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revision gas infrastructure representation 2

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This commit is contained in:
Fabian Neumann 2021-11-04 21:48:54 +01:00
parent 6a00d5bfca
commit 985705403e
7 changed files with 229 additions and 377 deletions
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24
Snakefile
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@ -81,8 +81,14 @@ rule build_simplified_population_layouts:
if config["sector"]["gas_network"]: if config["sector"]["gas_network"]:
datafiles = [
"IGGIELGN_LNGs.geojson",
"IGGIELGN_BorderPoints.geojson",
"IGGIELGN_Productions.geojson",
]
rule retrieve_gas_infrastructure_data: rule retrieve_gas_infrastructure_data:
output: "data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.csv" output: expand("data/gas_network/scigrid-gas/data/{files}", files=datafiles)
script: 'scripts/retrieve_gas_infrastructure_data.py' script: 'scripts/retrieve_gas_infrastructure_data.py'
rule build_gas_network: rule build_gas_network:
@ -93,20 +99,20 @@ if config["sector"]["gas_network"]:
resources: mem_mb=4000 resources: mem_mb=4000
script: "scripts/build_gas_network.py" script: "scripts/build_gas_network.py"
rule build_gas_import_locations: rule build_gas_input_locations:
input: input:
lng="data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.geojson" lng="data/gas_network/scigrid-gas/data/IGGIELGN_LNGs.geojson",
entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson" entry="data/gas_network/scigrid-gas/data/IGGIELGN_BorderPoints.geojson",
production="data/gas_network/scigrid-gas/data/IGGIELGN_Productions.geojson" production="data/gas_network/scigrid-gas/data/IGGIELGN_Productions.geojson",
regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"), regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
output: output:
gas_input_nodes="resources/gas_input_nodes_s{simpl}_{clusters}.csv" gas_input_nodes="resources/gas_input_locations_s{simpl}_{clusters}.csv"
resources: mem_mb=2000, resources: mem_mb=2000,
script: "scripts/build_gas_import_locations.py" script: "scripts/build_gas_input_locations.py"
rule cluster_gas_network: rule cluster_gas_network:
input: input:
cleaned_gas_network="data/gas_network/gas_network_dataset.csv", cleaned_gas_network="resources/gas_network.csv",
regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"), regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
regions_offshore=pypsaeur("resources/regions_offshore_elec_s{simpl}_{clusters}.geojson") regions_offshore=pypsaeur("resources/regions_offshore_elec_s{simpl}_{clusters}.geojson")
output: output:
@ -114,7 +120,7 @@ if config["sector"]["gas_network"]:
resources: mem_mb=4000 resources: mem_mb=4000
script: "scripts/cluster_gas_network.py" script: "scripts/cluster_gas_network.py"
gas_infrastructure = {**rules.cluster_gas_network.output, **rules.build_gas_import_locations.output} gas_infrastructure = {**rules.cluster_gas_network.output, **rules.build_gas_input_locations.output}
else: else:
gas_infrastructure = {} gas_infrastructure = {}

5
config.default.yaml
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@ -243,12 +243,14 @@ sector:
electricity_distribution_grid: false electricity_distribution_grid: false
electricity_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv electricity_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
electricity_grid_connection: true # only applies to onshore wind and utility PV electricity_grid_connection: true # only applies to onshore wind and utility PV
H2_network: true
gas_network: true gas_network: true
H2_retrofit: true # if set to True existing gas pipes can be retrofitted to H2 pipes H2_retrofit: true # if set to True existing gas pipes can be retrofitted to H2 pipes
# according to hydrogen backbone strategy (April, 2020) p.15 # according to hydrogen backbone strategy (April, 2020) p.15
# https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf # https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
# 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity # 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
H2_retrofit_capacity_per_CH4: 0.6 # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines H2_retrofit_capacity_per_CH4: 0.6 # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
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
gas_distribution_grid: true gas_distribution_grid: true
gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
biomass_transport: false # biomass transport between nodes biomass_transport: false # biomass transport between nodes
@ -449,6 +451,7 @@ plotting:
gas boilers: '#db6a25' gas boilers: '#db6a25'
gas boiler marginal: '#db6a25' gas boiler marginal: '#db6a25'
gas: '#e05b09' gas: '#e05b09'
fossil gas: '#e05b09'
