remove snakemake dependencies in functions, use as kwarg instead
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martacki
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config.yaml~
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317
config.yaml~
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@ -0,0 +1,317 @@
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# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: CC0-1.0
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version: 0.3.0
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tutorial: false
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logging:
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level: INFO
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format: '%(levelname)s:%(name)s:%(message)s'
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summary_dir: results
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scenario:
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simpl: ['']
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ll: ['copt']
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clusters: [37, 128, 256, 512, 1024]
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opts: [Co2L-3H]
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countries: ['AL', 'AT', 'BA', 'BE', 'BG', 'CH', 'CZ', 'DE', 'DK', 'EE', 'ES', 'FI', 'FR', 'GB', 'GR', 'HR', 'HU', 'IE', 'IT', 'LT', 'LU', 'LV', 'ME', 'MK', 'NL', 'NO', 'PL', 'PT', 'RO', 'RS', 'SE', 'SI', 'SK']
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snapshots:
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start: "2013-01-01"
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end: "2014-01-01"
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closed: 'left' # end is not inclusive
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enable:
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prepare_links_p_nom: false
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retrieve_databundle: true
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build_cutout: false
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retrieve_cutout: true
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build_natura_raster: false
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retrieve_natura_raster: true
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custom_busmap: false
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clustering:
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algorithm:
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name: kmeans #kmeans
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feature: coordinates #feature not supported yet
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electricity:
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voltages: [220., 300., 380.]
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co2limit: 7.75e+7 # 0.05 * 3.1e9*0.5
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co2base: 1.487e+9
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agg_p_nom_limits: data/agg_p_nom_minmax.csv
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extendable_carriers:
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Generator: []
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StorageUnit: [] # battery, H2
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Store: [battery, H2]
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Link: []
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max_hours:
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battery: 6
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H2: 168
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powerplants_filter: false # use pandas query strings here, e.g. Country not in ['Germany']
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custom_powerplants: false # use pandas query strings here, e.g. Country in ['Germany']
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conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
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renewable_capacities_from_OPSD: [] # onwind, offwind, solar
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# estimate_renewable_capacities_from_capacity_stats:
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# # Wind is the Fueltype in ppm.data.Capacity_stats, onwind, offwind-{ac,dc} the carrier in PyPSA-Eur
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# Wind: [onwind, offwind-ac, offwind-dc]
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# Solar: [solar]
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atlite:
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nprocesses: 4
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cutouts:
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# use 'base' to determine geographical bounds and time span from config
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# base:
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# module: era5
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europe-2013-era5:
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module: era5 # in priority order
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x: [-12., 35.]
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y: [33., 72]
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dx: 0.3
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dy: 0.3
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time: ['2013', '2013']
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europe-2013-sarah:
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module: [sarah, era5] # in priority order
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x: [-12., 45.]
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y: [33., 65]
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dx: 0.2
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dy: 0.2
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time: ['2013', '2013']
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sarah_interpolate: false
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sarah_dir:
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features: [influx, temperature]
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renewable:
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onwind:
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cutout: europe-2013-era5
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resource:
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method: wind
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turbine: Vestas_V112_3MW
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capacity_per_sqkm: 3 # ScholzPhd Tab 4.3.1: 10MW/km^2
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# correction_factor: 0.93
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corine:
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# Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
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# development of the REMix model and application for Europe. ( p.42 / p.28)
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grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
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24, 25, 26, 27, 28, 29, 31, 32]
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distance: 1000
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distance_grid_codes: [1, 2, 3, 4, 5, 6]
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natura: true
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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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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turbine: NREL_ReferenceTurbine_5MW_offshore
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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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max_shore_distance: 30000
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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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: 30000
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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solar:
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cutout: europe-2013-sarah
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resource:
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method: pv
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panel: CSi
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orientation:
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slope: 35.
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azimuth: 180.
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capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2
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# Determined by comparing uncorrected area-weighted full-load hours to those
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# published in Supplementary Data to
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# Pietzcker, Robert Carl, et al. "Using the sun to decarbonize the power
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# sector: The economic potential of photovoltaics and concentrating solar
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# power." Applied Energy 135 (2014): 704-720.
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correction_factor: 0.854337
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corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13,
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14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
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natura: true
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potential: simple # or conservative
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clip_p_max_pu: 1.e-2
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hydro:
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cutout: europe-2013-era5
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carriers: [ror, PHS, hydro]
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PHS_max_hours: 6
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hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float
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clip_min_inflow: 1.0
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lines:
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types:
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220.: "Al/St 240/40 2-bundle 220.0"
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300.: "Al/St 240/40 3-bundle 300.0"
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380.: "Al/St 240/40 4-bundle 380.0"
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s_max_pu: 0.7
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s_nom_max: .inf
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length_factor: 1.25
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under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
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links:
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p_max_pu: 1.0
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p_nom_max: .inf
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include_tyndp: true
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under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
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transformers:
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x: 0.1
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s_nom: 2000.
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type: ''
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load:
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url: https://data.open-power-system-data.org/time_series/2019-06-05/time_series_60min_singleindex.csv
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power_statistics: True # only for files from <2019; set false in order to get ENTSOE transparency data
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interpolate_limit: 3 # data gaps up until this size are interpolated linearly
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time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
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manual_adjustments: true # false
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scaling_factor: 1.0
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costs:
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year: 2030
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discountrate: 0.07 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
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USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html
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marginal_cost: # EUR/MWh
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solar: 0.01
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onwind: 0.015
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offwind: 0.015
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hydro: 0.
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H2: 0.
