Split offshore wind turbines into AC and DC connections
This commit is contained in:
Jonas Hoersch
parent
bddea86b1e
commit
db073d8daa
16
config.yaml
16
config.yaml
@ -72,7 +72,7 @@ renewable:
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distance_grid_codes: [1, 2, 3, 4, 5, 6]
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natura: true
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potential: conservative # or heuristic
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offwind:
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offwind-ac:
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cutout: europe-2013-era5
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resource:
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method: wind
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@ -83,6 +83,20 @@ renewable:
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corine: [44, 255]
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natura: true
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max_depth: 50
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max_shore_distance: 80000
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potential: conservative # or heuristic
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offwind-dc:
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cutout: europe-2013-era5
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resource:
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method: wind
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turbine: NREL_ReferenceTurbine_5MW_offshore
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# ScholzPhd Tab 4.3.1: 10MW/km^2
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capacity_per_sqkm: 3
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# correction_factor: 0.93
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corine: [44, 255]
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natura: true
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max_depth: 50
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min_shore_distance: 80000
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potential: conservative # or heuristic
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solar:
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cutout: europe-2013-sarah
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@ -16,10 +16,15 @@ lignite,2030,lifetime,40,years,IEA2010
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geothermal,2030,lifetime,40,years,IEA2010
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biomass,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
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oil,2030,lifetime,30,years,ECF2010 in DIW DataDoc http://hdl.handle.net/10419/80348
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onwind,2030,investment,910,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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onwind,2030,investment,1110,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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onwind-landcosts,2030,investment,200,EUR/kWel,Land costs and compensation payments conservatively estimated based on DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind,2030,investment,1640,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-grid,2030,investment,255,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node
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offwind-grid-perlength,2030,investment,0.97,EUR/kWel/km,Haertel 2017
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offwind-ac-station,2030,investment,250,EUR/kWel,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-ac-connection-submarine,2030,investment,2685,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-ac-connection-underground,2030,investment,1342,EUR/MW/km,DEA https://ens.dk/en/our-services/projections-and-models/technology-data
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offwind-dc-station,2030,investment,400,EUR/kWel,Haertel 2017; assuming one onshore and one offshore node + 13% learning reduction
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offwind-dc-connection-submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
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offwind-dc-connection-underground,2030,investment,1000,EUR/MW/km,Haertel 2017; average + 13% learning reduction
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solar,2030,investment,600,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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biomass,2030,investment,2209,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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geothermal,2030,investment,3392,EUR/kWel,DIW DataDoc http://hdl.handle.net/10419/80348
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@ -177,7 +182,7 @@ HVAC overhead,2030,FOM,2,%/year,Hagspiel
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HVDC overhead,2030,investment,400,EUR/MW/km,Hagspiel
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HVDC overhead,2030,lifetime,40,years,Hagspiel
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HVDC overhead,2030,FOM,2,%/year,Hagspiel
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HVDC submarine,2030,investment,2000,EUR/MW/km,Own analysis of European submarine HVDC projects since 2000
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HVDC submarine,2030,investment,2000,EUR/MW/km,DTU report based on Fig 34 of https://ec.europa.eu/energy/sites/ener/files/documents/2014_nsog_report.pdf
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HVDC submarine,2030,lifetime,40,years,Hagspiel
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HVDC submarine,2030,FOM,2,%/year,Hagspiel
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HVDC inverter pair,2030,investment,150000,EUR/MW,Hagspiel
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@ -161,15 +161,18 @@ def attach_wind_and_solar(n, costs):
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n.add("Carrier", name=tech)
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with xr.open_dataset(getattr(snakemake.input, 'profile_' + tech)) as ds:
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capital_cost = costs.at[tech, 'capital_cost']
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if tech + "-grid" in costs.index:
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if tech + "-grid-perlength" in costs.index:
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grid_cost = costs.at[tech + "-grid", "capital_cost"] + costs.at[tech + "-grid-perlength", 'capital_cost'] * ds['average_distance'].to_pandas()
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logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format(grid_cost.min(), grid_cost.max(), tech))
