe-2013-sarah3-era5
resource: resource:
method: wind method: wind
turbine: NREL_ReferenceTurbine_2020ATB_5.5MW turbine: NREL_ReferenceTurbine_2020ATB_5.5MW
@ -213,7 +203,7 @@ renewable:
excluder_resolution: 200 excluder_resolution: 200
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
offwind-float: offwind-float:
cutout: europe-2013-era5 cutout: europe-2013-sarah3-era5
resource: resource:
method: wind method: wind
turbine: NREL_ReferenceTurbine_5MW_offshore turbine: NREL_ReferenceTurbine_5MW_offshore
@ -231,7 +221,7 @@ renewable:
max_depth: 1000 max_depth: 1000
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
solar: solar:
cutout: europe-2013-sarah cutout: europe-2013-sarah3-era5
resource: resource:
method: pv method: pv
panel: CSi panel: CSi
@ -246,7 +236,7 @@ renewable:
excluder_resolution: 100 excluder_resolution: 100
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
solar-hsat: solar-hsat:
cutout: europe-2013-sarah cutout: europe-2013-sarah3-era5
resource: resource:
method: pv method: pv
panel: CSi panel: CSi
@ -261,7 +251,7 @@ renewable:
excluder_resolution: 100 excluder_resolution: 100
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
hydro: hydro:
cutout: europe-2013-era5 cutout: europe-2013-sarah3-era5
carriers: [ror, PHS, hydro] carriers: [ror, PHS, hydro]
PHS_max_hours: 6 PHS_max_hours: 6
hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float
@ -295,7 +285,7 @@ lines:
under_construction: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity under_construction: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
dynamic_line_rating: dynamic_line_rating:
activate: false activate: false
cutout: europe-2013-era5 cutout: europe-2013-sarah3-era5
correction_factor: 0.95 correction_factor: 0.95
max_voltage_difference: false max_voltage_difference: false
max_line_rating: false max_line_rating: false
@ -407,6 +397,7 @@ sector:
biomass: true biomass: true
industry: true industry: true
agriculture: true agriculture: true
fossil_fuels: true
district_heating: district_heating:
potential: 0.6 potential: 0.6
progress: progress:
@ -571,12 +562,12 @@ sector:
min_part_load_fischer_tropsch: 0.5 min_part_load_fischer_tropsch: 0.5
min_part_load_methanolisation: 0.3 min_part_load_methanolisation: 0.3
min_part_load_methanation: 0.3 min_part_load_methanation: 0.3
use_fischer_tropsch_waste_heat: true use_fischer_tropsch_waste_heat: 0.25
use_haber_bosch_waste_heat: true use_haber_bosch_waste_heat: 0.25
use_methanolisation_waste_heat: true use_methanolisation_waste_heat: 0.25
use_methanation_waste_heat: true use_methanation_waste_heat: 0.25
use_fuel_cell_waste_heat: true use_fuel_cell_waste_heat: 0.25
use_electrolysis_waste_heat: true use_electrolysis_waste_heat: 0.25
electricity_transmission_grid: true electricity_transmission_grid: true
electricity_distribution_grid: true electricity_distribution_grid: true
electricity_distribution_grid_cost_factor: 1.0 electricity_distribution_grid_cost_factor: 1.0
@ -606,6 +597,7 @@ sector:
conventional_generation: conventional_generation:
OCGT: gas OCGT: gas
biomass_to_liquid: false biomass_to_liquid: false
electrobiofuels: false
biosng: false biosng: false
limit_max_growth: limit_max_growth:
enable: false enable: false
@ -786,6 +778,7 @@ solving:
options: options:
clip_p_max_pu: 1.e-2 clip_p_max_pu: 1.e-2
load_shedding: false load_shedding: false
curtailment_mode: false
noisy_costs: true noisy_costs: true
skip_iterations: true skip_iterations: true
rolling_horizon: false rolling_horizon: false
@ -830,7 +823,7 @@ solving:
solver_options: solver_options:
highs-default: highs-default:
# refer to https://ergo-code.github.io/HiGHS/dev/options/definitions/ # refer to https://ergo-code.github.io/HiGHS/dev/options/definitions/
threads: 4 threads: 1
solver: "ipm" solver: "ipm"
run_crossover: "off" run_crossover: "off"
small_matrix_value: 1e-6 small_matrix_value: 1e-6
@ -841,7 +834,7 @@ solving:
parallel: "on" parallel: "on"
random_seed: 123 random_seed: 123
gurobi-default: gurobi-default:
threads: 4 threads: 8
method: 2 # barrier method: 2 # barrier
crossover: 0 crossover: 0
BarConvTol: 1.e-6 BarConvTol: 1.e-6
@ -879,6 +872,13 @@ solving:
Threads: 8 Threads: 8
LpMethod: 2 LpMethod: 2
Crossover: 0 Crossover: 0
RelGap: 1.e-6
Dualize: 0
copt-gpu:
LpMethod: 6
GPUMode: 1
PDLPTol: 1.e-5
Crossover: 0
cbc-default: {} # Used in CI cbc-default: {} # Used in CI
glpk-default: {} # Used in CI glpk-default: {} # Used in CI
@ -1030,6 +1030,7 @@ plotting:
services rural biomass boiler: '#c6cf98' services rural biomass boiler: '#c6cf98'
services urban decentral biomass boiler: '#dde5b5' services urban decentral biomass boiler: '#dde5b5'
biomass to liquid: '#32CD32' biomass to liquid: '#32CD32'
electrobiofuels: 'red'
BioSNG: '#123456' BioSNG: '#123456'
# power transmission # power transmission
lines: '#6c9459' lines: '#6c9459'
@ -1056,7 +1057,7 @@ plotting:
V2G: '#e5ffa8' V2G: '#e5ffa8'
land transport EV: '#baf238' land transport EV: '#baf238'
land transport demand: '#38baf2' land transport demand: '#38baf2'
Li ion: '#baf238' EV battery: '#baf238'
# hot water storage # hot water storage
water tanks: '#e69487' water tanks: '#e69487'
residential rural water tanks: '#f7b7a3' residential rural water tanks: '#f7b7a3'

151
data/GDP_PPP_30arcsec_v3_mapped_default.csv
View File

@ -1,151 +0,0 @@
name,GDP_PPP,country
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3139,806541.9318093687,MD
3142,1392454.6690911907,MD
3152,897871.2903553953,MD
3246,645554.8588933202,MD
7049,1150156.4449477682,MD
1924,162285.16792916053,UA
1970,751970.6071848695,UA
2974,368873.75840156944,UA
2977,294847.85539198935,UA
2979,197988.13680768458,UA
2980,301371.2491126519,UA
3031,56925.21878805953,UA
3032,139395.18279351242,UA
3033,145377.8061037629,UA
3035,52282.83655208812,UA
3036,497950.25890516065,UA
3037,1183293.1987702171,UA
3038,255005.98207636533,UA
3039,224711.50098325178,UA
3040,342959.943226467,UA
3044,69119.31486955672,UA
3045,246273.65986119965,UA
3047,146742.08407299497,UA
3049,107265.7028733467,UA
3050,1126147.985259493,UA
3051,69833.56303043803,UA
3052,67230.88206577855,UA
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3229,175692.61136747137,UA
3230,106722.62773321665,UA
3236,61542.06264321315,UA
3241,83752.90489164277,UA
4301,48419.52825967164,UA
4305,147759.74280349456,UA
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4315,218025.78516351627,UA
4317,155240.40554731718,UA
4318,1342144.2459407183,UA
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4360,47840.51245168512,UA
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4441,49159.485269198034,UA
7031,42001.73757065917,UA
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7063,39599.10564971086,UA
1 name GDP_PPP country
2 3140 632728.0438507323 MD
3 3139 806541.9318093687 MD
4 3142 1392454.6690911907 MD
5 3152 897871.2903553953 MD
6 3246 645554.8588933202 MD
7 7049 1150156.4449477682 MD
8 1924 162285.16792916053 UA
9 1970 751970.6071848695 UA
10 2974 368873.75840156944 UA
11 2977 294847.85539198935 UA
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13 2980 301371.2491126519 UA
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19 3037 1183293.1987702171 UA
20 3038 255005.98207636533 UA
21 3039 224711.50098325178 UA
22 3040 342959.943226467 UA
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29 3052 67230.88206577855 UA
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55 3088 347197.65113392205 UA
56 3089 3754718.141734592 UA
57 3090 521912.69768585655 UA
58 3093 232818.05269714879 UA
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61 3100 175689.10947424968 UA
62 3105 538438.9311459162 UA
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93 3199 61334.952541812374 UA
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95 3230 106722.62773321665 UA
96 3236 61542.06264321315 UA
97 3241 83752.90489164277 UA
98 4301 48419.52825967164 UA
99 4305 147759.74280349456 UA
100 4306 53156.905740992224 UA
101 4315 218025.78516351627 UA
102 4317 155240.40554731718 UA
103 4318 1342144.2459407183 UA
104 4319 91669.1449633853 UA
105 4321 85852.49282415409 UA
106 4347 67938.7698430624 UA
107 4357 20064.979012172935 UA
108 4360 47840.51245168512 UA
109 4361 55580.924388032574 UA
110 4362 165753.82588729708 UA
111 4363 46390.2448142152 UA
112 4365 96265.47592938849 UA
113 4366 272003.25510057947 UA
114 4367 80878.50229245829 UA
115 4370 330072.35444044066 UA
116 4371 7707066.181975477 UA
117 4373 2019766.7891575783 UA
118 4374 985354.331818515 UA
119 4377 230805.08833664874 UA
120 4382 125670.67125287943 UA
121 4383 46914.065511740075 UA
122 4384 48020.804310510954 UA
123 4385 55612.34707641123 UA
124 4387 74558.3475791577 UA
125 4388 245243.33449409154 UA
126 4389 95696.56767732685 UA
127 4391 251085.7523045193 UA
128 4401 66375.82996856027 UA
129 4403 111954.41038437477 UA
130 4405 46911.68560148837 UA
131 4408 150782.51691456966 UA
132 4409 112776.7399582134 UA
133 4410 153076.56860965435 UA
134 4412 192629.31238456024 UA
135 4413 181295.3120834606 UA
136 4414 995694.9413199169 UA
137 4416 157640.7868989174 UA
138 4418 77580.20674809469 UA
139 4420 122320.99275223716 UA
140 4424 184891.10924920067 UA
141 4425 84486.75974340564 UA
142 4431 50485.84380961137 UA
143 4435 231040.45446464577 UA
144 4436 81222.18707585508 UA
145 4438 114819.76472988473 UA
146 4439 76839.1052178896 UA
147 4440 135337.0313562152 UA
148 4441 49159.485269198034 UA
149 7031 42001.73757065917 UA
150 7059 159790.48382874 UA
151 7063 39599.10564971086 UA

2
doc/configtables/atlite.csv
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@ -3,7 +3,7 @@ default_cutout,--,str,"Defines a default cutout."
nprocesses,--,int,"Number of parallel processes in cutout preparation" nprocesses,--,int,"Number of parallel processes in cutout preparation"
show_progress,bool,true/false,"Whether progressbar for atlite conversion processes should be shown. False saves time." show_progress,bool,true/false,"Whether progressbar for atlite conversion processes should be shown. False saves time."
cutouts,,, cutouts,,,
-- {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." -- {name},--,"Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-sarah3-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>`_)" -- -- 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." -- -- 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." -- -- 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."

1 Unit Values Description
3 nprocesses -- int Number of parallel processes in cutout preparation
4 show_progress bool true/false Whether progressbar for atlite conversion processes should be shown. False saves time.
5 cutouts
6 -- {name} -- Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-era5``). Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-sarah3-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.
7 -- -- 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>`_)
8 -- -- 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.
9 -- -- 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.

2
doc/configtables/hydro.csv
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@ -1,5 +1,5 @@
,Unit,Values,Description ,Unit,Values,Description
cutout,--,Must be 'europe-2013-era5',Specifies the directory where the relevant weather data ist stored. cutout,--,Must be 'europe-2013-sarah3-era5',Specifies the directory where the relevant weather data ist stored.
carriers,--,"Any subset of {'ror', 'PHS', 'hydro'}","Specifies the types of hydro power plants to build per-unit availability time series for. 'ror' stands for run-of-river plants, 'PHS' represents pumped-hydro storage, and 'hydro' stands for hydroelectric dams." carriers,--,"Any subset of {'ror', 'PHS', 'hydro'}","Specifies the types of hydro power plants to build per-unit availability time series for. 'ror' stands for run-of-river plants, 'PHS' represents pumped-hydro storage, and 'hydro' stands for hydroelectric dams."
PHS_max_hours,h,float,Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_. PHS_max_hours,h,float,Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
hydro_max_hours,h,"Any of {float, 'energy_capacity_totals_by_country', 'estimate_by_large_installations'}",Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom`` or heuristically determined. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_. hydro_max_hours,h,"Any of {float, 'energy_capacity_totals_by_country', 'estimate_by_large_installations'}",Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom`` or heuristically determined. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.

1 Unit Values Description
2 cutout -- Must be 'europe-2013-era5' Must be 'europe-2013-sarah3-era5' Specifies the directory where the relevant weather data ist stored.
3 carriers -- Any subset of {'ror', 'PHS', 'hydro'} Specifies the types of hydro power plants to build per-unit availability time series for. 'ror' stands for run-of-river plants, 'PHS' represents pumped-hydro storage, and 'hydro' stands for hydroelectric dams.
4 PHS_max_hours h float Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
5 hydro_max_hours h Any of {float, 'energy_capacity_totals_by_country', 'estimate_by_large_installations'} Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity ``p_nom`` or heuristically determined. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.

