merge master?
This commit is contained in:
martacki
commit
e2ed561938
@ -29,6 +29,9 @@ before_install:
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# list packages for easier debugging
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- conda list
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before_script:
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- 'echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc'
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script:
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- cp ./test/config.test1.yaml ./config.yaml
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- snakemake -j all solve_all_networks
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58
Snakefile
58
Snakefile
@ -5,6 +5,9 @@
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from os.path import normpath, exists
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from shutil import copyfile
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from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
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HTTP = HTTPRemoteProvider()
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if not exists("config.yaml"):
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copyfile("config.default.yaml", "config.yaml")
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@ -135,10 +138,12 @@ rule build_bus_regions:
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resources: mem=1000
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script: "scripts/build_bus_regions.py"
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if config['enable'].get('build_cutout', False):
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rule build_cutout:
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output: directory("cutouts/{cutout}")
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input:
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regions_onshore="resources/regions_onshore.geojson",
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regions_offshore="resources/regions_offshore.geojson"
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output: "cutouts/{cutout}.nc"
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log: "logs/build_cutout/{cutout}.log"
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benchmark: "benchmarks/build_cutout_{cutout}"
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threads: ATLITE_NPROCESSES
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@ -148,16 +153,16 @@ if config['enable'].get('build_cutout', False):
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if config['enable'].get('retrieve_cutout', True):
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rule retrieve_cutout:
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output: directory(expand("cutouts/{cutouts}", **config['atlite'])),
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log: "logs/retrieve_cutout.log"
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script: 'scripts/retrieve_cutout.py'
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input: HTTP.remote("zenodo.org/record/4709858/files/{cutout}.nc", keep_local=True)
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output: "cutouts/{cutout}.nc"
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shell: "mv {input} {output}"
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if config['enable'].get('build_natura_raster', False):
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rule build_natura_raster:
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input:
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natura="data/bundle/natura/Natura2000_end2015.shp",
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cutouts=expand("cutouts/{cutouts}", **config['atlite'])
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cutouts=expand("cutouts/{cutouts}.nc", **config['atlite'])
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output: "resources/natura.tiff"
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log: "logs/build_natura_raster.log"
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script: "scripts/build_natura_raster.py"
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@ -165,9 +170,9 @@ if config['enable'].get('build_natura_raster', False):
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if config['enable'].get('retrieve_natura_raster', True):
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rule retrieve_natura_raster:
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input: HTTP.remote("zenodo.org/record/4706686/files/natura.tiff", keep_local=True)
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output: "resources/natura.tiff"
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log: "logs/retrieve_natura_raster.log"
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script: 'scripts/retrieve_natura_raster.py'
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shell: "mv {input} {output}"
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rule build_renewable_profiles:
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@ -181,11 +186,10 @@ rule build_renewable_profiles:
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country_shapes='resources/country_shapes.geojson',
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offshore_shapes='resources/offshore_shapes.geojson',
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regions=lambda w: ("resources/regions_onshore.geojson"
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if w.technology in ('onwind', 'solar')
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else "resources/regions_offshore.geojson"),
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cutout=lambda w: "cutouts/" + config["renewable"][w.technology]['cutout']
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output:
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profile="resources/profile_{technology}.nc",
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if w.technology in ('onwind', 'solar')
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else "resources/regions_offshore.geojson"),
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cutout=lambda w: "cutouts/" + config["renewable"][w.technology]['cutout'] + ".nc"
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output: profile="resources/profile_{technology}.nc",
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log: "logs/build_renewable_profile_{technology}.log"
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benchmark: "benchmarks/build_renewable_profiles_{technology}"
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threads: ATLITE_NPROCESSES
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@ -198,7 +202,7 @@ if 'hydro' in config['renewable'].keys():
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input:
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country_shapes='resources/country_shapes.geojson',
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eia_hydro_generation='data/bundle/EIA_hydro_generation_2000_2014.csv',
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cutout="cutouts/" + config["renewable"]['hydro']['cutout']
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cutout="cutouts/" + config["renewable"]['hydro']['cutout'] + ".nc"
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output: 'resources/profile_hydro.nc'
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log: "logs/build_hydro_profile.log"
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resources: mem=5000
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@ -388,29 +392,3 @@ rule plot_p_nom_max:
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log: "logs/plot_p_nom_max/elec_s{simpl}_{clusts}_{techs}_{country}_{ext}.log"
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script: "scripts/plot_p_nom_max.py"
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rule build_country_flh:
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input:
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base_network="networks/base.nc",
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corine="data/bundle/corine/g250_clc06_V18_5.tif",
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natura="resources/natura.tiff",
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gebco=lambda w: ("data/bundle/GEBCO_2014_2D.nc"
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if "max_depth" in config["renewable"][w.technology].keys()
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else []),
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country_shapes='resources/country_shapes.geojson',
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offshore_shapes='resources/offshore_shapes.geojson',
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pietzker="data/pietzker2014.xlsx",
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regions=lambda w: ("resources/country_shapes.geojson"
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if w.technology in ('onwind', 'solar')
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else "resources/offshore_shapes.geojson"),
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cutout=lambda w: "cutouts/" + config["renewable"][w.technology]['cutout']
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output:
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area="resources/country_flh_area_{technology}.csv",
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aggregated="resources/country_flh_aggregated_{technology}.csv",
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uncorrected="resources/country_flh_uncorrected_{technology}.csv",
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plot="resources/country_flh_{technology}.pdf",
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exclusion=directory("resources/country_exclusion_{technology}")
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log: "logs/build_country_flh_{technology}.log"
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resources: mem=10000
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benchmark: "benchmarks/build_country_flh_{technology}"
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script: "scripts/build_country_flh.py"
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@ -36,7 +36,7 @@ enable:
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electricity:
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voltages: [220., 300., 380.]
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co2limit: 7.75e+7 # 0.05 * 3.1e9*0.5
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co2base: 3.1e+9 # 1 * 3.1e9*0.5
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co2base: 1.487e9
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agg_p_nom_limits: data/agg_p_nom_minmax.csv
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extendable_carriers:
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@ -62,18 +62,28 @@ electricity:
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atlite:
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nprocesses: 4
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cutouts:
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# use 'base' to determine geographical bounds and time span from config
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# base:
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# module: era5
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europe-2013-era5:
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module: era5
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xs: [-12., 35.]
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ys: [72., 33.]
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years: [2013, 2013]
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module: era5 # in priority order
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x: [-12., 35.]
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y: [33., 72]
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dx: 0.3
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dy: 0.3
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time: ['2013', '2013']
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europe-2013-sarah:
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module: sarah
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resolution: 0.2
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xs: [-12., 42.]
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ys: [65., 33.]
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years: [2013, 2013]
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module: [sarah, era5] # in priority order
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x: [-12., 45.]
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y: [33., 65]
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dx: 0.2
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dy: 0.2
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time: ['2013', '2013']
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sarah_interpolate: false
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sarah_dir:
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features: [influx, temperature]
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renewable:
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onwind:
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cutout: europe-2013-era5
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@ -179,13 +189,16 @@ costs:
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year: 2030
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discountrate: 0.07 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
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USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
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marginal_cost:
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marginal_cost: # EUR/MWh
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solar: 0.01
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onwind: 0.015
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offwind: 0.015
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hydro: 0.
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H2: 0.
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electrolysis: 0.
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fuel cell: 0.
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battery: 0.
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battery inverter: 0.
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emission_prices: # in currency per tonne emission, only used with the option Ep
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co2: 0.
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@ -54,16 +54,15 @@ electricity:
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atlite:
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nprocesses: 4
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cutouts:
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europe-2013-era5:
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europe-2013-era5-tutorial:
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module: era5
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xs: [4., 15.]
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ys: [56., 46.]
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months: [3, 3]
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years: [2013, 2013]
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x: [4., 15.]
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y: [46., 56.]
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time: ["2013-03", "2013-03"]
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renewable:
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onwind:
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cutout: europe-2013-era5
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cutout: europe-2013-era5-tutorial
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resource:
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method: wind
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turbine: Vestas_V112_3MW
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@ -80,7 +79,7 @@ renewable:
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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offwind-ac:
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cutout: europe-2013-era5
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cutout: europe-2013-era5-tutorial
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resource:
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method: wind
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turbine: NREL_ReferenceTurbine_5MW_offshore
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@ -92,7 +91,7 @@ renewable:
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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offwind-dc:
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cutout: europe-2013-era5
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cutout: europe-2013-era5-tutorial
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resource:
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method: wind
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turbine: NREL_ReferenceTurbine_5MW_offshore
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@ -105,7 +104,7 @@ renewable:
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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solar:
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cutout: europe-2013-era5
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cutout: europe-2013-era5-tutorial
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resource:
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method: pv
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panel: CSi
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@ -1,8 +1,9 @@
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,Unit,Values,Description
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nprocesses,--,int,"Number of parallel processes in cutout preparation"
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cutouts,,,
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-- {name},--,"Convention is to name cutouts like ``<region>-<year>-<source>`` (e.g. ``europe-2013-era5``).","Directory to write cutout data to. The user may specify multiple cutouts under configuration ``atlite: cutouts:``. Reference is used in configuration ``renewable: {technology}: cutout:``"
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-- -- module,--,"One of {'era5','sarah'}","Source of the reanalysis weather dataset (e.g. `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_)"
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-- -- xs,°,"Float interval within [-180, 180]","Range of longitudes to download weather data for."
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-- -- ys,°,"Float interval within [-90, 90]","Range of latitudes to download weather data for."
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-- -- years,--,"Integer interval within [1979,2018]","Range of years to download weather data for."
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-- {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."
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-- -- 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>`_)"
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-- -- 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."
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-- -- 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."
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-- -- time,,"Time interval within ['1979', '2018'] (with valid pandas date time strings)","Time span to download weather data for. If not defined, it defaults to the time interval spanned by the snapshots."