natural gas: '#e05b09' natural gas: '#e05b09'
CCGT: '#a85522' CCGT: '#a85522'
CCGT marginal: '#a85522' CCGT marginal: '#a85522'
@ -457,6 +460,7 @@ plotting:
gas for industry: '#853403' gas for industry: '#853403'
gas for industry CC: '#692e0a' gas for industry CC: '#692e0a'
gas pipeline: '#ebbca0' gas pipeline: '#ebbca0'
gas pipeline new: '#a87c62'
# oil # oil
oil: '#c9c9c9' oil: '#c9c9c9'
oil boiler: '#adadad' oil boiler: '#adadad'
@ -546,6 +550,7 @@ plotting:
H2 storage: '#bf13a0' H2 storage: '#bf13a0'
land transport fuel cell: '#6b3161' land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc' H2 pipeline: '#f081dc'
H2 pipeline retrofitted: '#ba99b5'
H2 Fuel Cell: '#c251ae' H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9' H2 Electrolysis: '#ff29d9'
# syngas # syngas

2
scripts/build_biomass_potentials.py
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@ -175,7 +175,7 @@ def convert_nuts2_to_regions(bio_nuts2, regions):
# calculate area of nuts2 regions # calculate area of nuts2 regions
bio_nuts2["area_nuts2"] = area(bio_nuts2) bio_nuts2["area_nuts2"] = area(bio_nuts2)
overlay = gpd.overlay(regions, bio_nuts2) overlay = gpd.overlay(regions, bio_nuts2, keep_geom_type=True)
# calculate share of nuts2 area inside region # calculate share of nuts2 area inside region
overlay["share"] = area(overlay) / overlay["area_nuts2"] overlay["share"] = area(overlay) / overlay["area_nuts2"]

15
scripts/build_gas_import_locations.py → scripts/build_gas_input_locations.py
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@ -1,5 +1,5 @@
""" """
Build import locations for fossil gas from entry-points and LNG terminals. Build import locations for fossil gas from entry-points, LNG terminals and production sites.
""" """
import logging import logging
@ -16,11 +16,8 @@ def read_scigrid_gas(fn):
return df return df
def build_gas_input_locations(lng_fn, entry_fn, prod_fn): def build_gas_input_locations(lng_fn, entry_fn, prod_fn, countries):
countries = snakemake.config["countries"]
countries[countries.index('GB')] = 'UK'
# LNG terminals # LNG terminals
lng = read_scigrid_gas(lng_fn) lng = read_scigrid_gas(lng_fn)
@ -37,7 +34,8 @@ def build_gas_input_locations(lng_fn, entry_fn, prod_fn):
prod = read_scigrid_gas(prod_fn) prod = read_scigrid_gas(prod_fn)
prod = prod.loc[ prod = prod.loc[
(prod.geometry.y > 35) & (prod.geometry.y > 35) &
(prod.geometry.x < 30) (prod.geometry.x < 30) &
(prod.country_code != "DE")
] ]
return gpd.GeoDataFrame( return gpd.GeoDataFrame(
@ -60,10 +58,13 @@ if __name__ == "__main__":
onshore_regions = gpd.read_file(snakemake.input.regions_onshore).set_index('name') onshore_regions = gpd.read_file(snakemake.input.regions_onshore).set_index('name')
countries = onshore_regions.index.str[:2].unique().str.replace("GB", "UK")
gas_input_locations = build_gas_input_locations( gas_input_locations = build_gas_input_locations(
snakemake.input.lng, snakemake.input.lng,
snakemake.input.entry, snakemake.input.entry,
snakemake.input.production snakemake.input.production,
countries
) )
# recommended to use projected CRS rather than geographic CRS # recommended to use projected CRS rather than geographic CRS

5
scripts/cluster_gas_network.py
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@ -76,8 +76,9 @@ def aggregate_parallel_pipes(df):
"diameter_mm": "mean", "diameter_mm": "mean",
"length": 'mean', "length": 'mean',
'tags': ' '.join, 'tags': ' '.join,
"p_min_pu": 'min',
} }
df = df.groupby(df.index).agg(strategies) return df.groupby(df.index).agg(strategies)
if __name__ == "__main__": if __name__ == "__main__":
@ -105,6 +106,6 @@ if __name__ == "__main__":
gas_network = build_clustered_gas_network(df, bus_regions) gas_network = build_clustered_gas_network(df, bus_regions)
reindex_pipes(gas_network) reindex_pipes(gas_network)
aggregate_parallel_pipes(gas_network) gas_network = aggregate_parallel_pipes(gas_network)
gas_network.to_csv(snakemake.output.clustered_gas_network) gas_network.to_csv(snakemake.output.clustered_gas_network)

387
scripts/plot_network.py
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@ -236,8 +236,6 @@ def plot_h2_map(network):
linewidth_factor = 1e4 linewidth_factor = 1e4
# MW below which not drawn # MW below which not drawn
line_lower_threshold = 1e3 line_lower_threshold = 1e3
bus_color = "m"
link_color = "c"
# Drop non-electric buses so they don't clutter the plot # Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True) n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
@ -251,38 +249,68 @@ def plot_h2_map(network):
n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True) n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
link_widths = n.links.p_nom_opt / linewidth_factor h2_new = n.links.loc[n.links.carrier=="H2 pipeline", "p_nom_opt"]
link_widths[n.links.p_nom_opt < line_lower_threshold] = 0.
