revision gas infrastructure representation 2
This commit is contained in:
Fabian Neumann
parent
6a00d5bfca
commit
985705403e
24
Snakefile
24
Snakefile
@ -81,8 +81,14 @@ rule build_simplified_population_layouts:
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if config["sector"]["gas_network"]:
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datafiles = [
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"IGGIELGN_LNGs.geojson",
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"IGGIELGN_BorderPoints.geojson",
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"IGGIELGN_Productions.geojson",
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]
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rule retrieve_gas_infrastructure_data:
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output: "data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.csv"
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output: expand("data/gas_network/scigrid-gas/data/{files}", files=datafiles)
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script: 'scripts/retrieve_gas_infrastructure_data.py'
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rule build_gas_network:
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@ -93,20 +99,20 @@ if config["sector"]["gas_network"]:
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resources: mem_mb=4000
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script: "scripts/build_gas_network.py"
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rule build_gas_import_locations:
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rule build_gas_input_locations:
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input:
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lng="data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.geojson"
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entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson"
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production="data/gas_network/scigrid-gas/data/IGGIELGN_Productions.geojson"
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lng="data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.geojson",
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entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
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production="data/gas_network/scigrid-gas/data/IGGIELGN_Productions.geojson",
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regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
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output:
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gas_input_nodes="resources/gas_input_nodes_s{simpl}_{clusters}.csv"
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gas_input_nodes="resources/gas_input_locations_s{simpl}_{clusters}.csv"
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resources: mem_mb=2000,
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script: "scripts/build_gas_import_locations.py"
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script: "scripts/build_gas_input_locations.py"
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rule cluster_gas_network:
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input:
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cleaned_gas_network="data/gas_network/gas_network_dataset.csv",
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cleaned_gas_network="resources/gas_network.csv",
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regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
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regions_offshore=pypsaeur("resources/regions_offshore_elec_s{simpl}_{clusters}.geojson")
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output:
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@ -114,7 +120,7 @@ if config["sector"]["gas_network"]:
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resources: mem_mb=4000
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script: "scripts/cluster_gas_network.py"
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gas_infrastructure = {**rules.cluster_gas_network.output, **rules.build_gas_import_locations.output}
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gas_infrastructure = {**rules.cluster_gas_network.output, **rules.build_gas_input_locations.output}
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else:
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gas_infrastructure = {}
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@ -243,12 +243,14 @@ sector:
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electricity_distribution_grid: false
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electricity_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
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electricity_grid_connection: true # only applies to onshore wind and utility PV
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H2_network: true
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gas_network: true
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H2_retrofit: true # if set to True existing gas pipes can be retrofitted to H2 pipes
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# according to hydrogen backbone strategy (April, 2020) p.15
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# https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
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# 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
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H2_retrofit_capacity_per_CH4: 0.6 # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
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gas_network_connectivity_upgrade: 3 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
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gas_distribution_grid: true
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gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
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biomass_transport: false # biomass transport between nodes
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@ -449,6 +451,7 @@ plotting:
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gas boilers: '#db6a25'
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gas boiler marginal: '#db6a25'
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gas: '#e05b09'
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fossil gas: '#e05b09'
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natural gas: '#e05b09'
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CCGT: '#a85522'
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CCGT marginal: '#a85522'
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@ -457,6 +460,7 @@ plotting:
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gas for industry: '#853403'
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gas for industry CC: '#692e0a'
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gas pipeline: '#ebbca0'
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gas pipeline new: '#a87c62'
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# oil
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oil: '#c9c9c9'
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oil boiler: '#adadad'
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@ -546,6 +550,7 @@ plotting:
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H2 storage: '#bf13a0'
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land transport fuel cell: '#6b3161'
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H2 pipeline: '#f081dc'
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H2 pipeline retrofitted: '#ba99b5'
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H2 Fuel Cell: '#c251ae'
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H2 Electrolysis: '#ff29d9'
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# syngas
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@ -175,7 +175,7 @@ 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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overlay = gpd.overlay(regions, bio_nuts2, keep_geom_type=True)
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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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@ -1,5 +1,5 @@
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"""
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Build import locations for fossil gas from entry-points and LNG terminals.
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Build import locations for fossil gas from entry-points, LNG terminals and production sites.