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electrolysis: 0.
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fuel cell: 0.
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battery: 0.
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battery inverter: 0.
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emission_prices: # in currency per tonne emission, only used with the option Ep
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co2: 0.
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solving:
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options:
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formulation: kirchhoff
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load_shedding: true
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noisy_costs: true
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min_iterations: 4
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max_iterations: 6
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clip_p_max_pu: 0.01
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skip_iterations: false
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track_iterations: false
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#nhours: 10
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solver:
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name: gurobi
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threads: 4
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method: 2 # barrier
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crossover: 0
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BarConvTol: 1.e-5
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FeasibilityTol: 1.e-6
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AggFill: 0
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PreDual: 0
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GURO_PAR_BARDENSETHRESH: 200
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# solver:
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# name: cplex
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# threads: 4
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# lpmethod: 4 # barrier
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# solutiontype: 2 # non basic solution, ie no crossover
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# barrier_convergetol: 1.e-5
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# feasopt_tolerance: 1.e-6
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plotting:
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map:
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figsize: [7, 7]
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boundaries: [-10.2, 29, 35, 72]
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p_nom:
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bus_size_factor: 5.e+4
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linewidth_factor: 3.e+3
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costs_max: 80
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costs_threshold: 1
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energy_max: 15000.
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energy_min: -10000.
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energy_threshold: 50.
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vre_techs: ["onwind", "offwind-ac", "offwind-dc", "solar", "ror"]
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conv_techs: ["OCGT", "CCGT", "Nuclear", "Coal"]
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storage_techs: ["hydro+PHS", "battery", "H2"]
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load_carriers: ["AC load"]
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AC_carriers: ["AC line", "AC transformer"]
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link_carriers: ["DC line", "Converter AC-DC"]
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tech_colors:
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"onwind" : "#235ebc"
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"onshore wind" : "#235ebc"
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'offwind' : "#6895dd"
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'offwind-ac' : "#6895dd"
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'offshore wind' : "#6895dd"
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'offshore wind ac' : "#6895dd"
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'offwind-dc' : "#74c6f2"
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'offshore wind dc' : "#74c6f2"
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"hydro" : "#08ad97"
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"hydro+PHS" : "#08ad97"
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"PHS" : "#08ad97"
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"hydro reservoir" : "#08ad97"
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'hydroelectricity' : '#08ad97'
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"ror" : "#4adbc8"
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"run of river" : "#4adbc8"
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'solar' : "#f9d002"
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'solar PV' : "#f9d002"
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'solar thermal' : '#ffef60'
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'biomass' : '#0c6013'
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'solid biomass' : '#06540d'
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'biogas' : '#23932d'
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'waste' : '#68896b'
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'geothermal' : '#ba91b1'
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"OCGT" : "#d35050"
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"gas" : "#d35050"
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"natural gas" : "#d35050"
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"CCGT" : "#b20101"
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"nuclear" : "#ff9000"
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"coal" : "#707070"
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"lignite" : "#9e5a01"
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"oil" : "#262626"
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"H2" : "#ea048a"
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"hydrogen storage" : "#ea048a"
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"battery" : "#b8ea04"
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"Electric load" : "#f9d002"
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"electricity" : "#f9d002"
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"lines" : "#70af1d"
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"transmission lines" : "#70af1d"
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"AC-AC" : "#70af1d"
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"AC line" : "#70af1d"
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"links" : "#8a1caf"
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"HVDC links" : "#8a1caf"
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"DC-DC" : "#8a1caf"
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"DC link" : "#8a1caf"
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nice_names:
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OCGT: "Open-Cycle Gas"
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CCGT: "Combined-Cycle Gas"
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offwind-ac: "Offshore Wind (AC)"
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offwind-dc: "Offshore Wind (DC)"
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onwind: "Onshore Wind"
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solar: "Solar"
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PHS: "Pumped Hydro Storage"
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hydro: "Reservoir & Dam"
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battery: "Battery Storage"
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H2: "Hydrogen Storage"
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lines: "Transmission Lines"
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ror: "Run of River"
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@ -118,12 +118,7 @@ def _add_missing_carriers_from_costs(n, costs, carriers):
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n.import_components_from_dataframe(emissions, 'Carrier')
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def load_costs(Nyears=1., tech_costs=None, config=None, elec_config=None):
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if tech_costs is None:
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tech_costs = snakemake.input.tech_costs
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if config is None:
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config = snakemake.config['costs']
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def load_costs(tech_costs, config, elec_config, Nyears=1.):
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# set all asset costs and other parameters
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costs = pd.read_csv(tech_costs, index_col=list(range(3))).sort_index()
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@ -169,8 +164,6 @@ def load_costs(Nyears=1., tech_costs=None, config=None, elec_config=None):
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marginal_cost=0.,
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co2_emissions=0.))