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else:
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grid_cost = costs.at[tech + "-grid", "capital_cost"]
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logger.info("Added connection cost of {:0.0f} Eur/MW/a to {}".format(grid_cost, tech))
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capital_cost = capital_cost + grid_cost
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suptech = tech.split('-', 2)[0]
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if suptech == 'offwind':
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underwater_fraction = ds['underwater_fraction'].to_pandas()
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connection_cost = (snakemake.config['lines']['length_factor'] * ds['average_distance'].to_pandas() *
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(underwater_fraction * costs.at[tech + '-connection-submarine', 'capital_cost'] +
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(1. - underwater_fraction) * costs.at[tech + '-connection-underground', 'capital_cost']))
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capital_cost = costs.at['offwind', 'capital_cost'] + costs.at[tech + '-station', 'capital_cost'] + connection_cost
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logger.info("Added connection cost of {:0.0f}-{:0.0f} Eur/MW/a to {}".format(connection_cost.min(), connection_cost.max(), tech))
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elif suptech == 'onwind':
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capital_cost = costs.at['onwind', 'capital_cost'] + costs.at['onwind-landcosts', 'capital_cost']
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else:
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capital_cost = costs.at[tech, 'capital_cost']
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n.madd("Generator", ds.indexes['bus'], ' ' + tech,
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bus=ds.indexes['bus'],
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@ -177,9 +180,9 @@ def attach_wind_and_solar(n, costs):
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p_nom_extendable=True,
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p_nom_max=ds['p_nom_max'].to_pandas(),
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weight=ds['weight'].to_pandas(),
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marginal_cost=costs.at[tech, 'marginal_cost'],
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marginal_cost=costs.at[suptech, 'marginal_cost'],
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capital_cost=capital_cost,
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efficiency=costs.at[tech, 'efficiency'],
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efficiency=costs.at[suptech, 'efficiency'],
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p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())
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@ -63,22 +63,51 @@ def simplify_network_to_380(n):
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return n, trafo_map
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def _adjust_costs_using_distance(n, distance):
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def _prepare_connection_costs_per_link(n):
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costs = load_costs(n.snapshot_weightings.sum() / 8760, snakemake.input.tech_costs,
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snakemake.config['costs'], snakemake.config['electricity'])
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connection_costs_per_link = {}
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for tech in snakemake.config['renewable']:
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if tech + "-grid-perlength" in costs.index:
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cost_perlength = costs.at[tech + "-grid-perlength", "capital_cost"]
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tech_b = n.generators.carrier == tech
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generator_distance = n.generators.loc[tech_b, "bus"].map(distance).loc[lambda s: s>0]
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if not generator_distance.empty:
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n.generators.loc[generator_distance.index, "capital_cost"] += cost_perlength * generator_distance
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logger.info("Displacing generator(s) {}; capital_cost is adjusted accordingly"
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.format(", ".join("`{}` by {:.0f}km".format(b, d) for b, d in generator_distance.iteritems())))
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if tech.startswith('offwind'):
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connection_costs_per_link[tech] = (
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n.links.length * snakemake.config['lines']['length_factor'] *
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(n.links.underwater_fraction * costs.at[tech + '-connection-submarine', 'capital_cost'] +
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(1. - n.links.underwater_fraction) * costs.at[tech + '-connection-underground', 'capital_cost'])
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)
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return connection_costs_per_link
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def _aggregate_and_move_components(n, busmap, distance, aggregate_one_ports={"Load", "StorageUnit"}):
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def _compute_connection_costs_to_bus(n, busmap, connection_costs_per_link=None, buses=None):
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if connection_costs_per_link is None:
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connection_costs_per_link = _prepare_connection_costs_per_link(n)
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if buses is None:
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buses = busmap.index[busmap.index != busmap.values]
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connection_costs_to_bus = pd.DataFrame(index=buses)
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for tech in connection_costs_per_link:
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adj = n.adjacency_matrix(weights=pd.concat(dict(Link=connection_costs_per_link[tech].reindex(n.links.index),
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Line=pd.Series(0., n.lines.index))))
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costs_between_buses = dijkstra(adj, directed=False, indices=n.buses.index.get_indexer(buses))