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"""
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import logging
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@ -16,11 +16,8 @@ def read_scigrid_gas(fn):
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return df
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def build_gas_input_locations(lng_fn, entry_fn, prod_fn):
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def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
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countries = snakemake.config["countries"]
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countries[countries.index('GB')] = 'UK'
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# LNG terminals
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lng = read_scigrid_gas(lng_fn)
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@ -37,7 +34,8 @@ def build_gas_input_locations(lng_fn, entry_fn, prod_fn):
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prod = read_scigrid_gas(prod_fn)
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prod = prod.loc[
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(prod.geometry.y > 35) &
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(prod.geometry.x < 30)
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(prod.geometry.x < 30) &
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(prod.country_code != "DE")
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]
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return gpd.GeoDataFrame(
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@ -60,10 +58,13 @@ if __name__ == "__main__":
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onshore_regions = gpd.read_file(snakemake.input.regions_onshore).set_index('name')
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countries = onshore_regions.index.str[:2].unique().str.replace("GB", "UK")
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gas_input_locations = build_gas_input_locations(
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snakemake.input.lng,
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snakemake.input.entry,
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snakemake.input.production
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snakemake.input.production,
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countries
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)
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# recommended to use projected CRS rather than geographic CRS
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@ -76,8 +76,9 @@ def aggregate_parallel_pipes(df):
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"diameter_mm": "mean",
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"length": 'mean',
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'tags': ' '.join,
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"p_min_pu": 'min',
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}
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df = df.groupby(df.index).agg(strategies)
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return df.groupby(df.index).agg(strategies)
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if __name__ == "__main__":
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@ -105,6 +106,6 @@ if __name__ == "__main__":
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gas_network = build_clustered_gas_network(df, bus_regions)
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reindex_pipes(gas_network)
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aggregate_parallel_pipes(gas_network)
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gas_network = aggregate_parallel_pipes(gas_network)
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gas_network.to_csv(snakemake.output.clustered_gas_network)
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@ -236,8 +236,6 @@ def plot_h2_map(network):
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linewidth_factor = 1e4
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# MW below which not drawn
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line_lower_threshold = 1e3
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bus_color = "m"
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link_color = "c"
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# Drop non-electric buses so they don't clutter the plot
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n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
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@ -251,38 +249,68 @@ def plot_h2_map(network):
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n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
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link_widths = n.links.p_nom_opt / linewidth_factor
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link_widths[n.links.p_nom_opt < line_lower_threshold] = 0.
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link_color = n.links.carrier.map({"H2 pipeline":"red",
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"H2 pipeline retrofitted": "blue"})
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h2_new = n.links.loc[n.links.carrier=="H2 pipeline", "p_nom_opt"]
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h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
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positive_order = h2_retro.bus0 < h2_retro.bus1
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h2_retro_p = h2_retro[positive_order]
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swap_buses = {"bus0": "bus1", "bus1": "bus0"}
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h2_retro_n = h2_retro[~positive_order].rename(columns=swap_buses)
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h2_retro = pd.concat([h2_retro_p, h2_retro_n])
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h2_retro.index = h2_retro.apply(
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lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
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axis=1
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)
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h2_retro = h2_retro["p_nom_opt"]