2
doc/configtables/lines.csv
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@ -8,7 +8,7 @@ under_construction,--,"One of {'zero': set capacity to zero, 'remove': remove co
reconnect_crimea,--,"true or false","Whether to reconnect Crimea to the Ukrainian grid" reconnect_crimea,--,"true or false","Whether to reconnect Crimea to the Ukrainian grid"
dynamic_line_rating,,, dynamic_line_rating,,,
-- activate,bool,"true or false","Whether to take dynamic line rating into account" -- activate,bool,"true or false","Whether to take dynamic line rating into account"
-- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored." -- cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-era5') or reference an existing folder in the directory ``cutouts``. Source module must be ERA5.","Specifies the directory where the relevant weather data ist stored."
-- correction_factor,--,"float","Factor to compensate for overestimation of wind speeds in hourly averaged wind data" -- correction_factor,--,"float","Factor to compensate for overestimation of wind speeds in hourly averaged wind data"
-- max_voltage_difference,deg,"float","Maximum voltage angle difference in degrees or 'false' to disable" -- max_voltage_difference,deg,"float","Maximum voltage angle difference in degrees or 'false' to disable"
-- max_line_rating,--,"float","Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable" -- max_line_rating,--,"float","Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable"

1 Unit Values Description
8 reconnect_crimea -- true or false Whether to reconnect Crimea to the Ukrainian grid
9 dynamic_line_rating
10 -- activate bool true or false Whether to take dynamic line rating into account
11 -- 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. Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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.
12 -- correction_factor -- float Factor to compensate for overestimation of wind speeds in hourly averaged wind data
13 -- max_voltage_difference deg float Maximum voltage angle difference in degrees or 'false' to disable
14 -- max_line_rating -- float Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or 'false' to disable

2
doc/configtables/offwind-ac.csv
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@ -1,5 +1,5 @@
,Unit,Values,Description ,Unit,Values,Description
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." cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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,,, resource,,,
-- method,--,"Must be 'wind'","A superordinate technology type." -- 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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."

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. Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available. Specifies the turbine type and its characteristic power curve.

2
doc/configtables/offwind-dc.csv
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@ -1,5 +1,5 @@
,Unit,Values,Description ,Unit,Values,Description
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." cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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,,, resource,,,
-- method,--,"Must be 'wind'","A superordinate technology type." -- 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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."

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. Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available. Specifies the turbine type and its characteristic power curve.

2
doc/configtables/onwind.csv
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@ -1,5 +1,5 @@
,Unit,Values,Description ,Unit,Values,Description
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." cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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,,, resource,,,
-- method,--,"Must be 'wind'","A superordinate technology type." -- 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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the turbine type and its characteristic power curve."

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. Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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>`_. Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available. Specifies the turbine type and its characteristic power curve.

6
doc/configtables/sector.csv
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@ -4,8 +4,9 @@ heating,--,"{true, false}",Flag to include heating sector.
biomass,--,"{true, false}",Flag to include biomass sector. biomass,--,"{true, false}",Flag to include biomass sector.
industry,--,"{true, false}",Flag to include industry sector. industry,--,"{true, false}",Flag to include industry sector.
agriculture,--,"{true, false}",Flag to include agriculture sector. agriculture,--,"{true, false}",Flag to include agriculture sector.
fossil_fuels,--,"{true, false}","Flag to include imports of fossil fuels ( [""coal"", ""gas"", ""oil"", ""lignite""])"
district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_ district_heating,--,,`prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_
-- potential,--,float,maximum fraction of urban demand which can be supplied by district heating -- potential,--,float,maximum fraction of urban demand which can be supplied by district heating. Ignored where below current fraction.
-- progress,--,Dictionary with planning horizons as keys., Increase of today's district heating demand to potential maximum district heating share. Progress = 0 means today's district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating -- progress,--,Dictionary with planning horizons as keys., Increase of today's district heating demand to potential maximum district heating share. Progress = 0 means today's district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating
-- district_heating_loss,--,float,Share increase in district heat demand in urban central due to heat losses -- district_heating_loss,--,float,Share increase in district heat demand in urban central due to heat losses
cluster_heat_buses,--,"{true, false}",Cluster residential and service heat buses in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_ to one to save memory. cluster_heat_buses,--,"{true, false}",Cluster residential and service heat buses in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_ to one to save memory.
@ -71,7 +72,7 @@ boilers,--,"{true, false}",Add option for transforming gas into heat using gas b
resistive_heaters,--,"{true, false}",Add option for transforming electricity into heat using resistive heaters (independently from gas boilers) resistive_heaters,--,"{true, false}",Add option for transforming electricity into heat using resistive heaters (independently from gas boilers)
oil_boilers,--,"{true, false}",Add option for transforming oil into heat using boilers oil_boilers,--,"{true, false}",Add option for transforming oil into heat using boilers
biomass_boiler,--,"{true, false}",Add option for transforming biomass into heat using boilers biomass_boiler,--,"{true, false}",Add option for transforming biomass into heat using boilers
overdimension_individual_heating,--,"float",Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1. overdimension_individual_heating,--,float,Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1.
chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP) chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP)
micro_chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP) for decentral areas. micro_chp,--,"{true, false}",Add option for using Combined Heat and Power (CHP) for decentral areas.
solar_thermal,--,"{true, false}",Add option for using solar thermal to generate heat. solar_thermal,--,"{true, false}",Add option for using solar thermal to generate heat.
@ -137,6 +138,7 @@ biomass_transport,--,"{true, false}",Add option for transporting solid biomass b
biogas_upgrading_cc,--,"{true, false}",Add option to capture CO2 from biomass upgrading biogas_upgrading_cc,--,"{true, false}",Add option to capture CO2 from biomass upgrading
conventional_generation,,,Add a more detailed description of conventional carriers. Any power generation requires the consumption of fuel from nodes representing that fuel. conventional_generation,,,Add a more detailed description of conventional carriers. Any power generation requires the consumption of fuel from nodes representing that fuel.
biomass_to_liquid,--,"{true, false}",Add option for transforming solid biomass into liquid fuel with the same properties as oil biomass_to_liquid,--,"{true, false}",Add option for transforming solid biomass into liquid fuel with the same properties as oil
electrobiofuels,--,"{true, false}","Add option for transforming solid biomass and hydrogen into liquid fuel to make more use of biogenic carbon, as a combination of BtL and Fischer-Tropsch"
biosng,--,"{true, false}",Add option for transforming solid biomass into synthesis gas with the same properties as natural gas biosng,--,"{true, false}",Add option for transforming solid biomass into synthesis gas with the same properties as natural gas
limit_max_growth,,, limit_max_growth,,,
-- enable,--,"{true, false}",Add option to limit the maximum growth of a carrier -- enable,--,"{true, false}",Add option to limit the maximum growth of a carrier

1 Unit Values Description
4 biomass -- {true, false} Flag to include biomass sector.
5 industry -- {true, false} Flag to include industry sector.
6 agriculture -- {true, false} Flag to include agriculture sector.
7 fossil_fuels -- {true, false} Flag to include imports of fossil fuels ( ["coal", "gas", "oil", "lignite"])
8 district_heating -- `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_
9 -- potential -- float maximum fraction of urban demand which can be supplied by district heating maximum fraction of urban demand which can be supplied by district heating. Ignored where below current fraction.
10 -- progress -- Dictionary with planning horizons as keys. Increase of today's district heating demand to potential maximum district heating share. Progress = 0 means today's district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating
11 -- district_heating_loss -- float Share increase in district heat demand in urban central due to heat losses
12 cluster_heat_buses -- {true, false} Cluster residential and service heat buses in `prepare_sector_network.py <https://github.com/PyPSA/pypsa-eur-sec/blob/master/scripts/prepare_sector_network.py>`_ to one to save memory.
72 resistive_heaters -- {true, false} Add option for transforming electricity into heat using resistive heaters (independently from gas boilers)
73 oil_boilers -- {true, false} Add option for transforming oil into heat using boilers
74 biomass_boiler -- {true, false} Add option for transforming biomass into heat using boilers
75 overdimension_individual_heating -- float Add option for overdimensioning individual heating systems by a certain factor. This allows them to cover heat demand peaks e.g. 10% higher than those in the data with a setting of 1.1.
76 chp -- {true, false} Add option for using Combined Heat and Power (CHP)
77 micro_chp -- {true, false} Add option for using Combined Heat and Power (CHP) for decentral areas.
78 solar_thermal -- {true, false} Add option for using solar thermal to generate heat.
138 biogas_upgrading_cc -- {true, false} Add option to capture CO2 from biomass upgrading
139 conventional_generation Add a more detailed description of conventional carriers. Any power generation requires the consumption of fuel from nodes representing that fuel.
140 biomass_to_liquid -- {true, false} Add option for transforming solid biomass into liquid fuel with the same properties as oil
141 electrobiofuels -- {true, false} Add option for transforming solid biomass and hydrogen into liquid fuel to make more use of biogenic carbon, as a combination of BtL and Fischer-Tropsch
142 biosng -- {true, false} Add option for transforming solid biomass into synthesis gas with the same properties as natural gas
143 limit_max_growth
144 -- enable -- {true, false} Add option to limit the maximum growth of a carrier

2
doc/configtables/solar.csv
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@ -1,5 +1,5 @@
,Unit,Values,Description ,Unit,Values,Description
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." cutout,--,"Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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,,, resource,,,
-- method,--,"Must be 'pv'","A superordinate technology type." -- 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>`_ . Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","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>`_ . Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available.","Specifies the solar panel technology and its characteristic attributes."

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. Should be a folder listed in the configuration ``atlite: cutouts:`` (e.g. 'europe-2013-sarah3-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>`_ . Can be a string or a dictionary with years as keys which denote the year another turbine model becomes available. Specifies the solar panel technology and its characteristic attributes.

1
doc/configtables/solving.csv
View File

@ -2,6 +2,7 @@
options,,, options,,,
-- clip_p_max_pu,p.u.,float,To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero. -- clip_p_max_pu,p.u.,float,To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.
-- load_shedding,bool/float,"{'true','false', float}","Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh." -- load_shedding,bool/float,"{'true','false', float}","Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh."
-- curtailment_mode,bool/float,"{'true','false'}","Fixes the dispatch profiles of generators with time-varying p_max_pu by setting ``p_min_pu = p_max_pu`` and adds an auxiliary curtailment generator (with negative sign to absorb excess power) at every AC bus. This can speed up the solving process as the curtailment decision is aggregated into a single generator per region. Defaults to ``false``."
-- noisy_costs,bool,"{'true','false'}","Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`." -- noisy_costs,bool,"{'true','false'}","Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`."
-- skip_iterations,bool,"{'true','false'}","Skip iterating, do not update impedances of branches. Defaults to true." -- skip_iterations,bool,"{'true','false'}","Skip iterating, do not update impedances of branches. Defaults to true."
-- rolling_horizon,bool,"{'true','false'}","Switch for rule :mod:`solve_operations_network` whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size `horizon` which are solved consecutively. This setting has currently no effect on sector-coupled networks." -- rolling_horizon,bool,"{'true','false'}","Switch for rule :mod:`solve_operations_network` whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size `horizon` which are solved consecutively. This setting has currently no effect on sector-coupled networks."

1 Unit Values Description
2 options
3 -- clip_p_max_pu p.u. float To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.
4 -- load_shedding bool/float {'true','false', float} Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh.
5 -- curtailment_mode bool/float {'true','false'} Fixes the dispatch profiles of generators with time-varying p_max_pu by setting ``p_min_pu = p_max_pu`` and adds an auxiliary curtailment generator (with negative sign to absorb excess power) at every AC bus. This can speed up the solving process as the curtailment decision is aggregated into a single generator per region. Defaults to ``false``.
6 -- noisy_costs bool {'true','false'} Add random noise to marginal cost of generators by :math:`\mathcal{U}(0.009,0,011)` and capital cost of lines and links by :math:`\mathcal{U}(0.09,0,11)`.
7 -- skip_iterations bool {'true','false'} Skip iterating, do not update impedances of branches. Defaults to true.
8 -- rolling_horizon bool {'true','false'} Switch for rule :mod:`solve_operations_network` whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size `horizon` which are solved consecutively. This setting has currently no effect on sector-coupled networks.

2
doc/foresight.rst
View File

@ -242,7 +242,7 @@ Rule overview
file file
<https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__ <https://pypsa-eur.readthedocs.io/en/latest/preparation/build_powerplants.html?highlight=powerplants>`__
generated by pypsa-eur which, in turn, is based on the `powerplantmatching generated by pypsa-eur which, in turn, is based on the `powerplantmatching
<https://github.com/FRESNA/powerplantmatching>`__ database. <https://github.com/PyPSA/powerplantmatching>`__ database.
Existing wind and solar capacities are retrieved from `IRENA annual statistics Existing wind and solar capacities are retrieved from `IRENA annual statistics
<https://www.irena.org/Statistics/Download-Data>`__ and distributed among the <https://www.irena.org/Statistics/Download-Data>`__ and distributed among the

3
doc/installation.rst
View File

@ -81,7 +81,8 @@ Nevertheless, you can still use open-source solvers for smaller problems.
.. note:: .. note::
The rules :mod:`cluster_network` and :mod:`simplify_network` solve a mixed-integer quadratic optimisation problem for clustering. The rules :mod:`cluster_network` and :mod:`simplify_network` solve a mixed-integer quadratic optimisation problem for clustering.
The open-source solvers HiGHS, Cbc and GlPK cannot handle this. A fallback to SCIP is implemented in this case, which is included in the standard environment specifications. The open-source solvers HiGHS, Cbc and GlPK cannot handle this. A fallback to SCIP is implemented in this case, which is included in the standard environment specifications.
For an open-source solver setup install in your ``conda`` environment on OSX/Linux. To install the default solver Gurobi, run For an open-source solver setup install for example HiGHS **and** SCIP in your ``conda`` environment on OSX/Linux.
To install the default solver Gurobi, run
.. code:: bash .. code:: bash

4
doc/preparation.rst
View File

@ -16,7 +16,7 @@ using the ``retrieve*`` rules (:ref:`data`).
Having downloaded the necessary data, Having downloaded the necessary data,
- :mod:`build_shapes` generates GeoJSON files with shapes of the countries, exclusive economic zones and `NUTS3 <https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics>`__ areas. - :mod:`build_shapes` generates GeoJSON files with shapes of the countries, exclusive economic zones and `NUTS3 <https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics>`__ areas.
- :mod:`build_cutout` prepares smaller weather data portions from `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`__ for cutout ``europe-2013-era5`` and SARAH for cutout ``europe-2013-sarah``. - :mod:`build_cutout` prepares smaller weather data portions from `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`__ for cutout ``europe-2013-sarah3-era5`` and SARAH for cutout ``europe-2013-sarah``.
With these and the externally extracted ENTSO-E online map topology With these and the externally extracted ENTSO-E online map topology
(``data/entsoegridkit``), it can build a base PyPSA network with the following rules: (``data/entsoegridkit``), it can build a base PyPSA network with the following rules:
@ -25,7 +25,7 @@ With these and the externally extracted ENTSO-E online map topology
Then the process continues by calculating conventional power plant capacities, potentials, and per-unit availability time series for variable renewable energy carriers and hydro power plants with the following rules: Then the process continues by calculating conventional power plant capacities, potentials, and per-unit availability time series for variable renewable energy carriers and hydro power plants with the following rules:
- :mod:`build_powerplants` for today's thermal power plant capacities using `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`__ allocating these to the closest substation for each powerplant, - :mod:`build_powerplants` for today's thermal power plant capacities using `powerplantmatching <https://github.com/PyPSA/powerplantmatching>`__ allocating these to the closest substation for each powerplant,
- :mod:`build_ship_raster` for building shipping traffic density, - :mod:`build_ship_raster` for building shipping traffic density,
- :mod:`build_renewable_profiles` for the hourly capacity factors and installation potentials constrained by land-use in each substation's Voronoi cell for PV, onshore and offshore wind, and - :mod:`build_renewable_profiles` for the hourly capacity factors and installation potentials constrained by land-use in each substation's Voronoi cell for PV, onshore and offshore wind, and
- :mod:`build_hydro_profile` for the hourly per-unit hydro power availability time series. - :mod:`build_hydro_profile` for the hourly per-unit hydro power availability time series.