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-- -- features,,"String or list of strings with valid cutout features ('inlfux', 'wind').","When freshly building a cutout, retrieve data only for those features. If not defined, it defaults to all available features."
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|
@ -2,19 +2,18 @@
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voltages,kV,"Any subset of {220., 300., 380.}",Voltage levels to consider when,
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co2limit,:math:`t_{CO_2-eq}/a`,float,Cap on total annual system carbon dioxide emissions,
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co2base,:math:`t_{CO_2-eq}/a`,float,Reference value of total annual system carbon dioxide emissions if relative emission reduction target is specified in ``{opts}`` wildcard.,
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agg_p_nom_limits,--,file,path,Reference to ``.csv`` file specifying per carrier generator nominal capacity constraints for individual countries if ``'CCL'`` is in ``{opts}`` wildcard. Defaults to ``data/agg_p_nom_minmax.csv``.
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agg_p_nom_limits,file,path,Reference to ``.csv`` file specifying per carrier generator nominal capacity constraints for individual countries if ``'CCL'`` is in ``{opts}`` wildcard. Defaults to ``data/agg_p_nom_minmax.csv``.
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extendable_carriers,,,,
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--,Generator,--,"Any subset of {'OCGT','CCGT'}",Places extendable conventional power plants (OCGT and/or CCGT) where gas power plants are located today without capacity limits.
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--,StorageUnit,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
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--,Store,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
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--,Link,--,Any subset of {'H2 pipeline'},Adds extendable links (H2 pipelines only) at every connection where there are lines or HVDC links without capacity limits and with zero initial capacity. Hydrogen pipelines require hydrogen storage to be modelled as ``Store``.
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-- Generator,--,"Any subset of {'OCGT','CCGT'}",Places extendable conventional power plants (OCGT and/or CCGT) where gas power plants are located today without capacity limits.
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-- StorageUnit,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
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-- Store,--,"Any subset of {'battery','H2'}",Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
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-- Link,--,Any subset of {'H2 pipeline'},Adds extendable links (H2 pipelines only) at every connection where there are lines or HVDC links without capacity limits and with zero initial capacity. Hydrogen pipelines require hydrogen storage to be modelled as ``Store``.
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max_hours,,,,
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--,battery,h,float,Maximum state of charge capacity of the battery in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
|
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--,H2,h,float,Maximum state of charge capacity of the hydrogen storage in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
|
||||
-- battery,h,float,Maximum state of charge capacity of the battery in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
|
||||
-- H2,h,float,Maximum state of charge capacity of the hydrogen storage in terms of hours at full output capacity ``p_nom``. Cf. `PyPSA documentation <https://pypsa.readthedocs.io/en/latest/components.html#storage-unit>`_.
|
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powerplants_filter,--,"use `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ strings here, e.g. Country not in ['Germany']",Filter query for the default powerplant database.,
|
||||
custom_powerplants,--,"use `pandas.query <https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.query.html>`_ strings here, e.g. Country in ['Germany']",Filter query for the custom powerplant database.,
|
||||
conventional_carriers,--,"Any subset of {nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass}",List of conventional power plants to include in the model from ``resources/powerplants.csv``.,
|
||||
renewable_capacities_from_OPSD,,"[solar, onwind, offwind]",List of carriers (offwind-ac and offwind-dc are included in offwind) whose capacities 'p_nom' are aligned to the `OPSD renewable power plant list <https://data.open-power-system-data.org/renewable_power_plants/>`_,
|
||||
,"Fueltype [ppm], e.g. “Wind”","list of fueltypes stings in PyPSA-EUR, eg. “[onwind, offwind-ac, offwind-dc]”",converts ppm Fueltype to PyPSA-EUR Fueltype,
|
||||
estimate_renewable_capacities_from_capacitiy_stats,,,,
|
||||
,"Fueltype [ppm], e.g. “Wind”","list of fueltypes stings in PyPSA-EUR, eg. “[onwind, offwind-ac, offwind-dc]”",converts ppm Fueltype to PyPSA-EUR Fueltype,
|
||||
"-- Fueltype [ppm], e.g. Wind",,"list of fueltypes strings in PyPSA-Eur, e.g. [onwind, offwind-ac, offwind-dc]",converts ppm Fueltype to PyPSA-EUR Fueltype,
|
||||
|
||||
|
Can't render this file because it has a wrong number of fields in line 5.
|
@ -95,9 +95,12 @@ Specifies the temporal range to build an energy system model for as arguments to
|
||||
``atlite``
|
||||
=============
|
||||
|
||||
Define and specify the ``atlite.Cutout`` used for calculating renewable potentials and time-series. All options except for ``features`` are directly used as `cutout parameters <https://atlite.readthedocs.io/en/latest/ref_api.html#cutout>`_.
|
||||
|
||||
.. literalinclude:: ../config.default.yaml
|
||||
:language: yaml
|
||||
:lines: 62-75
|
||||
:start-at: atlite:
|
||||
:end-before: renewable:
|
||||
|
||||
.. csv-table::
|
||||
:header-rows: 1
|
||||
|
||||
@ -9,50 +9,6 @@ Plotting and Summary
|
||||
|
||||
.. warning:: The corresponding code is currently under revision and has only minimal documentation.
|
||||
|
||||
.. _flh:
|
||||
|
||||
Rule ``build_country_flh``
|
||||
=============================
|
||||
|
||||
.. graphviz::
|
||||
:align: center
|
||||
|
||||
digraph snakemake_dag {
|
||||
graph [bgcolor=white,
|
||||
margin=0,
|
||||
size="8,5"
|
||||
];
|
||||
node [fontname=sans,
|
||||
fontsize=10,
|
||||
penwidth=2,
|
||||
shape=box,
|
||||
style=rounded
|
||||
];
|
||||
edge [color=grey,
|
||||
penwidth=2
|
||||
];
|
||||
0 [color="0.31 0.6 0.85",
|
||||
fillcolor=gray,
|
||||
label=build_country_flh,
|
||||
style=filled];
|
||||
1 [color="0.06 0.6 0.85",
|
||||
label=base_network];
|
||||
1 -> 0;
|
||||
2 [color="0.42 0.6 0.85",
|
||||
label=build_natura_raster];
|
||||
2 -> 0;
|
||||
3 [color="0.58 0.6 0.85",
|
||||
label=build_shapes];
|
||||
3 -> 0;
|
||||
4 [color="0.14 0.6 0.85",
|
||||
label=build_cutout];
|
||||
4 -> 0;
|
||||
}
|
||||
|
||||
|
|
||||
|
||||
.. automodule:: build_country_flh
|
||||
|
||||
.. _plot_potentials:
|
||||
|
||||
Rule ``plot_p_nom_max``
|
||||
|
||||
@ -21,9 +21,59 @@ Rule ``retrieve_databundle``
|
||||
Rule ``retrieve_cutout``
|
||||
------------------------
|
||||
|
||||
.. automodule:: retrieve_cutout
|
||||
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3517949.svg
|
||||
:target: https://doi.org/10.5281/zenodo.3517949
|
||||
|
||||
Cutouts are spatio-temporal subsets of the European weather data from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ reanalysis dataset and the `CMSAF SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_ solar surface radiation dataset for the year 2013.
|
||||
They have been prepared by and are for use with the `atlite <https://github.com/PyPSA/atlite>`_ tool. You can either generate them yourself using the ``build_cutouts`` rule or retrieve them directly from `zenodo <https://doi.org/10.5281/zenodo.3517949>`_ through the rule ``retrieve_cutout``.
|
||||
The :ref:`tutorial` uses a smaller cutout than required for the full model (30 MB), which is also automatically downloaded.
|
||||
|
||||
.. note::
|
||||
To download cutouts yourself from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ you need to `set up the CDS API <https://cds.climate.copernicus.eu/api-how-to>`_.
|
||||
|
||||
|
||||
**Relevant Settings**
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
tutorial:
|
||||
enable:
|
||||
build_cutout:
|
||||
|
||||
.. seealso::
|
||||
Documentation of the configuration file ``config.yaml`` at
|
||||
:ref:`toplevel_cf`
|
||||
|
||||
**Outputs**
|
||||
|
||||
- ``cutouts/{cutout}``: weather data from either the `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ reanalysis weather dataset or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewProduktSearch>`_ satellite-based historic weather data.
|
||||
|
||||
.. seealso::
|
||||
For details see :mod:`build_cutout` and read the `atlite documentation <https://atlite.readthedocs.io>`_.
|
||||
|
||||
|
||||
Rule ``retrieve_natura_raster``
|
||||
-------------------------------
|
||||
|
||||
.. automodule:: retrieve_natura_raster
|
||||
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.4706686.svg
|
||||
:target: https://doi.org/10.5281/zenodo.4706686
|
||||
|
||||
This rule, as a substitute for :mod:`build_natura_raster`, downloads an already rasterized version (`natura.tiff <https://zenodo.org/record/4706686/files/natura.tiff>`_) of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times. The file is placed into the ``resources`` sub-directory.
|
||||
|
||||
**Relevant Settings**
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
enable:
|
||||
build_natura_raster:
|
||||
|
||||
.. seealso::
|
||||
Documentation of the configuration file ``config.yaml`` at
|
||||
:ref:`toplevel_cf`
|
||||
|
||||
**Outputs**
|
||||
|
||||
- ``resources/natura.tiff``: Rasterized version of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times.
|
||||
|
||||
.. seealso::
|
||||
For details see :mod:`build_natura_raster`.