link_color = n.links.carrier.map({"H2 pipeline":"red",
"H2 pipeline retrofitted": "blue"})
h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
positive_order = h2_retro.bus0 < h2_retro.bus1
h2_retro_p = h2_retro[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
h2_retro_n = h2_retro[~positive_order].rename(columns=swap_buses)
h2_retro = pd.concat([h2_retro_p, h2_retro_n])
h2_retro.index = h2_retro.apply(
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
axis=1
)
h2_retro = h2_retro["p_nom_opt"]
link_widths_total = (h2_new + h2_retro) / linewidth_factor
link_widths_total = link_widths_total.groupby(level=0).sum().reindex(n.links.index).fillna(0.)
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
retro = n.links.p_nom_opt.where(n.links.carrier=='H2 pipeline retrofitted', other=0.)
link_widths_retro = retro / linewidth_factor
link_widths_retro[n.links.p_nom_opt < line_lower_threshold] = 0.
n.links.bus0 = n.links.bus0.str.replace(" H2", "") n.links.bus0 = n.links.bus0.str.replace(" H2", "")
n.links.bus1 = n.links.bus1.str.replace(" H2", "") n.links.bus1 = n.links.bus1.str.replace(" H2", "")
print(link_widths.sort_values())
print(n.links[["bus0", "bus1"]])
fig, ax = plt.subplots( fig, ax = plt.subplots(
figsize=(7, 6), figsize=(7, 6),
subplot_kw={"projection": ccrs.PlateCarree()} subplot_kw={"projection": ccrs.PlateCarree()}
) )
bus_colors = {
"H2 Electrolysis": "m",
"H2 Fuel Cell": "slateblue"
}
n.plot( n.plot(
bus_sizes=bus_sizes, bus_sizes=bus_sizes,
bus_colors={"H2 Electrolysis": bus_color, bus_colors=bus_colors,
"H2 Fuel Cell": "slateblue"}, link_colors='#afc6c7',
link_colors=link_color, link_widths=link_widths_total,
link_widths=link_widths,
branch_components=["Link"], branch_components=["Link"],
ax=ax, **map_opts ax=ax,
**map_opts
)
n.plot(
geomap=False,
bus_sizes=0,
link_colors='#72d3d6',
link_widths=link_widths_retro,
branch_components=["Link"],
ax=ax,
**map_opts
) )
handles = make_legend_circles_for( handles = make_legend_circles_for(
[50000, 10000], [50000, 10000],
scale=bus_size_factor, scale=bus_size_factor,
facecolor=bus_color facecolor='k'
) )
labels = ["{} GW".format(s) for s in (50, 10)] labels = ["{} GW".format(s) for s in (50, 10)]
@ -330,261 +358,127 @@ def plot_ch4_map(network):
n = network.copy() n = network.copy()
supply_energy = get_nodal_balance().droplevel([0,1]).sort_index()
if "gas pipeline" not in n.links.carrier.unique(): if "gas pipeline" not in n.links.carrier.unique():
return return
assign_location(n) assign_location(n)
bus_size_factor = 1e7 bus_size_factor = 4e7
linewidth_factor = 1e4 linewidth_factor = 1e4
# MW below which not drawn # MW below which not drawn
line_lower_threshold = 5e3 line_lower_threshold = 500
bus_color = "maroon"
link_color = "lightcoral"