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link_widths_total = (h2_new + h2_retro) / linewidth_factor
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link_widths_total = link_widths_total.groupby(level=0).sum().reindex(n.links.index).fillna(0.)
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link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
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retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)
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link_widths_retro = retro / linewidth_factor
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link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.
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n.links.bus0 = n.links.bus0.str.replace(" H2", "")
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n.links.bus1 = n.links.bus1.str.replace(" H2", "")
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print(link_widths.sort_values())
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print(n.links[["bus0", "bus1"]])
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fig, ax = plt.subplots(
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figsize=(7, 6),
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subplot_kw={"projection": ccrs.PlateCarree()}
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)
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bus_colors = {
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"H2 Electrolysis": "m",
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"H2 Fuel Cell": "slateblue"
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}
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n.plot(
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bus_sizes=bus_sizes,
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bus_colors={"H2 Electrolysis": bus_color,
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"H2 Fuel Cell": "slateblue"},
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link_colors=link_color,
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link_widths=link_widths,
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bus_colors=bus_colors,
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link_colors='#afc6c7',
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link_widths=link_widths_total,
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branch_components=["Link"],
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ax=ax, **map_opts
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ax=ax,
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**map_opts
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)
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n.plot(
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geomap=False,
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bus_sizes=0,
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link_colors='#72d3d6',
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link_widths=link_widths_retro,
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branch_components=["Link"],
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ax=ax,
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**map_opts
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)
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handles = make_legend_circles_for(
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[50000, 10000],
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scale=bus_size_factor,
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facecolor=bus_color
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facecolor='k'
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)
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labels = ["{} GW".format(s) for s in (50, 10)]
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@ -330,261 +358,127 @@ def plot_ch4_map(network):
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n = network.copy()
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supply_energy = get_nodal_balance().droplevel([0,1]).sort_index()
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if "gas pipeline" not in n.links.carrier.unique():
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return
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assign_location(n)
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bus_size_factor = 1e7
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bus_size_factor = 4e7
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linewidth_factor = 1e4
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# MW below which not drawn
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line_lower_threshold = 5e3
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bus_color = "maroon"
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link_color = "lightcoral"
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line_lower_threshold = 500
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# Drop non-electric buses so they don't clutter the plot
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n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
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elec = n.generators[n.generators.carrier=="gas"].index
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methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
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bus_sizes = n.generators_t.p.loc[:,elec].mul(n.snapshot_weightings, axis=0).sum().groupby(n.generators.loc[elec,"bus"]).sum() / bus_size_factor
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bus_sizes = n.generators_t.p.loc[:,elec].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.generators.loc[elec,"bus"]).sum() / bus_size_factor
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bus_sizes.rename(index=lambda x: x.replace(" gas", ""), inplace=True)
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bus_sizes = bus_sizes.reindex(n.buses.index).fillna(0)
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bus_sizes.index = pd.MultiIndex.from_product(
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[bus_sizes.index, ["fossil gas"]])
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bus_sizes.index = pd.MultiIndex.from_product([bus_sizes.index, ["fossil gas"]])