39
doc/release_notes.rst
View File

@ -10,6 +10,18 @@ Release Notes
Upcoming Release Upcoming Release
================ ================
* Add option to produce electrobiofuels from solid biomass and hydrogen, as a combination of BtL and Fischer-Tropsch to make more use of the biogenic carbon
* Add flag ``sector: fossil_fuels`` in config to remove the option of importing fossil fuels
* Renamed the carrier of batteries in BEVs from `battery storage` to `EV battery` and the corresponding bus carrier from `Li ion` to `EV battery`. This is to avoid confusion with stationary battery storage.
* Changed default assumptions about waste heat usage from PtX and fuel cells in district heating.
The default value for the link efficiency scaling factor was changed from 100% to 25%.
It can be set to other values in the configuration ``sector: use_TECHNOLOGY_waste_heat``.
* In simplifying polygons in :mod:`build_shapes` default to no tolerance.
* Set non-zero capital_cost for methanol stores to avoid unrealistic storage sizes * Set non-zero capital_cost for methanol stores to avoid unrealistic storage sizes
* Set p_nom = p_nom_min for generators with baseyear == grouping_year in add_existing_baseyear. This has no effect on the optimization but helps n.statistics to correctly report already installed capacities. * Set p_nom = p_nom_min for generators with baseyear == grouping_year in add_existing_baseyear. This has no effect on the optimization but helps n.statistics to correctly report already installed capacities.
@ -25,6 +37,33 @@ Upcoming Release
* Bugfix: Correctly read in threshold capacity below which to remove components from previous planning horizons in :mod:`add_brownfield`. * Bugfix: Correctly read in threshold capacity below which to remove components from previous planning horizons in :mod:`add_brownfield`.
* For countries not contained in the NUTS3-specific datasets (i.e. MD and UA), the mapping of GDP per capita and population per bus region used to spatially distribute electricity demand is now endogenised in a new rule :mod:`build_gdp_ppp_non_nuts3`. https://github.com/PyPSA/pypsa-eur/pull/1146
* The databundle has been updated to release v0.3.0, which includes raw GDP and population data for countries outside the NUTS system (UA, MD). https://github.com/PyPSA/pypsa-eur/pull/1146
* Updated filtering in :mod:`determine_availability_matrix_MD_UA.py` to improve speed. https://github.com/PyPSA/pypsa-eur/pull/1146
* Bugfix: Impose minimum value of zero for district heating progress between current and future market share in :mod:`build_district_heat_share`.
* The ``{scope}`` wildcard was removed, since its outputs were not used.
* Enable parallelism in :mod:`determine_availability_matrix_MD_UA.py` and remove plots. This requires the use of temporary files.
* Updated pre-built `weather data cutouts
<https://zenodo.org/records/12791128>`__. These are now merged cutouts with
solar irradiation from the new SARAH-3 dataset while taking all other
variables from ERA5. Cutouts are now available for multiple years (2010, 2013,
2019, and 2023).
* Added option ``solving: curtailment_mode``` which fixes the dispatch profiles
of generators with time-varying p_max_pu by setting ``p_min_pu = p_max_pu``
and adds an auxiliary curtailment generator with negative sign (to absorb
excess power) at every AC bus. This can speed up the solving process as the
curtailment decision is aggregated into a single generator per region.
* In :mod:`base_network`, replace own voronoi polygon calculation function with
Geopandas `gdf.voronoi_polygons` method.
PyPSA-Eur 0.11.0 (25th May 2024) PyPSA-Eur 0.11.0 (25th May 2024)
===================================== =====================================

7
doc/wildcards.rst
View File

@ -142,13 +142,6 @@ The ``{sector_opts}`` wildcard is only used for sector-coupling studies.
:widths: 10,20,10,10 :widths: 10,20,10,10
:file: configtables/sector-opts.csv :file: configtables/sector-opts.csv
.. _scope:
The ``{scope}`` wildcard
========================
Takes values ``residential``, ``urban``, ``total``.
.. _planning_horizons: .. _planning_horizons:
The ``{planning_horizons}`` wildcard The ``{planning_horizons}`` wildcard

2
envs/environment.yaml
View File

@ -28,7 +28,7 @@ dependencies:
- powerplantmatching>=0.5.15 - powerplantmatching>=0.5.15
- numpy - numpy
- pandas>=2.1 - pandas>=2.1
- geopandas>=0.11.0 - geopandas>=1
- xarray>=2023.11.0 - xarray>=2023.11.0
- rioxarray - rioxarray
- netcdf4 - netcdf4

36
rules/build_electricity.smk
View File

@ -58,7 +58,7 @@ rule build_powerplants:
logs("build_powerplants.log"), logs("build_powerplants.log"),
threads: 1 threads: 1
resources: resources:
mem_mb=5000, mem_mb=7000,
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -202,7 +202,6 @@ rule determine_availability_matrix_MD_UA:
+ ".nc", + ".nc",
output: output:
availability_matrix=resources("availability_matrix_MD-UA_{technology}.nc"), availability_matrix=resources("availability_matrix_MD-UA_{technology}.nc"),
availability_map=resources("availability_matrix_MD-UA_{technology}.png"),
log: log:
logs("determine_availability_matrix_MD_UA_{technology}.log"), logs("determine_availability_matrix_MD_UA_{technology}.log"),
threads: config["atlite"].get("nprocesses", 4) threads: config["atlite"].get("nprocesses", 4)
@ -375,6 +374,37 @@ def input_conventional(w):
} }
# Optional input when having Ukraine (UA) or Moldova (MD) in the countries list
def input_gdp_pop_non_nuts3(w):
countries = set(config_provider("countries")(w))
if {"UA", "MD"}.intersection(countries):
return {"gdp_pop_non_nuts3": resources("gdp_pop_non_nuts3.geojson")}
return {}
rule build_gdp_pop_non_nuts3:
params:
countries=config_provider("countries"),
input:
base_network=resources("networks/base.nc"),
regions=resources("regions_onshore.geojson"),
gdp_non_nuts3="data/bundle/GDP_per_capita_PPP_1990_2015_v2.nc",
pop_non_nuts3="data/bundle/ppp_2013_1km_Aggregated.tif",
output:
resources("gdp_pop_non_nuts3.geojson"),
log:
logs("build_gdp_pop_non_nuts3.log"),
benchmark:
benchmarks("build_gdp_pop_non_nuts3")
threads: 1
resources:
mem_mb=8000,
conda:
"../envs/environment.yaml"
script:
"../scripts/build_gdp_pop_non_nuts3.py"
rule add_electricity: rule add_electricity:
params: params:
length_factor=config_provider("lines", "length_factor"), length_factor=config_provider("lines", "length_factor"),
@ -390,6 +420,7 @@ rule add_electricity:
input: input:
unpack(input_profile_tech), unpack(input_profile_tech),
unpack(input_conventional), unpack(input_conventional),
unpack(input_gdp_pop_non_nuts3),
base_network=resources("networks/base.nc"), base_network=resources("networks/base.nc"),
line_rating=lambda w: ( line_rating=lambda w: (
resources("networks/line_rating.nc") resources("networks/line_rating.nc")
@ -411,7 +442,6 @@ rule add_electricity:
), ),
load=resources("electricity_demand.csv"), load=resources("electricity_demand.csv"),
nuts3_shapes=resources("nuts3_shapes.geojson"), nuts3_shapes=resources("nuts3_shapes.geojson"),
ua_md_gdp="data/GDP_PPP_30arcsec_v3_mapped_default.csv",
output: output:
resources("networks/elec.nc"), resources("networks/elec.nc"),
log: log:

60
rules/build_sector.smk
View File

@ -151,18 +151,18 @@ rule build_daily_heat_demand:
snapshots=config_provider("snapshots"), snapshots=config_provider("snapshots"),
drop_leap_day=config_provider("enable", "drop_leap_day"), drop_leap_day=config_provider("enable", "drop_leap_day"),
input: input:
pop_layout=resources("pop_layout_{scope}.nc"), pop_layout=resources("pop_layout_total.nc"),
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"), regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
cutout=heat_demand_cutout, cutout=heat_demand_cutout,
output: output:
heat_demand=resources("daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"), heat_demand=resources("daily_heat_demand_total_elec_s{simpl}_{clusters}.nc"),
resources: resources:
mem_mb=20000, mem_mb=20000,
threads: 8 threads: 8
log: log:
logs("build_daily_heat_demand_{scope}_{simpl}_{clusters}.loc"), logs("build_daily_heat_demand_total_{simpl}_{clusters}.loc"),
benchmark: benchmark:
benchmarks("build_daily_heat_demand/{scope}_s{simpl}_{clusters}") benchmarks("build_daily_heat_demand/total_s{simpl}_{clusters}")
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -175,16 +175,16 @@ rule build_hourly_heat_demand:
drop_leap_day=config_provider("enable", "drop_leap_day"), drop_leap_day=config_provider("enable", "drop_leap_day"),
input: input:
heat_profile="data/heat_load_profile_BDEW.csv", heat_profile="data/heat_load_profile_BDEW.csv",
heat_demand=resources("daily_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"), heat_demand=resources("daily_heat_demand_total_elec_s{simpl}_{clusters}.nc"),
output: output:
heat_demand=resources("hourly_heat_demand_{scope}_elec_s{simpl}_{clusters}.nc"), heat_demand=resources("hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc"),
resources: resources:
mem_mb=2000, mem_mb=2000,
threads: 8 threads: 8
log: log:
logs("build_hourly_heat_demand_{scope}_{simpl}_{clusters}.loc"), logs("build_hourly_heat_demand_total_{simpl}_{clusters}.loc"),
benchmark: benchmark:
benchmarks("build_hourly_heat_demand/{scope}_s{simpl}_{clusters}") benchmarks("build_hourly_heat_demand/total_s{simpl}_{clusters}")
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -196,19 +196,19 @@ rule build_temperature_profiles:
snapshots=config_provider("snapshots"), snapshots=config_provider("snapshots"),
drop_leap_day=config_provider("enable", "drop_leap_day"), drop_leap_day=config_provider("enable", "drop_leap_day"),
input: input:
pop_layout=resources("pop_layout_{scope}.nc"), pop_layout=resources("pop_layout_total.nc"),
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"), regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
cutout=heat_demand_cutout, cutout=heat_demand_cutout,
output: output:
temp_soil=resources("temp_soil_{scope}_elec_s{simpl}_{clusters}.nc"), temp_soil=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
temp_air=resources("temp_air_{scope}_elec_s{simpl}_{clusters}.nc"), temp_air=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
resources: resources:
mem_mb=20000, mem_mb=20000,
threads: 8 threads: 8
log: log:
logs("build_temperature_profiles_{scope}_{simpl}_{clusters}.log"), logs("build_temperature_profiles_total_{simpl}_{clusters}.log"),
benchmark: benchmark:
benchmarks("build_temperature_profiles/{scope}_s{simpl}_{clusters}") benchmarks("build_temperature_profiles/total_s{simpl}_{clusters}")
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -220,18 +220,10 @@ rule build_cop_profiles:
heat_pump_sink_T=config_provider("sector", "heat_pump_sink_T"), heat_pump_sink_T=config_provider("sector", "heat_pump_sink_T"),
input: input:
temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"), temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
temp_soil_rural=resources("temp_soil_rural_elec_s{simpl}_{clusters}.nc"),
temp_soil_urban=resources("temp_soil_urban_elec_s{simpl}_{clusters}.nc"),
temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"), temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
temp_air_rural=resources("temp_air_rural_elec_s{simpl}_{clusters}.nc"),
temp_air_urban=resources("temp_air_urban_elec_s{simpl}_{clusters}.nc"),
output: output:
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"), cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
cop_soil_rural=resources("cop_soil_rural_elec_s{simpl}_{clusters}.nc"),
cop_soil_urban=resources("cop_soil_urban_elec_s{simpl}_{clusters}.nc"),
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"), cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
cop_air_rural=resources("cop_air_rural_elec_s{simpl}_{clusters}.nc"),
cop_air_urban=resources("cop_air_urban_elec_s{simpl}_{clusters}.nc"),
resources: resources:
mem_mb=20000, mem_mb=20000,
log: log:
@ -263,18 +255,18 @@ rule build_solar_thermal_profiles:
drop_leap_day=config_provider("enable", "drop_leap_day"), drop_leap_day=config_provider("enable", "drop_leap_day"),
solar_thermal=config_provider("solar_thermal"), solar_thermal=config_provider("solar_thermal"),
input: input:
pop_layout=resources("pop_layout_{scope}.nc"), pop_layout=resources("pop_layout_total.nc"),
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"), regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
cutout=solar_thermal_cutout, cutout=solar_thermal_cutout,
output: output:
solar_thermal=resources("solar_thermal_{scope}_elec_s{simpl}_{clusters}.nc"), solar_thermal=resources("solar_thermal_total_elec_s{simpl}_{clusters}.nc"),
resources: resources:
mem_mb=20000, mem_mb=20000,
threads: 16 threads: 16
log: log:
logs("build_solar_thermal_profiles_{scope}_s{simpl}_{clusters}.log"), logs("build_solar_thermal_profiles_total_s{simpl}_{clusters}.log"),
benchmark: benchmark:
benchmarks("build_solar_thermal_profiles/{scope}_s{simpl}_{clusters}") benchmarks("build_solar_thermal_profiles/total_s{simpl}_{clusters}")
conda: conda:
"../envs/environment.yaml" "../envs/environment.yaml"
script: script:
@ -1024,32 +1016,14 @@ rule prepare_sector_network:
"district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv" "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
), ),
temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"), temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
temp_soil_rural=resources("temp_soil_rural_elec_s{simpl}_{clusters}.nc"),
temp_soil_urban=resources("temp_soil_urban_elec_s{simpl}_{clusters}.nc"),
temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"), temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
temp_air_rural=resources("temp_air_rural_elec_s{simpl}_{clusters}.nc"),
temp_air_urban=resources("temp_air_urban_elec_s{simpl}_{clusters}.nc"),
cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"), cop_soil_total=resources("cop_soil_total_elec_s{simpl}_{clusters}.nc"),
cop_soil_rural=resources("cop_soil_rural_elec_s{simpl}_{clusters}.nc"),
cop_soil_urban=resources("cop_soil_urban_elec_s{simpl}_{clusters}.nc"),
cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"), cop_air_total=resources("cop_air_total_elec_s{simpl}_{clusters}.nc"),
cop_air_rural=resources("cop_air_rural_elec_s{simpl}_{clusters}.nc"),
cop_air_urban=resources("cop_air_urban_elec_s{simpl}_{clusters}.nc"),
solar_thermal_total=lambda w: ( solar_thermal_total=lambda w: (
resources("solar_thermal_total_elec_s{simpl}_{clusters}.nc") resources("solar_thermal_total_elec_s{simpl}_{clusters}.nc")
if config_provider("sector", "solar_thermal")(w) if config_provider("sector", "solar_thermal")(w)
else [] else []
), ),
solar_thermal_urban=lambda w: (
resources("solar_thermal_urban_elec_s{simpl}_{clusters}.nc")
if config_provider("sector", "solar_thermal")(w)
else []
),
solar_thermal_rural=lambda w: (
resources("solar_thermal_rural_elec_s{simpl}_{clusters}.nc")
if config_provider("sector", "solar_thermal")(w)
else []
),
egs_potentials=lambda w: ( egs_potentials=lambda w: (
resources("egs_potentials_s{simpl}_{clusters}.csv") resources("egs_potentials_s{simpl}_{clusters}.csv")
if config_provider("sector", "enhanced_geothermal", "enable")(w) if config_provider("sector", "enhanced_geothermal", "enable")(w)