|
||||
|
||||
@ -11,6 +11,14 @@ Release Notes
|
||||
Upcoming Release
|
||||
================
|
||||
|
||||
* Switch to new major release, ``>=v0.2.1`` of ``atlite``. The version upgrade comes along with significant speed up for the rule ``build_renewable_profiles.py`` (~factor 2). A lot of the code which calculated the landuse availability is now outsourced and does not rely on ``glaes``, ``geokit`` anymore. This facilitates the environment building and version compatibility of ``gdal``, ``libgdal`` with other packages.
|
||||
* The minimum python version was set to ``3.8``.
|
||||
* The rule and script ``build_country_flh`` are removed as they're no longer used or maintained.
|
||||
* The flag ``keep_all_available_areas`` in the configuration for renewable potentials (config.yaml -> renewable -> {technology}) was deprecated and now defaults to ``True``.
|
||||
* The tutorial cutout was renamed from ``cutouts/europe-2013-era5.nc`` to ``cutouts/europe-2013-era5-tutorial.nc`` to accomodate tutorial and productive cutouts side-by-side.
|
||||
* Fix: Value for ``co2base`` in ``config.yaml`` adjusted to 1.487e9 t CO2-eq (from 3.1e9 t CO2-eq). The new value represents emissions related to the electricity sector for EU+UK. The old value was ~2x too high and used when the emissions wildcard in ``{opts}`` was used.
|
||||
* Add option to include marginal costs of links representing fuel cells, electrolysis, and battery inverters
|
||||
[`#232 <https://github.com/PyPSA/pypsa-eur/pull/232>`_].
|
||||
|
||||
PyPSA-Eur 0.3.0 (7th December 2020)
|
||||
==================================
|
||||
|
||||
@ -130,8 +130,7 @@ It can take the values ``onwind``, ``offwind-ac``, ``offwind-dc``, and ``solar``
|
||||
|
||||
The wildcard can moreover be used to create technology specific figures and summaries.
|
||||
For instance ``{technology}`` can be used to plot regionally disaggregated potentials
|
||||
with the rule :mod:`plot_p_nom_max` or to summarize a particular technology's
|
||||
full load hours in various countries with the rule :mod:`build_country_flh`.
|
||||
with the rule :mod:`plot_p_nom_max`.
|
||||
|
||||
.. _attr:
|
||||
|
||||
|
||||
@ -9,7 +9,8 @@ dependencies:
|
||||
- python<=3.7
|
||||
- pip
|
||||
- pypsa>=0.17.1
|
||||
- atlite=0.0.3
|
||||
- atlite>=0.2.2
|
||||
- dask<=2021.3.1 # until https://github.com/dask/dask/issues/7583 is solved
|
||||
- pre-commit
|
||||
|
||||
# Dependencies of the workflow itself
|
||||
@ -19,27 +20,13 @@ dependencies:
|
||||
- memory_profiler
|
||||
- yaml
|
||||
- pytables
|
||||
- powerplantmatching>=0.4.3
|
||||
|
||||
# Second order dependencies which should really be deps of atlite
|
||||
- xarray
|
||||
- progressbar2
|
||||
- pyyaml>=5.1.0
|
||||
- powerplantmatching>=0.4.8
|
||||
|
||||
# GIS dependencies have to come all from conda-forge
|
||||
- cartopy
|
||||
- fiona
|
||||
- proj
|
||||
- pyshp
|
||||
- geopandas
|
||||
- rasterio
|
||||
- shapely
|
||||
- libgdal
|
||||
- descartes
|
||||
|
||||
- pip:
|
||||
- vresutils==0.3.1
|
||||
- git+https://github.com/PyPSA/glaes.git#egg=glaes
|
||||
- git+https://github.com/PyPSA/geokit.git#egg=geokit
|
||||
- cdsapi
|
||||
- sphinx
|
||||
- sphinx_rtd_theme
|
||||
|
||||
@ -8,12 +8,13 @@ channels:
|
||||
- bioconda
|
||||
- http://conda.anaconda.org/gurobi
|
||||
dependencies:
|
||||
- python
|
||||
- python>=3.8
|
||||
- pip
|
||||
- mamba # esp for windows build
|
||||
|
||||
- pypsa>=0.17.1
|
||||
- atlite=0.0.3
|
||||
- pypsa>=0.17.1
|
||||
- atlite>=0.2.2
|
||||
- dask<=2021.3.1 # until https://github.com/dask/dask/issues/7583 is solved
|
||||
|
||||
# Dependencies of the workflow itself
|
||||
- xlrd
|
||||
@ -29,32 +30,14 @@ dependencies:
|
||||
- powerplantmatching>=0.4.8
|
||||
- numpy<=1.19.0 # otherwise macos fails
|
||||
|
||||
# Second order dependencies which should really be deps of atlite
|
||||
- xarray
|
||||
- netcdf4
|
||||
- bottleneck
|
||||
- toolz
|
||||
- dask
|
||||
- progressbar2
|
||||
- pyyaml>=5.1.0
|
||||
|
||||
# Keep in conda environment when calling ipython
|
||||
- ipython
|
||||
|
||||
# GIS dependencies:
|
||||
- cartopy
|
||||
- fiona
|
||||
- proj
|
||||
- pyshp
|
||||
- geopandas
|
||||
- rasterio
|
||||
- shapely
|
||||
- libgdal<=3.0.4
|
||||
- descartes
|
||||
|
||||
- pip:
|
||||
- vresutils==0.3.1
|
||||
- tsam>=1.1.0
|
||||
- git+https://github.com/PyPSA/glaes.git#egg=glaes
|
||||
- git+https://github.com/PyPSA/geokit.git#egg=geokit
|
||||
- cdsapi
|
||||
|
||||
@ -114,7 +114,8 @@ def attach_stores(n, costs):
|
||||
carrier='H2 electrolysis',
|
||||
p_nom_extendable=True,
|
||||
efficiency=costs.at["electrolysis", "efficiency"],
|
||||
capital_cost=costs.at["electrolysis", "capital_cost"])
|
||||
capital_cost=costs.at["electrolysis", "capital_cost"],
|
||||
marginal_cost=costs.at["electrolysis", "marginal_cost"])
|
||||
|
||||
n.madd("Link", h2_buses_i + " Fuel Cell",
|
||||
bus0=h2_buses_i,
|
||||
@ -123,7 +124,8 @@ def attach_stores(n, costs):
|
||||
p_nom_extendable=True,
|
||||
efficiency=costs.at["fuel cell", "efficiency"],
|
||||
#NB: fixed cost is per MWel
|
||||
capital_cost=costs.at["fuel cell", "capital_cost"] * costs.at["fuel cell", "efficiency"])
|
||||
capital_cost=costs.at["fuel cell", "capital_cost"] * costs.at["fuel cell", "efficiency"],
|
||||
marginal_cost=costs.at["fuel cell", "marginal_cost"])
|
||||
|
||||
if 'battery' in carriers:
|
||||
b_buses_i = n.madd("Bus", buses_i + " battery", carrier="battery", **bus_sub_dict)
|
||||
@ -133,7 +135,8 @@ def attach_stores(n, costs):
|
||||
carrier='battery',
|
||||
e_cyclic=True,
|
||||
e_nom_extendable=True,
|
||||
capital_cost=costs.at['battery storage', 'capital_cost'])
|
||||
capital_cost=costs.at['battery storage', 'capital_cost'],
|
||||
marginal_cost=costs.at["battery", "marginal_cost"])
|
||||
|
||||
n.madd("Link", b_buses_i + " charger",
|
||||
bus0=buses_i,
|
||||
@ -141,15 +144,16 @@ def attach_stores(n, costs):
|
||||
carrier='battery charger',
|
||||
efficiency=costs.at['battery inverter', 'efficiency'],
|
||||
capital_cost=costs.at['battery inverter', 'capital_cost'],
|
||||
p_nom_extendable=True)
|
||||
p_nom_extendable=True,
|
||||
marginal_cost=costs.at["battery inverter", "marginal_cost"])
|
||||
|
||||
n.madd("Link", b_buses_i + " discharger",
|
||||
bus0=b_buses_i,
|
||||
bus1=buses_i,
|
||||
carrier='battery discharger',
|
||||
efficiency=costs.at['battery inverter','efficiency'],
|
||||
capital_cost=costs.at['battery inverter', 'capital_cost'],
|
||||
p_nom_extendable=True)
|
||||
p_nom_extendable=True,
|
||||
marginal_cost=costs.at["battery inverter", "marginal_cost"])
|
||||
|
||||
|
||||
def attach_hydrogen_pipelines(n, costs):
|
||||
|
||||
@ -1,243 +0,0 @@
|
||||
#!/usr/bin/env python
|
||||
|
||||
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: GPL-3.0-or-later
|
||||
|
||||
"""
|
||||
Create ``.csv`` files and plots for comparing per country full load hours of renewable time series.
|
||||
|
||||
Relevant Settings
|
||||
-----------------
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
snapshots:
|
||||
|
||||
renewable:
|
||||
{technology}:
|
||||
cutout:
|
||||
resource:
|
||||
correction_factor:
|
||||
|
||||
.. seealso::
|
||||
Documentation of the configuration file ``config.yaml`` at
|
||||
:ref:`snapshots_cf`, :ref:`renewable_cf`
|
||||
|
||||
Inputs
|
||||
------
|
||||
|
||||
- ``data/bundle/corine/g250_clc06_V18_5.tif``: `CORINE Land Cover (CLC) <https://land.copernicus.eu/pan-european/corine-land-cover>`_ inventory on `44 classes <https://wiki.openstreetmap.org/wiki/Corine_Land_Cover#Tagging>`_ of land use (e.g. forests, arable land, industrial, urban areas).
|
||||
|
||||
.. image:: img/corine.png
|
||||
:scale: 33 %
|
||||
|
||||
- ``data/bundle/GEBCO_2014_2D.nc``: A `bathymetric <https://en.wikipedia.org/wiki/Bathymetry>`_ data set with a global terrain model for ocean and land at 15 arc-second intervals by the `General Bathymetric Chart of the Oceans (GEBCO) <https://www.gebco.net/data_and_products/gridded_bathymetry_data/>`_.