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methanation = abs(n.links_t.p1.loc[:,methanation_i].mul(n.snapshot_weightings, axis=0)).sum().groupby(n.links.loc[methanation_i,"bus1"]).sum() / bus_size_factor
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methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
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methanation = abs(n.links_t.p1.loc[:,methanation_i].mul(n.snapshot_weightings.generators, axis=0)).sum().groupby(n.links.loc[methanation_i,"bus1"]).sum() / bus_size_factor
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methanation = methanation.groupby(methanation.index).sum().rename(index=lambda x: x.replace(" gas", ""))
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# make a fake MultiIndex so that area is correct for legend
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methanation.index = pd.MultiIndex.from_product(
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[methanation.index, ["methanation"]])
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methanation.index = pd.MultiIndex.from_product([methanation.index, ["methanation"]])
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biogas_i = n.stores[n.stores.carrier=="biogas"].index
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biogas = n.stores_t.p.loc[:,biogas_i].mul(n.snapshot_weightings, axis=0).sum().groupby(n.stores.loc[biogas_i,"bus"]).sum() / bus_size_factor
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biogas = n.stores_t.p.loc[:,biogas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.stores.loc[biogas_i,"bus"]).sum() / bus_size_factor
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biogas = biogas.groupby(biogas.index).sum().rename(index=lambda x: x.replace(" biogas", ""))
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# make a fake MultiIndex so that area is correct for legend
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biogas.index = pd.MultiIndex.from_product(
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[biogas.index, ["biogas"]])
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biogas.index = pd.MultiIndex.from_product([biogas.index, ["biogas"]])
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bus_sizes = pd.concat([bus_sizes, methanation, biogas])
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bus_sizes.sort_index(inplace=True)
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n.links.drop(n.links.index[n.links.carrier != "gas pipeline"], inplace=True)
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to_remove = n.links.index[~n.links.carrier.str.contains("gas pipeline")]
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n.links.drop(to_remove, inplace=True)
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link_widths = n.links.p_nom_opt / linewidth_factor
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link_widths = n.links.p_nom_opt / linewidth_factor
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link_widths[n.links.p_nom_opt < line_lower_threshold] = 0.
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link_widths_orig = n.links.p_nom / linewidth_factor
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link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.
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link_color = n.links.carrier.map({"gas pipeline": "lightcoral",
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"gas pipeline new": "red"})
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n.links.bus0 = n.links.bus0.str.replace(" gas", "")
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n.links.bus1 = n.links.bus1.str.replace(" gas", "")
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print(link_widths.sort_values())
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bus_colors = {
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"fossil gas": 'maroon',
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"methanation": "steelblue",
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"biogas": "seagreen"
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}
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print(n.links[["bus0", "bus1"]])
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fig, ax = plt.subplots(figsize=(7,6), subplot_kw={"projection": ccrs.PlateCarree()})
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fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
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n.plot(
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bus_sizes=bus_sizes,
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bus_colors=bus_colors,
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link_colors='lightgrey',
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link_widths=link_widths_orig,
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branch_components=["Link"],
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ax=ax,
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**map_opts
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)
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fig.set_size_inches(7, 6)
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n.plot(bus_sizes=bus_sizes,
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bus_colors={"fossil gas": bus_color,
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"methanation": "steelblue",
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"biogas": "seagreen"},
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link_colors=link_color,
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link_widths=link_widths,
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branch_components=["Link"],
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ax=ax, boundaries=(-10, 30, 34, 70))
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n.plot(
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geomap=False,
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ax=ax,
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bus_sizes=0.,
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link_colors=link_color,
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link_widths=link_widths,
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branch_components=["Link"],
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**map_opts
