Use hotmaps industrial database for distribution in each country
I.e. per sector geographical distribution of industrial facilities within each country. Drop facilities outside Europe and with no geocoordinates. Use ETS emissions as a distribution key; where emissions data is missing, substitute with an average for that sector and that country (strong assumption).
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Tom Brown
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@ -25,6 +25,7 @@ gurobi.log
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/data/transport_data.csv
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/data/switzerland*
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/data/.nfs*
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/data/Industrial_Database.csv
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*.org
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15
Snakefile
15
Snakefile
@ -198,6 +198,21 @@ rule build_industrial_production_per_country_tomorrow:
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resources: mem_mb=1000
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script: 'scripts/build_industrial_production_per_country_tomorrow.py'
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rule build_industrial_production_per_node:
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input:
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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europe_shape=pypsaeur('resources/europe_shape.geojson'),
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hotmaps_industrial_database="data/Industrial_Database.csv",
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network=pypsaeur('networks/{network}_s{simpl}_{clusters}.nc'),
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industrial_production_per_country_tomorrow="resources/industrial_production_per_country_tomorrow.csv"
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output:
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industrial_production_per_node="resources/industrial_production_{network}_s{simpl}_{clusters}.csv"
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threads: 1
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resources: mem_mb=1000
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script: 'scripts/build_industrial_production_per_node.py'
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rule build_industrial_energy_demand_per_country_today:
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input:
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ammonia_production="resources/ammonia_production.csv",
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174
scripts/build_industrial_production_per_node.py
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174
scripts/build_industrial_production_per_node.py
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@ -0,0 +1,174 @@
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import pypsa
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import pandas as pd
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import geopandas as gpd
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from shapely import wkt, prepared
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from scipy.spatial import cKDTree as KDTree
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def prepare_hotmaps_database():
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df = pd.read_csv(snakemake.input.hotmaps_industrial_database,
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sep=";",
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index_col=0)
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#remove those sites without valid geometries
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df.drop(df.index[df.geom.isna()],
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inplace=True)
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#parse geometry
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#https://geopandas.org/gallery/create_geopandas_from_pandas.html?highlight=parse#from-wkt-format
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df["Coordinates"] = df.geom.apply(lambda x : wkt.loads(x[x.find(";POINT")+1:]))
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gdf = gpd.GeoDataFrame(df, geometry='Coordinates')
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europe_shape = gpd.read_file(snakemake.input.europe_shape).loc[0, 'geometry']
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europe_shape_prepped = prepared.prep(europe_shape)
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not_in_europe = gdf.index[~gdf.geometry.apply(europe_shape_prepped.contains)]
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print("Removing the following industrial facilities since they are not in European area:")
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print(gdf.loc[not_in_europe])
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gdf.drop(not_in_europe,
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inplace=True)
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country_to_code = {
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'Belgium' : 'BE',
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'Bulgaria' : 'BG',
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'Czech Republic' : 'CZ',
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'Denmark' : 'DK',
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'Germany' : 'DE',
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'Estonia' : 'EE',
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'Ireland' : 'IE',
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'Greece' : 'GR',
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'Spain' : 'ES',
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'France' : 'FR',
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'Croatia' : 'HR',
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'Italy' : 'IT',
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'Cyprus' : 'CY',
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'Latvia' : 'LV',
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'Lithuania' : 'LT',
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'Luxembourg' : 'LU',
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'Hungary' : 'HU',
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'Malta' : 'MA',
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'Netherland' : 'NL',
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'Austria' : 'AT',
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'Poland' : 'PL',
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'Portugal' : 'PT',
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'Romania' : 'RO',
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'Slovenia' : 'SI',
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'Slovakia' : 'SK',
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'Finland' : 'FI',
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'Sweden' : 'SE',
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'United Kingdom' : 'GB',
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'Iceland' : 'IS',
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'Norway' : 'NO',
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'Montenegro' : 'ME',
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'FYR of Macedonia' : 'MK',
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'Albania' : 'AL',
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'Serbia' : 'RS',
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'Turkey' : 'TU',