|
||||
|
||||
.. image:: img/gebco_2019_grid_image.jpg
|
||||
:scale: 50 %
|
||||
|
||||
**Source:** `GEBCO <https://www.gebco.net/data_and_products/images/gebco_2019_grid_image.jpg>`_
|
||||
|
||||
- ``data/pietzker2014.xlsx``: `Supplementary material 2 <https://ars.els-cdn.com/content/image/1-s2.0-S0306261914008149-mmc2.xlsx>`_ from `Pietzcker et al. <https://doi.org/10.1016/j.apenergy.2014.08.011>`_; not part of the data bundle; download and place here yourself.
|
||||
- ``resources/natura.tiff``: confer :ref:`natura`
|
||||
- ``resources/country_shapes.geojson``: confer :ref:`shapes`
|
||||
- ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
|
||||
- ``resources/regions_onshore.geojson``: (if not offshore wind), confer :ref:`busregions`
|
||||
- ``resources/regions_offshore.geojson``: (if offshore wind), :ref:`busregions`
|
||||
- ``"cutouts/" + config["renewable"][{technology}]['cutout']``: :ref:`cutout`
|
||||
- ``networks/base.nc``: :ref:`base`
|
||||
|
||||
Outputs
|
||||
-------
|
||||
|
||||
- ``resources/country_flh_area_{technology}.csv``:
|
||||
- ``resources/country_flh_aggregated_{technology}.csv``:
|
||||
- ``resources/country_flh_uncorrected_{technology}.csv``:
|
||||
- ``resources/country_flh_{technology}.pdf``:
|
||||
- ``resources/country_exclusion_{technology}``:
|
||||
|
||||
Description
|
||||
-----------
|
||||
|
||||
"""
|
||||
|
||||
import logging
|
||||
from _helpers import configure_logging
|
||||
|
||||
import os
|
||||
import atlite
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
import pandas as pd
|
||||
|
||||
import geokit as gk
|
||||
from scipy.sparse import vstack
|
||||
import pycountry as pyc
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from vresutils import landuse as vlanduse
|
||||
from vresutils.array import spdiag
|
||||
|
||||
import progressbar as pgb
|
||||
|
||||
from build_renewable_profiles import init_globals, calculate_potential
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def build_area(flh, countries, areamatrix, breaks, fn):
|
||||
area_unbinned = xr.DataArray(areamatrix.todense(), [countries, capacity_factor.coords['spatial']])
|
||||
bins = xr.DataArray(pd.cut(flh.to_series(), bins=breaks), flh.coords, name="bins")
|
||||
area = area_unbinned.groupby(bins).sum(dim="spatial").to_pandas()
|
||||
area.loc[:,slice(*area.sum()[lambda s: s > 0].index[[0,-1]])].to_csv(fn)
|
||||
area.columns = area.columns.map(lambda s: s.left)
|
||||
return area
|
||||
|
||||
|
||||
def plot_area_not_solar(area, countries):
|
||||
# onshore wind/offshore wind
|
||||
a = area.T
|
||||
|
||||
fig, axes = plt.subplots(nrows=len(countries), sharex=True)
|
||||
for c, ax in zip(countries, axes):
|
||||
d = a[[c]] / 1e3
|
||||
d.plot.bar(ax=ax, legend=False, align='edge', width=1.)
|
||||
ax.set_ylabel(f"Potential {c} / GW")
|
||||
ax.set_title(c)
|
||||
ax.legend()
|
||||
ax.set_xlabel("Full-load hours")
|
||||
fig.savefig(snakemake.output.plot, transparent=True, bbox_inches='tight')
|
||||
|
||||
def plot_area_solar(area, p_area, countries):
|
||||
# onshore wind/offshore wind
|
||||
p = p_area.T
|
||||
a = area.T
|
||||
|
||||
fig, axes = plt.subplots(nrows=len(countries), sharex=True, squeeze=False)
|
||||
for c, ax in zip(countries, axes.flat):
|
||||
d = pd.concat([a[c], p[c]], keys=['PyPSA-Eur', 'Pietzker'], axis=1) / 1e3
|
||||
d.plot.bar(ax=ax, legend=False, align='edge', width=1.)
|
||||
# ax.set_ylabel(f"Potential {c} / GW")
|
||||
ax.set_title(c)
|
||||
ax.legend()
|
||||
ax.set_xlabel("Full-load hours")
|
||||
|
||||
fig.savefig(snakemake.output.plot, transparent=True, bbox_inches='tight')
|
||||
|
||||
|
||||
def build_aggregate(flh, countries, areamatrix, breaks, p_area, fn):
|
||||
agg_a = pd.Series(np.ravel((areamatrix / areamatrix.sum(axis=1)).dot(flh.values)),
|
||||
countries, name="PyPSA-Eur")
|
||||
|
||||
if p_area is None:
|
||||
agg_a['Overall'] = float((np.asarray((areamatrix.sum(axis=0) / areamatrix.sum())
|
||||
.dot(flh.values)).squeeze()))
|
||||
|
||||
agg = pd.DataFrame({'PyPSA-Eur': agg_a})
|
||||
else:
|
||||
# Determine indices of countries which are also in Pietzcker
|
||||
inds = pd.Index(countries).get_indexer(p_area.index)
|
||||
areamatrix = areamatrix[inds]
|
||||
|
||||
agg_a['Overall'] = float((np.asarray((areamatrix.sum(axis=0) / areamatrix.sum())
|
||||
.dot(flh.values)).squeeze()))
|
||||
|
||||
midpoints = (breaks[1:] + breaks[:-1])/2.
|
||||
p = p_area.T
|
||||
|
||||
# Per-country FLH comparison
|
||||
agg_p = pd.Series((p / p.sum()).multiply(midpoints, axis=0).sum(), name="Pietzker")
|
||||
agg_p['Overall'] = float((p.sum(axis=1) / p.sum().sum()).multiply(midpoints, axis=0).sum())
|
||||
|
||||
agg = pd.DataFrame({'PyPSA-Eur': agg_a, 'Pietzcker': agg_p, 'Ratio': agg_p / agg_a})
|
||||
|
||||
agg.to_csv(fn)
|
||||
|
||||
if __name__ == '__main__':
|
||||
if 'snakemake' not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
snakemake = mock_snakemake('build_country_flh', technology='solar')
|
||||
configure_logging(snakemake)
|
||||
|
||||
pgb.streams.wrap_stderr()
|
||||
|
||||
|
||||
config = snakemake.config['renewable'][snakemake.wildcards.technology]
|
||||
|
||||
time = pd.date_range(freq='m', **snakemake.config['snapshots'])
|
||||
params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]]))
|
||||
|
||||
cutout = atlite.Cutout(config['cutout'],
|
||||
cutout_dir=os.path.dirname(snakemake.input.cutout),
|
||||
**params)
|
||||
|
||||
minx, maxx, miny, maxy = cutout.extent
|
||||
dx = (maxx - minx) / (cutout.shape[1] - 1)
|
||||
dy = (maxy - miny) / (cutout.shape[0] - 1)
|
||||
bounds = gk.Extent.from_xXyY((minx - dx/2., maxx + dx/2.,
|
||||
miny - dy/2., maxy + dy/2.))
|
||||
|
||||
# Use GLAES to compute available potentials and the transition matrix
|
||||
paths = dict(snakemake.input)
|
||||
|
||||
init_globals(bounds.xXyY, dx, dy, config, paths)
|
||||
regions = gk.vector.extractFeatures(paths["regions"], onlyAttr=True)
|
||||
countries = pd.Index(regions["name"], name="country")
|
||||
|
||||
widgets = [
|
||||
pgb.widgets.Percentage(),
|
||||
' ', pgb.widgets.SimpleProgress(format='(%s)' % pgb.widgets.SimpleProgress.DEFAULT_FORMAT),
|
||||
' ', pgb.widgets.Bar(),
|
||||
' ', pgb.widgets.Timer(),
|
||||
' ', pgb.widgets.ETA()
|
||||
]
|
||||
progressbar = pgb.ProgressBar(prefix='Compute GIS potentials: ', widgets=widgets, max_value=len(countries))
|
||||
|
||||
if not os.path.isdir(snakemake.output.exclusion):
|
||||
os.makedirs(snakemake.output.exclusion)
|
||||
|
||||
matrix = vstack([calculate_potential(i, save_map=os.path.join(snakemake.output.exclusion, countries[i]))
|
||||
for i in progressbar(regions.index)])
|
||||
|
||||
areamatrix = matrix * spdiag(vlanduse._cutout_cell_areas(cutout).ravel())
|
||||
areamatrix.data[areamatrix.data < 1.] = 0 # ignore weather cells where only less than 1 km^2 can be installed
|
||||
areamatrix.eliminate_zeros()
|
||||
|
||||
resource = config['resource']
|
||||
func = getattr(cutout, resource.pop('method'))
|
||||
correction_factor = config.get('correction_factor', 1.)