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)
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handles = make_legend_circles_for(
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[200, 1000], scale=bus_size_factor, facecolor=bus_color)
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[200000, 1000000],
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scale=bus_size_factor,
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facecolor='k'
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)
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labels = ["{} MW".format(s) for s in (200, 1000)]
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l2 = ax.legend(handles, labels,
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loc="upper left", bbox_to_anchor=(0.01, 1.01),
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labelspacing=1.0,
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framealpha=1.,
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title='Biomass potential',
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handler_map=make_handler_map_to_scale_circles_as_in(ax))
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l2 = ax.legend(
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handles, labels,
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loc="upper left",
|
||||
bbox_to_anchor=(-0.03, 1.01),
|
||||
labelspacing=1.0,
|
||||
frameon=False,
|
||||
title='gas generation',
|
||||
handler_map=make_handler_map_to_scale_circles_as_in(ax)
|
||||
)
|
||||
|
||||
ax.add_artist(l2)
|
||||
|
||||
handles = []
|
||||
labels = []
|
||||
|
||||
for s in (50, 10):
|
||||
handles.append(plt.Line2D([0], [0], color=link_color,
|
||||
linewidth=s * 1e3 / linewidth_factor))
|
||||
handles.append(plt.Line2D([0], [0], color="k", linewidth=s * 1e3 / linewidth_factor))
|
||||
labels.append("{} GW".format(s))
|
||||
l1_1 = ax.legend(handles, labels,
|
||||
loc="upper left", bbox_to_anchor=(0.30, 1.01),
|
||||
framealpha=1,
|
||||
labelspacing=0.8, handletextpad=1.5,
|
||||
title='CH4 pipeline capacity')
|
||||
|
||||
l1_1 = ax.legend(
|
||||
handles, labels,
|
||||
loc="upper left",
|
||||
bbox_to_anchor=(0.28, 1.01),
|
||||
frameon=False,
|
||||
labelspacing=0.8,
|
||||
handletextpad=1.5,
|
||||
title='gas pipeline capacity'
|
||||
)
|
||||
|
||||
ax.add_artist(l1_1)
|
||||
|
||||
fig.savefig(snakemake.output.map.replace("-costs-all","-ch4_network"), transparent=True,
|
||||
bbox_inches="tight")
|
||||
fig.savefig(
|
||||
snakemake.output.map.replace("-costs-all","-ch4_network"),
|
||||
bbox_inches="tight"
|
||||
)
|
||||
|
||||
##################################################
|
||||
supply_energy.drop("gas pipeline", level=1, inplace=True)
|
||||
supply_energy = supply_energy[abs(supply_energy)>5]
|
||||
supply_energy.rename(index=lambda x: x.replace(" gas",""), level=0, inplace=True)
|
||||
|
||||
|
||||
demand = supply_energy[supply_energy<0].groupby(level=[0,1]).sum()
|
||||
supply = supply_energy[supply_energy>0].groupby(level=[0,1]).sum()
|
||||
|
||||
#### DEMAND #######################################
|
||||
bus_size_factor = 2e7
|
||||
bus_sizes = abs(demand) / bus_size_factor
|
||||
|
||||
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
|
||||
|
||||
fig.set_size_inches(7, 6)
|
||||
|
||||
n.plot(bus_sizes=bus_sizes,
|
||||
bus_colors={"CHP": "r",
|
||||
"OCGT": "wheat",
|
||||
"SMR": "darkkhaki",
|
||||
"SMR CC": "tan",
|
||||
"gas boiler": "orange",
|
||||
"gas for industry": "grey",
|
||||
'gas for industry CC': "lightgrey"},
|
||||
link_colors=link_color,
|
||||
link_widths=link_widths,
|
||||
branch_components=["Link"],
|
||||
ax=ax, boundaries=(-10, 30, 34, 70))
|
||||
|
||||
handles = make_legend_circles_for(
|
||||
[10e6, 20e6], scale=bus_size_factor, facecolor=bus_color)
|
||||
labels = ["{} TWh".format(s) for s in (10, 20)]
|
||||
l2 = ax.legend(handles, labels,
|
||||
loc="upper left", bbox_to_anchor=(0.01, 1.01),
|
||||
labelspacing=1.0,
|
||||
framealpha=1.,
|
||||
title='CH4 demand',
|
||||
handler_map=make_handler_map_to_scale_circles_as_in(ax))
|
||||
ax.add_artist(l2)
|
||||
|
||||
handles = []
|
||||
labels = []
|
||||
|
||||
for s in (50, 10):
|
||||
handles.append(plt.Line2D([0], [0], color=link_color,
|
||||
linewidth=s * 1e3 / linewidth_factor))
|
||||
labels.append("{} GW".format(s))
|
||||
l1_1 = ax.legend(handles, labels,
|
||||
loc="upper left", bbox_to_anchor=(0.30, 1.01),
|
||||
framealpha=1,
|
||||
labelspacing=0.8, handletextpad=1.5,
|
||||
title='CH4 pipeline capacity')
|
||||
ax.add_artist(l1_1)
|
||||
|
||||
fig.savefig(snakemake.output.map.replace("-costs-all","-ch4_demand"), transparent=True,
|
||||
bbox_inches="tight")
|
||||
|
||||
|
||||
#### SUPPLY #######################################
|
||||
bus_size_factor = 2e7
|
||||
bus_sizes = supply / bus_size_factor
|
||||
|
||||
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
|
||||
|
||||
fig.set_size_inches(7, 6)
|
||||
|
||||
n.plot(bus_sizes=bus_sizes,
|
||||
bus_colors={"gas": "maroon",
|
||||
"methanation": "steelblue",
|
||||
"helmeth": "slateblue",
|
||||
"biogas": "seagreen"},
|
||||
link_colors=link_color,
|
||||
link_widths=link_widths,
|
||||
branch_components=["Link"],
|
||||
ax=ax, boundaries=(-10, 30, 34, 70))
|
||||
|
||||
handles = make_legend_circles_for(
|
||||
[10e6, 20e6], scale=bus_size_factor, facecolor="black")
|
||||
labels = ["{} TWh".format(s) for s in (10, 20)]
|
||||
l2 = ax.legend(handles, labels,
|
||||
loc="upper left", bbox_to_anchor=(0.01, 1.01),
|
||||
labelspacing=1.0,
|
||||
framealpha=1.,
|
||||
title='CH4 supply',
|
||||
handler_map=make_handler_map_to_scale_circles_as_in(ax))
|
||||
ax.add_artist(l2)
|
||||
|
||||
handles = []
|
||||
labels = []
|
||||
|
||||
for s in (50, 10):
|
||||
handles.append(plt.Line2D([0], [0], color=link_color,
|
||||
linewidth=s * 1e3 / linewidth_factor))
|
||||
labels.append("{} GW".format(s))
|
||||
l1_1 = ax.legend(handles, labels,
|
||||
loc="upper left", bbox_to_anchor=(0.30, 1.01),
|
||||
framealpha=1,
|
||||
labelspacing=0.8, handletextpad=1.5,
|
||||
title='CH4 pipeline capacity')
|
||||
ax.add_artist(l1_1)
|
||||
|
||||
fig.savefig(snakemake.output.map.replace("-costs-all","-ch4_supply"), transparent=True,
|
||||
bbox_inches="tight")
|
||||
|
||||
###########################################################################
|
||||