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if elec_config is None:
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elec_config = snakemake.config['electricity']
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max_hours = elec_config['max_hours']
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costs.loc["battery"] = \
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costs_for_storage(costs.loc["battery storage"], costs.loc["battery inverter"],
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@ -188,9 +181,7 @@ def load_costs(Nyears=1., tech_costs=None, config=None, elec_config=None):
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return costs
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def load_powerplants(ppl_fn=None):
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if ppl_fn is None:
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ppl_fn = snakemake.input.powerplants
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def load_powerplants(ppl_fn):
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carrier_dict = {'ocgt': 'OCGT', 'ccgt': 'CCGT', 'bioenergy': 'biomass',
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'ccgt, thermal': 'CCGT', 'hard coal': 'coal'}
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return (pd.read_csv(ppl_fn, index_col=0, dtype={'bus': 'str'})
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@ -199,18 +190,17 @@ def load_powerplants(ppl_fn=None):
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.replace({'carrier': carrier_dict}))
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def attach_load(n):
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def attach_load(n, regions, load, nuts3_shapes, cntries = [], scaling = 1.):
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substation_lv_i = n.buses.index[n.buses['substation_lv']]
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regions = (gpd.read_file(snakemake.input.regions).set_index('name')
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regions = (gpd.read_file(regions).set_index('name')
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.reindex(substation_lv_i))
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opsd_load = (pd.read_csv(snakemake.input.load, index_col=0, parse_dates=True)
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.filter(items=snakemake.config['countries']))
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opsd_load = (pd.read_csv(load, index_col=0, parse_dates=True)
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.filter(items=cntries))
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scaling = snakemake.config.get('load', {}).get('scaling_factor', 1.0)
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logger.info(f"Load data scaled with scalling factor {scaling}.")
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opsd_load *= scaling
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nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('index')
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nuts3 = gpd.read_file(nuts3_shapes).set_index('index')
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def upsample(cntry, group):
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l = opsd_load[cntry]
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@ -263,18 +253,20 @@ def update_transmission_costs(n, costs, length_factor=1.0, simple_hvdc_costs=Fal
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n.links.loc[dc_b, 'capital_cost'] = costs
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def attach_wind_and_solar(n, costs):
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for tech in snakemake.config['renewable']:
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def attach_wind_and_solar(n, costs, input_profiles,
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technologies = ['onwind', 'offwind-ac', 'offwind-dc', 'solar'],
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line_length_factor = 1.):
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for tech in technologies:
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if tech == 'hydro': continue
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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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with xr.open_dataset(getattr(input_profiles, 'profile_' + tech)) as ds:
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if ds.indexes['bus'].empty: continue
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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'] *
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connection_cost = (line_length_factor *
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ds['average_distance'].to_pandas() *
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(underwater_fraction *
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costs.at[tech + '-connection-submarine', 'capital_cost'] +