|
||||
|
||||
capacity_factor = func(capacity_factor=True, show_progress='Compute capacity factors: ', **resource).stack(spatial=('y', 'x'))
|
||||
flh_uncorr = capacity_factor * 8760
|
||||
flh_corr = correction_factor * flh_uncorr
|
||||
|
||||
if snakemake.wildcards.technology == 'solar':
|
||||
pietzcker = pd.read_excel(snakemake.input.pietzker, sheet_name="PV on all area", skiprows=2, header=[0,1]).iloc[1:177]
|
||||
p_area1_50 = pietzcker['Usable Area at given FLh in 1-50km distance to settlement '].dropna(axis=1)
|
||||
p_area1_50.columns = p_area1_50.columns.str.split(' ').str[0]
|
||||
|
||||
p_area50_100 = pietzcker['Usable Area at given FLh in 50-100km distance to settlement ']
|
||||
|
||||
p_area = p_area1_50 + p_area50_100
|
||||
cols = p_area.columns
|
||||
breaks = cols.str.split('-').str[0].append(pd.Index([cols[-1].split('-')[1]])).astype(int)
|
||||
p_area.columns = breaks[:-1]
|
||||
|
||||
p_area = p_area.reindex(countries.map(lambda c: pyc.countries.get(alpha_2=c).name))
|
||||
p_area.index = countries
|
||||
p_area = p_area.dropna() # Pietzcker does not have data for CZ and MK
|
||||
else:
|
||||
breaks = np.r_[0:8000:50]
|
||||
p_area = None
|
||||
|
||||
|
||||
area = build_area(flh_corr, countries, areamatrix, breaks, snakemake.output.area)
|
||||
|
||||
if snakemake.wildcards.technology == 'solar':
|
||||
plot_area_solar(area, p_area, p_area.index)
|
||||
else:
|
||||
plot_area_not_solar(area, countries)
|
||||
|
||||
build_aggregate(flh_uncorr, countries, areamatrix, breaks, p_area, snakemake.output.uncorrected)
|
||||
build_aggregate(flh_corr, countries, areamatrix, breaks, p_area, snakemake.output.aggregated)
|
||||
@ -1,4 +1,4 @@
|
||||
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
|
||||
# SPDX-FileCopyrightText: : 2017-2021 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: GPL-3.0-or-later
|
||||
|
||||
@ -92,10 +92,11 @@ Description
|
||||
"""
|
||||
|
||||
import logging
|
||||
import atlite
|
||||
import geopandas as gpd
|
||||
import pandas as pd
|
||||
from _helpers import configure_logging
|
||||
|
||||
import os
|
||||
import atlite
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -106,14 +107,24 @@ if __name__ == "__main__":
|
||||
configure_logging(snakemake)
|
||||
|
||||
cutout_params = snakemake.config['atlite']['cutouts'][snakemake.wildcards.cutout]
|
||||
for p in ('xs', 'ys', 'years', 'months'):
|
||||
if p in cutout_params:
|
||||
cutout_params[p] = slice(*cutout_params[p])
|
||||
|
||||
cutout = atlite.Cutout(snakemake.wildcards.cutout,
|
||||
cutout_dir=os.path.dirname(snakemake.output[0]),
|
||||
**cutout_params)
|
||||
snapshots = pd.date_range(freq='h', **snakemake.config['snapshots'])
|
||||
time = [snapshots[0], snapshots[-1]]
|
||||
cutout_params['time'] = slice(*cutout_params.get('time', time))
|
||||
|
||||
nprocesses = snakemake.config['atlite'].get('nprocesses', 4)
|
||||
if {'x', 'y', 'bounds'}.isdisjoint(cutout_params):
|
||||
# Determine the bounds from bus regions with a buffer of two grid cells
|
||||
onshore = gpd.read_file(snakemake.input.regions_onshore)
|
||||
offshore = gpd.read_file(snakemake.input.regions_offshore)
|
||||
regions = onshore.append(offshore)
|
||||
d = max(cutout_params.get('dx', 0.25), cutout_params.get('dy', 0.25))*2
|
||||
cutout_params['bounds'] = regions.total_bounds + [-d, -d, d, d]
|
||||
elif {'x', 'y'}.issubset(cutout_params):
|
||||
cutout_params['x'] = slice(*cutout_params['x'])
|
||||
cutout_params['y'] = slice(*cutout_params['y'])
|
||||
|
||||
cutout.prepare(nprocesses=nprocesses)
|
||||
|
||||
logging.info(f"Preparing cutout with parameters {cutout_params}.")
|
||||
features = cutout_params.pop('features', None)
|
||||
cutout = atlite.Cutout(snakemake.output[0], **cutout_params)
|
||||
cutout.prepare(features=features)
|
||||
|
||||
@ -62,7 +62,6 @@ Description
|
||||
import logging
|
||||
from _helpers import configure_logging
|
||||
|
||||
import os
|
||||
import atlite
|
||||
import geopandas as gpd
|
||||
from vresutils import hydro as vhydro
|
||||
@ -76,20 +75,21 @@ if __name__ == "__main__":
|
||||
configure_logging(snakemake)
|
||||
|
||||
config = snakemake.config['renewable']['hydro']
|
||||
cutout_dir = os.path.dirname(snakemake.input.cutout)
|
||||
cutout = atlite.Cutout(config['cutout'], cutout_dir=cutout_dir)
|
||||
cutout = atlite.Cutout(snakemake.input.cutout)
|
||||
|
||||
countries = snakemake.config['countries']
|
||||
country_shapes = gpd.read_file(snakemake.input.country_shapes).set_index('name')['geometry'].reindex(countries)
|
||||
country_shapes = (gpd.read_file(snakemake.input.country_shapes)
|
||||
.set_index('name')['geometry'].reindex(countries))
|
||||
country_shapes.index.name = 'countries'
|
||||
|
||||
eia_stats = vhydro.get_eia_annual_hydro_generation(snakemake.input.eia_hydro_generation).reindex(columns=countries)
|
||||
eia_stats = vhydro.get_eia_annual_hydro_generation(
|
||||
snakemake.input.eia_hydro_generation).reindex(columns=countries)
|
||||
inflow = cutout.runoff(shapes=country_shapes,
|
||||
smooth=True,
|
||||
lower_threshold_quantile=True,
|
||||
normalize_using_yearly=eia_stats)
|
||||
|
||||
if 'clip_min_inflow' in config:
|
||||
inflow.values[inflow.values < config['clip_min_inflow']] = 0.
|
||||
inflow = inflow.where(inflow > config['clip_min_inflow'], 0)
|
||||
|
||||
inflow.to_netcdf(snakemake.output[0])
|
||||
|
||||
@ -43,30 +43,49 @@ import logging
|
||||
from _helpers import configure_logging
|
||||
|
||||
import atlite
|
||||
import geokit as gk
|
||||
from pathlib import Path
|
||||
import geopandas as gpd
|
||||
import rasterio as rio
|
||||
from rasterio.features import geometry_mask
|
||||
from rasterio.warp import transform_bounds
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def determine_cutout_xXyY(cutout_name):
|
||||
cutout = atlite.Cutout(cutout_name, cutout_dir=cutout_dir)
|
||||
cutout = atlite.Cutout(cutout_name)
|
||||
assert cutout.crs.to_epsg() == 4326
|
||||
x, X, y, Y = cutout.extent
|
||||
dx = (X - x) / (cutout.shape[1] - 1)
|
||||
dy = (Y - y) / (cutout.shape[0] - 1)
|
||||
dx, dy = cutout.dx, cutout.dy
|
||||
return [x - dx/2., X + dx/2., y - dy/2., Y + dy/2.]
|
||||
|
||||
|
||||
def get_transform_and_shape(bounds, res):
|
||||
left, bottom = [(b // res)* res for b in bounds[:2]]
|
||||
right, top = [(b // res + 1) * res for b in bounds[2:]]
|
||||
shape = int((top - bottom) // res), int((right - left) / res)
|
||||
transform = rio.Affine(res, 0, left, 0, -res, top)
|
||||
return transform, shape
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if 'snakemake' not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
snakemake = mock_snakemake('build_natura_raster')
|
||||
configure_logging(snakemake)
|
||||
|
||||
cutout_dir = Path(snakemake.input.cutouts[0]).parent.resolve()
|
||||
cutout_names = {res['cutout'] for res in snakemake.config['renewable'].values()}
|
||||
xs, Xs, ys, Ys = zip(*(determine_cutout_xXyY(cutout) for cutout in cutout_names))
|
||||
xXyY = min(xs), max(Xs), min(ys), max(Ys)
|
||||
|
||||
natura = gk.vector.loadVector(snakemake.input.natura)
|
||||
extent = gk.Extent.from_xXyY(xXyY).castTo(3035).fit(100)
|
||||
extent.rasterize(natura, pixelWidth=100, pixelHeight=100, output=snakemake.output[0])
|
||||
cutouts = snakemake.input.cutouts
|
||||
xs, Xs, ys, Ys = zip(*(determine_cutout_xXyY(cutout) for cutout in cutouts))
|
||||
bounds = transform_bounds(4326, 3035, min(xs), min(ys), max(Xs), max(Ys))
|
||||
transform, out_shape = get_transform_and_shape(bounds, res=100)
|
||||
|
||||
# adjusted boundaries
|
||||
shapes = gpd.read_file(snakemake.input.natura).to_crs(3035)
|
||||
raster = ~geometry_mask(shapes.geometry, out_shape[::-1], transform)
|
||||
raster = raster.astype(rio.uint8)
|
||||
|
||||
with rio.open(snakemake.output[0], 'w', driver='GTiff', dtype=rio.uint8,
|
||||
count=1, transform=transform, crs=3035, compress='lzw',
|
||||
width=raster.shape[1], height=raster.shape[0]) as dst:
|
||||
dst.write(raster, indexes=1)
|
||||
|
||||
|
||||
@ -60,7 +60,6 @@ Inputs
|
||||
**Source:** `GEBCO <https://www.gebco.net/data_and_products/images/gebco_2019_grid_image.jpg>`_
|
||||
|
||||
- ``resources/natura.tiff``: confer :ref:`natura`
|
||||
- ``resources/country_shapes.geojson``: confer :ref:`shapes`
|
||||
- ``resources/offshore_shapes.geojson``: confer :ref:`shapes`
|
||||
- ``resources/regions_onshore.geojson``: (if not offshore wind), confer :ref:`busregions`
|
||||
- ``resources/regions_offshore.geojson``: (if offshore wind), :ref:`busregions`
|
||||
@ -180,220 +179,154 @@ node (`p_nom_max`): ``simple`` and ``conservative``:
|
||||
reached.