net = pd.concat([demand.groupby(level=0).sum().rename("demand"),
|
||||
supply.groupby(level=0).sum().rename("supply")], axis=1).sum(axis=1)
|
||||
mask = net>0
|
||||
net_importer = net.mask(mask).rename("net_importer")
|
||||
net_exporter = net.mask(~mask).rename("net_exporter")
|
||||
|
||||
bus_size_factor = 2e7
|
||||
linewidth_factor = 1e-1
|
||||
bus_sizes = pd.concat([abs(net_importer), net_exporter], axis=1).fillna(0).stack() / bus_size_factor
|
||||
|
||||
link_widths = abs(n.links_t.p0).max().loc[n.links.index] / n.links.p_nom_opt
|
||||
link_widths /= linewidth_factor
|
||||
|
||||
|
||||
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()})
|
||||
|
||||
fig.set_size_inches(7, 6)
|
||||
|
||||
n.plot(bus_sizes=bus_sizes,
|
||||
bus_colors={"net_importer": "r",
|
||||
"net_exporter": "darkgreen",
|
||||
},
|
||||
link_colors="lightgrey",
|
||||
link_widths=link_widths,
|
||||
branch_components=["Link"],
|
||||
ax=ax, boundaries=(-10, 30, 34, 70))
|
||||
|
||||
handles = make_legend_circles_for(
|
||||
[10e6, 20e6], scale=bus_size_factor, facecolor="black")
|
||||
labels = ["{} TWh".format(s) for s in (10, 20)]
|
||||
l2 = ax.legend(handles, labels,
|
||||
loc="upper left", bbox_to_anchor=(0.01, 1.01),
|
||||
labelspacing=1.0,
|
||||
framealpha=1.,
|
||||
title='Net Import/Export',
|
||||
handler_map=make_handler_map_to_scale_circles_as_in(ax))
|
||||
ax.add_artist(l2)
|
||||
|
||||
handles = []
|
||||
labels = []
|
||||
|
||||
for s in (0.5, 1):
|
||||
handles.append(plt.Line2D([0], [0], color="lightgrey",
|
||||
linewidth=s / linewidth_factor))
|
||||
labels.append("{} per unit".format(s))
|
||||
l1_1 = ax.legend(handles, labels,
|
||||
loc="upper left", bbox_to_anchor=(0.30, 1.01),
|
||||
framealpha=1,
|
||||
labelspacing=0.8, handletextpad=1.5,
|
||||
title='maximum used CH4 pipeline capacity')
|
||||
ax.add_artist(l1_1)
|
||||
|
||||
fig.savefig(snakemake.output.map.replace("-costs-all","-ch4_net_balance"), transparent=True,
|
||||
bbox_inches="tight")
|
||||
|
||||
def plot_map_without(network):
|
||||
|
||||
@ -785,51 +679,6 @@ def plot_series(network, carrier="AC", name="test"):
|
||||
transparent=True)
|
||||
|
||||
|
||||
def get_nodal_balance(carrier="gas"):
|
||||
|
||||
bus_map = (n.buses.carrier == carrier)
|
||||
bus_map.at[""] = False
|
||||
supply_energy = pd.Series(dtype="float64")
|
||||
|
||||
for c in n.iterate_components(n.one_port_components):
|
||||
|
||||
items = c.df.index[c.df.bus.map(bus_map).fillna(False)]
|
||||
|
||||
if len(items) == 0:
|
||||
continue
|
||||
|
||||
s = round(c.pnl.p.multiply(n.snapshot_weightings,axis=0).sum().multiply(c.df['sign']).loc[items]
|
||||
.groupby([c.df.bus, c.df.carrier]).sum())
|
||||
s = pd.concat([s], keys=[c.list_name])
|
||||
s = pd.concat([s], keys=[carrier])
|
||||
|
||||
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
|
||||
supply_energy.loc[s.index] = s
|
||||
|
||||
|
||||
for c in n.iterate_components(n.branch_components):
|
||||
|
||||
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
|
||||
|
||||
items = c.df.index[c.df["bus" + str(end)].map(bus_map,na_action=False)]
|
||||
|
||||
if len(items) == 0:
|
||||
continue
|
||||
|
||||
s = ((-1)*c.pnl["p"+end][items].multiply(n.snapshot_weightings,axis=0).sum()
|
||||
.groupby([c.df.loc[items,'bus{}'.format(end)], c.df.loc[items,'carrier']]).sum())
|
||||
s.index = s.index
|
||||
s = pd.concat([s], keys=[c.list_name])
|
||||
s = pd.concat([s], keys=[carrier])
|
||||
|
||||
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
|
||||
|
||||
supply_energy.loc[s.index] = s
|
||||
|
||||
supply_energy = supply_energy.rename(index=lambda x: rename_techs(x), level=3)
|
||||
return supply_energy
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if 'snakemake' not in globals():
|
||||
from helper import mock_snakemake
|
||||
|
||||
@ -8,6 +8,7 @@ import pytz
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
import xarray as xr
|
||||
import networkx as nx
|
||||
|
||||
from itertools import product
|
||||
from scipy.stats import beta
|
||||
@ -16,6 +17,10 @@ from vresutils.costdata import annuity
|
||||
from build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
|
||||
from helper import override_component_attrs
|
||||
|
||||
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
|
||||
from networkx.algorithms import complement
|
||||
from pypsa.geo import haversine_pts
|
||||
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
@ -131,40 +136,6 @@ def get(item, investment_year=None):
|
||||
return item
|
||||
|
||||
|
||||
def create_network_topology(n, prefix, connector=" -> "):
|
||||
"""
|
||||
Create a network topology like the power transmission network.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : pypsa.Network
|
||||
prefix : str
|
||||
connector : str
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame with columns bus0, bus1 and length
|
||||
"""
|
||||
|
||||
ln_attrs = ["bus0", "bus1", "length"]
|
||||
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
|
||||
|
||||
candidates = pd.concat([
|
||||
n.lines[ln_attrs],
|
||||
n.links.loc[n.links.carrier == "DC", lk_attrs]
|
||||
]).fillna(0)
|
||||
|
||||
positive_order = candidates.bus0 < candidates.bus1
|
||||
candidates_p = candidates[positive_order]
|
||||
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
|
||||
candidates_n = candidates[~positive_order].rename(columns=swap_buses)
|
||||
candidates = pd.concat([candidates_p, candidates_n])
|
||||
|
||||
topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
|
||||
topo.index = topo.apply(lambda c: prefix + c.bus0 + connector + c.bus1, axis=1)
|
||||
return topo
|
||||
|
||||
|
||||
def co2_emissions_year(countries, opts, year):
|
||||
"""
|
||||
Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
|
||||
@ -252,14 +223,21 @@ def add_lifetime_wind_solar(n, costs):
|
||||
n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, 'lifetime']
|
||||
|
||||
|
||||
def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
|
||||
def haversine(p):
|
||||
coord0 = n.buses.loc[p.bus0, ['x', 'y']].values
|
||||
coord1 = n.buses.loc[p.bus1, ['x', 'y']].values
|
||||
return 1.5 * haversine_pts(coord0, coord1)
|
||||
|
||||
|
||||
def create_network_topology(n, prefix, carriers=["DC"], connector=" -> ", bidirectional=True):
|
||||
"""
|
||||
Create a network topology like the power transmission network.
