use spatial namespace to manage biomass transport resolution
This commit is contained in:
Fabian Neumann
parent
fd1121af4a
commit
d1298fa93d
@ -67,4 +67,4 @@ def build_biomass_transport_costs():
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if __name__ == "__main__":
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prepare_biomass_transport_costs()
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build_biomass_transport_costs()
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@ -19,10 +19,43 @@ from helper import override_component_attrs
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import logging
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logger = logging.getLogger(__name__)
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from types import SimpleNamespace
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spatial = SimpleNamespace()
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def define_spatial(nodes):
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"""
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Namespace for spatial
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Parameters
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----------
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nodes : list-like
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"""
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global spatial
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global options
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spatial.nodes = nodes
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spatial.biomass = SimpleNamespace()
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if options["biomass_transport"]:
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spatial.biomass.nodes = nodes + " solid biomass"
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spatial.biomass.locations = nodes
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spatial.biomass.industry = nodes + " solid biomass for industry"
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spatial.biomass.industry_cc = nodes + " solid biomass for industry CC"
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else:
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spatial.biomass.nodes = ["EU solid biomass"]
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spatial.biomass.locations = "EU"
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spatial.biomass.industry = ["solid biomass for industry"]
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spatial.biomass.industry_cc = ["solid biomass for industry CC"]
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spatial.biomass.df = pd.DataFrame(vars(spatial.biomass), index=nodes)
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def emission_sectors_from_opts(opts):
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sectors = ["electricity"]
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sectors = ["electricity"]
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if "T" in opts:
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sectors += [
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"rail non-elec",
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@ -144,25 +177,50 @@ def add_lifetime_wind_solar(n, costs):
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n.generators.loc[gen_i, "lifetime"] = costs.at[carrier, 'lifetime']
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def create_network_topology(n, prefix, connector=" -> "):
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def create_network_topology(n, prefix, connector=" -> ", bidirectional=True):
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"""
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create a network topology as the electric network,
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returns a pandas dataframe with bus0, bus1 and length
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Create a network topology like the power transmission network.
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Parameters
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----------
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n : pypsa.Network
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prefix : str
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connector : str
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bidirectional : bool, default True
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True: one link for each connection
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False: one link for each connection and direction (back and forth)
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Returns
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-------
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pd.DataFrame with columns bus0, bus1 and length
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"""
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attrs = ["bus0", "bus1", "length"]
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ln_attrs = ["bus0", "bus1", "length"]
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lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
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candidates = pd.concat([n.lines[attrs],
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n.links.loc[n.links.carrier == "DC", attrs]])
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candidates = pd.concat([
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n.lines[ln_attrs],
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n.links.loc[n.links.carrier == "DC", lk_attrs]
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]).fillna(0)
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positive_order = candidates.bus0 < candidates.bus1
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candidates_p = candidates[positive_order]
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candidates_n = (candidates[~ positive_order]
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.rename(columns={"bus0": "bus1", "bus1": "bus0"}))
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candidates = pd.concat((candidates_p, candidates_n), sort=False)
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swap_buses = {"bus0": "bus1", "bus1": "bus0"}
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candidates_n = candidates[~positive_order].rename(columns=swap_buses)
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candidates = pd.concat([candidates_p, candidates_n])
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def make_index(c):
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return prefix + c.bus0 + connector + c.bus1
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topo = candidates.groupby(["bus0", "bus1"], as_index=False).mean()
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topo.index = topo.apply(lambda x: prefix + x.bus0 + connector + x.bus1, axis=1)
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topo.index = topo.apply(make_index, axis=1)
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if not bidirectional:
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topo_reverse = topo.copy()
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topo_reverse.rename(columns=swap_buses, inplace=True)
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topo_reverse.index = topo_reverse.apply(make_index, axis=1)
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topo = topo.append(topo_reverse)
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return topo
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@ -1554,8 +1612,11 @@ def add_biomass(n, costs):
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biomass_potentials = pd.read_csv(snakemake.input.biomass_potentials, index_col=0)
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transport_costs = pd.read_csv(snakemake.input.biomass_transport,
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index_col=0)
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transport_costs = pd.read_csv(
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snakemake.input.biomass_transport,
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index_col=0,
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squeeze=True
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)
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# potential per node distributed within country by population
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biomass_pot_node = (biomass_potentials.loc[pop_layout.ct]
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@ -1572,7 +1633,8 @@ def add_biomass(n, costs):
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)
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n.madd("Bus",
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biomass_pot_node.index + " solid biomass",
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spatial.biomass.nodes,
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location=spatial.biomass.locations,
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carrier="solid biomass"
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)
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@ -1586,8 +1648,8 @@ def add_biomass(n, costs):
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)