|
||||
|
||||
"""
|
||||
import progressbar as pgb
|
||||
import geopandas as gpd
|
||||
import xarray as xr
|
||||
import numpy as np
|
||||
import atlite
|
||||
import logging
|
||||
from pypsa.geo import haversine
|
||||
from shapely.geometry import LineString
|
||||
import time
|
||||
|
||||
from _helpers import configure_logging
|
||||
|
||||
import os
|
||||
import atlite
|
||||
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
import pandas as pd
|
||||
import multiprocessing as mp
|
||||
import matplotlib.pyplot as plt
|
||||
import progressbar as pgb
|
||||
|
||||
from scipy.sparse import csr_matrix, vstack
|
||||
from pypsa.geo import haversine
|
||||
from vresutils import landuse as vlanduse
|
||||
from vresutils.array import spdiag
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
bounds = dx = dy = config = paths = gebco = clc = natura = None
|
||||
|
||||
|
||||
def init_globals(bounds_xXyY, n_dx, n_dy, n_config, n_paths):
|
||||
# Late import so that the GDAL Context is only created in the new processes
|
||||
global gl, gk, gdal
|
||||
import glaes as gl
|
||||
import geokit as gk
|
||||
from osgeo import gdal as gdal
|
||||
|
||||
# global in each process of the multiprocessing.Pool
|
||||
global bounds, dx, dy, config, paths, gebco, clc, natura
|
||||
|
||||
bounds = gk.Extent.from_xXyY(bounds_xXyY)
|
||||
dx = n_dx
|
||||
dy = n_dy
|
||||
config = n_config
|
||||
paths = n_paths
|
||||
|
||||
if "max_depth" in config:
|
||||
gebco = gk.raster.loadRaster(paths["gebco"])
|
||||
gebco.SetProjection(gk.srs.loadSRS(4326).ExportToWkt())
|
||||
|
||||
clc = gk.raster.loadRaster(paths["corine"])
|
||||
clc.SetProjection(gk.srs.loadSRS(3035).ExportToWkt())
|
||||
|
||||
natura = gk.raster.loadRaster(paths["natura"])
|
||||
|
||||
|
||||
def downsample_to_coarse_grid(bounds, dx, dy, mask, data):
|
||||
# The GDAL warp function with the 'average' resample algorithm needs a band of zero values of at least
|
||||
# the size of one coarse cell around the original raster or it produces erroneous results
|
||||
orig = mask.createRaster(data=data)
|
||||
padded_extent = mask.extent.castTo(bounds.srs).pad(max(dx, dy)).castTo(mask.srs)
|
||||
padded = padded_extent.fit((mask.pixelWidth, mask.pixelHeight)).warp(orig, mask.pixelWidth, mask.pixelHeight)
|
||||
orig = None # free original raster
|
||||
average = bounds.createRaster(dx, dy, dtype=gdal.GDT_Float32)
|
||||
assert gdal.Warp(average, padded, resampleAlg='average') == 1, "gdal warp failed: %s" % gdal.GetLastErrorMsg()
|
||||
return average
|
||||
|
||||
|
||||
def calculate_potential(gid, save_map=None):
|
||||
feature = gk.vector.extractFeature(paths["regions"], where=gid)
|
||||
ec = gl.ExclusionCalculator(feature.geom)
|
||||
|
||||
corine = config.get("corine", {})
|
||||
if isinstance(corine, list):
|
||||
corine = {'grid_codes': corine}
|
||||
if "grid_codes" in corine:
|
||||
ec.excludeRasterType(clc, value=corine["grid_codes"], invert=True)
|
||||
if corine.get("distance", 0.) > 0.:
|
||||
ec.excludeRasterType(clc, value=corine["distance_grid_codes"], buffer=corine["distance"])
|
||||
|
||||
if config.get("natura", False):
|
||||
ec.excludeRasterType(natura, value=1)
|
||||
if "max_depth" in config:
|
||||
ec.excludeRasterType(gebco, (None, -config["max_depth"]))
|
||||
|
||||
# TODO compute a distance field as a raster beforehand
|
||||
if 'max_shore_distance' in config:
|
||||
ec.excludeVectorType(paths["country_shapes"], buffer=config['max_shore_distance'], invert=True)
|
||||
if 'min_shore_distance' in config:
|
||||
ec.excludeVectorType(paths["country_shapes"], buffer=config['min_shore_distance'])
|
||||
|
||||
if save_map is not None:
|
||||
ec.draw()
|
||||
plt.savefig(save_map, transparent=True)
|
||||
plt.close()
|
||||
|
||||
availability = downsample_to_coarse_grid(bounds, dx, dy, ec.region, np.where(ec.region.mask, ec._availability, 0))
|
||||
|
||||
return csr_matrix(gk.raster.extractMatrix(availability).flatten() / 100.)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if 'snakemake' not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
snakemake = mock_snakemake('build_renewable_profiles', technology='solar')
|
||||
configure_logging(snakemake)
|
||||
|
||||
pgb.streams.wrap_stderr()
|
||||
|
||||
paths = snakemake.input
|
||||
nprocesses = snakemake.config['atlite'].get('nprocesses')
|
||||
noprogress = not snakemake.config['atlite'].get('show_progress', True)
|
||||
config = snakemake.config['renewable'][snakemake.wildcards.technology]
|
||||
|
||||
time = pd.date_range(freq='m', **snakemake.config['snapshots'])
|
||||
params = dict(years=slice(*time.year[[0, -1]]), months=slice(*time.month[[0, -1]]))
|
||||
|
||||
cutout = atlite.Cutout(config['cutout'],
|
||||
cutout_dir=os.path.dirname(snakemake.input.cutout),
|
||||
**params)
|
||||
|
||||
minx, maxx, miny, maxy = cutout.extent
|
||||
dx = (maxx - minx) / (cutout.shape[1] - 1)
|
||||
dy = (maxy - miny) / (cutout.shape[0] - 1)
|
||||
bounds_xXyY = (minx - dx/2., maxx + dx/2., miny - dy/2., maxy + dy/2.)
|
||||
|
||||
# Use GLAES to compute available potentials and the transition matrix
|
||||
paths = dict(snakemake.input)
|
||||
|
||||
# Use the following for testing the default windows method on linux
|
||||
# mp.set_start_method('spawn')
|
||||
with mp.Pool(initializer=init_globals, initargs=(bounds_xXyY, dx, dy, config, paths),
|
||||
maxtasksperchild=20, processes=snakemake.config['atlite'].get('nprocesses', 2)) as pool:
|
||||
|
||||
# The GDAL library creates a GDAL context on module import, which may not be shared over multiple
|
||||
# processes or the PROJ4 library has a hickup, so we import only after forking.
|
||||
import geokit as gk
|
||||
|
||||
regions = gk.vector.extractFeatures(paths["regions"], onlyAttr=True)
|
||||
buses = pd.Index(regions['name'], name="bus")
|
||||
widgets = [
|
||||
pgb.widgets.Percentage(),
|
||||
' ', pgb.widgets.SimpleProgress(format='(%s)' % pgb.widgets.SimpleProgress.DEFAULT_FORMAT),
|
||||
' ', pgb.widgets.Bar(),
|
||||
' ', pgb.widgets.Timer(),
|
||||
' ', pgb.widgets.ETA()
|
||||
]
|
||||
progressbar = pgb.ProgressBar(prefix='Compute GIS potentials: ', widgets=widgets, max_value=len(regions))
|
||||
matrix = vstack(list(progressbar(pool.imap(calculate_potential, regions.index))))
|
||||
|
||||
potentials = config['capacity_per_sqkm'] * vlanduse._cutout_cell_areas(cutout)
|
||||
potmatrix = matrix * spdiag(potentials.ravel())
|
||||
if not config.get('keep_all_available_areas', False):
|
||||
potmatrix.data[potmatrix.data < 1.] = 0 # ignore weather cells where only less than 1 MW can be installed
|
||||
potmatrix.eliminate_zeros()
|
||||
|
||||
resource = config['resource']
|
||||
func = getattr(cutout, resource.pop('method'))
|
||||
resource = config['resource'] # pv panel config / wind turbine config
|
||||
correction_factor = config.get('correction_factor', 1.)
|
||||
if correction_factor != 1.:
|
||||
logger.warning('correction_factor is set as {}'.format(correction_factor))
|
||||
capacity_factor = correction_factor * func(capacity_factor=True, show_progress='Compute capacity factors: ', **resource).stack(spatial=('y', 'x')).values
|
||||
layoutmatrix = potmatrix * spdiag(capacity_factor)
|
||||
|
||||
profile, capacities = func(matrix=layoutmatrix, index=buses, per_unit=True,
|
||||
return_capacity=True, show_progress='Compute profiles: ',
|
||||
**resource)
|
||||
|
||||
capacity_per_sqkm = config['capacity_per_sqkm']
|
||||
p_nom_max_meth = config.get('potential', 'conservative')
|
||||
|
||||
if p_nom_max_meth == 'simple':
|
||||
p_nom_max = xr.DataArray(np.asarray(potmatrix.sum(axis=1)).squeeze(), [buses])
|
||||
elif p_nom_max_meth == 'conservative':
|
||||
# p_nom_max has to be calculated for each bus and is the minimal ratio
|
||||
# (min over all weather grid cells of the bus region) between the available
|
||||
# potential (potmatrix) and the used normalised layout (layoutmatrix /
|
||||
# capacities), so we would like to calculate i.e. potmatrix / (layoutmatrix /
|
||||
# capacities). Since layoutmatrix = potmatrix * capacity_factor, this
|
||||
# corresponds to capacities/max(capacity factor in the voronoi cell)
|
||||
p_nom_max = xr.DataArray([1./np.max(capacity_factor[inds]) if len(inds) else 0.