5
rules/common.smk
View File

@ -55,7 +55,7 @@ def dynamic_getter(wildcards, keys, default):
scenario_name = wildcards.run scenario_name = wildcards.run
if scenario_name not in scenarios: if scenario_name not in scenarios:
raise ValueError( raise ValueError(
f"Scenario {scenario_name} not found in file {config['run']['scenario']['file']}." f"Scenario {scenario_name} not found in file {config['run']['scenarios']['file']}."
) )
config_with_scenario = scenario_config(scenario_name) config_with_scenario = scenario_config(scenario_name)
config_with_wildcards = update_config_from_wildcards( config_with_wildcards = update_config_from_wildcards(
@ -81,7 +81,8 @@ def config_provider(*keys, default=None):
def solver_threads(w): def solver_threads(w):
solver_options = config_provider("solving", "solver_options")(w) solver_options = config_provider("solving", "solver_options")(w)
option_set = config_provider("solving", "solver", "options")(w) option_set = config_provider("solving", "solver", "options")(w)
threads = solver_options[option_set].get("threads", 4) solver_option_set = solver_options[option_set]
threads = solver_option_set.get("threads") or solver_option_set.get("Threads") or 4
return threads return threads

6
rules/retrieve.smk
View File

@ -29,6 +29,8 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_databundle",
"h2_salt_caverns_GWh_per_sqkm.geojson", "h2_salt_caverns_GWh_per_sqkm.geojson",
"natura/natura.tiff", "natura/natura.tiff",
"gebco/GEBCO_2014_2D.nc", "gebco/GEBCO_2014_2D.nc",
"GDP_per_capita_PPP_1990_2015_v2.nc",
"ppp_2013_1km_Aggregated.tif",
] ]
rule retrieve_databundle: rule retrieve_databundle:
@ -69,7 +71,7 @@ if config["enable"]["retrieve"] and config["enable"].get("retrieve_cutout", True
rule retrieve_cutout: rule retrieve_cutout:
input: input:
storage( storage(
"https://zenodo.org/records/6382570/files/{cutout}.nc", "https://zenodo.org/records/12791128/files/{cutout}.nc",
), ),
output: output:
protected("cutouts/" + CDIR + "{cutout}.nc"), protected("cutouts/" + CDIR + "{cutout}.nc"),
@ -163,7 +165,7 @@ if config["enable"]["retrieve"]:
rule retrieve_ship_raster: rule retrieve_ship_raster:
input: input:
storage( storage(
"https://zenodo.org/records/10973944/files/shipdensity_global.zip", "https://zenodo.org/records/12760663/files/shipdensity_global.zip",
keep_local=True, keep_local=True,
), ),
output: output:

8
scripts/add_brownfield.py
View File

@ -89,10 +89,6 @@ def add_brownfield(n, n_p, year):
# deal with gas network # deal with gas network
pipe_carrier = ["gas pipeline"] pipe_carrier = ["gas pipeline"]
if snakemake.params.H2_retrofit: if snakemake.params.H2_retrofit:
# drop capacities of previous year to avoid duplicating
to_drop = n.links.carrier.isin(pipe_carrier) & (n.links.build_year != year)
n.mremove("Link", n.links.loc[to_drop].index)
# subtract the already retrofitted from today's gas grid capacity # subtract the already retrofitted from today's gas grid capacity
h2_retrofitted_fixed_i = n.links[ h2_retrofitted_fixed_i = n.links[
(n.links.carrier == "H2 pipeline retrofitted") (n.links.carrier == "H2 pipeline retrofitted")
@ -115,10 +111,6 @@ def add_brownfield(n, n_p, year):
index=pipe_capacity.index index=pipe_capacity.index
).fillna(0) ).fillna(0)
n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity
else:
new_pipes = n.links.carrier.isin(pipe_carrier) & (n.links.build_year == year)
n.links.loc[new_pipes, "p_nom"] = 0.0
n.links.loc[new_pipes, "p_nom_min"] = 0.0
def disable_grid_expansion_if_limit_hit(n): def disable_grid_expansion_if_limit_hit(n):

34
scripts/add_electricity.py
View File

@ -287,26 +287,26 @@ def shapes_to_shapes(orig, dest):
transfer = sparse.lil_matrix((len(dest), len(orig)), dtype=float) transfer = sparse.lil_matrix((len(dest), len(orig)), dtype=float)
for i, j in product(range(len(dest)), range(len(orig))): for i, j in product(range(len(dest)), range(len(orig))):
if orig_prepped[j].intersects(dest[i]): if orig_prepped[j].intersects(dest.iloc[i]):
area = orig[j].intersection(dest[i]).area area = orig.iloc[j].intersection(dest.iloc[i]).area
transfer[i, j] = area / dest[i].area transfer[i, j] = area / dest.iloc[i].area
return transfer return transfer
def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.0): def attach_load(
n, regions, load, nuts3_shapes, gdp_pop_non_nuts3, countries, scaling=1.0
):
substation_lv_i = n.buses.index[n.buses["substation_lv"]] substation_lv_i = n.buses.index[n.buses["substation_lv"]]
regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i) gdf_regions = gpd.read_file(regions).set_index("name").reindex(substation_lv_i)
opsd_load = pd.read_csv(load, index_col=0, parse_dates=True).filter(items=countries) opsd_load = pd.read_csv(load, index_col=0, parse_dates=True).filter(items=countries)
ua_md_gdp = pd.read_csv(ua_md_gdp, dtype={"name": "str"}).set_index("name")
logger.info(f"Load data scaled by factor {scaling}.") logger.info(f"Load data scaled by factor {scaling}.")
opsd_load *= scaling opsd_load *= scaling
nuts3 = gpd.read_file(nuts3_shapes).set_index("index") nuts3 = gpd.read_file(nuts3_shapes).set_index("index")
def upsample(cntry, group): def upsample(cntry, group, gdp_pop_non_nuts3):
load = opsd_load[cntry] load = opsd_load[cntry]
if len(group) == 1: if len(group) == 1:
@ -325,7 +325,15 @@ def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n)) factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
if cntry in ["UA", "MD"]: if cntry in ["UA", "MD"]:
# overwrite factor because nuts3 provides no data for UA+MD # overwrite factor because nuts3 provides no data for UA+MD
factors = normed(ua_md_gdp.loc[group.index, "GDP_PPP"].squeeze()) gdp_pop_non_nuts3 = gpd.read_file(gdp_pop_non_nuts3).set_index("Bus")
gdp_pop_non_nuts3 = gdp_pop_non_nuts3.loc[
(gdp_pop_non_nuts3.country == cntry)
& (gdp_pop_non_nuts3.index.isin(substation_lv_i))
]
factors = normed(
0.6 * normed(gdp_pop_non_nuts3["gdp"])
+ 0.4 * normed(gdp_pop_non_nuts3["pop"])
)
return pd.DataFrame( return pd.DataFrame(
factors.values * load.values[:, np.newaxis], factors.values * load.values[:, np.newaxis],
index=load.index, index=load.index,
@ -334,8 +342,8 @@ def attach_load(n, regions, load, nuts3_shapes, ua_md_gdp, countries, scaling=1.
load = pd.concat( load = pd.concat(
[ [
upsample(cntry, group) upsample(cntry, group, gdp_pop_non_nuts3)
for cntry, group in regions.geometry.groupby(regions.country) for cntry, group in gdf_regions.geometry.groupby(gdf_regions.country)
], ],
axis=1, axis=1,
) )
@ -821,7 +829,7 @@ if __name__ == "__main__":
snakemake.input.regions, snakemake.input.regions,
snakemake.input.load, snakemake.input.load,
snakemake.input.nuts3_shapes, snakemake.input.nuts3_shapes,
snakemake.input.ua_md_gdp, snakemake.input.get("gdp_pop_non_nuts3"),
params.countries, params.countries,
params.scaling_factor, params.scaling_factor,
) )
@ -844,7 +852,7 @@ if __name__ == "__main__":
fuel_price = pd.read_csv( fuel_price = pd.read_csv(
snakemake.input.fuel_price, index_col=0, header=0, parse_dates=True snakemake.input.fuel_price, index_col=0, header=0, parse_dates=True
) )
fuel_price = fuel_price.reindex(n.snapshots).fillna(method="ffill") fuel_price = fuel_price.reindex(n.snapshots).ffill()
else: else:
fuel_price = None fuel_price = None