# Drop non-electric buses so they don't clutter the plot # Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True) n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
elec = n.generators[n.generators.carrier=="gas"].index elec = n.generators[n.generators.carrier=="gas"].index
methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
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
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
bus_sizes.rename(index=lambda x: x.replace(" gas", ""), inplace=True) bus_sizes.rename(index=lambda x: x.replace(" gas", ""), inplace=True)
bus_sizes = bus_sizes.reindex(n.buses.index).fillna(0) bus_sizes = bus_sizes.reindex(n.buses.index).fillna(0)
bus_sizes.index = pd.MultiIndex.from_product( bus_sizes.index = pd.MultiIndex.from_product([bus_sizes.index, ["fossil gas"]])
[bus_sizes.index, ["fossil gas"]])
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 methanation_i = n.links[n.links.carrier.isin(["helmeth", "Sabatier"])].index
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
methanation = methanation.groupby(methanation.index).sum().rename(index=lambda x: x.replace(" gas", "")) methanation = methanation.groupby(methanation.index).sum().rename(index=lambda x: x.replace(" gas", ""))
# make a fake MultiIndex so that area is correct for legend # make a fake MultiIndex so that area is correct for legend
methanation.index = pd.MultiIndex.from_product( methanation.index = pd.MultiIndex.from_product([methanation.index, ["methanation"]])
[methanation.index, ["methanation"]])
biogas_i = n.stores[n.stores.carrier=="biogas"].index biogas_i = n.stores[n.stores.carrier=="biogas"].index
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 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
biogas = biogas.groupby(biogas.index).sum().rename(index=lambda x: x.replace(" biogas", "")) biogas = biogas.groupby(biogas.index).sum().rename(index=lambda x: x.replace(" biogas", ""))
# make a fake MultiIndex so that area is correct for legend # make a fake MultiIndex so that area is correct for legend
biogas.index = pd.MultiIndex.from_product( biogas.index = pd.MultiIndex.from_product([biogas.index, ["biogas"]])
[biogas.index, ["biogas"]])
bus_sizes = pd.concat([bus_sizes, methanation, biogas]) bus_sizes = pd.concat([bus_sizes, methanation, biogas])
bus_sizes.sort_index(inplace=True) bus_sizes.sort_index(inplace=True)
n.links.drop(n.links.index[n.links.carrier != "gas pipeline"], inplace=True) to_remove = n.links.index[~n.links.carrier.str.contains("gas pipeline")]
n.links.drop(to_remove, inplace=True)
link_widths = n.links.p_nom_opt / linewidth_factor link_widths = n.links.p_nom_opt / linewidth_factor
link_widths[n.links.p_nom_opt < line_lower_threshold] = 0. link_widths[n.links.p_nom_opt < line_lower_threshold] = 0.
link_widths_orig = n.links.p_nom / linewidth_factor
link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.