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n.madd("Store",
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biomass_pot_node.index + " solid biomass",
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bus=biomass_pot_node.index + " solid biomass",
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spatial.biomass.nodes,
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bus=spatial.biomass.nodes,
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carrier="solid biomass",
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e_nom=biomass_pot_node["solid biomass"].values,
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marginal_cost=costs.at['solid biomass', 'fuel'],
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@ -1606,34 +1668,26 @@ def add_biomass(n, costs):
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p_nom_extendable=True
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)
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# add biomass transport
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biomass_transport = create_network_topology(n, "Biomass transport ")
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if options["biomass_transport"]:
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# add biomass transport
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biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
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# make transport in both directions
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df = biomass_transport.copy()
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df["bus1"] = biomass_transport.bus0
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df["bus0"] = biomass_transport.bus1
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df.rename(index=lambda x: "Biomass transport " + df.at[x, "bus0"]
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+ " -> " + df.at[x, "bus1"], inplace=True)
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biomass_transport = pd.concat([biomass_transport, df])
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# costs
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bus0_costs = biomass_transport.bus0.apply(lambda x: transport_costs.loc[x[:2]])
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bus1_costs = biomass_transport.bus1.apply(lambda x: transport_costs.loc[x[:2]])
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biomass_transport["costs"] = pd.concat([bus0_costs, bus1_costs],
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axis=1).mean(axis=1)
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n.madd("Link",
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biomass_transport.index,
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bus0=biomass_transport.bus0 + " solid biomass",
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bus1=biomass_transport.bus1 + " solid biomass",
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p_nom_extendable=True,
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length=biomass_transport.length.values,
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marginal_cost=biomass_transport.costs * biomass_transport.length.values,
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capital_cost=1,
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carrier="solid biomass transport"
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)
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# costs
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bus0_costs = biomass_transport.bus0.apply(lambda x: transport_costs[x[:2]])
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bus1_costs = biomass_transport.bus1.apply(lambda x: transport_costs[x[:2]])
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biomass_transport["costs"] = pd.concat([bus0_costs, bus1_costs], axis=1).mean(axis=1)
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n.madd("Link",
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biomass_transport.index,
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bus0=biomass_transport.bus0 + " solid biomass",
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bus1=biomass_transport.bus1 + " solid biomass",
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p_nom_extendable=True,
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length=biomass_transport.length.values,
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marginal_cost=biomass_transport.costs * biomass_transport.length.values,
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capital_cost=1,
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carrier="solid biomass transport"
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)
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#AC buses with district heating
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urban_central = n.buses.index[n.buses.carrier == "urban central heat"]
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@ -1644,7 +1698,7 @@ def add_biomass(n, costs):
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n.madd("Link",
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urban_central + " urban central solid biomass CHP",
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bus0=urban_central + " solid biomass",
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bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
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bus1=urban_central,
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bus2=urban_central + " urban central heat",
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carrier="urban central solid biomass CHP",
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@ -1658,7 +1712,7 @@ def add_biomass(n, costs):
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n.madd("Link",
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urban_central + " urban central solid biomass CHP CC",
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bus0=urban_central + " solid biomass",
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bus0=spatial.biomass.df.loc[urban_central, "nodes"].values,
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bus1=urban_central,
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bus2=urban_central + " urban central heat",
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bus3="co2 atmosphere",
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@ -1684,36 +1738,34 @@ def add_industry(n, costs):
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# 1e6 to convert TWh to MWh
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industrial_demand = pd.read_csv(snakemake.input.industrial_demand, index_col=0) * 1e6
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solid_biomass_by_country = industrial_demand["solid biomass"]
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n.madd("Bus",
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industrial_demand.index + " solid biomass for industry",
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location="EU",
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spatial.biomass.df.loc[industrial_demand.index, "industry"].values,
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location=spatial.biomass.df.loc[industrial_demand.index, "locations"].values,
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carrier="solid biomass for industry"
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)
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p_set = industrial_demand["solid biomass"].rename(index=lambda x: x + " solid biomass for industry") / 8760
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n.madd("Load",
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industrial_demand.index + " solid biomass for industry",
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bus=industrial_demand.index + " solid biomass for industry",
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spatial.biomass.df.loc[industrial_demand.index, "industry"].values,
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bus=spatial.biomass.df.loc[industrial_demand.index, "industry"].values,
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carrier="solid biomass for industry",
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p_set=p_set
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)
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n.madd("Link",
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industrial_demand.index + " solid biomass for industry",
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bus0=industrial_demand.index + " solid biomass",
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bus1=industrial_demand.index + " solid biomass for industry",
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spatial.biomass.df.loc[industrial_demand.index, "industry"].values,
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bus0=spatial.biomass.df.loc[industrial_demand.index, "nodes"].values,
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bus1=spatial.biomass.df.loc[industrial_demand.index, "industry"].values,
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carrier="solid biomass for industry",
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p_nom_extendable=True,
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efficiency=1.
