Merge pull request #71 from nworbmot/master
Use hotmaps industrial database for distribution of industry in each country
This commit is contained in:
Tom Brown
GitHub
commit
4e6c323ac3
1
.gitignore
vendored
1
.gitignore
vendored
@ -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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55
Snakefile
55
Snakefile
@ -3,6 +3,7 @@ configfile: "config.yaml"
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wildcard_constraints:
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lv="[a-z0-9\.]+",
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network="[a-zA-Z0-9]*",
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simpl="[a-zA-Z0-9]*",
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clusters="[0-9]+m?",
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sectors="[+a-zA-Z0-9]+",
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@ -198,6 +199,46 @@ 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_distribution_key:
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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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output:
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industrial_distribution_key="resources/industrial_distribution_key_{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_distribution_key.py'
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rule build_industrial_production_per_node:
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input:
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industrial_distribution_key="resources/industrial_distribution_key_{network}_s{simpl}_{clusters}.csv",
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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_node:
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input:
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industry_sector_ratios="resources/industry_sector_ratios.csv",
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industrial_production_per_node="resources/industrial_production_{network}_s{simpl}_{clusters}.csv",
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industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_{network}_s{simpl}_{clusters}.csv"
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output:
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industrial_energy_demand_per_node="resources/industrial_energy_demand_{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_energy_demand_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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@ -209,6 +250,18 @@ rule build_industrial_energy_demand_per_country_today:
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script: 'scripts/build_industrial_energy_demand_per_country_today.py'
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rule build_industrial_energy_demand_per_node_today:
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input:
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industrial_distribution_key="resources/industrial_distribution_key_{network}_s{simpl}_{clusters}.csv",
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industrial_energy_demand_per_country_today="resources/industrial_energy_demand_per_country_today.csv"
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output:
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industrial_energy_demand_per_node_today="resources/industrial_energy_demand_today_{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_energy_demand_per_node_today.py'
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rule build_industrial_energy_demand_per_country:
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input:
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industry_sector_ratios="resources/industry_sector_ratios.csv",
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@ -248,7 +301,7 @@ rule prepare_sector_network:
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clustermaps=pypsaeur('resources/clustermaps_{network}_s{simpl}_{clusters}.h5'),
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clustered_pop_layout="resources/pop_layout_{network}_s{simpl}_{clusters}.csv",
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simplified_pop_layout="resources/pop_layout_{network}_s{simpl}.csv",
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industrial_demand="resources/industrial_demand_{network}_s{simpl}_{clusters}.csv",
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industrial_demand="resources/industrial_energy_demand_{network}_s{simpl}_{clusters}.csv",
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heat_demand_urban="resources/heat_demand_urban_{network}_s{simpl}_{clusters}.nc",
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heat_demand_rural="resources/heat_demand_rural_{network}_s{simpl}_{clusters}.nc",
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heat_demand_total="resources/heat_demand_total_{network}_s{simpl}_{clusters}.nc",
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19
doc/data.csv
Normal file
19
doc/data.csv
Normal file
@ -0,0 +1,19 @@
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description,file/folder,licence,source
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JRC IDEES database,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees
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urban/rural fraction,urban_percent.csv,unknown,unknown
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JRC biomass potentials,biomass/,unknown,https://doi.org/10.2790/39014
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EEA emission statistics,eea/,unknown,https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-14
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Eurostat Energy Balances,eurostat-energy_balances-*/,Eurostat,https://ec.europa.eu/eurostat/web/energy/data/energy-balances
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Swiss energy statistics from Swiss Federal Office of Energy,switzerland-sfoe/,unknown,http://www.bfe.admin.ch/themen/00526/00541/00542/02167/index.html?dossier_id=02169
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BASt emobility statistics,emobility/,unknown,http://www.bast.de/DE/Verkehrstechnik/Fachthemen/v2-verkehrszaehlung/Stundenwerte.html?nn=626916
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timezone mappings,timezone_mappings.csv,CC BY 4.0,Tom Brown
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BDEW heating profile,heat_load_profile_BDEW.csv,unknown,https://github.com/oemof/demandlib
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heating profiles for Aarhus,heat_load_profile_DK_AdamJensen.csv,unknown,Adam Jensen MA thesis at Aarhus University
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George Lavidas wind/wave costs,WindWaveWEC_GLTB.xlsx,unknown,George Lavidas
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country codes,Country_codes.csv,CC BY 4.0,Marta Victoria
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co2 budgets,co2_budget.csv,CC BY 4.0,https://arxiv.org/abs/2004.11009
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existing heating potentials,existing_infrastructure/existing_heating_raw.csv,unknown,https://ec.europa.eu/energy/studies/mapping-and-analyses-current-and-future-2020-2030-heatingcooling-fuel-deployment_en?redir=1
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IRENA existing VRE capacities,existing_infrastructure/{solar|onwind|offwind}_capcity_IRENA.csv,unknown,https://www.irena.org/Statistics/Download-Data
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USGS ammonia production,myb1-2017-nitro.xls,unknown,https://www.usgs.gov/centers/nmic/nitrogen-statistics-and-information
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hydrogen salt cavern potentials,hydrogen_salt_cavern_potentials.csv,CC BY 4.0,https://doi.org/10.1016/j.ijhydene.2019.12.161
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hotmaps industrial site database,Industrial_Database.csv,CC BY 4.0,https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database
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@ -58,19 +58,26 @@ atlite version 0.0.2.