link_color = n.links.carrier.map({"gas pipeline": "lightcoral",
"gas pipeline new": "red"})
n.links.bus0 = n.links.bus0.str.replace(" gas", "") n.links.bus0 = n.links.bus0.str.replace(" gas", "")
n.links.bus1 = n.links.bus1.str.replace(" gas", "") n.links.bus1 = n.links.bus1.str.replace(" gas", "")
print(link_widths.sort_values()) bus_colors = {
"fossil gas": 'maroon',
"methanation": "steelblue",
"biogas": "seagreen"
}
print(n.links[["bus0", "bus1"]]) fig, ax = plt.subplots(figsize=(7,6), subplot_kw={"projection": ccrs.PlateCarree()})
fig, ax = plt.subplots(subplot_kw={"projection": ccrs.PlateCarree()}) n.plot(
bus_sizes=bus_sizes,
bus_colors=bus_colors,
link_colors='lightgrey',
link_widths=link_widths_orig,
branch_components=["Link"],
ax=ax,
**map_opts
)
fig.set_size_inches(7, 6) n.plot(
geomap=False,
n.plot(bus_sizes=bus_sizes, ax=ax,
bus_colors={"fossil gas": bus_color, bus_sizes=0.,
"methanation": "steelblue", link_colors=link_color,
"biogas": "seagreen"}, link_widths=link_widths,
link_colors=link_color, branch_components=["Link"],
link_widths=link_widths, **map_opts
branch_components=["Link"], )
ax=ax, boundaries=(-10, 30, 34, 70))
handles = make_legend_circles_for( handles = make_legend_circles_for(
[200, 1000], scale=bus_size_factor, facecolor=bus_color) [200000, 1000000],
scale=bus_size_factor,
facecolor='k'
)
labels = ["{} MW".format(s) for s in (200, 1000)] labels = ["{} MW".format(s) for s in (200, 1000)]
l2 = ax.legend(handles, labels,
loc="upper left", bbox_to_anchor=(0.01, 1.01), l2 = ax.legend(
labelspacing=1.0, handles, labels,
framealpha=1., loc="upper left",
title='Biomass potential', bbox_to_anchor=(-0.03, 1.01),
handler_map=make_handler_map_to_scale_circles_as_in(ax)) labelspacing=1.0,
frameon=False,
title='gas generation',
handler_map=make_handler_map_to_scale_circles_as_in(ax)
)
ax.add_artist(l2) ax.add_artist(l2)
handles = [] handles = []
labels = [] labels = []
for s in (50, 10): for s in (50, 10):
handles.append(plt.Line2D([0], [0], color=link_color, handles.append(plt.Line2D([0], [0], color="k", linewidth=s * 1e3 / linewidth_factor))
linewidth=s * 1e3 / linewidth_factor))
labels.append("{} GW".format(s)) labels.append("{} GW".format(s))
l1_1 = ax.legend(handles, labels,
loc="upper left", bbox_to_anchor=(0.30, 1.01), l1_1 = ax.legend(
framealpha=1, handles, labels,
labelspacing=0.8, handletextpad=1.5, loc="upper left",
title='CH4 pipeline capacity') bbox_to_anchor=(0.28, 1.01),
frameon=False,
labelspacing=0.8,
handletextpad=1.5,
title='gas pipeline capacity'
)
ax.add_artist(l1_1) ax.add_artist(l1_1)
fig.savefig(snakemake.output.map.replace("-costs-all","-ch4_network"), transparent=True, fig.savefig(
bbox_inches="tight") 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): def plot_map_without(network):
@ -785,51 +679,6 @@ def plot_series(network, carrier="AC", name="test"):
transparent=True) 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 __name__ == "__main__":
if 'snakemake' not in globals(): if 'snakemake' not in globals():
from helper import mock_snakemake from helper import mock_snakemake

168
scripts/prepare_sector_network.py
View File

@ -8,6 +8,7 @@ import pytz
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import xarray as xr import xarray as xr
import networkx as nx
from itertools import product from itertools import product
from scipy.stats import beta 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 build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
from helper import override_component_attrs 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 import logging
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -131,40 +136,6 @@ def get(item, investment_year=None):
return item 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): def co2_emissions_year(countries, opts, year):
""" """
Calculate CO2 emissions in one specific year (e.g. 1990 or 2018). 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'] 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 Parameters
---------- ----------
n : pypsa.Network n : pypsa.Network
prefix : str prefix : str
carriers : list-like
connector : str connector : str
bidirectional : bool, default True bidirectional : bool, default True
True: one link for each connection True: one link for each connection
@ -267,7 +245,7 @@ def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
Returns Returns
------- -------
pd.DataFrame with columns bus0, bus1 and length pd.DataFrame with columns bus0, bus1, length, underwater_fraction
""" """
ln_attrs = ["bus0", "bus1", "length"] ln_attrs = ["bus0", "bus1", "length"]
@ -275,9 +253,13 @@ def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
candidates = pd.concat([ candidates = pd.concat([
n.lines[ln_attrs], 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) ]).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 positive_order = candidates.bus0 < candidates.bus1
candidates_p = candidates[positive_order] candidates_p = candidates[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"} swap_buses = {"bus0": "bus1", "bus1": "bus0"}
@ -1110,25 +1092,16 @@ def add_storage_and_grids(n, costs):
logger.info("Add gas network") logger.info("Add gas network")
cols = [
"bus0",
"bus1",
"p_min_pu",
"p_nom",
"tags",
"length"
"build_year"
]
fn = snakemake.input.clustered_gas_network 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"]: 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["p_nom_min"] = 0.