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)
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n.madd("Link",
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industrial_demand.index + " solid biomass for industry CC",
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bus0=industrial_demand.index + " solid biomass",
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bus1=industrial_demand.index + " solid biomass for industry",
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spatial.biomass.df.loc[industrial_demand.index, "industry_cc"].values,
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bus0=spatial.biomass.df.loc[industrial_demand.index, "nodes"].values,
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bus1=spatial.biomass.df.loc[industrial_demand.index, "industry_cc"].values,
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bus2="co2 atmosphere",
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bus3="co2 stored",
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carrier="solid biomass for industry CC",
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@ -2000,93 +2052,6 @@ def maybe_adjust_costs_and_potentials(n, opts):
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print("changing", attr , "for", carrier, "by factor", factor)
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def remove_biomass_transport(n):
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print("no transport of solid biomass considered")
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# remove country specific biomass buses
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n.buses = n.buses[~n.buses.carrier.str.contains("biomass")]
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# biomass potential
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biomass_pot = n.stores[n.stores.carrier=="solid biomass"].e_nom.sum()
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# remove biomass store per country
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n.stores = n.stores[n.stores.carrier!="solid biomass"]
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# remove biomass transport links
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n.links = n.links[n.links.carrier!="solid biomass transport"]
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# total industry demand for biomass
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biomass_demand = n.loads[n.loads.carrier=="solid biomass for industry"].p_set.sum()
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# remove industry demand
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n.loads = n.loads[n.loads.carrier!="solid biomass for industry"]
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# drop transport and industry links
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sel = [
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'solid biomass transport',
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'solid biomass for industry',
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'solid biomass for industry CC'
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]
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n.links = n.links[~n.links.carrier.isin(sel)]
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# add back EU bus + load + store + industry links
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n.add("Bus",
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"EU solid biomass",
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location="EU",
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carrier="solid biomass"
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)
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n.add("Bus",
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"solid biomass for industry",
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location="EU",
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carrier="solid biomass for industry"
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)
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n.add("Load",
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"solid biomass for industry",
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bus="solid biomass for industry",
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carrier="solid biomass for industry",
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p_set=biomass_demand
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)
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n.add("Store",
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"EU solid biomass",
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bus="EU solid biomass",
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carrier="solid biomass",
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e_nom=biomass_pot,
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marginal_cost=costs.at['solid biomass','fuel'],
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e_initial=biomass_pot
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)
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n.add("Link",
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"solid biomass for industry",
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bus0="EU solid biomass",
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bus1="solid biomass for industry",
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carrier="solid biomass for industry",
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p_nom_extendable=True,
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efficiency=1.
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)
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n.add("Link",
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"solid biomass for industry CC",
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bus0="EU solid biomass",
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bus1="solid biomass for industry",
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bus2="co2 atmosphere",
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bus3="co2 stored",
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carrier="solid biomass for industry CC",
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p_nom_extendable=True,
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capital_cost=costs.at["cement capture", "fixed"] * costs.at['solid biomass', 'CO2 intensity'],
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efficiency=0.9,
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efficiency2=-costs.at['solid biomass', 'CO2 intensity'] * costs.at["cement capture", "capture_rate"],
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efficiency3=costs.at['solid biomass', 'CO2 intensity'] * costs.at["cement capture", "capture_rate"],
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lifetime=costs.at['cement capture', 'lifetime']
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)
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# set CHP buses from country to single EU bus
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n.links.loc[n.links.carrier.str.contains("biomass CHP"), "bus0"] = "EU solid biomass"
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# TODO this should rather be a config no wildcard
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def limit_individual_line_extension(n, maxext):
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print(f"limiting new HVAC and HVDC extensions to {maxext} MW")
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@ -2183,9 +2148,6 @@ if __name__ == "__main__":
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if "noH2network" in opts:
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remove_h2_network(n)
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if not options["biomass_transport"]:
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remove_biomass_transport(n)
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for o in opts:
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m = re.match(r'^\d+h$', o, re.IGNORECASE)
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if m is not None:
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