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'Bosnia and Herzegovina' : 'BA',
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'Switzerland' : 'CH',
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'Liechtenstein' : 'AT',
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}
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gdf["country_code"] = gdf.Country.map(country_to_code)
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if gdf["country_code"].isna().any():
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print("Warning, some countries not assigned an ISO code")
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gdf["x"] = gdf.geometry.x
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gdf["y"] = gdf.geometry.y
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return gdf
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def assign_buses(gdf):
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gdf["bus"] = ""
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for c in n.buses.country.unique():
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buses_i = n.buses.index[n.buses.country == c]
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kdtree = KDTree(n.buses.loc[buses_i, ['x','y']].values)
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industry_i = gdf.index[(gdf.country_code == c)]
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if industry_i.empty:
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print("Skipping country with no industry:",c)
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else:
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tree_i = kdtree.query(gdf.loc[industry_i, ['x','y']].values)[1]
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gdf.loc[industry_i, 'bus'] = buses_i[tree_i]
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if (gdf.bus == "").any():
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print("Some industrial facilities have empty buses")
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if gdf.bus.isna().any():
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print("Some industrial facilities have NaN buses")
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def build_nodal_industrial_production(gdf):
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industrial_production = pd.read_csv(snakemake.input.industrial_production_per_country_tomorrow,
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index_col=0)
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nodal_industrial_production = pd.DataFrame(index=n.buses.index,
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columns=industrial_production.columns,
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dtype=float)
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pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,index_col=0)
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pop_layout["ct"] = pop_layout.index.str[:2]
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ct_total = pop_layout.total.groupby(pop_layout["ct"]).sum()
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pop_layout["ct_total"] = pop_layout["ct"].map(ct_total)
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pop_layout["fraction"] = pop_layout["total"]/pop_layout["ct_total"]
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#map JRC/our sectors to hotmaps sector, where mapping exist
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sector_mapping = {'Electric arc' : 'Iron and steel',
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'Integrated steelworks' : 'Iron and steel',
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'DRI + Electric arc' : 'Iron and steel',
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'Ammonia' : 'Chemical industry',
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'Basic chemicals (without ammonia)' : 'Chemical industry',
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'Other chemicals' : 'Chemical industry',
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'Pharmaceutical products etc.' : 'Chemical industry',
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'Cement' : 'Cement',
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'Ceramics & other NMM' : 'Non-metallic mineral products',
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'Glass production' : 'Glass',
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'Pulp production' : 'Paper and printing',
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'Paper production' : 'Paper and printing',
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'Printing and media reproduction' : 'Paper and printing',
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'Alumina production' : 'Non-ferrous metals',
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'Aluminium - primary production' : 'Non-ferrous metals',
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'Aluminium - secondary production' : 'Non-ferrous metals',
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'Other non-ferrous metals' : 'Non-ferrous metals',
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}
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for c in n.buses.country.unique():
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buses = n.buses.index[n.buses.country == c]
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for sector in industrial_production.columns:
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facilities = gdf.index[(gdf.country_code == c) & (gdf.Subsector == sector_mapping.get(sector,"None"))]
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if (sector in sector_mapping) and not facilities.empty:
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emissions = gdf.loc[facilities,"Emissions_ETS_2014"]
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if emissions.sum() == 0:
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distribution_key = pd.Series(1/len(facilities),
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facilities)
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else:
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#BEWARE: this is a strong assumption
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emissions = emissions.fillna(emissions.mean())
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distribution_key = emissions/emissions.sum()
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distribution_key = distribution_key.groupby(gdf.loc[facilities,"bus"]).sum().reindex(buses,fill_value=0.)
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else:
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distribution_key = pop_layout.loc[buses,"fraction"]
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if abs(distribution_key.sum() - 1) > 1e-4:
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print(c,sector,distribution_key)
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nodal_industrial_production.loc[buses,sector] = industrial_production.at[c,sector]*distribution_key
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nodal_industrial_production.to_csv(snakemake.output.industrial_production_per_node)
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if __name__ == "__main__":
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n = pypsa.Network(snakemake.input.network)
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hotmaps_database = prepare_hotmaps_database()
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assign_buses(hotmaps_database)
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build_nodal_industrial_production(hotmaps_database)
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