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Data requirements
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=================
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The data requirements include the JRC-IDEES-2015 database, JRC biomass
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potentials, EEA emission statistics, Eurostat Energy Balances, urban
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district heating potentials, emobility statistics, timezone mappings
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and heating profiles.
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Small data files are included directly in the git repository, while
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larger ones are archived in a data bundle. The data bundle's size is
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around 640 MB.
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The data bundle is about 640 MB.
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To download and extract it on the command line:
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To download and extract the data bundle on the command line:
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.. code:: bash
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projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-200921.tar.gz"
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projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-200921.tar.gz
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projects/pypsa-eur-sec/data % wget "https://nworbmot.org/pypsa-eur-sec-data-bundle-201012.tar.gz"
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projects/pypsa-eur-sec/data % tar xvzf pypsa-eur-sec-data-bundle-201012.tar.gz
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The data licences and sources are given in the following table.
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.. csv-table::
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:header-rows: 1
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:file: data.csv
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Set up the default configuration
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================================
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@ -102,4 +102,4 @@ To make a new release of the data bundle, make an archive of the files in ``data
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.. code:: bash
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data % tar pczf pypsa-eur-sec-data-bundle-date.tar.gz eea switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility urban_percent.csv timezone_mappings.csv heat_load_profile_DK_AdamJensen.csv WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls
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data % tar pczf pypsa-eur-sec-data-bundle-date.tar.gz eea switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility urban_percent.csv timezone_mappings.csv heat_load_profile_DK_AdamJensen.csv WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv
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153
scripts/build_industrial_distribution_key.py
Normal file
153
scripts/build_industrial_distribution_key.py
Normal file
@ -0,0 +1,153 @@
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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_distribution_key(gdf):
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sectors = ['Iron and steel','Chemical industry','Cement','Non-metallic mineral products','Glass','Paper and printing','Non-ferrous metals']
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distribution_keys = pd.DataFrame(index=n.buses.index,
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columns=sectors,
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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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distribution_keys["population"] = pop_layout["total"]/pop_layout["ct_total"]
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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 sectors:
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facilities = gdf.index[(gdf.country_code == c) & (gdf.Subsector == sector)]
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if 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 = distribution_keys.loc[buses,"population"]
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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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distribution_keys.loc[buses,sector] = distribution_key
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distribution_keys.to_csv(snakemake.output.industrial_distribution_key)
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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_distribution_key(hotmaps_database)
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33
scripts/build_industrial_energy_demand_per_node.py
Normal file
33
scripts/build_industrial_energy_demand_per_node.py
Normal file
@ -0,0 +1,33 @@
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import pandas as pd
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import numpy as np
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# import EU ratios df as csv
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industry_sector_ratios=pd.read_csv(snakemake.input.industry_sector_ratios,
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index_col=0)
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#material demand per node and industry (kton/a)
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nodal_production = pd.read_csv(snakemake.input.industrial_production_per_node,
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index_col=0)
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#energy demand today to get current electricity
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nodal_today = pd.read_csv(snakemake.input.industrial_energy_demand_per_node_today,
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index_col=0)
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#final energy consumption per node and industry (TWh/a)
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nodal_df = nodal_production.dot(industry_sector_ratios.T)
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nodal_df*= 0.001 #GWh -> TWh (ktCO2 -> MtCO2)
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rename_sectors = {'elec':'electricity',
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'biomass':'solid biomass',
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'heat':'low-temperature heat'}