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connection_costs_to_bus[tech] = costs_between_buses[np.arange(len(buses)),
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n.buses.index.get_indexer(busmap.loc[buses])]
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return connection_costs_to_bus
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def _adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus):
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for tech in connection_costs_to_bus:
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tech_b = n.generators.carrier == tech
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costs = n.generators.loc[tech_b, "bus"].map(connection_costs_to_bus[tech]).loc[lambda s: s>0]
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if not costs.empty:
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n.generators.loc[costs.index, "capital_cost"] += costs
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logger.info("Displacing {} generator(s) and adding connection costs {} to capital_costs"
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.format(tech, ", ".join("of {:.0f} Eur/MW to `{}`".format(d, b) for b, d in costs.iteritems())))
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def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, aggregate_one_ports={"Load", "StorageUnit"}):
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def replace_components(n, c, df, pnl):
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n.mremove(c, n.df(c).index)
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@ -87,7 +116,7 @@ def _aggregate_and_move_components(n, busmap, distance, aggregate_one_ports={"Lo
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if not df.empty:
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import_series_from_dataframe(n, df, c, attr)
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_adjust_costs_using_distance(n, distance)
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_adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus)
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generators, generators_pnl = aggregategenerators(n, busmap)
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replace_components(n, "Generator", generators, generators_pnl)
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@ -102,16 +131,6 @@ def _aggregate_and_move_components(n, busmap, distance, aggregate_one_ports={"Lo
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df = n.df(c)
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n.mremove(c, df.index[df.bus0.isin(buses_to_del) | df.bus1.isin(buses_to_del)])
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def _compute_distance(n, busmap, buses=None, adjacency_matrix=None):
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if buses is None:
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buses = busmap.index[busmap.index != busmap.values]
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if adjacency_matrix is None:
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adjacency_matrix = n.adjacency_matrix(weights=pd.concat(dict(Link=n.links.length, Line=pd.Series(0., n.lines.index))))
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dist = dijkstra(adjacency_matrix, directed=False, indices=n.buses.index.get_indexer(buses))
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return pd.Series(dist[np.arange(len(buses)), n.buses.index.get_indexer(busmap.loc[buses])], buses)
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def simplify_links(n):
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## Complex multi-node links are folded into end-points
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logger.info("Simplifying connected link components")
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@ -155,8 +174,9 @@ def simplify_links(n):
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seen.add(u)
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busmap = n.buses.index.to_series()
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distance = pd.Series(0., n.buses.index)
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adjacency_matrix = n.adjacency_matrix(weights=pd.concat(dict(Link=n.links.length, Line=pd.Series(0., n.lines.index))))
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connection_costs_per_link = _prepare_connection_costs_per_link(n)
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connection_costs_to_bus = pd.DataFrame(0., index=n.buses.index, columns=list(connection_costs_per_link))
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for lbl in labels.value_counts().loc[lambda s: s > 2].index:
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@ -169,7 +189,7 @@ def simplify_links(n):
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m = sp.spatial.distance_matrix(n.buses.loc[b, ['x', 'y']],
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n.buses.loc[buses[1:-1], ['x', 'y']])
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busmap.loc[buses] = b[np.r_[0, m.argmin(axis=0), 1]]
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distance.loc[buses] += _compute_distance(n, busmap, buses)
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connection_costs_to_bus.loc[buses] += _compute_connection_costs_to_bus(n, busmap, connection_costs_per_link, buses)
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all_links = [i for _, i in sum(links, [])]
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@ -200,7 +220,7 @@ def simplify_links(n):
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logger.debug("Collecting all components using the busmap")
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_aggregate_and_move_components(n, busmap, distance)
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_aggregate_and_move_components(n, busmap, connection_costs_to_bus)
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return n, busmap
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def remove_stubs(n):
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@ -208,9 +228,9 @@ def remove_stubs(n):
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busmap = busmap_by_stubs(n) # ['country'])
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distance = _compute_distance(n, busmap)
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connection_costs_to_bus = _compute_connection_costs_to_bus(n, busmap)
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_aggregate_and_move_components(n, busmap, distance)
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_aggregate_and_move_components(n, busmap, connection_costs_to_bus)
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return n, busmap
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