|
||||
for inds in np.split(potmatrix.indices, potmatrix.indptr[1:-1])], [buses]) * capacities
|
||||
if isinstance(config.get("corine", {}), list):
|
||||
config['corine'] = {'grid_codes': config['corine']}
|
||||
|
||||
if correction_factor != 1.:
|
||||
logger.info(f'correction_factor is set as {correction_factor}')
|
||||
|
||||
|
||||
cutout = atlite.Cutout(paths['cutout'])
|
||||
regions = gpd.read_file(paths.regions).set_index('name').rename_axis('bus')
|
||||
buses = regions.index
|
||||
|
||||
excluder = atlite.ExclusionContainer(crs=3035, res=100)
|
||||
|
||||
if config['natura']:
|
||||
excluder.add_raster(paths.natura, nodata=0, allow_no_overlap=True)
|
||||
|
||||
corine = config.get("corine", {})
|
||||
if "grid_codes" in corine:
|
||||
codes = corine["grid_codes"]
|
||||
excluder.add_raster(paths.corine, codes=codes, invert=True, crs=3035)
|
||||
if corine.get("distance", 0.) > 0.:
|
||||
codes = corine["distance_grid_codes"]
|
||||
buffer = corine["distance"]
|
||||
excluder.add_raster(paths.corine, codes=codes, buffer=buffer, crs=3035)
|
||||
|
||||
if "max_depth" in config:
|
||||
func = lambda v: v <= -config['max_depth']
|
||||
excluder.add_raster(paths.gebco, codes=func, crs=4236, nodata=-1000)
|
||||
|
||||
if 'min_shore_distance' in config:
|
||||
buffer = config['min_shore_distance']
|
||||
excluder.add_geometry(paths.country_shapes, buffer=buffer)
|
||||
|
||||
if 'max_shore_distance' in config:
|
||||
buffer = config['max_shore_distance']
|
||||
excluder.add_geometry(paths.country_shapes, buffer=buffer, invert=True)
|
||||
|
||||
kwargs = dict(nprocesses=nprocesses, disable_progressbar=noprogress)
|
||||
if noprogress:
|
||||
logger.info('Calculate landuse availabilities...')
|
||||
start = time.time()
|
||||
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
|
||||
duration = time.time() - start
|
||||
logger.info(f'Completed availability calculation ({duration:2.2f}s)')
|
||||
else:
|
||||
raise AssertionError('Config key `potential` should be one of "simple" (default) or "conservative",'
|
||||
' not "{}"'.format(p_nom_max_meth))
|
||||
availability = cutout.availabilitymatrix(regions, excluder, **kwargs)
|
||||
|
||||
layout = xr.DataArray(np.asarray(potmatrix.sum(axis=0)).reshape(cutout.shape),
|
||||
[cutout.meta.indexes[ax] for ax in ['y', 'x']])
|
||||
area = cutout.grid.to_crs(3035).area / 1e6
|
||||
area = xr.DataArray(area.values.reshape(cutout.shape),
|
||||
[cutout.coords['y'], cutout.coords['x']])
|
||||
|
||||
# Determine weighted average distance from substation
|
||||
cell_coords = cutout.grid_coordinates()
|
||||
potential = capacity_per_sqkm * availability.sum('bus') * area
|
||||
func = getattr(cutout, resource.pop('method'))
|
||||
resource['dask_kwargs'] = {'num_workers': nprocesses}
|
||||
capacity_factor = correction_factor * func(capacity_factor=True, **resource)
|
||||
layout = capacity_factor * area * capacity_per_sqkm
|
||||
profile, capacities = func(matrix=availability.stack(spatial=['y','x']),
|
||||
layout=layout, index=buses,
|
||||
per_unit=True, return_capacity=True, **resource)
|
||||
|
||||
logger.info(f"Calculating maximal capacity per bus (method '{p_nom_max_meth}')")
|
||||
if p_nom_max_meth == 'simple':
|
||||
p_nom_max = capacity_per_sqkm * availability @ area
|
||||
elif p_nom_max_meth == 'conservative':
|
||||
max_cap_factor = capacity_factor.where(availability!=0).max(['x', 'y'])
|
||||
p_nom_max = capacities / max_cap_factor
|
||||
else:
|
||||
raise AssertionError('Config key `potential` should be one of "simple" '
|
||||
f'(default) or "conservative", not "{p_nom_max_meth}"')
|
||||
|
||||
|
||||
|
||||
logger.info('Calculate average distances.')
|
||||
layoutmatrix = (layout * availability).stack(spatial=['y','x'])
|
||||
|
||||
coords = cutout.grid[['x', 'y']]
|
||||
bus_coords = regions[['x', 'y']]
|
||||
|
||||
average_distance = []
|
||||
for i in regions.index:
|
||||
row = layoutmatrix[i]
|
||||
distances = haversine(regions.loc[i, ['x', 'y']], cell_coords[row.indices])[0]
|
||||
average_distance.append((distances * (row.data / row.data.sum())).sum())
|
||||
centre_of_mass = []
|
||||
for bus in buses:
|
||||
row = layoutmatrix.sel(bus=bus).data
|
||||
nz_b = row != 0
|
||||
row = row[nz_b]
|
||||
co = coords[nz_b]
|
||||
distances = haversine(bus_coords.loc[bus], co)
|
||||
average_distance.append((distances * (row / row.sum())).sum())
|
||||
centre_of_mass.append(co.values.T @ (row / row.sum()))
|
||||
|
||||
average_distance = xr.DataArray(average_distance, [buses])
|
||||
centre_of_mass = xr.DataArray(centre_of_mass, [buses, ('spatial', ['x', 'y'])])
|
||||
|
||||
|
||||
ds = xr.merge([(correction_factor * profile).rename('profile'),
|
||||
capacities.rename('weight'),
|
||||
p_nom_max.rename('p_nom_max'),
|
||||
layout.rename('potential'),
|
||||
average_distance.rename('average_distance')])
|
||||
capacities.rename('weight'),
|
||||
p_nom_max.rename('p_nom_max'),
|
||||
potential.rename('potential'),
|
||||
average_distance.rename('average_distance')])
|
||||
|
||||
|
||||
if snakemake.wildcards.technology.startswith("offwind"):
|
||||
import geopandas as gpd
|
||||
from shapely.geometry import LineString
|
||||
|
||||
offshore_shape = gpd.read_file(snakemake.input.offshore_shapes).unary_union
|
||||
logger.info('Calculate underwater fraction of connections.')
|
||||
offshore_shape = gpd.read_file(paths['offshore_shapes']).unary_union
|
||||
underwater_fraction = []
|
||||
for i in regions.index:
|
||||
row = layoutmatrix[i]
|
||||
centre_of_mass = (cell_coords[row.indices] * (row.data / row.data.sum())[:,np.newaxis]).sum(axis=0)
|
||||
line = LineString([centre_of_mass, regions.loc[i, ['x', 'y']]])
|
||||
underwater_fraction.append(line.intersection(offshore_shape).length / line.length)
|
||||
for bus in buses:
|
||||
p = centre_of_mass.sel(bus=bus).data
|
||||
line = LineString([p, regions.loc[bus, ['x', 'y']]])
|
||||
frac = line.intersection(offshore_shape).length/line.length
|
||||
underwater_fraction.append(frac)
|
||||
|
||||
ds['underwater_fraction'] = xr.DataArray(underwater_fraction, [buses])
|
||||
|
||||
# select only buses with some capacity and minimal capacity factor
|
||||
ds = ds.sel(bus=((ds['profile'].mean('time') > config.get('min_p_max_pu', 0.)) &
|
||||
(ds['p_nom_max'] > config.get('min_p_nom_max', 0.))))
|
||||
(ds['p_nom_max'] > config.get('min_p_nom_max', 0.))))
|
||||
|
||||
if 'clip_p_max_pu' in config:
|
||||
ds['profile'].values[ds['profile'].values < config['clip_p_max_pu']] = 0.