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@ -300,8 +292,8 @@ def attach_wind_and_solar(n, costs):
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p_max_pu=ds['profile'].transpose('time', 'bus').to_pandas())
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def attach_conventional_generators(n, costs, ppl):
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carriers = snakemake.config['electricity']['conventional_carriers']
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def attach_conventional_generators(n, costs, ppl, carriers=['nuclear', 'oil', 'OCGT', 'CCGT',
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'coal', 'lignite', 'geothermal', 'biomass']):
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_add_missing_carriers_from_costs(n, costs, carriers)
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@ -322,10 +314,9 @@ def attach_conventional_generators(n, costs, ppl):
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logger.warning(f'Capital costs for conventional generators put to 0 EUR/MW.')
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def attach_hydro(n, costs, ppl):
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if 'hydro' not in snakemake.config['renewable']: return
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c = snakemake.config['renewable']['hydro']
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carriers = c.get('carriers', ['ror', 'PHS', 'hydro'])
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def attach_hydro(n, costs, ppl, profile_hydro, hydro_capacities,
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config_hydro = {'carriers': {'ror', 'PHS', 'hydro'}}):
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carriers = config_hydro.get('carriers', ['ror', 'PHS', 'hydro'])
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|
||||
_add_missing_carriers_from_costs(n, costs, carriers)
|
||||
|
||||
@ -341,11 +332,11 @@ def attach_hydro(n, costs, ppl):
|
||||
if not inflow_idx.empty:
|
||||
dist_key = ppl.loc[inflow_idx, 'p_nom'].groupby(country).transform(normed)
|
||||
|
||||
with xr.open_dataarray(snakemake.input.profile_hydro) as inflow:
|
||||
with xr.open_dataarray(profile_hydro) as inflow:
|
||||
inflow_countries = pd.Index(country[inflow_idx])
|
||||
missing_c = (inflow_countries.unique()
|
||||
.difference(inflow.indexes['countries']))
|
||||
assert missing_c.empty, (f"'{snakemake.input.profile_hydro}' is missing "
|
||||
assert missing_c.empty, (f"'{profile_hydro}' is missing "
|
||||
f"inflow time-series for at least one country: {', '.join(missing_c)}")
|
||||
|
||||
inflow_t = (inflow.sel(countries=inflow_countries)
|
||||
@ -370,7 +361,7 @@ def attach_hydro(n, costs, ppl):
|
||||
if 'PHS' in carriers and not phs.empty:
|
||||
# fill missing max hours to config value and
|
||||
# assume no natural inflow due to lack of data
|
||||
phs = phs.replace({'max_hours': {0: c['PHS_max_hours']}})
|
||||
phs = phs.replace({'max_hours': {0: config_hydro['PHS_max_hours']}})
|
||||
n.madd('StorageUnit', phs.index,
|
||||
carrier='PHS',
|
||||
bus=phs['bus'],
|
||||
@ -382,8 +373,8 @@ def attach_hydro(n, costs, ppl):
|
||||
cyclic_state_of_charge=True)
|
||||
|
||||
if 'hydro' in carriers and not hydro.empty:
|
||||
hydro_max_hours = c.get('hydro_max_hours')
|
||||
hydro_stats = pd.read_csv(snakemake.input.hydro_capacities,
|
||||
hydro_max_hours = config_hydro.get('hydro_max_hours')
|
||||
hydro_stats = pd.read_csv(hydro_capacities,
|
||||
comment="#", na_values='-', index_col=0)
|
||||
e_target = hydro_stats["E_store[TWh]"].clip(lower=0.2) * 1e6
|
||||
e_installed = hydro.eval('p_nom * max_hours').groupby(hydro.country).sum()
|
||||
@ -412,7 +403,7 @@ def attach_hydro(n, costs, ppl):
|
||||
p_nom=hydro['p_nom'],
|
||||
max_hours=hydro_max_hours,
|
||||
capital_cost=(costs.at['hydro', 'capital_cost']
|
||||
if c.get('hydro_capital_cost') else 0.),
|
||||
if config_hydro.get('hydro_capital_cost') else 0.),
|
||||
marginal_cost=costs.at['hydro', 'marginal_cost'],
|
||||
p_max_pu=1., # dispatch
|
||||
p_min_pu=0., # store
|
||||
@ -422,8 +413,7 @@ def attach_hydro(n, costs, ppl):
|
||||
inflow=inflow_t.loc[:, hydro.index])
|
||||
|
||||
|
||||
def attach_extendable_generators(n, costs, ppl):
|
||||
elec_opts = snakemake.config['electricity']
|
||||
def attach_extendable_generators(n, costs, ppl, elec_opts = {'extendable_carriers': {'Generator': []}}):
|
||||
carriers = pd.Index(elec_opts['extendable_carriers']['Generator'])
|
||||
|
||||
_add_missing_carriers_from_costs(n, costs, carriers)
|
||||
@ -472,12 +462,11 @@ def attach_extendable_generators(n, costs, ppl):
|
||||
|
||||
|
||||
|
||||
def attach_OPSD_renewables(n):
|
||||
def attach_OPSD_renewables(n, techs=[]):
|
||||
|
||||
available = ['DE', 'FR', 'PL', 'CH', 'DK', 'CZ', 'SE', 'GB']
|
||||
tech_map = {'Onshore': 'onwind', 'Offshore': 'offwind', 'Solar': 'solar'}
|
||||
countries = set(available) & set(n.buses.country)
|
||||