91
scripts/base_network.py
View File

@ -72,6 +72,7 @@ Creates the network topology from an ENTSO-E map extract, and create Voronoi sha
""" """
import logging import logging
import warnings
from itertools import product from itertools import product
import geopandas as gpd import geopandas as gpd
@ -85,9 +86,9 @@ import shapely.wkt
import yaml import yaml
from _helpers import REGION_COLS, configure_logging, get_snapshots, set_scenario_config from _helpers import REGION_COLS, configure_logging, get_snapshots, set_scenario_config
from packaging.version import Version, parse from packaging.version import Version, parse
from scipy import spatial
from scipy.sparse import csgraph from scipy.sparse import csgraph
from shapely.geometry import LineString, Point, Polygon from scipy.spatial import KDTree
from shapely.geometry import LineString, Point
PD_GE_2_2 = parse(pd.__version__) >= Version("2.2") PD_GE_2_2 = parse(pd.__version__) >= Version("2.2")
@ -118,7 +119,7 @@ def _find_closest_links(links, new_links, distance_upper_bound=1.5):
querycoords = np.vstack( querycoords = np.vstack(
[new_links[["x1", "y1", "x2", "y2"]], new_links[["x2", "y2", "x1", "y1"]]] [new_links[["x1", "y1", "x2", "y2"]], new_links[["x2", "y2", "x1", "y1"]]]
) )
tree = spatial.KDTree(treecoords) tree = KDTree(treecoords)
dist, ind = tree.query(querycoords, distance_upper_bound=distance_upper_bound) dist, ind = tree.query(querycoords, distance_upper_bound=distance_upper_bound)
found_b = ind < len(links) found_b = ind < len(links)
found_i = np.arange(len(new_links) * 2)[found_b] % len(new_links) found_i = np.arange(len(new_links) * 2)[found_b] % len(new_links)
@ -273,7 +274,7 @@ def _add_links_from_tyndp(buses, links, links_tyndp, europe_shape):
return buses, links return buses, links
tree_buses = buses.query("carrier=='AC'") tree_buses = buses.query("carrier=='AC'")
tree = spatial.KDTree(tree_buses[["x", "y"]]) tree = KDTree(tree_buses[["x", "y"]])
_, ind0 = tree.query(links_tyndp[["x1", "y1"]]) _, ind0 = tree.query(links_tyndp[["x1", "y1"]])
ind0_b = ind0 < len(tree_buses) ind0_b = ind0 < len(tree_buses)
links_tyndp.loc[ind0_b, "bus0"] = tree_buses.index[ind0[ind0_b]] links_tyndp.loc[ind0_b, "bus0"] = tree_buses.index[ind0[ind0_b]]
@ -671,7 +672,7 @@ def _set_links_underwater_fraction(n, offshore_shapes):
if not hasattr(n.links, "geometry"): if not hasattr(n.links, "geometry"):
n.links["underwater_fraction"] = 0.0 n.links["underwater_fraction"] = 0.0
else: else:
offshore_shape = gpd.read_file(offshore_shapes).unary_union offshore_shape = gpd.read_file(offshore_shapes).union_all()
links = gpd.GeoSeries(n.links.geometry.dropna().map(shapely.wkt.loads)) links = gpd.GeoSeries(n.links.geometry.dropna().map(shapely.wkt.loads))
n.links["underwater_fraction"] = ( n.links["underwater_fraction"] = (
links.intersection(offshore_shape).length / links.length links.intersection(offshore_shape).length / links.length
@ -788,59 +789,26 @@ def base_network(
return n return n
def voronoi_partition_pts(points, outline): def voronoi(points, outline, crs=4326):
""" """
Compute the polygons of a voronoi partition of `points` within the polygon Create Voronoi polygons from a set of points within an outline.
`outline`. Taken from
https://github.com/FRESNA/vresutils/blob/master/vresutils/graph.py.
Attributes
----------
points : Nx2 - ndarray[dtype=float]
outline : Polygon
Returns
-------
polygons : N - ndarray[dtype=Polygon|MultiPolygon]
""" """
points = np.asarray(points) pts = gpd.GeoSeries(
gpd.points_from_xy(points.x, points.y),
if len(points) == 1: index=points.index,
polygons = [outline] crs=crs,
else:
xmin, ymin = np.amin(points, axis=0)
xmax, ymax = np.amax(points, axis=0)
xspan = xmax - xmin
yspan = ymax - ymin
# to avoid any network positions outside all Voronoi cells, append
# the corners of a rectangle framing these points
vor = spatial.Voronoi(
np.vstack(
(
points,
[
[xmin - 3.0 * xspan, ymin - 3.0 * yspan],
[xmin - 3.0 * xspan, ymax + 3.0 * yspan],
[xmax + 3.0 * xspan, ymin - 3.0 * yspan],
[xmax + 3.0 * xspan, ymax + 3.0 * yspan],
],
)
)
) )
voronoi = pts.voronoi_polygons(extend_to=outline).clip(outline)
polygons = [] # can be removed with shapely 2.1 where order is preserved
for i in range(len(points)): # https://github.com/shapely/shapely/issues/2020
poly = Polygon(vor.vertices[vor.regions[vor.point_region[i]]]) with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
pts = gpd.GeoDataFrame(geometry=pts)
voronoi = gpd.GeoDataFrame(geometry=voronoi)
joined = gpd.sjoin_nearest(pts, voronoi, how="right")
if not poly.is_valid: return joined.dissolve(by="Bus").squeeze()
poly = poly.buffer(0)
with np.errstate(invalid="ignore"):
poly = poly.intersection(outline)
polygons.append(poly)
return polygons
def build_bus_shapes(n, country_shapes, offshore_shapes, countries): def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
@ -870,11 +838,10 @@ def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
"name": onshore_locs.index, "name": onshore_locs.index,
"x": onshore_locs["x"], "x": onshore_locs["x"],
"y": onshore_locs["y"], "y": onshore_locs["y"],
"geometry": voronoi_partition_pts( "geometry": voronoi(onshore_locs, onshore_shape),
onshore_locs.values, onshore_shape
),
"country": country, "country": country,
} },
crs=n.crs,
) )
) )
@ -887,14 +854,16 @@ def build_bus_shapes(n, country_shapes, offshore_shapes, countries):
"name": offshore_locs.index, "name": offshore_locs.index,
"x": offshore_locs["x"], "x": offshore_locs["x"],
"y": offshore_locs["y"], "y": offshore_locs["y"],
"geometry": voronoi_partition_pts(offshore_locs.values, offshore_shape), "geometry": voronoi(offshore_locs, offshore_shape),
"country": country, "country": country,
} },
crs=n.crs,
) )
offshore_regions_c = offshore_regions_c.loc[offshore_regions_c.area > 1e-2] sel = offshore_regions_c.to_crs(3035).area > 10 # m2
offshore_regions_c = offshore_regions_c.loc[sel]
offshore_regions.append(offshore_regions_c) offshore_regions.append(offshore_regions_c)
shapes = pd.concat(onshore_regions, ignore_index=True) shapes = pd.concat(onshore_regions, ignore_index=True).set_crs(n.crs)
return onshore_regions, offshore_regions, shapes, offshore_shapes return onshore_regions, offshore_regions, shapes, offshore_shapes

13
scripts/build_cop_profiles.py
View File

@ -21,20 +21,12 @@ Relevant Settings
Inputs: Inputs:
------- -------
- ``resources/<run_name>/temp_soil_total_elec_s<simpl>_<clusters>.nc``: Soil temperature (total) time series. - ``resources/<run_name>/temp_soil_total_elec_s<simpl>_<clusters>.nc``: Soil temperature (total) time series.
- ``resources/<run_name>/temp_soil_rural_elec_s<simpl>_<clusters>.nc``: Soil temperature (rural) time series.
- ``resources/<run_name>/temp_soil_urban_elec_s<simpl>_<clusters>.nc``: Soil temperature (urban) time series.
- ``resources/<run_name>/temp_air_total_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (total) time series. - ``resources/<run_name>/temp_air_total_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (total) time series.
- ``resources/<run_name>/temp_air_rural_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (rural) time series.
- ``resources/<run_name>/temp_air_urban_elec_s<simpl>_<clusters>.nc``: Ambient air temperature (urban) time series.
Outputs: Outputs:
-------- --------
- ``resources/cop_soil_total_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (total). - ``resources/cop_soil_total_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (total).
- ``resources/cop_soil_rural_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (rural).
- ``resources/cop_soil_urban_elec_s<simpl>_<clusters>.nc``: COP (ground-sourced) time series (urban).
- ``resources/cop_air_total_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (total). - ``resources/cop_air_total_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (total).
- ``resources/cop_air_rural_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (rural).
- ``resources/cop_air_urban_elec_s<simpl>_<clusters>.nc``: COP (air-sourced) time series (urban).
References References
@ -67,12 +59,11 @@ if __name__ == "__main__":
set_scenario_config(snakemake) set_scenario_config(snakemake)
for area in ["total", "urban", "rural"]:
for source in ["air", "soil"]: for source in ["air", "soil"]:
source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_{area}"]) source_T = xr.open_dataarray(snakemake.input[f"temp_{source}_total"])
delta_T = snakemake.params.heat_pump_sink_T - source_T delta_T = snakemake.params.heat_pump_sink_T - source_T
cop = coefficient_of_performance(delta_T, source) cop = coefficient_of_performance(delta_T, source)
cop.to_netcdf(snakemake.output[f"cop_{source}_{area}"]) cop.to_netcdf(snakemake.output[f"cop_{source}_total"])

2
scripts/build_cutout.py
View File

@ -103,7 +103,7 @@ if __name__ == "__main__":
if "snakemake" not in globals(): if "snakemake" not in globals():
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake("build_cutout", cutout="europe-2013-era5") snakemake = mock_snakemake("build_cutout", cutout="europe-2013-sarah3-era5")
configure_logging(snakemake) configure_logging(snakemake)
set_scenario_config(snakemake) set_scenario_config(snakemake)

2
scripts/build_district_heat_share.py
View File

@ -86,7 +86,7 @@ if __name__ == "__main__":
urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1) urban_fraction = pd.concat([urban_fraction, dist_fraction_node], axis=1).max(axis=1)
# difference of max potential and today's share of district heating # difference of max potential and today's share of district heating
diff = (urban_fraction * central_fraction) - dist_fraction_node diff = ((urban_fraction * central_fraction) - dist_fraction_node).clip(lower=0)
progress = get( progress = get(
snakemake.config["sector"]["district_heating"]["progress"], investment_year snakemake.config["sector"]["district_heating"]["progress"], investment_year
) )

13
scripts/build_gas_input_locations.py
View File

@ -7,6 +7,7 @@ Build import locations for fossil gas from entry-points, LNG terminals and
production sites with data from SciGRID_gas and Global Energy Monitor. production sites with data from SciGRID_gas and Global Energy Monitor.
""" """
import json
import logging import logging
import geopandas as gpd import geopandas as gpd
@ -19,7 +20,8 @@ logger = logging.getLogger(__name__)
def read_scigrid_gas(fn): def read_scigrid_gas(fn):
df = gpd.read_file(fn) df = gpd.read_file(fn)
df = pd.concat([df, df.param.apply(pd.Series)], axis=1) expanded_param = df.param.apply(json.loads).apply(pd.Series)
df = pd.concat([df, expanded_param], axis=1)
df.drop(["param", "uncertainty", "method"], axis=1, inplace=True) df.drop(["param", "uncertainty", "method"], axis=1, inplace=True)
return df return df
@ -97,11 +99,11 @@ def build_gas_input_locations(gem_fn, entry_fn, sto_fn, countries):
~(entry.from_country.isin(countries) & entry.to_country.isin(countries)) ~(entry.from_country.isin(countries) & entry.to_country.isin(countries))
& ~entry.name.str.contains("Tegelen") # only take non-EU entries & ~entry.name.str.contains("Tegelen") # only take non-EU entries
| (entry.from_country == "NO") # malformed datapoint # entries from NO to GB | (entry.from_country == "NO") # malformed datapoint # entries from NO to GB
] ].copy()
sto = read_scigrid_gas(sto_fn) sto = read_scigrid_gas(sto_fn)
remove_country = ["RU", "UA", "TR", "BY"] # noqa: F841 remove_country = ["RU", "UA", "TR", "BY"] # noqa: F841
sto = sto.query("country_code not in @remove_country") sto = sto.query("country_code not in @remove_country").copy()
# production sites inside the model scope # production sites inside the model scope
prod = build_gem_prod_data(gem_fn) prod = build_gem_prod_data(gem_fn)
@ -132,7 +134,8 @@ if __name__ == "__main__":
snakemake = mock_snakemake( snakemake = mock_snakemake(
"build_gas_input_locations", "build_gas_input_locations",
simpl="", simpl="",
clusters="128", clusters="5",
configfiles="config/test/config.overnight.yaml",
) )
configure_logging(snakemake) configure_logging(snakemake)
@ -162,7 +165,7 @@ if __name__ == "__main__":
gas_input_nodes = gpd.sjoin(gas_input_locations, regions, how="left") gas_input_nodes = gpd.sjoin(gas_input_locations, regions, how="left")
gas_input_nodes.rename(columns={"index_right": "bus"}, inplace=True) gas_input_nodes.rename(columns={"name": "bus"}, inplace=True)
gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver="GeoJSON") gas_input_nodes.to_file(snakemake.output.gas_input_nodes, driver="GeoJSON")

6
scripts/build_gas_network.py
View File

@ -7,6 +7,7 @@ Preprocess gas network based on data from bthe SciGRID_gas project
(https://www.gas.scigrid.de/). (https://www.gas.scigrid.de/).
""" """
import json
import logging import logging
import geopandas as gpd import geopandas as gpd
@ -54,8 +55,9 @@ def diameter_to_capacity(pipe_diameter_mm):
def load_dataset(fn): def load_dataset(fn):
df = gpd.read_file(fn) df = gpd.read_file(fn)
param = df.param.apply(pd.Series) param = df.param.apply(json.loads).apply(pd.Series)
method = df.method.apply(pd.Series)[["diameter_mm", "max_cap_M_m3_per_d"]] cols = ["diameter_mm", "max_cap_M_m3_per_d"]
method = df.method.apply(json.loads).apply(pd.Series)[cols]
method.columns = method.columns + "_method" method.columns = method.columns + "_method"
df = pd.concat([df, param, method], axis=1) df = pd.concat([df, param, method], axis=1)
to_drop = ["param", "uncertainty", "method", "tags"] to_drop = ["param", "uncertainty", "method", "tags"]

153
scripts/build_gdp_pop_non_nuts3.py Normal file
View File

@ -0,0 +1,153 @@
# -*- coding: utf-8 -*-
# SPDX-FileCopyrightText: : 2017-2024 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
"""
Maps the per-capita GDP and population values to non-NUTS3 regions.
The script takes as input the country code, a GeoDataFrame containing
the regions, and the file paths to the datasets containing the GDP and
POP values for non-NUTS3 countries.
"""
import logging
import geopandas as gpd
import numpy as np
import pandas as pd
import pypsa
import rasterio
import xarray as xr
from _helpers import configure_logging, set_scenario_config
from rasterio.mask import mask
from shapely.geometry import box
logger = logging.getLogger(__name__)
def calc_gdp_pop(country, regions, gdp_non_nuts3, pop_non_nuts3):
"""
Calculate the GDP p.c. and population values for non NUTS3 regions.
Parameters:
country (str): The two-letter country code of the non-NUTS3 region.
regions (GeoDataFrame): A GeoDataFrame containing the regions.
gdp_non_nuts3 (str): The file path to the dataset containing the GDP p.c values
for non NUTS3 countries (e.g. MD, UA)
pop_non_nuts3 (str): The file path to the dataset containing the POP values
for non NUTS3 countries (e.g. MD, UA)
Returns:
tuple: A tuple containing two GeoDataFrames:
- gdp: A GeoDataFrame with the mean GDP p.c. values mapped to each bus.
- pop: A GeoDataFrame with the summed POP values mapped to each bus.
"""
regions = (
regions.rename(columns={"name": "Bus"})
.drop(columns=["x", "y"])
.set_index("Bus")
)
regions = regions[regions.country == country]
# Create a bounding box for UA, MD from region shape, including a buffer of 10000 metres
bounding_box = (
gpd.GeoDataFrame(geometry=[box(*regions.total_bounds)], crs=regions.crs)
.to_crs(epsg=3857)
.buffer(10000)
.to_crs(regions.crs)
)
# GDP Mapping
logger.info(f"Mapping mean GDP p.c. to non-NUTS3 region: {country}")
with xr.open_dataset(gdp_non_nuts3) as src_gdp:
src_gdp = src_gdp.where(
(src_gdp.longitude >= bounding_box.bounds.minx.min())
& (src_gdp.longitude <= bounding_box.bounds.maxx.max())
& (src_gdp.latitude >= bounding_box.bounds.miny.min())
& (src_gdp.latitude <= bounding_box.bounds.maxy.max()),
drop=True,
)
gdp = src_gdp.to_dataframe().reset_index()
gdp = gdp.rename(columns={"GDP_per_capita_PPP": "gdp"})
gdp = gdp[gdp.time == gdp.time.max()]
gdp_raster = gpd.GeoDataFrame(
gdp,
geometry=gpd.points_from_xy(gdp.longitude, gdp.latitude),
crs="EPSG:4326",
)
gdp_mapped = gpd.sjoin(gdp_raster, regions, predicate="within")
gdp = (
gdp_mapped.copy()
.groupby(["Bus", "country"])
.agg({"gdp": "mean"})
.reset_index(level=["country"])
)
# Population Mapping
logger.info(f"Mapping summed population to non-NUTS3 region: {country}")
with rasterio.open(pop_non_nuts3) as src_pop:
# Mask the raster with the bounding box
out_image, out_transform = mask(src_pop, bounding_box, crop=True)
out_meta = src_pop.meta.copy()
out_meta.update(
{
"driver": "GTiff",
"height": out_image.shape[1],
"width": out_image.shape[2],
"transform": out_transform,
}
)
masked_data = out_image[0] # Use the first band (rest is empty)
row_indices, col_indices = np.where(masked_data != src_pop.nodata)
values = masked_data[row_indices, col_indices]
# Affine transformation from pixel coordinates to geo coordinates
x_coords, y_coords = rasterio.transform.xy(out_transform, row_indices, col_indices)
pop_raster = pd.DataFrame({"x": x_coords, "y": y_coords, "pop": values})
pop_raster = gpd.GeoDataFrame(
pop_raster,
geometry=gpd.points_from_xy(pop_raster.x, pop_raster.y),
crs=src_pop.crs,
)
pop_mapped = gpd.sjoin(pop_raster, regions, predicate="within")
pop = (
pop_mapped.groupby(["Bus", "country"])
.agg({"pop": "sum"})
.reset_index()
.set_index("Bus")
)
gdp_pop = regions.join(gdp.drop(columns="country"), on="Bus").join(
pop.drop(columns="country"), on="Bus"
)
gdp_pop.fillna(0, inplace=True)
return gdp_pop
if __name__ == "__main__":
if "snakemake" not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake("build_gdp_pop_non_nuts3")
configure_logging(snakemake)
set_scenario_config(snakemake)
n = pypsa.Network(snakemake.input.base_network)
regions = gpd.read_file(snakemake.input.regions)
gdp_non_nuts3 = snakemake.input.gdp_non_nuts3
pop_non_nuts3 = snakemake.input.pop_non_nuts3
subset = {"MD", "UA"}.intersection(snakemake.params.countries)
gdp_pop = pd.concat(
[
calc_gdp_pop(country, regions, gdp_non_nuts3, pop_non_nuts3)
for country in subset
],
axis=0,
)
logger.info(
f"Exporting GDP and POP values for non-NUTS3 regions {snakemake.output}"
)
gdp_pop.reset_index().to_file(snakemake.output, driver="GeoJSON")