|
||||
Create a network topology from transmission lines and link carrier selection.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
n : pypsa.Network
|
||||
prefix : str
|
||||
carriers : list-like
|
||||
connector : str
|
||||
bidirectional : bool, default True
|
||||
True: one link for each connection
|
||||
@ -267,7 +245,7 @@ def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame with columns bus0, bus1 and length
|
||||
pd.DataFrame with columns bus0, bus1, length, underwater_fraction
|
||||
"""
|
||||
|
||||
ln_attrs = ["bus0", "bus1", "length"]
|
||||
@ -275,9 +253,13 @@ def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
|
||||
|
||||
candidates = pd.concat([
|
||||
n.lines[ln_attrs],
|
||||
n.links.loc[n.links.carrier == "DC", lk_attrs]
|
||||
n.links.loc[n.links.carrier.isin(carriers), lk_attrs]
|
||||
]).fillna(0)
|
||||
|
||||
# base network topology purely on location not carrier
|
||||
candidates["bus0"] = candidates.bus0.map(n.buses.location)
|
||||
candidates["bus1"] = candidates.bus1.map(n.buses.location)
|
||||
|
||||
positive_order = candidates.bus0 < candidates.bus1
|
||||
candidates_p = candidates[positive_order]
|
||||
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
|
||||
@ -1110,25 +1092,16 @@ def add_storage_and_grids(n, costs):
|
||||
|
||||
logger.info("Add gas network")
|
||||
|
||||
cols = [
|
||||
"bus0",
|
||||
"bus1",
|
||||
"p_min_pu",
|
||||
"p_nom",
|
||||
"tags",
|
||||
"length"
|
||||
"build_year"
|
||||
]
|
||||
fn = snakemake.input.clustered_gas_network
|
||||
gas_pipes = pd.read_csv(fn, usecols=cols, index_col=0)
|
||||
gas_pipes = pd.read_csv(fn, index_col=0)
|
||||
|
||||
if options["H2_retrofit"]:
|
||||
gas_pipes["p_nom_max"] = gas_pipes.gas_pipes.p_nom
|
||||
gas_pipes["p_nom_max"] = gas_pipes.p_nom
|
||||
gas_pipes["p_nom_min"] = 0.
|
||||
gas_pipes["capital_cost"] = 0.
|
||||
else:
|
||||
gas_pipes["p_nom_max"] = np.inf
|
||||
gas_pipes["p_nom_min"] = gas_pipes.gas_pipes.p_nom
|
||||
gas_pipes["p_nom_min"] = gas_pipes.p_nom
|
||||
gas_pipes["capital_cost"] = gas_pipes.length * costs.at['CH4 (g) pipeline', 'fixed']
|
||||
|
||||
n.madd("Link",
|
||||
@ -1144,11 +1117,12 @@ def add_storage_and_grids(n, costs):
|
||||
capital_cost=gas_pipes.capital_cost,
|
||||
tags=gas_pipes.tags,
|
||||
carrier="gas pipeline",
|
||||
lifetime=50
|
||||
lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
|
||||
)
|
||||
|
||||
# remove fossil generators where there is neither
|
||||
# production, LNG terminal, nor entry-point beyond system scope
|
||||
|
||||
fn = snakemake.input.gas_input_nodes
|
||||
gas_input_nodes = pd.read_csv(fn, index_col=0, squeeze=True).values
|
||||
remove_i = n.generators[
|
||||
@ -1157,8 +1131,41 @@ def add_storage_and_grids(n, costs):
|
||||
].index
|
||||
n.generators.drop(remove_i, inplace=True)
|
||||
|
||||
# TODO candidate gas network topology
|
||||
# add candidates for new gas pipelines to achieve full connectivity
|
||||
|
||||
G = nx.Graph()
|
||||
|
||||
gas_buses = n.buses.loc[n.buses.carrier=='gas', 'location']
|
||||
G.add_nodes_from(np.unique(gas_buses.values))
|
||||
|
||||
sel = gas_pipes.p_nom > 1500
|
||||
attrs = ["bus0", "bus1", "length"]
|
||||
G.add_weighted_edges_from(gas_pipes.loc[sel, attrs].values)
|
||||
|
||||
# find all complement edges
|
||||
complement_edges = pd.DataFrame(complement(G).edges, columns=["bus0", "bus1"])
|
||||
complement_edges["length"] = complement_edges.apply(haversine, axis=1)
|
||||
|
||||
# apply k_edge_augmentation weighted by length of complement edges
|
||||
k_edge = options.get("gas_network_connectivity_upgrade", 3)
|
||||
augmentation = k_edge_augmentation(G, k_edge, avail=complement_edges.values)