techs = snakemake.config['electricity'].get('renewable_capacities_from_OPSD', [])
|
||||
tech_map = {k: v for k, v in tech_map.items() if v in techs}
|
||||
|
||||
if not tech_map:
|
||||
@ -505,10 +494,7 @@ def attach_OPSD_renewables(n):
|
||||
|
||||
|
||||
|
||||
def estimate_renewable_capacities(n, tech_map=None):
|
||||
if tech_map is None:
|
||||
tech_map = (snakemake.config['electricity']
|
||||
.get('estimate_renewable_capacities_from_capacity_stats', {}))
|
||||
def estimate_renewable_capacities(n, tech_map={}):
|
||||
|
||||
if len(tech_map) == 0: return
|
||||
|
||||
@ -540,8 +526,7 @@ def estimate_renewable_capacities(n, tech_map=None):
|
||||
n.generators.loc[tech_i, 'p_nom_min'] = n.generators.loc[tech_i, 'p_nom']
|
||||
|
||||
|
||||
def add_nice_carrier_names(n, config=None):
|
||||
if config is None: config = snakemake.config
|
||||
def add_nice_carrier_names(n, config):
|
||||
carrier_i = n.carriers.index
|
||||
nice_names = (pd.Series(config['plotting']['nice_names'])
|
||||
.reindex(carrier_i).fillna(carrier_i.to_series().str.title()))
|
||||
@ -563,22 +548,32 @@ if __name__ == "__main__":
|
||||
n = pypsa.Network(snakemake.input.base_network)
|
||||
Nyears = n.snapshot_weightings.objective.sum() / 8760.
|
||||
|
||||
costs = load_costs(Nyears)
|
||||
ppl = load_powerplants()
|
||||
costs = load_costs(tech_costs = snakemake.input.tech_costs, config = snakemake.config['costs'],
|
||||
elec_config = snakemake.config['electricity'], Nyears = Nyears)
|
||||
ppl = load_powerplants(snakemake.input.powerplants)
|
||||
|
||||
attach_load(n)
|
||||
attach_load(n, regions = snakemake.input.regions, load = snakemake.input.load,
|
||||
nuts3_shapes = snakemake.input.nuts3_shapes,
|
||||
cntries = snakemake.config['countries'],
|
||||
scaling = snakemake.config.get('load', {}).get('scaling_factor', 1.0))
|
||||
|
||||
update_transmission_costs(n, costs)
|
||||
|
||||
attach_conventional_generators(n, costs, ppl)
|
||||
attach_wind_and_solar(n, costs)
|
||||
attach_hydro(n, costs, ppl)
|
||||
attach_extendable_generators(n, costs, ppl)
|
||||
attach_conventional_generators(n, costs, ppl, carriers = snakemake.config['electricity']['conventional_carriers'])
|
||||
attach_wind_and_solar(n, costs, snakemake.input, technologies = snakemake.config['renewable'],
|
||||
line_length_factor = snakemake.config['lines']['length_factor'])
|
||||
|
||||
estimate_renewable_capacities(n)
|
||||
attach_OPSD_renewables(n)
|
||||
if 'hydro' in snakemake.config['renewable']:
|
||||
attach_hydro(n, costs, ppl, snakemake.input.profile_hydro, snakemake.input.hydro_capacities,
|
||||
config_hydro = snakemake.config['renewable']['hydro'])
|
||||
|
||||
attach_extendable_generators(n, costs, ppl, elec_opts = snakemake.config['electricity'])
|
||||
|
||||
estimate_renewable_capacities(n, tech_map = (snakemake.config['electricity']
|
||||
.get('estimate_renewable_capacities_from_capacity_stats', {})))
|
||||
attach_OPSD_renewables(n, techs = snakemake.config['electricity'].get('renewable_capacities_from_OPSD', []))
|
||||
update_p_nom_max(n)
|
||||
|
||||
add_nice_carrier_names(n)
|
||||
add_nice_carrier_names(n, config = snakemake.config)
|
||||
|
||||
n.export_to_netcdf(snakemake.output[0])
|
||||
|
||||
@ -64,8 +64,7 @@ idx = pd.IndexSlice
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def attach_storageunits(n, costs):
|
||||
elec_opts = snakemake.config['electricity']
|
||||
def attach_storageunits(n, costs, elec_opts = {'extendable_carriers': {'StorageUnit': []}, 'max_hours': {'battery': 6, 'H2': 168}}):
|
||||
carriers = elec_opts['extendable_carriers']['StorageUnit']
|
||||
max_hours = elec_opts['max_hours']
|
||||
|
||||
@ -89,8 +88,7 @@ def attach_storageunits(n, costs):
|
||||
cyclic_state_of_charge=True)
|
||||
|
||||
|
||||
def attach_stores(n, costs):
|
||||
elec_opts = snakemake.config['electricity']
|
||||
def attach_stores(n, costs, elec_opts = {'extendable_carriers': {'Store': ['battery', 'H2']}}):
|
||||
carriers = elec_opts['extendable_carriers']['Store']
|
||||
|
||||
_add_missing_carriers_from_costs(n, costs, carriers)
|
||||
@ -156,8 +154,7 @@ def attach_stores(n, costs):
|
||||
marginal_cost=costs.at["battery inverter", "marginal_cost"])
|
||||
|
||||
|
||||
def attach_hydrogen_pipelines(n, costs):
|
||||
elec_opts = snakemake.config['electricity']
|
||||
def attach_hydrogen_pipelines(n, costs, elec_opts = {'extendable_carriers': {'Store': ['H2', 'battery']}}):
|
||||
ext_carriers = elec_opts['extendable_carriers']
|
||||
as_stores = ext_carriers.get('Store', [])
|
||||
|
||||
@ -198,13 +195,13 @@ if __name__ == "__main__":
|
||||
|
||||
n = pypsa.Network(snakemake.input.network)
|
||||
Nyears = n.snapshot_weightings.objective.sum() / 8760.
|
||||
costs = load_costs(Nyears, tech_costs=snakemake.input.tech_costs,
|
||||
config=snakemake.config['costs'],
|
||||
elec_config=snakemake.config['electricity'])
|
||||
costs = load_costs(tech_costs = snakemake.input.tech_costs,
|
||||
config = snakemake.config['costs'],