10
scripts/build_hourly_heat_demand.py
View File

@ -22,12 +22,12 @@ Inputs
------ ------
- ``data/heat_load_profile_BDEW.csv``: Intraday heat profile for water and space heating demand for the residential and services sectors for weekends and weekdays. - ``data/heat_load_profile_BDEW.csv``: Intraday heat profile for water and space heating demand for the residential and services sectors for weekends and weekdays.
- ``resources/daily_heat_demand_<scope>_elec_s<simpl>_<clusters>.nc``: Daily heat demand per cluster. - ``resources/daily_heat_demand_total_elec_s<simpl>_<clusters>.nc``: Daily heat demand per cluster.
Outputs Outputs
------- -------
- ``resources/hourly_heat_demand_<scope>_elec_s<simpl>_<clusters>.nc``: - ``resources/hourly_heat_demand_total_elec_s<simpl>_<clusters>.nc``:
""" """
from itertools import product from itertools import product
@ -41,10 +41,10 @@ if __name__ == "__main__":
from _helpers import mock_snakemake from _helpers import mock_snakemake
snakemake = mock_snakemake( snakemake = mock_snakemake(
"build_hourly_heat_demands", "build_hourly_heat_demand",
scope="total", scope="total",
simpl="", simpl="",
clusters=48, clusters=5,
) )
set_scenario_config(snakemake) set_scenario_config(snakemake)
@ -85,6 +85,6 @@ if __name__ == "__main__":
heat_demand.index.name = "snapshots" heat_demand.index.name = "snapshots"
ds = heat_demand.stack().to_xarray() ds = heat_demand.stack(future_stack=True).to_xarray()
ds.to_netcdf(snakemake.output.heat_demand) ds.to_netcdf(snakemake.output.heat_demand)

2
scripts/build_industrial_distribution_key.py
View File

@ -116,7 +116,7 @@ def prepare_hotmaps_database(regions):
gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within") gdf = gpd.sjoin(gdf, regions, how="inner", predicate="within")
gdf.rename(columns={"index_right": "bus"}, inplace=True) gdf.rename(columns={"name": "bus"}, inplace=True)
gdf["country"] = gdf.bus.str[:2] gdf["country"] = gdf.bus.str[:2]
# the .sjoin can lead to duplicates if a geom is in two overlapping regions # the .sjoin can lead to duplicates if a geom is in two overlapping regions

4
scripts/build_industrial_energy_demand_per_country_today.py
View File

@ -184,7 +184,7 @@ def separate_basic_chemicals(demand, production):
demand.drop(columns="Basic chemicals", inplace=True) demand.drop(columns="Basic chemicals", inplace=True)
demand["HVC"].clip(lower=0, inplace=True) demand["HVC"] = demand["HVC"].clip(lower=0)
return demand return demand
@ -248,7 +248,7 @@ if __name__ == "__main__":
demand = add_non_eu28_industrial_energy_demand(countries, demand, production) demand = add_non_eu28_industrial_energy_demand(countries, demand, production)
# for format compatibility # for format compatibility
demand = demand.stack(dropna=False).unstack(level=[0, 2]) demand = demand.stack(future_stack=True).unstack(level=[0, 2])
# style and annotation # style and annotation
demand.index.name = "TWh/a" demand.index.name = "TWh/a"

3
scripts/build_industrial_production_per_country.py
View File

@ -301,7 +301,8 @@ def separate_basic_chemicals(demand, year):
demand["Basic chemicals"] -= demand["Ammonia"] demand["Basic chemicals"] -= demand["Ammonia"]
# EE, HR and LT got negative demand through subtraction - poor data # EE, HR and LT got negative demand through subtraction - poor data
demand["Basic chemicals"].clip(lower=0.0, inplace=True) col = "Basic chemicals"
demand[col] = demand[col].clip(lower=0.0)
# assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals # assume HVC, methanol, chlorine production proportional to non-ammonia basic chemicals
distribution_key = ( distribution_key = (

3
scripts/build_industry_sector_ratios_intermediate.py
View File

@ -129,11 +129,12 @@ def build_industry_sector_ratios_intermediate():
] ]
today_sector_ratios_ct.loc[:, ~missing_mask] = today_sector_ratios_ct.loc[ today_sector_ratios_ct.loc[:, ~missing_mask] = today_sector_ratios_ct.loc[
:, ~missing_mask :, ~missing_mask
].fillna(0) ].fillna(future_sector_ratios)
intermediate_sector_ratios[ct] = ( intermediate_sector_ratios[ct] = (
today_sector_ratios_ct * (1 - fraction_future) today_sector_ratios_ct * (1 - fraction_future)
+ future_sector_ratios * fraction_future + future_sector_ratios * fraction_future
) )
intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1) intermediate_sector_ratios = pd.concat(intermediate_sector_ratios, axis=1)
intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios) intermediate_sector_ratios.to_csv(snakemake.output.industry_sector_ratios)

4
scripts/build_population_layouts.py
View File

@ -92,7 +92,9 @@ if __name__ == "__main__":
# The first low density grid cells to reach rural fraction are rural # The first low density grid cells to reach rural fraction are rural
asc_density_i = density_cells_ct.sort_values().index asc_density_i = density_cells_ct.sort_values().index
asc_density_cumsum = pop_cells_ct[asc_density_i].cumsum() / pop_cells_ct.sum() asc_density_cumsum = (
pop_cells_ct.iloc[asc_density_i].cumsum() / pop_cells_ct.sum()
)
rural_fraction_ct = 1 - urban_fraction[ct] rural_fraction_ct = 1 - urban_fraction[ct]
pop_ct_rural_b = asc_density_cumsum < rural_fraction_ct pop_ct_rural_b = asc_density_cumsum < rural_fraction_ct
pop_ct_urban_b = ~pop_ct_rural_b pop_ct_urban_b = ~pop_ct_rural_b

8
scripts/build_powerplants.py
View File

@ -6,7 +6,7 @@
# coding: utf-8 # coding: utf-8
""" """
Retrieves conventional powerplant capacities and locations from Retrieves conventional powerplant capacities and locations from
`powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_, assigns `powerplantmatching <https://github.com/PyPSA/powerplantmatching>`_, assigns
these to buses and creates a ``.csv`` file. It is possible to amend the these to buses and creates a ``.csv`` file. It is possible to amend the
powerplant database with custom entries provided in powerplant database with custom entries provided in
``data/custom_powerplants.csv``. ``data/custom_powerplants.csv``.
@ -30,17 +30,17 @@ Inputs
------ ------
- ``networks/base.nc``: confer :ref:`base`. - ``networks/base.nc``: confer :ref:`base`.
- ``data/custom_powerplants.csv``: custom powerplants in the same format as `powerplantmatching <https://github.com/FRESNA/powerplantmatching>`_ provides - ``data/custom_powerplants.csv``: custom powerplants in the same format as `powerplantmatching <https://github.com/PyPSA/powerplantmatching>`_ provides
Outputs Outputs
------- -------
- ``resource/powerplants.csv``: A list of conventional power plants (i.e. neither wind nor solar) with fields for name, fuel type, technology, country, capacity in MW, duration, commissioning year, retrofit year, latitude, longitude, and dam information as documented in the `powerplantmatching README <https://github.com/FRESNA/powerplantmatching/blob/master/README.md>`_; additionally it includes information on the closest substation/bus in ``networks/base.nc``. - ``resource/powerplants.csv``: A list of conventional power plants (i.e. neither wind nor solar) with fields for name, fuel type, technology, country, capacity in MW, duration, commissioning year, retrofit year, latitude, longitude, and dam information as documented in the `powerplantmatching README <https://github.com/PyPSA/powerplantmatching/blob/master/README.md>`_; additionally it includes information on the closest substation/bus in ``networks/base.nc``.
.. image:: img/powerplantmatching.png .. image:: img/powerplantmatching.png
:scale: 30 % :scale: 30 %
**Source:** `powerplantmatching on GitHub <https://github.com/FRESNA/powerplantmatching>`_ **Source:** `powerplantmatching on GitHub <https://github.com/PyPSA/powerplantmatching>`_
Description Description
----------- -----------

2
scripts/build_renewable_profiles.py
View File

@ -406,7 +406,7 @@ if __name__ == "__main__":
if snakemake.wildcards.technology.startswith("offwind"): if snakemake.wildcards.technology.startswith("offwind"):
logger.info("Calculate underwater fraction of connections.") logger.info("Calculate underwater fraction of connections.")
offshore_shape = gpd.read_file(snakemake.input["offshore_shapes"]).unary_union offshore_shape = gpd.read_file(snakemake.input["offshore_shapes"]).union_all()
underwater_fraction = [] underwater_fraction = []
for bus in buses: for bus in buses:
p = centre_of_mass.sel(bus=bus).data p = centre_of_mass.sel(bus=bus).data

2
scripts/build_retro_cost.py
View File

@ -890,7 +890,7 @@ def calculate_gain_utilisation_factor(heat_transfer_perm2, Q_ht, Q_gain):
Calculates gain utilisation factor nu. Calculates gain utilisation factor nu.
""" """
# time constant of the building tau [h] = c_m [Wh/(m^2K)] * 1 /(H_tr_e+H_tb*H_ve) [m^2 K /W] # time constant of the building tau [h] = c_m [Wh/(m^2K)] * 1 /(H_tr_e+H_tb*H_ve) [m^2 K /W]
tau = c_m / heat_transfer_perm2.T.groupby(axis=1).sum().T tau = c_m / heat_transfer_perm2.groupby().sum()
alpha = alpha_H_0 + (tau / tau_H_0) alpha = alpha_H_0 + (tau / tau_H_0)
# heat balance ratio # heat balance ratio
gamma = (1 / Q_ht).mul(Q_gain.sum(axis=1), axis=0) gamma = (1 / Q_ht).mul(Q_gain.sum(axis=1), axis=0)

20
scripts/build_shapes.py
View File

@ -91,7 +91,7 @@ def _get_country(target, **keys):
return np.nan return np.nan
def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True): def _simplify_polys(polys, minarea=0.1, tolerance=None, filterremote=True):
if isinstance(polys, MultiPolygon): if isinstance(polys, MultiPolygon):
polys = sorted(polys.geoms, key=attrgetter("area"), reverse=True) polys = sorted(polys.geoms, key=attrgetter("area"), reverse=True)
mainpoly = polys[0] mainpoly = polys[0]
@ -106,7 +106,9 @@ def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
) )
else: else:
polys = mainpoly polys = mainpoly
return polys.simplify(tolerance=tolerance) if tolerance is not None:
polys = polys.simplify(tolerance=tolerance)
return polys
def countries(naturalearth, country_list): def countries(naturalearth, country_list):
@ -124,7 +126,7 @@ def countries(naturalearth, country_list):
df = df.loc[ df = df.loc[
df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5)) df.name.isin(country_list) & ((df["scalerank"] == 0) | (df["scalerank"] == 5))
] ]
s = df.set_index("name")["geometry"].map(_simplify_polys) s = df.set_index("name")["geometry"].map(_simplify_polys).set_crs(df.crs)
if "RS" in country_list: if "RS" in country_list:
s["RS"] = s["RS"].union(s.pop("KV")) s["RS"] = s["RS"].union(s.pop("KV"))
# cleanup shape union # cleanup shape union
@ -145,7 +147,8 @@ def eez(country_shapes, eez, country_list):
lambda s: _simplify_polys(s, filterremote=False) lambda s: _simplify_polys(s, filterremote=False)
) )
s = gpd.GeoSeries( s = gpd.GeoSeries(
{k: v for k, v in s.items() if v.distance(country_shapes[k]) < 1e-3} {k: v for k, v in s.items() if v.distance(country_shapes[k]) < 1e-3},
crs=df.crs,
) )
s = s.to_frame("geometry") s = s.to_frame("geometry")
s.index.name = "name" s.index.name = "name"
@ -156,7 +159,7 @@ def country_cover(country_shapes, eez_shapes=None):
shapes = country_shapes shapes = country_shapes
if eez_shapes is not None: if eez_shapes is not None:
shapes = pd.concat([shapes, eez_shapes]) shapes = pd.concat([shapes, eez_shapes])
europe_shape = shapes.unary_union europe_shape = shapes.union_all()
if isinstance(europe_shape, MultiPolygon): if isinstance(europe_shape, MultiPolygon):
europe_shape = max(europe_shape.geoms, key=attrgetter("area")) europe_shape = max(europe_shape.geoms, key=attrgetter("area"))
return Polygon(shell=europe_shape.exterior) return Polygon(shell=europe_shape.exterior)
@ -235,11 +238,11 @@ def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
[["BA1", "BA", 3871.0], ["RS1", "RS", 7210.0], ["AL1", "AL", 2893.0]], [["BA1", "BA", 3871.0], ["RS1", "RS", 7210.0], ["AL1", "AL", 2893.0]],
columns=["NUTS_ID", "country", "pop"], columns=["NUTS_ID", "country", "pop"],
geometry=gpd.GeoSeries(), geometry=gpd.GeoSeries(),
crs=df.crs,
) )
manual["geometry"] = manual["country"].map(country_shapes) manual["geometry"] = manual["country"].map(country_shapes.to_crs(df.crs))
manual = manual.dropna() manual = manual.dropna()
manual = manual.set_index("NUTS_ID") manual = manual.set_index("NUTS_ID")
manual = manual.set_crs("ETRS89")
df = pd.concat([df, manual], sort=False) df = pd.concat([df, manual], sort=False)
@ -265,7 +268,8 @@ if __name__ == "__main__":
offshore_shapes.reset_index().to_file(snakemake.output.offshore_shapes) offshore_shapes.reset_index().to_file(snakemake.output.offshore_shapes)
europe_shape = gpd.GeoDataFrame( europe_shape = gpd.GeoDataFrame(
geometry=[country_cover(country_shapes, offshore_shapes.geometry)] geometry=[country_cover(country_shapes, offshore_shapes.geometry)],
crs=country_shapes.crs,
) )
europe_shape.reset_index().to_file(snakemake.output.europe_shape) europe_shape.reset_index().to_file(snakemake.output.europe_shape)