|
||||
min_p_max_pu = config['clip_p_max_pu']
|
||||
ds['profile'] = ds['profile'].where(ds['profile'] >= min_p_max_pu, 0)
|
||||
|
||||
ds.to_netcdf(snakemake.output.profile)
|
||||
|
||||
@ -71,7 +71,7 @@ opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"}
|
||||
|
||||
|
||||
def _add_indexed_rows(df, raw_index):
|
||||
new_index = df.index|pd.MultiIndex.from_product(raw_index)
|
||||
new_index = df.index.union(pd.MultiIndex.from_product(raw_index))
|
||||
if isinstance(new_index, pd.Index):
|
||||
new_index = pd.MultiIndex.from_tuples(new_index)
|
||||
|
||||
@ -126,7 +126,7 @@ def calculate_costs(n, label, costs):
|
||||
|
||||
marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
|
||||
|
||||
costs = costs.reindex(costs.index|pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index]))
|
||||
costs = costs.reindex(costs.index.union(pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index])))
|
||||
|
||||
costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values
|
||||
|
||||
@ -222,7 +222,7 @@ def calculate_supply(n, label, supply):
|
||||
#lots of sign compensation for direction and to do maximums
|
||||
s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items,'carrier']).sum()
|
||||
|
||||
supply = supply.reindex(supply.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
|
||||
supply = supply.reindex(supply.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
|
||||
supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values
|
||||
|
||||
return supply
|
||||
@ -268,7 +268,7 @@ def calculate_supply_energy(n, label, supply_energy):
|
||||
|
||||
s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'carrier']).sum()
|
||||
|
||||
supply_energy = supply_energy.reindex(supply_energy.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
|
||||
supply_energy = supply_energy.reindex(supply_energy.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
|
||||
supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values
|
||||
|
||||
return supply_energy
|
||||
@ -276,7 +276,7 @@ def calculate_supply_energy(n, label, supply_energy):
|
||||
|
||||
def calculate_metrics(n,label,metrics):
|
||||
|
||||
metrics = metrics.reindex(metrics.index|pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"]))
|
||||
metrics = metrics.reindex(metrics.index.union(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])))
|
||||
|
||||
metrics.at["line_volume_DC",label] = (n.links.length*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
|
||||
metrics.at["line_volume_AC",label] = (n.lines.length*n.lines.s_nom_opt).sum()
|
||||
@ -298,7 +298,7 @@ def calculate_prices(n,label,prices):
|
||||
|
||||
bus_type = pd.Series(n.buses.index.str[3:],n.buses.index).replace("","electricity")
|
||||
|
||||
prices = prices.reindex(prices.index|bus_type.value_counts().index)
|
||||
prices = prices.reindex(prices.index.union(bus_type.value_counts().index))
|
||||
|
||||
logger.warning("Prices are time-averaged, not load-weighted")
|
||||
prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).mean()
|
||||
|
||||
@ -164,7 +164,7 @@ def plot_map(n, ax=None, attribute='p_nom', opts={}):
|
||||
handler_map=make_handler_map_to_scale_circles_as_in(ax))
|
||||
ax.add_artist(l2)
|
||||
|
||||
techs = (bus_sizes.index.levels[1]) & pd.Index(opts['vre_techs'] + opts['conv_techs'] + opts['storage_techs'])
|
||||
techs = (bus_sizes.index.levels[1]).intersection(pd.Index(opts['vre_techs'] + opts['conv_techs'] + opts['storage_techs']))
|
||||
handles = []
|
||||
labels = []
|
||||
for t in techs:
|
||||
|
||||
@ -1,75 +0,0 @@
|
||||
# SPDX-FileCopyrightText: 2019-2020 Fabian Hofmann (FIAS)
|
||||
#
|
||||
# SPDX-License-Identifier: GPL-3.0-or-later
|
||||
|
||||
"""
|
||||
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3517949.svg
|
||||
:target: https://doi.org/10.5281/zenodo.3517949
|
||||
|
||||
Cutouts are spatiotemporal subsets of the European weather data from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ reanalysis dataset and the `CMSAF SARAH-2 <https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=SARAH_V002>`_ solar surface radiation dataset for the year 2013 (3.9 GB).
|
||||
They have been prepared by and are for use with the `atlite <https://github.com/PyPSA/atlite>`_ tool. You can either generate them yourself using the ``build_cutouts`` rule or retrieve them directly from `zenodo <https://doi.org/10.5281/zenodo.3517949>`_ through the rule ``retrieve_cutout`` described here.
|
||||
|
||||
.. note::
|
||||
To download cutouts yourself from the `ECMWF ERA5 <https://software.ecmwf.int/wiki/display/CKB/ERA5+data+documentation>`_ you need to `set up the CDS API <https://cds.climate.copernicus.eu/api-how-to>`_.
|
||||
|
||||
The :ref:`tutorial` uses smaller `cutouts <https://zenodo.org/record/3518020/files/pypsa-eur-tutorial-cutouts.tar.xz>`_ than required for the full model (19 MB)
|
||||
|
||||
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3518020.svg
|
||||
:target: https://doi.org/10.5281/zenodo.3518020
|
||||
|
||||
|
||||
**Relevant Settings**
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
tutorial:
|
||||
enable:
|
||||
build_cutout:
|
||||
|
||||
.. seealso::
|
||||
Documentation of the configuration file ``config.yaml`` at
|
||||
:ref:`toplevel_cf`
|
||||
|
||||
**Outputs**
|
||||
|
||||
- ``cutouts/{cutout}``: weather data from either the `ERA5 <https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5>`_ reanalysis weather dataset or `SARAH-2 <https://wui.cmsaf.eu/safira/action/viewProduktSearch>`_ satellite-based historic weather data.
|
||||
|
||||
.. seealso::
|
||||
For details see :mod:`build_cutout` and read the `atlite documentation <https://atlite.readthedocs.io>`_.
|
||||
|
||||
"""
|
||||
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
from pathlib import Path
|
||||
import tarfile
|
||||
from _helpers import progress_retrieve, configure_logging
|
||||
|
||||
if __name__ == "__main__":
|
||||
if 'snakemake' not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
snakemake = mock_snakemake('retrieve_cutout')
|
||||
rootpath = '..'
|
||||
else:
|
||||
rootpath = '.'
|
||||
|
||||
configure_logging(snakemake) # TODO Make logging compatible with progressbar (see PR #102)
|
||||
|
||||
if snakemake.config['tutorial']:
|
||||
url = "https://zenodo.org/record/3518020/files/pypsa-eur-tutorial-cutouts.tar.xz"
|
||||
else:
|
||||
url = "https://zenodo.org/record/3517949/files/pypsa-eur-cutouts.tar.xz"
|
||||
|
||||
# Save location
|
||||
tarball_fn = Path(f"{rootpath}/cutouts.tar.xz")
|
||||
|
||||
logger.info(f"Downloading cutouts from '{url}'.")
|
||||
progress_retrieve(url, tarball_fn)
|
||||
|
||||
logger.info(f"Extracting cutouts.")
|
||||
tarfile.open(tarball_fn).extractall(path=rootpath)
|
||||
|
||||
tarball_fn.unlink()
|
||||
|
||||
logger.info(f"Cutouts available in '{Path(tarball_fn.stem).stem}'.")
|
||||
@ -1,49 +0,0 @@
|
||||
# Copyright 2019-2020 Fabian Hofmann (FIAS)
|
||||
# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
|
||||
#
|
||||
# SPDX-License-Identifier: GPL-3.0-or-later
|
||||
|
||||
"""
|
||||
.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.3518215.svg
|
||||
:target: https://doi.org/10.5281/zenodo.3518215
|
||||
|
||||
This rule, as a substitute for :mod:`build_natura_raster`, downloads an already rasterized version (`natura.tiff <https://zenodo.org/record/3518215/files/natura.tiff>`_) of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times. The file is placed into the ``resources`` sub-directory.
|
||||
|
||||
**Relevant Settings**
|
||||
|
||||
.. code:: yaml
|
||||
|
||||
enable:
|
||||
build_natura_raster:
|
||||
|
||||
.. seealso::
|
||||
Documentation of the configuration file ``config.yaml`` at
|
||||
:ref:`toplevel_cf`
|
||||
|
||||
**Outputs**
|
||||
|
||||
- ``resources/natura.tiff``: Rasterized version of `Natura 2000 <https://en.wikipedia.org/wiki/Natura_2000>`_ natural protection areas to reduce computation times.
|
||||
|
||||
.. seealso::
|
||||
For details see :mod:`build_natura_raster`.
|
||||
|
||||
"""
|
||||
|
||||
import logging
|
||||
|
||||
from _helpers import progress_retrieve, configure_logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
if __name__ == "__main__":
|
||||
if 'snakemake' not in globals():
|
||||
from _helpers import mock_snakemake
|
||||
snakemake = mock_snakemake('retrieve_natura_raster')
|
||||
configure_logging(snakemake) # TODO Make logging compatible with progressbar (see PR #102)
|
||||
|
||||
url = "https://zenodo.org/record/3518215/files/natura.tiff"
|
||||
|
||||
logger.info(f"Downloading natura raster from '{url}'.")
|
||||
progress_retrieve(url, snakemake.output[0])
|
||||
|
||||
logger.info(f"Natura raster available as '{snakemake.output[0]}'.")
|
||||
@ -53,16 +53,15 @@ electricity:
|
||||
atlite:
|
||||
nprocesses: 4
|
||||
cutouts:
|
||||
europe-2013-era5:
|
||||
europe-2013-era5-tutorial:
|
||||
module: era5
|
||||
xs: [4., 15.]
|
||||
ys: [56., 46.]
|
||||
months: [3, 3]
|
||||
years: [2013, 2013]
|
||||
x: [4., 15.]
|
||||
y: [46., 56.]
|
||||
time: ["2013-03", "2013-03"]
|
||||
|
||||
renewable:
|
||||
onwind:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-era5-tutorial
|
||||
resource:
|
||||
method: wind
|
||||
turbine: Vestas_V112_3MW
|
||||
@ -79,7 +78,7 @@ renewable:
|
||||
potential: simple # or conservative
|
||||
clip_p_max_pu: 1.e-2
|
||||
offwind-ac:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-era5-tutorial
|
||||
resource:
|
||||
method: wind
|
||||
turbine: NREL_ReferenceTurbine_5MW_offshore
|
||||
@ -91,7 +90,7 @@ renewable:
|
||||
potential: simple # or conservative
|
||||
clip_p_max_pu: 1.e-2
|
||||
offwind-dc:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-era5-tutorial
|
||||
resource:
|
||||
method: wind
|
||||
turbine: NREL_ReferenceTurbine_5MW_offshore
|
||||
@ -104,7 +103,7 @@ renewable:
|
||||
potential: simple # or conservative
|
||||
clip_p_max_pu: 1.e-2
|
||||
solar:
|
||||
cutout: europe-2013-era5
|
||||
cutout: europe-2013-era5-tutorial
|
||||
resource:
|
||||
method: pv
|
||||
panel: CSi
|
||||
@ -147,9 +146,9 @@ transformers:
|
||||
|
||||
load:
|
||||
url: https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv
|
||||
power_statistics: True # only for files from <2019; set false in order to get ENTSOE transparency data
|
||||
power_statistics: True # only for files from <2019; set false in order to get ENTSOE transparency data
|
||||
interpolate_limit: 3 # data gaps up until this size are interpolated linearly
|
||||
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
|
||||
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
|
||||
manual_adjustments: true # false
|
||||
scaling_factor: 1.0
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user