|
||||
elec_config = snakemake.config['electricity'], Nyears = Nyears)
|
||||
|
||||
attach_storageunits(n, costs)
|
||||
attach_stores(n, costs)
|
||||
attach_hydrogen_pipelines(n, costs)
|
||||
attach_storageunits(n, costs, elec_opts = snakemake.config['electricity'])
|
||||
attach_stores(n, costs, elec_opts = snakemake.config['electricity'])
|
||||
attach_hydrogen_pipelines(n, costs, elec_opts = snakemake.config['electricity'])
|
||||
|
||||
add_nice_carrier_names(n, config=snakemake.config)
|
||||
|
||||
|
||||
@ -84,11 +84,10 @@ from scipy.spatial import cKDTree as KDTree
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def add_custom_powerplants(ppl):
|
||||
custom_ppl_query = snakemake.config['electricity']['custom_powerplants']
|
||||
def add_custom_powerplants(ppl, custom_powerplants, custom_ppl_query=False):
|
||||
if not custom_ppl_query:
|
||||
return ppl
|
||||
add_ppls = pd.read_csv(snakemake.input.custom_powerplants, index_col=0,
|
||||
add_ppls = pd.read_csv(custom_powerplants, index_col=0,
|
||||
dtype={'bus': 'str'})
|
||||
if isinstance(custom_ppl_query, str):
|
||||
add_ppls.query(custom_ppl_query, inplace=True)
|
||||
@ -119,7 +118,9 @@ if __name__ == "__main__":
|
||||
if isinstance(ppl_query, str):
|
||||
ppl.query(ppl_query, inplace=True)
|
||||
|
||||
ppl = add_custom_powerplants(ppl) # add carriers from own powerplant files
|
||||
# add carriers from own powerplant files:
|
||||
ppl = add_custom_powerplants(ppl, custom_powerplants = snakemake.input.custom_powerplants,
|
||||
custom_ppl_query = snakemake.config['electricity']['custom_powerplants'])
|
||||
|
||||
cntries_without_ppl = [c for c in countries if c not in ppl.Country.unique()]
|
||||
|
||||
|
||||
@ -107,11 +107,10 @@ def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True):
|
||||
return polys.simplify(tolerance=tolerance)
|
||||
|
||||
|
||||
def countries():
|
||||
cntries = snakemake.config['countries']
|
||||
def countries(naturalearth, cntries=[]):
|
||||
if 'RS' in cntries: cntries.append('KV')
|
||||
|
||||
df = gpd.read_file(snakemake.input.naturalearth)
|
||||
df = gpd.read_file(naturalearth)
|
||||
|
||||
# Names are a hassle in naturalearth, try several fields
|
||||
fieldnames = (df[x].where(lambda s: s!='-99') for x in ('ISO_A2', 'WB_A2', 'ADM0_A3'))
|
||||
@ -124,9 +123,9 @@ def countries():
|
||||
return s
|
||||
|
||||
|
||||
def eez(country_shapes):
|
||||
df = gpd.read_file(snakemake.input.eez)
|
||||
df = df.loc[df['ISO_3digit'].isin([_get_country('alpha_3', alpha_2=c) for c in snakemake.config['countries']])]
|
||||
def eez(country_shapes, eez, cntries=[]):
|
||||
df = gpd.read_file(eez)
|
||||
df = df.loc[df['ISO_3digit'].isin([_get_country('alpha_3', alpha_2=c) for c in cntries])]
|
||||
df['name'] = df['ISO_3digit'].map(lambda c: _get_country('alpha_2', alpha_3=c))
|
||||
s = df.set_index('name').geometry.map(lambda s: _simplify_polys(s, filterremote=False))
|
||||
s = gpd.GeoSeries({k:v for k,v in s.iteritems() if v.distance(country_shapes[k]) < 1e-3})
|
||||
@ -145,29 +144,29 @@ def country_cover(country_shapes, eez_shapes=None):
|
||||
return Polygon(shell=europe_shape.exterior)
|
||||
|
||||
|
||||
def nuts3(country_shapes):
|
||||
df = gpd.read_file(snakemake.input.nuts3)
|
||||
def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp):
|
||||
df = gpd.read_file(nuts3)
|
||||
df = df.loc[df['STAT_LEVL_'] == 3]
|
||||
df['geometry'] = df['geometry'].map(_simplify_polys)
|
||||
df = df.rename(columns={'NUTS_ID': 'id'})[['id', 'geometry']].set_index('id')
|
||||
|
||||
pop = pd.read_table(snakemake.input.nuts3pop, na_values=[':'], delimiter=' ?\t', engine='python')
|
||||
pop = pd.read_table(nuts3pop, na_values=[':'], delimiter=' ?\t', engine='python')
|
||||
pop = (pop
|
||||
.set_index(pd.MultiIndex.from_tuples(pop.pop('unit,geo\\time').str.split(','))).loc['THS']
|
||||
.applymap(lambda x: pd.to_numeric(x, errors='coerce'))
|
||||
.fillna(method='bfill', axis=1))['2014']
|
||||
|
||||
gdp = pd.read_table(snakemake.input.nuts3gdp, na_values=[':'], delimiter=' ?\t', engine='python')
|
||||
gdp = pd.read_table(nuts3gdp, na_values=[':'], delimiter=' ?\t', engine='python')
|
||||
gdp = (gdp
|
||||
.set_index(pd.MultiIndex.from_tuples(gdp.pop('unit,geo\\time').str.split(','))).loc['EUR_HAB']
|
||||
.applymap(lambda x: pd.to_numeric(x, errors='coerce'))
|
||||
.fillna(method='bfill', axis=1))['2014']
|
||||
|
||||
cantons = pd.read_csv(snakemake.input.ch_cantons)
|
||||
cantons = pd.read_csv(ch_cantons)
|
||||
cantons = cantons.set_index(cantons['HASC'].str[3:])['NUTS']
|
||||
cantons = cantons.str.pad(5, side='right', fillchar='0')
|
||||
|
||||
swiss = pd.read_excel(snakemake.input.ch_popgdp, skiprows=3, index_col=0)
|
||||
swiss = pd.read_excel(ch_popgdp, skiprows=3, index_col=0)
|
||||
swiss.columns = swiss.columns.to_series().map(cantons)
|
||||
|
||||
pop = pop.append(pd.to_numeric(swiss.loc['Residents in 1000', 'CH040':]))
|
||||
@ -220,14 +219,16 @@ if __name__ == "__main__":