4
scripts/build_shipping_demand.py
View File

@ -45,9 +45,7 @@ if __name__ == "__main__":
# assign ports to nearest region # assign ports to nearest region
p = european_ports.to_crs(3857) p = european_ports.to_crs(3857)
r = regions.to_crs(3857) r = regions.to_crs(3857)
outflows = ( outflows = p.sjoin_nearest(r).groupby("name").properties_outflows.sum().div(1e3)
p.sjoin_nearest(r).groupby("index_right").properties_outflows.sum().div(1e3)
)
# calculate fraction of each country's port outflows # calculate fraction of each country's port outflows
countries = outflows.index.str[:2] countries = outflows.index.str[:2]

6
scripts/build_temperature_profiles.py
View File

@ -25,15 +25,15 @@ Relevant Settings
Inputs Inputs
------ ------
- ``resources/<run_name>/pop_layout_<scope>.nc``: - ``resources/<run_name>/pop_layout_total.nc``:
- ``resources/<run_name>/regions_onshore_elec_s<simpl>_<clusters>.geojson``: - ``resources/<run_name>/regions_onshore_elec_s<simpl>_<clusters>.geojson``:
- ``cutout``: Weather data cutout, as specified in config - ``cutout``: Weather data cutout, as specified in config
Outputs Outputs
------- -------
- ``resources/temp_soil_<scope>_elec_s<simpl>_<clusters>.nc``: - ``resources/temp_soil_total_elec_s<simpl>_<clusters>.nc``:
- ``resources/temp_air_<scope>_elec_s<simpl>_<clusters>.nc` - ``resources/temp_air_total_elec_s<simpl>_<clusters>.nc`
""" """
import atlite import atlite

9
scripts/cluster_gas_network.py
View File

@ -41,9 +41,9 @@ def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
for i in [0, 1]: for i in [0, 1]:
gdf = gpd.GeoDataFrame(geometry=df[f"point{i}"], crs="EPSG:4326") gdf = gpd.GeoDataFrame(geometry=df[f"point{i}"], crs="EPSG:4326")
bus_mapping = gpd.sjoin( bus_mapping = gpd.sjoin(gdf, bus_regions, how="left", predicate="within")[
gdf, bus_regions, how="left", predicate="within" "name"
).index_right ]
bus_mapping = bus_mapping.groupby(bus_mapping.index).first() bus_mapping = bus_mapping.groupby(bus_mapping.index).first()
df[f"bus{i}"] = bus_mapping df[f"bus{i}"] = bus_mapping
@ -58,6 +58,9 @@ def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
# drop pipes within the same region # drop pipes within the same region
df = df.loc[df.bus1 != df.bus0] df = df.loc[df.bus1 != df.bus0]
if df.empty:
return df
# recalculate lengths as center to center * length factor # recalculate lengths as center to center * length factor
df["length"] = df.apply( df["length"] = df.apply(
lambda p: length_factor lambda p: length_factor

41
scripts/determine_availability_matrix_MD_UA.py
View File

@ -8,16 +8,15 @@ Create land elibility analysis for Ukraine and Moldova with different datasets.
import functools import functools
import logging import logging
import os
import time import time
from tempfile import NamedTemporaryFile
import atlite import atlite
import fiona import fiona
import geopandas as gpd import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np import numpy as np
from _helpers import configure_logging, set_scenario_config from _helpers import configure_logging, set_scenario_config
from atlite.gis import shape_availability
from rasterio.plot import show
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -40,7 +39,7 @@ if __name__ == "__main__":
configure_logging(snakemake) configure_logging(snakemake)
set_scenario_config(snakemake) set_scenario_config(snakemake)
nprocesses = None # snakemake.config["atlite"].get("nprocesses") nprocesses = int(snakemake.threads)
noprogress = not snakemake.config["atlite"].get("show_progress", True) noprogress = not snakemake.config["atlite"].get("show_progress", True)
config = snakemake.config["renewable"][snakemake.wildcards.technology] config = snakemake.config["renewable"][snakemake.wildcards.technology]
@ -48,7 +47,9 @@ if __name__ == "__main__":
regions = ( regions = (
gpd.read_file(snakemake.input.regions).set_index("name").rename_axis("bus") gpd.read_file(snakemake.input.regions).set_index("name").rename_axis("bus")
) )
buses = regions.index # Limit to "UA" and "MD" regions
buses = regions.loc[regions["country"].isin(["UA", "MD"])].index.values
regions = regions.loc[buses]
excluder = atlite.ExclusionContainer(crs=3035, res=100) excluder = atlite.ExclusionContainer(crs=3035, res=100)
@ -93,8 +94,15 @@ if __name__ == "__main__":
bbox=regions.geometry, bbox=regions.geometry,
layer=layer, layer=layer,
).to_crs(3035) ).to_crs(3035)
# temporary file needed for parallelization
with NamedTemporaryFile(suffix=".geojson", delete=False) as f:
plg_tmp_fn = f.name
if not wdpa.empty: if not wdpa.empty:
excluder.add_geometry(wdpa.geometry) wdpa[["geometry"]].to_file(plg_tmp_fn)
while not os.path.exists(plg_tmp_fn):
time.sleep(1)
excluder.add_geometry(plg_tmp_fn)
layer = get_wdpa_layer_name(wdpa_fn, "points") layer = get_wdpa_layer_name(wdpa_fn, "points")
wdpa_pts = gpd.read_file( wdpa_pts = gpd.read_file(
@ -107,8 +115,15 @@ if __name__ == "__main__":
wdpa_pts = wdpa_pts.set_geometry( wdpa_pts = wdpa_pts.set_geometry(
wdpa_pts["geometry"].buffer(wdpa_pts["buffer_radius"]) wdpa_pts["geometry"].buffer(wdpa_pts["buffer_radius"])
) )
# temporary file needed for parallelization
with NamedTemporaryFile(suffix=".geojson", delete=False) as f:
pts_tmp_fn = f.name
if not wdpa_pts.empty: if not wdpa_pts.empty:
excluder.add_geometry(wdpa_pts.geometry) wdpa_pts[["geometry"]].to_file(pts_tmp_fn)
while not os.path.exists(pts_tmp_fn):
time.sleep(1)
excluder.add_geometry(pts_tmp_fn)
if "max_depth" in config: if "max_depth" in config:
# lambda not supported for atlite + multiprocessing # lambda not supported for atlite + multiprocessing
@ -144,16 +159,10 @@ if __name__ == "__main__":
else: else:
availability = cutout.availabilitymatrix(regions, excluder, **kwargs) availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
regions_geometry = regions.to_crs(3035).geometry for fn in [pts_tmp_fn, plg_tmp_fn]:
band, transform = shape_availability(regions_geometry, excluder) if os.path.exists(fn):
fig, ax = plt.subplots(figsize=(4, 8)) os.remove(fn)
gpd.GeoSeries(regions_geometry.unary_union).plot(ax=ax, color="none")
show(band, transform=transform, cmap="Greens", ax=ax)
plt.axis("off")
plt.savefig(snakemake.output.availability_map, bbox_inches="tight", dpi=500)
# Limit results only to buses for UA and MD
buses = regions.loc[regions["country"].isin(["UA", "MD"])].index.values
availability = availability.sel(bus=buses) availability = availability.sel(bus=buses)
# Save and plot for verification # Save and plot for verification

2
scripts/make_summary_perfect.py
View File

@ -631,7 +631,7 @@ def calculate_co2_emissions(n, label, df):
weightings = n.snapshot_weightings.generators.mul( weightings = n.snapshot_weightings.generators.mul(
n.investment_period_weightings["years"] n.investment_period_weightings["years"]
.reindex(n.snapshots) .reindex(n.snapshots)
.fillna(method="bfill") .bfill()
.fillna(1.0), .fillna(1.0),
axis=0, axis=0,
) )

2
scripts/plot_validation_electricity_production.py
View File

@ -70,7 +70,7 @@ if __name__ == "__main__":
optimized = optimized[["Generator", "StorageUnit"]].droplevel(0, axis=1) optimized = optimized[["Generator", "StorageUnit"]].droplevel(0, axis=1)
optimized = optimized.rename(columns=n.buses.country, level=0) optimized = optimized.rename(columns=n.buses.country, level=0)
optimized = optimized.rename(columns=carrier_groups, level=1) optimized = optimized.rename(columns=carrier_groups, level=1)
optimized = optimized.groupby(axis=1, level=[0, 1]).sum() optimized = optimized.T.groupby(level=[0, 1]).sum().T
data = pd.concat([historic, optimized], keys=["Historic", "Optimized"], axis=1) data = pd.concat([historic, optimized], keys=["Historic", "Optimized"], axis=1)
data.columns.names = ["Kind", "Country", "Carrier"] data.columns.names = ["Kind", "Country", "Carrier"]

4
scripts/prepare_network.py
View File

@ -137,9 +137,7 @@ def add_emission_prices(n, emission_prices={"co2": 0.0}, exclude_co2=False):
def add_dynamic_emission_prices(n): def add_dynamic_emission_prices(n):
co2_price = pd.read_csv(snakemake.input.co2_price, index_col=0, parse_dates=True) co2_price = pd.read_csv(snakemake.input.co2_price, index_col=0, parse_dates=True)
co2_price = co2_price[~co2_price.index.duplicated()] co2_price = co2_price[~co2_price.index.duplicated()]
co2_price = ( co2_price = co2_price.reindex(n.snapshots).ffill().bfill()
co2_price.reindex(n.snapshots).fillna(method="ffill").fillna(method="bfill")
)
emissions = ( emissions = (
n.generators.carrier.map(n.carriers.co2_emissions) / n.generators.efficiency n.generators.carrier.map(n.carriers.co2_emissions) / n.generators.efficiency

2
scripts/prepare_perfect_foresight.py
View File

@ -250,7 +250,7 @@ def adjust_stores(n):
n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True n.stores.loc[cyclic_i, "e_cyclic_per_period"] = True
n.stores.loc[cyclic_i, "e_cyclic"] = False n.stores.loc[cyclic_i, "e_cyclic"] = False
# non cyclic store assumptions # non cyclic store assumptions
non_cyclic_store = ["co2", "co2 stored", "solid biomass", "biogas", "Li ion"] non_cyclic_store = ["co2", "co2 stored", "solid biomass", "biogas", "EV battery"]
co2_i = n.stores[n.stores.carrier.isin(non_cyclic_store)].index co2_i = n.stores[n.stores.carrier.isin(non_cyclic_store)].index
n.stores.loc[co2_i, "e_cyclic_per_period"] = False n.stores.loc[co2_i, "e_cyclic_per_period"] = False
n.stores.loc[co2_i, "e_cyclic"] = False n.stores.loc[co2_i, "e_cyclic"] = False