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nodal_df.rename(columns=rename_sectors,inplace=True)
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nodal_df["current electricity"] = nodal_today["electricity"]
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nodal_df.index.name = "TWh/a (MtCO2/a)"
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nodal_df.to_csv(snakemake.output.industrial_energy_demand_per_node,
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float_format='%.2f')
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54
scripts/build_industrial_energy_demand_per_node_today.py
Normal file
54
scripts/build_industrial_energy_demand_per_node_today.py
Normal file
@ -0,0 +1,54 @@
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import pandas as pd
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import numpy as np
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def build_nodal_demand():
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industrial_demand = pd.read_csv(snakemake.input.industrial_energy_demand_per_country_today,
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header=[0,1],
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index_col=0)
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distribution_keys = pd.read_csv(snakemake.input.industrial_distribution_key,
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index_col=0)
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distribution_keys["country"] = distribution_keys.index.str[:2]
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nodal_demand = pd.DataFrame(0.,
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index=distribution_keys.index,
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columns=industrial_demand.index,
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dtype=float)
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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 distribution_keys.country.unique():
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buses = distribution_keys.index[distribution_keys.country == c]
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for sector in industrial_demand.columns.levels[1]:
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distribution_key = distribution_keys.loc[buses,sector_mapping.get(sector,"population")]
|
||||
demand = industrial_demand[c,sector]
|
||||
outer = pd.DataFrame(np.outer(distribution_key,demand),index=distribution_key.index,columns=demand.index)
|
||||
nodal_demand.loc[buses] += outer
|
||||
|
||||
nodal_demand.index.name = "TWh/a"
|
||||
|
||||
nodal_demand.to_csv(snakemake.output.industrial_energy_demand_per_node_today)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
build_nodal_demand()
|
||||
47
scripts/build_industrial_production_per_node.py
Normal file
47
scripts/build_industrial_production_per_node.py
Normal file
@ -0,0 +1,47 @@
|
||||
|
||||
import pandas as pd
|
||||
|
||||
def build_nodal_industrial_production():
|
||||
|
||||
industrial_production = pd.read_csv(snakemake.input.industrial_production_per_country_tomorrow,
|
||||
index_col=0)
|
||||
|
||||
distribution_keys = pd.read_csv(snakemake.input.industrial_distribution_key,
|
||||
index_col=0)
|
||||
distribution_keys["country"] = distribution_keys.index.str[:2]
|
||||
|
||||
nodal_industrial_production = pd.DataFrame(index=distribution_keys.index,
|
||||
columns=industrial_production.columns,
|
||||
dtype=float)
|
||||
|
||||
#map JRC/our sectors to hotmaps sector, where mapping exist
|
||||
sector_mapping = {'Electric arc' : 'Iron and steel',
|
||||
'Integrated steelworks' : 'Iron and steel',
|
||||
'DRI + Electric arc' : 'Iron and steel',
|
||||
'Ammonia' : 'Chemical industry',
|
||||
'Basic chemicals (without ammonia)' : 'Chemical industry',
|
||||
'Other chemicals' : 'Chemical industry',
|
||||
'Pharmaceutical products etc.' : 'Chemical industry',
|
||||
'Cement' : 'Cement',
|
||||
'Ceramics & other NMM' : 'Non-metallic mineral products',
|
||||
'Glass production' : 'Glass',
|
||||
'Pulp production' : 'Paper and printing',
|
||||
'Paper production' : 'Paper and printing',
|
||||
'Printing and media reproduction' : 'Paper and printing',
|
||||
'Alumina production' : 'Non-ferrous metals',
|
||||
'Aluminium - primary production' : 'Non-ferrous metals',
|
||||
'Aluminium - secondary production' : 'Non-ferrous metals',
|
||||
'Other non-ferrous metals' : 'Non-ferrous metals',
|
||||
}
|
||||
|
||||
for c in distribution_keys.country.unique():
|
||||
buses = distribution_keys.index[distribution_keys.country == c]
|
||||
for sector in industrial_production.columns:
|
||||
distribution_key = distribution_keys.loc[buses,sector_mapping.get(sector,"population")]
|
||||
nodal_industrial_production.loc[buses,sector] = industrial_production.at[c,sector]*distribution_key
|
||||
|
||||
nodal_industrial_production.to_csv(snakemake.output.industrial_production_per_node)
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
build_nodal_industrial_production()
|
||||
@ -666,7 +666,7 @@ def insert_electricity_distribution_grid(network):
|
||||
capital_cost=costs.at['electricity distribution grid','fixed']*snakemake.config["sector"]['electricity_distribution_grid_cost_factor'])
|
||||
|
||||
|
||||
#this catches regular electricity load and "industry new electricity"
|
||||
#this catches regular electricity load and "industry electricity"
|
||||
loads = network.loads.index[network.loads.carrier.str.contains("electricity")]
|
||||
network.loads.loc[loads,"bus"] += " low voltage"
|
||||
|
||||
@ -1635,12 +1635,18 @@ def add_industry(network):
|
||||
carrier="low-temperature heat for industry",
|
||||
p_set=industrial_demand.loc[nodes,"low-temperature heat"]/8760.)
|
||||
|
||||
#remove today's industrial electricity demand by scaling down total electricity demand
|
||||
for ct in n.buses.country.unique():
|
||||
loads = n.loads.index[(n.loads.index.str[:2] == ct) & (n.loads.carrier == "electricity")]
|
||||
factor = 1 - industrial_demand.loc[loads,"current electricity"].sum()/n.loads_t.p_set[loads].sum().sum()
|
||||
n.loads_t.p_set[loads] *= factor
|
||||
|
||||
network.madd("Load",
|
||||
nodes,
|
||||
suffix=" industry new electricity",
|
||||
suffix=" industry electricity",
|
||||
bus=nodes,
|
||||
carrier="industry new electricity",
|
||||
p_set = (industrial_demand.loc[nodes,"electricity"]-industrial_demand.loc[nodes,"current electricity"])/8760.)
|
||||
carrier="industry electricity",
|
||||
p_set=industrial_demand.loc[nodes,"electricity"]/8760.)
|
||||
|
||||
network.madd("Bus",
|
||||
["process emissions"],
|
||||
|
||||
Loading…
Reference in New Issue
Block a user