|
||||
|
||||
out = snakemake.output
|
||||
|
||||
country_shapes = countries()
|
||||
country_shapes = countries(snakemake.input.naturalearth, snakemake.config['countries'])
|
||||
save_to_geojson(country_shapes, out.country_shapes)
|
||||
|
||||
offshore_shapes = eez(country_shapes)
|
||||
offshore_shapes = eez(country_shapes, snakemake.input.eez, cntries=snakemake.config['countries'])
|
||||
save_to_geojson(offshore_shapes, out.offshore_shapes)
|
||||
|
||||
europe_shape = country_cover(country_shapes, offshore_shapes)
|
||||
save_to_geojson(gpd.GeoSeries(europe_shape), out.europe_shape)
|
||||
|
||||
nuts3_shapes = nuts3(country_shapes)
|
||||
nuts3_shapes = nuts3(country_shapes, snakemake.input.nuts3, snakemake.input.nuts3pop,
|
||||
snakemake.input.nuts3gdp, snakemake.input.ch_cantons, snakemake.input.ch_popgdp)
|
||||
|
||||
save_to_geojson(nuts3_shapes, out.nuts3_shapes)
|
||||
|
||||
@ -170,12 +170,9 @@ def weighting_for_country(n, x):
|
||||
return (w * (100. / w.max())).clip(lower=1.).astype(int)
|
||||
|
||||
|
||||
def distribute_clusters(n, n_clusters, focus_weights=None, solver_name=None):
|
||||
def distribute_clusters(n, n_clusters, focus_weights=None, solver_name="cbc"):
|
||||
"""Determine the number of clusters per country"""
|
||||
|
||||
if solver_name is None:
|
||||
solver_name = snakemake.config['solving']['solver']['name']
|
||||
|
||||
L = (n.loads_t.p_set.mean()
|
||||
.groupby(n.loads.bus).sum()
|
||||
.groupby([n.buses.country, n.buses.sub_network]).sum()
|
||||
@ -268,12 +265,10 @@ def clustering_for_n_clusters(n, n_clusters, custom_busmap=False, aggregate_carr
|
||||
else:
|
||||
raise AttributeError(f"potential_mode should be one of 'simple' or 'conservative' but is '{potential_mode}'")
|
||||
|
||||
if custom_busmap:
|
||||
busmap = pd.read_csv(snakemake.input.custom_busmap, index_col=0, squeeze=True)
|
||||
busmap.index = busmap.index.astype(str)
|
||||
logger.info(f"Imported custom busmap from {snakemake.input.custom_busmap}")
|
||||
else:
|
||||
if custom_busmap is False:
|
||||
busmap = busmap_for_n_clusters(n, n_clusters, solver_name, focus_weights, algorithm)
|
||||
else:
|
||||
busmap = custom_busmap
|
||||
|
||||
clustering = get_clustering_from_busmap(
|
||||
n, busmap,
|
||||
@ -306,8 +301,6 @@ def save_to_geojson(s, fn):
|
||||
|
||||
|
||||
def cluster_regions(busmaps, input=None, output=None):
|
||||
if input is None: input = snakemake.input
|
||||
if output is None: output = snakemake.output
|
||||
|
||||
busmap = reduce(lambda x, y: x.map(y), busmaps[1:], busmaps[0])
|
||||
|
||||
@ -358,10 +351,9 @@ if __name__ == "__main__":
|
||||
else:
|
||||
line_length_factor = snakemake.config['lines']['length_factor']
|
||||
Nyears = n.snapshot_weightings.objective.sum()/8760
|
||||
hvac_overhead_cost = (load_costs(Nyears,
|
||||
tech_costs=snakemake.input.tech_costs,
|
||||
config=snakemake.config['costs'],
|
||||
elec_config=snakemake.config['electricity'])
|
||||
hvac_overhead_cost = (load_costs(tech_costs = snakemake.input.tech_costs,
|
||||
config = snakemake.config['costs'],
|
||||
elec_config=snakemake.config['electricity'], Nyears = Nyears)
|
||||
.at['HVAC overhead', 'capital_cost'])
|
||||
|
||||
def consense(x):
|
||||
@ -373,6 +365,10 @@ if __name__ == "__main__":
|
||||
potential_mode = consense(pd.Series([snakemake.config['renewable'][tech]['potential']
|
||||
for tech in renewable_carriers]))
|
||||
custom_busmap = snakemake.config["enable"].get("custom_busmap", False)
|
||||
if custom_busmap:
|
||||
custom_busmap = pd.read_csv(snakemake.input.custom_busmap, index_col=0, squeeze=True)
|
||||
custom_busmap.index = custom_busmap.index.astype(str)
|
||||
logger.info(f"Imported custom busmap from {snakemake.input.custom_busmap}")
|
||||
clustering = clustering_for_n_clusters(n, n_clusters, custom_busmap, aggregate_carriers,
|
||||
line_length_factor=line_length_factor,
|
||||
potential_mode=potential_mode,
|
||||
@ -386,4 +382,4 @@ if __name__ == "__main__":
|
||||
for attr in ('busmap', 'linemap'): #also available: linemap_positive, linemap_negative
|
||||
getattr(clustering, attr).to_csv(snakemake.output[attr])
|
||||
|
||||
cluster_regions((clustering.busmap,))
|
||||
cluster_regions((clustering.busmap,), snakemake.input, snakemake.output)
|
||||
|
||||
@ -403,8 +403,8 @@ def make_summaries(networks_dict, country='all'):
|
||||
n = n[n.buses.country == country]
|
||||
|
||||
Nyears = n.snapshot_weightings.objective.sum() / 8760.
|
||||
costs = load_costs(Nyears, snakemake.input[0],
|
||||
snakemake.config['costs'], snakemake.config['electricity'])
|
||||
costs = load_costs(tech_costs = snakemake.input[0], config = snakemake.config['costs'],
|
||||
elec_config = snakemake.config['electricity'], Nyears = Nyears)
|
||||
update_transmission_costs(n, costs, simple_hvdc_costs=False)
|
||||
|
||||
assign_carriers(n)
|
||||
@ -415,8 +415,7 @@ def make_summaries(networks_dict, country='all'):
|
||||
return dfs
|
||||
|
||||
|
||||