gas_pipes["capital_cost"] = 0. gas_pipes["capital_cost"] = 0.
else: else:
gas_pipes["p_nom_max"] = np.inf 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'] gas_pipes["capital_cost"] = gas_pipes.length * costs.at['CH4 (g) pipeline', 'fixed']
n.madd("Link", n.madd("Link",
@ -1144,11 +1117,12 @@ def add_storage_and_grids(n, costs):
capital_cost=gas_pipes.capital_cost, capital_cost=gas_pipes.capital_cost,
tags=gas_pipes.tags, tags=gas_pipes.tags,
carrier="gas pipeline", carrier="gas pipeline",
lifetime=50 lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
) )
# remove fossil generators where there is neither # remove fossil generators where there is neither
# production, LNG terminal, nor entry-point beyond system scope # production, LNG terminal, nor entry-point beyond system scope
fn = snakemake.input.gas_input_nodes fn = snakemake.input.gas_input_nodes
gas_input_nodes = pd.read_csv(fn, index_col=0, squeeze=True).values gas_input_nodes = pd.read_csv(fn, index_col=0, squeeze=True).values
remove_i = n.generators[ remove_i = n.generators[
@ -1157,8 +1131,41 @@ def add_storage_and_grids(n, costs):
].index ].index
n.generators.drop(remove_i, inplace=True) 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 # retroftting existing CH4 pipes to H2 pipes
if options["gas_network"] and options["H2_retrofit"]: if options["gas_network"] and options["H2_retrofit"]:
@ -1172,49 +1179,32 @@ def add_storage_and_grids(n, costs):
h2_pipes.index, h2_pipes.index,
bus0=h2_pipes.bus0 + " H2", bus0=h2_pipes.bus0 + " H2",
bus1=h2_pipes.bus1 + " H2", bus1=h2_pipes.bus1 + " H2",
p_min_pu=-1., # allow that all H2 pipelines can be used in other direction p_min_pu=-1., # allow that all H2 retrofit pipelines can be used in both directions
p_nom_max=h2_pipes.pipe_capacity_MW * options["H2_retrofit_capacity_per_CH4"], p_nom_max=h2_pipes.p_nom * options["H2_retrofit_capacity_per_CH4"],
p_nom_extendable=True, p_nom_extendable=True,
length=h2_pipes.length_km, length=h2_pipes.length,
capital_cost=costs.at['H2 (g) pipeline repurposed', 'fixed'] * h2_pipes.length_km, capital_cost=costs.at['H2 (g) pipeline repurposed', 'fixed'] * h2_pipes.length,
type=gas_pipes.num_parallel, tags=h2_pipes.tags,
tags=h2_pipes.id,
carrier="H2 pipeline retrofitted", carrier="H2 pipeline retrofitted",
lifetime=50 lifetime=costs.at['H2 (g) pipeline repurposed', 'lifetime']
) )
attrs = ["bus0", "bus1", "length"] if options.get("H2_network", True):
h2_links = pd.DataFrame(columns=attrs)
lines_sel = n.lines[attrs] h2_pipes = create_network_topology(n, "H2 pipeline ", carriers=["DC", "gas pipeline"])
links_sel = n.links.loc[n.links.carrier.isin(["DC", "gas pipeline"]), attrs]
candidates = pd.concat({ # TODO Add efficiency losses
"lines": lines_sel, n.madd("Link",
"links": links_sel, h2_pipes.index,
}) bus0=h2_pipes.bus0.values + " H2",
bus1=h2_pipes.bus1.values + " H2",
for candidate in candidates.index: p_min_pu=-1,
buses = [candidates.at[candidate, "bus0"], candidates.at[candidate, "bus1"]] p_nom_extendable=True,
buses.sort() length=h2_pipes.length.values,
name = f"H2 pipeline {buses[0]} -> {buses[1]}" capital_cost=costs.at['H2 (g) pipeline', 'fixed'] * h2_pipes.length.values,
if name not in h2_links.index: carrier="H2 pipeline",
h2_links.at[name, "bus0"] = buses[0] lifetime=costs.at['H2 (g) pipeline', 'lifetime']
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']
)
n.add("Carrier", "battery") n.add("Carrier", "battery")