85
scripts/prepare_sector_network.py
View File

@ -542,6 +542,9 @@ def add_carrier_buses(n, carrier, nodes=None):
capital_cost=capital_cost, capital_cost=capital_cost,
) )
fossils = ["coal", "gas", "oil", "lignite"]
if options.get("fossil_fuels", True) and carrier in fossils:
n.madd( n.madd(
"Generator", "Generator",
nodes, nodes,
@ -688,6 +691,7 @@ def add_co2_tracking(n, costs, options):
e_nom_extendable=True, e_nom_extendable=True,
e_nom_max=e_nom_max, e_nom_max=e_nom_max,
capital_cost=options["co2_sequestration_cost"], capital_cost=options["co2_sequestration_cost"],
marginal_cost=-0.1,
bus=sequestration_buses, bus=sequestration_buses,
lifetime=options["co2_sequestration_lifetime"], lifetime=options["co2_sequestration_lifetime"],
carrier="co2 sequestered", carrier="co2 sequestered",
@ -1237,12 +1241,14 @@ def add_storage_and_grids(n, costs):
gas_pipes["p_nom_min"] = 0.0 gas_pipes["p_nom_min"] = 0.0
# 0.1 EUR/MWkm/a to prefer decommissioning to address degeneracy # 0.1 EUR/MWkm/a to prefer decommissioning to address degeneracy
gas_pipes["capital_cost"] = 0.1 * gas_pipes.length gas_pipes["capital_cost"] = 0.1 * gas_pipes.length
gas_pipes["p_nom_extendable"] = True
else: else:
gas_pipes["p_nom_max"] = np.inf gas_pipes["p_nom_max"] = np.inf
gas_pipes["p_nom_min"] = gas_pipes.p_nom gas_pipes["p_nom_min"] = gas_pipes.p_nom
gas_pipes["capital_cost"] = ( gas_pipes["capital_cost"] = (
gas_pipes.length * costs.at["CH4 (g) pipeline", "fixed"] gas_pipes.length * costs.at["CH4 (g) pipeline", "fixed"]
) )
gas_pipes["p_nom_extendable"] = False
n.madd( n.madd(
"Link", "Link",
@ -1251,14 +1257,14 @@ def add_storage_and_grids(n, costs):
bus1=gas_pipes.bus1 + " gas", bus1=gas_pipes.bus1 + " gas",
p_min_pu=gas_pipes.p_min_pu, p_min_pu=gas_pipes.p_min_pu,
p_nom=gas_pipes.p_nom, p_nom=gas_pipes.p_nom,
p_nom_extendable=True, p_nom_extendable=gas_pipes.p_nom_extendable,
p_nom_max=gas_pipes.p_nom_max, p_nom_max=gas_pipes.p_nom_max,
p_nom_min=gas_pipes.p_nom_min, p_nom_min=gas_pipes.p_nom_min,
length=gas_pipes.length, length=gas_pipes.length,
capital_cost=gas_pipes.capital_cost, capital_cost=gas_pipes.capital_cost,
tags=gas_pipes.name, tags=gas_pipes.name,
carrier="gas pipeline", carrier="gas pipeline",
lifetime=costs.at["CH4 (g) pipeline", "lifetime"], lifetime=np.inf,
) )
# remove fossil generators where there is neither # remove fossil generators where there is neither
@ -1540,14 +1546,14 @@ def add_EVs(
temperature, temperature,
): ):
n.add("Carrier", "Li ion") n.add("Carrier", "EV battery")
n.madd( n.madd(
"Bus", "Bus",
spatial.nodes, spatial.nodes,
suffix=" EV battery", suffix=" EV battery",
location=spatial.nodes, location=spatial.nodes,
carrier="Li ion", carrier="EV battery",
unit="MWh_el", unit="MWh_el",
) )
@ -1620,9 +1626,9 @@ def add_EVs(
n.madd( n.madd(
"Store", "Store",
spatial.nodes, spatial.nodes,
suffix=" battery storage", suffix=" EV battery",
bus=spatial.nodes + " EV battery", bus=spatial.nodes + " EV battery",
carrier="battery storage", carrier="EV battery",
e_cyclic=True, e_cyclic=True,
e_nom=e_nom, e_nom=e_nom,
e_max_pu=1, e_max_pu=1,
@ -2501,6 +2507,43 @@ def add_biomass(n, costs):
marginal_cost=costs.at["BtL", "VOM"] * costs.at["BtL", "efficiency"], marginal_cost=costs.at["BtL", "VOM"] * costs.at["BtL", "efficiency"],
) )
# Electrobiofuels (BtL with hydrogen addition to make more use of biogenic carbon).
# Combination of efuels and biomass to liquid, both based on Fischer-Tropsch.
# Experimental version - use with caution
if options["electrobiofuels"]:
efuel_scale_factor = costs.at["BtL", "C stored"]
name = (
pd.Index(spatial.biomass.nodes)
+ " "
+ pd.Index(spatial.h2.nodes.str.replace(" H2", ""))
)
n.madd(
"Link",
name,
suffix=" electrobiofuels",
bus0=spatial.biomass.nodes,
bus1=spatial.oil.nodes,
bus2=spatial.h2.nodes,
bus3="co2 atmosphere",
carrier="electrobiofuels",
lifetime=costs.at["electrobiofuels", "lifetime"],
efficiency=costs.at["electrobiofuels", "efficiency-biomass"],
efficiency2=-costs.at["electrobiofuels", "efficiency-hydrogen"],
efficiency3=-costs.at["solid biomass", "CO2 intensity"]
+ costs.at["BtL", "CO2 stored"]
* (1 - costs.at["Fischer-Tropsch", "capture rate"]),
p_nom_extendable=True,
capital_cost=costs.at["BtL", "fixed"] * costs.at["BtL", "efficiency"]
+ efuel_scale_factor
* costs.at["Fischer-Tropsch", "fixed"]
* costs.at["Fischer-Tropsch", "efficiency"],
marginal_cost=costs.at["BtL", "VOM"] * costs.at["BtL", "efficiency"]
+ efuel_scale_factor
* costs.at["Fischer-Tropsch", "VOM"]
* costs.at["Fischer-Tropsch", "efficiency"],
)
# BioSNG from solid biomass # BioSNG from solid biomass
if options["biosng"]: if options["biosng"]:
n.madd( n.madd(
@ -2769,10 +2812,11 @@ def add_industry(n, costs):
) )
domestic_navigation = pop_weighted_energy_totals.loc[ domestic_navigation = pop_weighted_energy_totals.loc[
nodes, "total domestic navigation" nodes, ["total domestic navigation"]
].squeeze() ].squeeze()
international_navigation = ( international_navigation = (
pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze() * nyears pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze(axis=1)
* nyears
) )
all_navigation = domestic_navigation + international_navigation all_navigation = domestic_navigation + international_navigation
p_set = all_navigation * 1e6 / nhours p_set = all_navigation * 1e6 / nhours
@ -2891,7 +2935,7 @@ def add_industry(n, costs):
carrier="oil", carrier="oil",
) )
if "oil" not in n.generators.carrier.unique(): if options.get("fossil_fuels", True) and "oil" not in n.generators.carrier.unique():
n.madd( n.madd(
"Generator", "Generator",
spatial.oil.nodes, spatial.oil.nodes,
@ -3349,7 +3393,7 @@ def add_waste_heat(n):
) )
n.links.loc[urban_central + " Fischer-Tropsch", "efficiency3"] = ( n.links.loc[urban_central + " Fischer-Tropsch", "efficiency3"] = (
0.95 - n.links.loc[urban_central + " Fischer-Tropsch", "efficiency"] 0.95 - n.links.loc[urban_central + " Fischer-Tropsch", "efficiency"]
) ) * options["use_fischer_tropsch_waste_heat"]
if options["use_methanation_waste_heat"] and "Sabatier" in link_carriers: if options["use_methanation_waste_heat"] and "Sabatier" in link_carriers:
n.links.loc[urban_central + " Sabatier", "bus3"] = ( n.links.loc[urban_central + " Sabatier", "bus3"] = (
@ -3357,7 +3401,7 @@ def add_waste_heat(n):
) )
n.links.loc[urban_central + " Sabatier", "efficiency3"] = ( n.links.loc[urban_central + " Sabatier", "efficiency3"] = (
0.95 - n.links.loc[urban_central + " Sabatier", "efficiency"] 0.95 - n.links.loc[urban_central + " Sabatier", "efficiency"]
) ) * options["use_methanation_waste_heat"]
# DEA quotes 15% of total input (11% of which are high-value heat) # DEA quotes 15% of total input (11% of which are high-value heat)
if options["use_haber_bosch_waste_heat"] and "Haber-Bosch" in link_carriers: if options["use_haber_bosch_waste_heat"] and "Haber-Bosch" in link_carriers:
@ -3374,7 +3418,7 @@ def add_waste_heat(n):
) )
n.links.loc[urban_central + " Haber-Bosch", "efficiency3"] = ( n.links.loc[urban_central + " Haber-Bosch", "efficiency3"] = (
0.15 * total_energy_input / electricity_input 0.15 * total_energy_input / electricity_input
) ) * options["use_haber_bosch_waste_heat"]
if ( if (
options["use_methanolisation_waste_heat"] options["use_methanolisation_waste_heat"]
@ -3386,11 +3430,11 @@ def add_waste_heat(n):
n.links.loc[urban_central + " methanolisation", "efficiency4"] = ( n.links.loc[urban_central + " methanolisation", "efficiency4"] = (
costs.at["methanolisation", "heat-output"] costs.at["methanolisation", "heat-output"]
/ costs.at["methanolisation", "hydrogen-input"] / costs.at["methanolisation", "hydrogen-input"]
) ) * options["use_methanolisation_waste_heat"]
# TODO integrate usable waste heat efficiency into technology-data from DEA # TODO integrate usable waste heat efficiency into technology-data from DEA
if ( if (
options.get("use_electrolysis_waste_heat", False) options["use_electrolysis_waste_heat"]
and "H2 Electrolysis" in link_carriers and "H2 Electrolysis" in link_carriers
): ):
n.links.loc[urban_central + " H2 Electrolysis", "bus2"] = ( n.links.loc[urban_central + " H2 Electrolysis", "bus2"] = (
@ -3398,7 +3442,7 @@ def add_waste_heat(n):
) )
n.links.loc[urban_central + " H2 Electrolysis", "efficiency2"] = ( n.links.loc[urban_central + " H2 Electrolysis", "efficiency2"] = (
0.84 - n.links.loc[urban_central + " H2 Electrolysis", "efficiency"] 0.84 - n.links.loc[urban_central + " H2 Electrolysis", "efficiency"]
) ) * options["use_electrolysis_waste_heat"]
if options["use_fuel_cell_waste_heat"] and "H2 Fuel Cell" in link_carriers: if options["use_fuel_cell_waste_heat"] and "H2 Fuel Cell" in link_carriers:
n.links.loc[urban_central + " H2 Fuel Cell", "bus2"] = ( n.links.loc[urban_central + " H2 Fuel Cell", "bus2"] = (
@ -3406,7 +3450,7 @@ def add_waste_heat(n):
) )
n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = ( n.links.loc[urban_central + " H2 Fuel Cell", "efficiency2"] = (
0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"] 0.95 - n.links.loc[urban_central + " H2 Fuel Cell", "efficiency"]
) ) * options["use_fuel_cell_waste_heat"]
def add_agriculture(n, costs): def add_agriculture(n, costs):
@ -3717,7 +3761,7 @@ def lossy_bidirectional_links(n, carrier, efficiencies={}):
rev_links.index = rev_links.index.map(lambda x: x + "-reversed") rev_links.index = rev_links.index.map(lambda x: x + "-reversed")
n.links = pd.concat([n.links, rev_links], sort=False) n.links = pd.concat([n.links, rev_links], sort=False)
n.links["reversed"] = n.links["reversed"].fillna(False) n.links["reversed"] = n.links["reversed"].fillna(False).infer_objects(copy=False)
n.links["length_original"] = n.links["length_original"].fillna(n.links.length) n.links["length_original"] = n.links["length_original"].fillna(n.links.length)
# do compression losses after concatenation to take electricity consumption at bus0 in either direction # do compression losses after concatenation to take electricity consumption at bus0 in either direction
@ -3942,12 +3986,11 @@ if __name__ == "__main__":
snakemake = mock_snakemake( snakemake = mock_snakemake(
"prepare_sector_network", "prepare_sector_network",
# configfiles="test/config.overnight.yaml",
simpl="", simpl="",
opts="", opts="",
clusters="37", clusters="1",
ll="v1.0", ll="vopt",
sector_opts="730H-T-H-B-I-A-dist1", sector_opts="",
planning_horizons="2050", planning_horizons="2050",
) )

2
scripts/retrieve_databundle.py
View File

@ -48,7 +48,7 @@ if __name__ == "__main__":
configure_logging(snakemake) configure_logging(snakemake)
set_scenario_config(snakemake) set_scenario_config(snakemake)
url = "https://zenodo.org/records/10973944/files/bundle.tar.xz" url = "https://zenodo.org/records/12760663/files/bundle.tar.xz"
tarball_fn = Path(f"{rootpath}/bundle.tar.xz") tarball_fn = Path(f"{rootpath}/bundle.tar.xz")
to_fn = Path(rootpath) / Path(snakemake.output[0]).parent.parent to_fn = Path(rootpath) / Path(snakemake.output[0]).parent.parent

3
scripts/retrieve_eurostat_data.py
View File

@ -28,7 +28,8 @@ if __name__ == "__main__":
disable_progress = snakemake.config["run"].get("disable_progressbar", False) disable_progress = snakemake.config["run"].get("disable_progressbar", False)
url_eurostat = ( url_eurostat = (
"https://ec.europa.eu/eurostat/documents/38154/4956218/Balances-April2023.zip" # "https://ec.europa.eu/eurostat/documents/38154/4956218/Balances-April2023.zip" # link down
"https://tubcloud.tu-berlin.de/s/prkJpL7B9M3cDPb/download/Balances-April2023.zip"
) )
tarball_fn = Path(f"{rootpath}/data/eurostat/eurostat_2023.zip") tarball_fn = Path(f"{rootpath}/data/eurostat/eurostat_2023.zip")
to_fn = Path(f"{rootpath}/data/eurostat/Balances-April2023/") to_fn = Path(f"{rootpath}/data/eurostat/Balances-April2023/")

20
scripts/solve_network.py
View File

@ -155,7 +155,7 @@ def _add_land_use_constraint(n):
existing_large, "p_nom_min" existing_large, "p_nom_min"
] ]
n.generators.p_nom_max.clip(lower=0, inplace=True) n.generators["p_nom_max"] = n.generators["p_nom_max"].clip(lower=0)
def _add_land_use_constraint_m(n, planning_horizons, config): def _add_land_use_constraint_m(n, planning_horizons, config):
@ -207,7 +207,7 @@ def _add_land_use_constraint_m(n, planning_horizons, config):
existing_large, "p_nom_min" existing_large, "p_nom_min"
] ]
n.generators.p_nom_max.clip(lower=0, inplace=True) n.generators["p_nom_max"] = n.generators["p_nom_max"].clip(lower=0)
def add_solar_potential_constraints(n, config): def add_solar_potential_constraints(n, config):
@ -471,6 +471,22 @@ def prepare_network(
p_nom=1e9, # kW p_nom=1e9, # kW
) )
if solve_opts.get("curtailment_mode"):
n.add("Carrier", "curtailment", color="#fedfed", nice_name="Curtailment")
n.generators_t.p_min_pu = n.generators_t.p_max_pu
buses_i = n.buses.query("carrier == 'AC'").index
n.madd(
"Generator",
buses_i,
suffix=" curtailment",
bus=buses_i,
p_min_pu=-1,
p_max_pu=0,
marginal_cost=-0.1,
carrier="curtailment",
p_nom=1e6,
)
if solve_opts.get("noisy_costs"): if solve_opts.get("noisy_costs"):
for t in n.iterate_components(): for t in n.iterate_components():
# if 'capital_cost' in t.df: # if 'capital_cost' in t.df: