spatially-explicit biomass potentials from ENSPRESO (NUTS2)
This commit is contained in:
Fabian Neumann
parent
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commit
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19
Snakefile
19
Snakefile
@ -1,4 +1,7 @@
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from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider
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HTTP = HTTPRemoteProvider()
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configfile: "config.yaml"
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@ -170,13 +173,19 @@ rule build_energy_totals:
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rule build_biomass_potentials:
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input:
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jrc_potentials="data/biomass/JRC Biomass Potentials.xlsx"
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enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
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nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
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regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
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nuts3_population=pypsaeur("data/bundle/nama_10r_3popgdp.tsv.gz"),
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swiss_cantons=pypsaeur("data/bundle/ch_cantons.csv"),
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swiss_population=pypsaeur("data/bundle/je-e-21.03.02.xls"),
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country_shapes=pypsaeur('resources/country_shapes.geojson')
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output:
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biomass_potentials_all='resources/biomass_potentials_all.csv',
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biomass_potentials='resources/biomass_potentials.csv'
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biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',
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biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv'
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threads: 1
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resources: mem_mb=1000
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benchmark: "benchmarks/build_biomass_potentials"
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benchmark: "benchmarks/build_biomass_potentials_s{simpl}_{clusters}"
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script: 'scripts/build_biomass_potentials.py'
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@ -323,7 +332,7 @@ rule prepare_sector_network:
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transport_name='resources/transport_data.csv',
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traffic_data_KFZ = "data/emobility/KFZ__count",
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traffic_data_Pkw = "data/emobility/Pkw__count",
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biomass_potentials='resources/biomass_potentials.csv',
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biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
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heat_profile="data/heat_load_profile_BDEW.csv",
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costs=CDIR + "costs_{planning_horizons}.csv",
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profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"),
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@ -99,28 +99,28 @@ energy:
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biomass:
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year: 2030
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scenario: Med
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scenario: ENS_Med
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classes:
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solid biomass:
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- Primary agricultural residues
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- Forestry energy residue
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- Secondary forestry residues
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- Secondary Forestry residues sawdust
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- Forestry residues from landscape care biomass
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- Argicultural waste
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- Municipal waste
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- Residues from landscape care
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- Sawdust
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- Secondary Forestry residues - woodchips
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not included:
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- Bioethanol sugar beet biomass
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- Rapeseeds for biodiesel
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- sunflower and soya for Biodiesel
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- Starchy crops biomass
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- Grassy crops biomass
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- Willow biomass
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- Poplar biomass potential
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- Roundwood fuelwood
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- Roundwood Chips & Pellets
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- Bioethanol barley, wheat, grain maize, oats, other cereals and rye
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- Fuelwood residues
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- C&P_RW
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- FuelwoodRW
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- Rape seed
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- Sugar from sugar beet
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- Miscanthus, switchgrass, RCG
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- "Sunflower, soya seed "
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- Poplar
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- Willow
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biogas:
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- Manure biomass potential
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- Sludge biomass
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- Manure solid, liquid
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- Sludge
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solar_thermal:
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@ -1,55 +1,148 @@
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import pandas as pd
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rename = {"UK" : "GB", "BH" : "BA"}
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import geopandas as gpd
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def build_biomass_potentials():
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def build_nuts_population_data(year=2013):
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config = snakemake.config['biomass']
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year = config["year"]
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scenario = config["scenario"]
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pop = pd.read_csv(
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snakemake.input.nuts3_population,
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sep=r'\,| \t|\t',
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engine='python',
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na_values=[":"],
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index_col=1
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)[str(year)]
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# only countries
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pop.drop("EU28", inplace=True)
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df = pd.read_excel(snakemake.input.jrc_potentials,
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"Potentials (PJ)",
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index_col=[0,1])
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# mapping from Cantons to NUTS3
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cantons = pd.read_csv(snakemake.input.swiss_cantons)
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cantons = cantons.set_index(cantons.HASC.str[3:]).NUTS
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cantons = cantons.str.pad(5, side='right', fillchar='0')
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df.rename(columns={"Unnamed: 18": "Municipal waste"}, inplace=True)
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df.drop(columns="Total", inplace=True)
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df.replace("-", 0., inplace=True)
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# get population by NUTS3
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swiss = pd.read_excel(snakemake.input.swiss_population, skiprows=3, index_col=0).loc["Residents in 1000"]
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swiss = swiss.rename(cantons).filter(like="CH")
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column = df.iloc[:,0]
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countries = column.where(column.str.isalpha()).pad()
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countries = [rename.get(ct, ct) for ct in countries]
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countries_i = pd.Index(countries, name='country')
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df.set_index(countries_i, append=True, inplace=True)
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# aggregate also to higher order NUTS levels
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swiss = [swiss.groupby(swiss.index.str[:i]).sum() for i in range(2, 6)]
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df.drop(index='MS', level=0, inplace=True)
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# merge Europe + Switzerland
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pop = pd.DataFrame(pop.append(swiss), columns=["total"])
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# add missing manually
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pop["AL"] = 2893
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pop["BA"] = 3871
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pop["RS"] = 7210
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pop["ct"] = pop.index.str[:2]
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return pop
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# convert from PJ to MWh
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df = df / 3.6 * 1e6
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df.to_csv(snakemake.output.biomass_potentials_all)
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def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
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glossary = pd.read_excel(
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snakemake.input.enspreso_biomass,
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sheet_name="Glossary",
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usecols="B:D",
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skiprows=1,
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index_col=0
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)
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df = pd.read_excel(
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snakemake.input.enspreso_biomass,
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sheet_name="ENER - NUTS2 BioCom E",
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usecols="A:H"
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)
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# solid biomass includes:
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# Primary agricultural residues (MINBIOAGRW1),
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# Forestry energy residue (MINBIOFRSF1),
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# Secondary forestry residues (MINBIOWOOW1),
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# Secondary Forestry residues – sawdust (MINBIOWOO1a)',
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# Forestry residues from landscape care biomass (MINBIOFRSF1a),
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# Municipal waste (MINBIOMUN1)',
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df["group"] = df["E-Comm"].map(glossary.group)
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df["commodity"] = df["E-Comm"].map(glossary.description)
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# biogas includes:
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# Manure biomass potential (MINBIOGAS1),
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# Sludge biomass (MINBIOSLU1),
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to_rename = {
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"NUTS2 Potential available by Bio Commodity": "potential",
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"NUST2": "NUTS2",
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}
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df.rename(columns=to_rename, inplace=True)
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# fill up with NUTS0 if NUTS2 is not given
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df.NUTS2 = df.apply(lambda x: x.NUTS0 if x.NUTS2 == '-' else x.NUTS2, axis=1)
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df = df.loc[year, scenario, :]
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# convert PJ to TWh
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df.potential /= 3.6
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df.Unit = "TWh/a"
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grouper = {v: k for k, vv in config["classes"].items() for v in vv}
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df = df.groupby(grouper, axis=1).sum()
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dff = df.query("Year == @year and Scenario == @scenario")
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df.index.name = "MWh/a"
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bio = dff.groupby(["NUTS2", "commodity"]).potential.sum().unstack()
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# currently Serbia and Kosovo not split, so aggregate
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bio.loc["RS"] += bio.loc["XK"]
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bio.drop("XK", inplace=True)
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return bio
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df.to_csv(snakemake.output.biomass_potentials)
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def disaggregate_nuts0(bio):
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pop = build_nuts_population_data()
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# get population in nuts2
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pop_nuts2 = pop.loc[pop.index.str.len() == 4]
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by_country = pop_nuts2.total.groupby(pop_nuts2.ct).sum()
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pop_nuts2["fraction"] = pop_nuts2.total / pop_nuts2.ct.map(by_country)
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# distribute nuts0 data to nuts2 by population
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bio_nodal = bio.loc[pop_nuts2.ct]
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bio_nodal.index = pop_nuts2.index
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bio_nodal = bio_nodal.mul(pop_nuts2.fraction, axis=0)
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# update inplace
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bio.update(bio_nodal)
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return bio
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def build_nuts2_shapes():
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"""
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- load NUTS2 geometries
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- add RS, AL, BA country shapes (not covered in NUTS 2013)
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- consistently name ME, MK
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"""
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nuts2 = gpd.GeoDataFrame(gpd.read_file(snakemake.input.nuts2).set_index('id').geometry)
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countries = gpd.read_file(snakemake.input.country_shapes).set_index('name')
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missing = countries.loc[["AL", "RS", "BA"]]
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nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
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return nuts2.append(missing)
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def area(gdf):
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"""Returns area of GeoDataFrame geometries in square kilometers."""
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return gdf.to_crs(epsg=3035).area.div(1e6)
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def convert_nuts2_to_regions(bio_nuts2, regions):
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# calculate area of nuts2 regions
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bio_nuts2["area_nuts2"] = area(bio_nuts2)
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overlay = gpd.overlay(regions, bio_nuts2)
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# calculate share of nuts2 area inside region
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overlay["share"] = area(overlay) / overlay["area_nuts2"]
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# multiply all nuts2-level values with share of nuts2 inside region
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adjust_cols = overlay.columns.difference({"name", "area_nuts2", "geometry", "share"})
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overlay[adjust_cols] = overlay[adjust_cols].multiply(overlay["share"], axis=0)
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bio_regions = overlay.groupby("name").sum()
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bio_regions.drop(["area_nuts2", "share"], axis=1, inplace=True)
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return bio_regions
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if __name__ == "__main__":
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@ -57,12 +150,28 @@ if __name__ == "__main__":
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from helper import mock_snakemake
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snakemake = mock_snakemake('build_biomass_potentials')
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config = snakemake.config['biomass']
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year = config["year"]
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scenario = config["scenario"]
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# This is a hack, to be replaced once snakemake is unicode-conform
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enspreso = enspreso_biomass_potentials(year, scenario)
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solid_biomass = snakemake.config['biomass']['classes']['solid biomass']
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if 'Secondary Forestry residues sawdust' in solid_biomass:
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solid_biomass.remove('Secondary Forestry residues sawdust')
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solid_biomass.append('Secondary Forestry residues – sawdust')
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enspreso = disaggregate_nuts0(enspreso)
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build_biomass_potentials()
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nuts2 = build_nuts2_shapes()
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df_nuts2 = gpd.GeoDataFrame(nuts2.geometry).join(enspreso)
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regions = gpd.read_file(snakemake.input.regions_onshore)
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df = convert_nuts2_to_regions(df_nuts2, regions)
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df.to_csv(snakemake.output.biomass_potentials_all)
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grouper = {v: k for k, vv in config["classes"].items() for v in vv}
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df = df.groupby(grouper, axis=1).sum()
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df *= 1e6 # TWh/a to MWh/a
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df.index.name = "MWh/a"
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df.to_csv(snakemake.output.biomass_potentials)
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@ -1527,9 +1527,6 @@ def add_biomass(n, costs):
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print("adding biomass")
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# biomass distributed at country level - i.e. transport within country allowed
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countries = n.buses.country.dropna().unique()
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biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
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n.add("Carrier", "biogas")
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@ -1552,18 +1549,18 @@ def add_biomass(n, costs):
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"EU biogas",
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bus="EU biogas",
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carrier="biogas",
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e_nom=biomass_potentials.loc[countries, "biogas"].sum(),
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e_nom=biomass_potentials["biogas"].sum(),
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marginal_cost=costs.at['biogas', 'fuel'],
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e_initial=biomass_potentials.loc[countries, "biogas"].sum()
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e_initial=biomass_potentials["biogas"].sum()
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)
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n.add("Store",
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"EU solid biomass",
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bus="EU solid biomass",
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carrier="solid biomass",
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e_nom=biomass_potentials.loc[countries, "solid biomass"].sum(),
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e_nom=biomass_potentials["solid biomass"].sum(),
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marginal_cost=costs.at['solid biomass', 'fuel'],
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e_initial=biomass_potentials.loc[countries, "solid biomass"].sum()
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e_initial=biomass_potentials["solid biomass"].sum()
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)
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n.add("Link",
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