|
||||
new_gas_pipes = pd.DataFrame(augmentation, columns=["bus0", "bus1"])
|
||||
new_gas_pipes["length"] = new_gas_pipes.apply(haversine, axis=1)
|
||||
|
||||
new_gas_pipes.index = new_gas_pipes.apply(
|
||||
lambda x: f"gas pipeline new {x.bus0} <-> {x.bus1}", axis=1)
|
||||
|
||||
n.madd("Link",
|
||||
new_gas_pipes.index,
|
||||
bus0=new_gas_pipes.bus0 + " gas",
|
||||
bus1=new_gas_pipes.bus1 + " gas",
|
||||
p_min_pu=-1, # new gas pipes are bidirectional
|
||||
p_nom_extendable=True,
|
||||
length=new_gas_pipes.length,
|
||||
capital_cost=new_gas_pipes.length * costs.at['CH4 (g) pipeline', 'fixed'],
|
||||
carrier="gas pipeline new",
|
||||
lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
|
||||
)
|
||||
|
||||
# retroftting existing CH4 pipes to H2 pipes
|
||||
if options["gas_network"] and options["H2_retrofit"]:
|
||||
@ -1172,49 +1179,32 @@ def add_storage_and_grids(n, costs):
|
||||
h2_pipes.index,
|
||||
bus0=h2_pipes.bus0 + " H2",
|
||||
bus1=h2_pipes.bus1 + " H2",
|
||||
p_min_pu=-1., # allow that all H2 pipelines can be used in other direction
|
||||
p_nom_max=h2_pipes.pipe_capacity_MW * options["H2_retrofit_capacity_per_CH4"],
|
||||
p_min_pu=-1., # allow that all H2 retrofit pipelines can be used in both directions
|
||||
p_nom_max=h2_pipes.p_nom * options["H2_retrofit_capacity_per_CH4"],
|
||||
p_nom_extendable=True,
|
||||
length=h2_pipes.length_km,
|
||||
capital_cost=costs.at['H2 (g) pipeline repurposed', 'fixed'] * h2_pipes.length_km,
|
||||
type=gas_pipes.num_parallel,
|
||||
tags=h2_pipes.id,
|
||||
length=h2_pipes.length,
|
||||
capital_cost=costs.at['H2 (g) pipeline repurposed', 'fixed'] * h2_pipes.length,
|
||||
tags=h2_pipes.tags,
|
||||
carrier="H2 pipeline retrofitted",
|
||||
lifetime=50
|
||||
lifetime=costs.at['H2 (g) pipeline repurposed', 'lifetime']
|
||||
)
|
||||
|
||||
attrs = ["bus0", "bus1", "length"]
|
||||
h2_links = pd.DataFrame(columns=attrs)
|
||||
if options.get("H2_network", True):
|
||||
|
||||
lines_sel = n.lines[attrs]
|
||||
links_sel = n.links.loc[n.links.carrier.isin(["DC", "gas pipeline"]), attrs]
|
||||
h2_pipes = create_network_topology(n, "H2 pipeline ", carriers=["DC", "gas pipeline"])
|
||||
|
||||
candidates = pd.concat({
|
||||
"lines": lines_sel,
|
||||
"links": links_sel,
|
||||
})
|
||||
|
||||
for candidate in candidates.index:
|
||||
buses = [candidates.at[candidate, "bus0"], candidates.at[candidate, "bus1"]]
|
||||
buses.sort()
|
||||
name = f"H2 pipeline {buses[0]} -> {buses[1]}"
|
||||
if name not in h2_links.index:
|
||||
h2_links.at[name, "bus0"] = buses[0]
|
||||
h2_links.at[name, "bus1"] = buses[1]
|
||||
h2_links.at[name, "length"] = candidates.at[candidate, "length"]
|
||||
|
||||
# TODO Add efficiency losses
|
||||
n.madd("Link",
|
||||
h2_links.index,
|
||||
bus0=h2_links.bus0.values + " H2",
|
||||
bus1=h2_links.bus1.values + " H2",
|
||||
p_min_pu=-1,
|
||||
p_nom_extendable=True,
|
||||
length=h2_links.length.values,
|
||||
capital_cost=costs.at['H2 (g) pipeline', 'fixed'] * h2_links.length.values,
|
||||
carrier="H2 pipeline",
|
||||
lifetime=costs.at['H2 (g) pipeline', 'lifetime']
|
||||
)
|
||||
# TODO Add efficiency losses
|
||||
n.madd("Link",
|
||||
h2_pipes.index,
|
||||
bus0=h2_pipes.bus0.values + " H2",
|
||||
bus1=h2_pipes.bus1.values + " H2",
|
||||
p_min_pu=-1,
|
||||
p_nom_extendable=True,
|
||||
length=h2_pipes.length.values,
|
||||
capital_cost=costs.at['H2 (g) pipeline', 'fixed'] * h2_pipes.length.values,
|
||||
carrier="H2 pipeline",
|
||||
lifetime=costs.at['H2 (g) pipeline', 'lifetime']
|
||||
)
|
||||
|
||||
n.add("Carrier", "battery")
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user