def to_csv(dfs):
|
||||
dir = snakemake.output[0]
|
||||
def to_csv(dfs, dir):
|
||||
os.makedirs(dir, exist_ok=True)
|
||||
for key, df in dfs.items():
|
||||
df.to_csv(os.path.join(dir, f"{key}.csv"))
|
||||
@ -453,4 +452,4 @@ if __name__ == "__main__":
|
||||
|
||||
dfs = make_summaries(networks_dict, country=snakemake.wildcards.country)
|
||||
|
||||
to_csv(dfs)
|
||||
to_csv(dfs, snakemake.output[0])
|
||||
|
||||
@ -70,21 +70,14 @@ idx = pd.IndexSlice
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def add_co2limit(n, Nyears=1., factor=None):
|
||||
|
||||
if factor is not None:
|
||||
annual_emissions = factor*snakemake.config['electricity']['co2base']
|
||||
else:
|
||||
annual_emissions = snakemake.config['electricity']['co2limit']
|
||||
def add_co2limit(n, co2limit=1.487e+9, Nyears=1.):
|
||||
|
||||
n.add("GlobalConstraint", "CO2Limit",
|
||||
carrier_attribute="co2_emissions", sense="<=",
|
||||
constant=annual_emissions * Nyears)
|
||||
constant=co2limit * Nyears)
|
||||
|
||||
|
||||
def add_emission_prices(n, emission_prices=None, exclude_co2=False):
|
||||
if emission_prices is None:
|
||||
emission_prices = snakemake.config['costs']['emission_prices']
|
||||
def add_emission_prices(n, emission_prices={'co2': 0.}, exclude_co2=False):
|
||||
if exclude_co2: emission_prices.pop('co2')
|
||||
ep = (pd.Series(emission_prices).rename(lambda x: x+'_emissions') *
|
||||
n.carriers.filter(like='_emissions')).sum(axis=1)
|
||||
@ -94,13 +87,12 @@ def add_emission_prices(n, emission_prices=None, exclude_co2=False):
|
||||
n.storage_units['marginal_cost'] += su_ep
|
||||
|
||||
|
||||
def set_line_s_max_pu(n):
|
||||
s_max_pu = snakemake.config['lines']['s_max_pu']
|
||||
def set_line_s_max_pu(n, s_max_pu = 0.7):
|
||||
n.lines['s_max_pu'] = s_max_pu
|
||||
logger.info(f"N-1 security margin of lines set to {s_max_pu}")
|
||||
|
||||
|
||||
def set_transmission_limit(n, ll_type, factor, Nyears=1):
|
||||
def set_transmission_limit(n, ll_type, factor, costs, Nyears=1):
|
||||
links_dc_b = n.links.carrier == 'DC' if not n.links.empty else pd.Series()
|
||||
|
||||
_lines_s_nom = (np.sqrt(3) * n.lines.type.map(n.line_types.i_nom) *
|
||||
@ -112,9 +104,6 @@ def set_transmission_limit(n, ll_type, factor, Nyears=1):
|
||||
ref = (lines_s_nom @ n.lines[col] +
|
||||
n.links.loc[links_dc_b, "p_nom"] @ n.links.loc[links_dc_b, col])
|
||||
|
||||
costs = load_costs(Nyears, snakemake.input.tech_costs,
|
||||
snakemake.config['costs'],
|
||||
snakemake.config['electricity'])
|
||||
update_transmission_costs(n, costs, simple_hvdc_costs=False)
|
||||
|
||||
if factor == 'opt' or float(factor) > 1.0:
|
||||
@ -151,7 +140,7 @@ def average_every_nhours(n, offset):
|
||||
return m
|
||||
|
||||
|
||||
def apply_time_segmentation(n, segments):
|
||||
def apply_time_segmentation(n, segments, solver_name="cplex"):
|
||||
logger.info(f"Aggregating time series to {segments} segments.")
|
||||
try:
|
||||
import tsam.timeseriesaggregation as tsam
|
||||
@ -170,8 +159,6 @@ def apply_time_segmentation(n, segments):
|
||||
|
||||
raw = pd.concat([p_max_pu, load, inflow], axis=1, sort=False)
|
||||
|
||||
solver_name = snakemake.config["solving"]["solver"]["name"]
|
||||
|
||||
agg = tsam.TimeSeriesAggregation(raw, hoursPerPeriod=len(raw),
|
||||
noTypicalPeriods=1, noSegments=int(segments),
|
||||
segmentation=True, solver=solver_name)
|
||||
@ -208,9 +195,7 @@ def enforce_autarky(n, only_crossborder=False):
|
||||
n.mremove("Line", lines_rm)
|
||||
n.mremove("Link", links_rm)
|
||||
|
||||
def set_line_nom_max(n):
|
||||
s_nom_max_set = snakemake.config["lines"].get("s_nom_max,", np.inf)
|
||||
p_nom_max_set = snakemake.config["links"].get("p_nom_max", np.inf)
|
||||
def set_line_nom_max(n, s_nom_max_set=np.inf, p_nom_max_set=np.inf):
|
||||
n.lines.s_nom_max.clip(upper=s_nom_max_set, inplace=True)
|
||||
n.links.p_nom_max.clip(upper=p_nom_max_set, inplace=True)
|
||||
|
||||
@ -225,8 +210,11 @@ if __name__ == "__main__":
|
||||
|
||||
n = pypsa.Network(snakemake.input[0])
|
||||
Nyears = n.snapshot_weightings.objective.sum() / 8760.
|
||||
costs = load_costs(tech_costs = snakemake.input.tech_costs,
|
||||
config = snakemake.config['costs'],
|
||||
elec_config = snakemake.config['electricity'], Nyears = Nyears)
|
||||
|
||||
set_line_s_max_pu(n)
|
||||
set_line_s_max_pu(n, s_max_pu=snakemake.config['lines']['s_max_pu'])
|
||||
|
||||
for o in opts:
|
||||
m = re.match(r'^\d+h$', o, re.IGNORECASE)
|
||||
@ -237,16 +225,17 @@ if __name__ == "__main__":
|
||||
for o in opts:
|
||||
m = re.match(r'^\d+seg$', o, re.IGNORECASE)
|
||||
if m is not None:
|
||||
n = apply_time_segmentation(n, m.group(0)[:-3])
|
||||
n = apply_time_segmentation(n, m.group(0)[:-3], solver_name=snakemake.config["solving"]["solver"]["name"])
|
||||
break
|
||||
|
||||
for o in opts:
|
||||
if "Co2L" in o:
|
||||
m = re.findall("[0-9]*\.?[0-9]+$", o)
|
||||
if len(m) > 0:
|
||||
add_co2limit(n, Nyears, float(m[0]))
|
||||
co2limit=float(m[0])*snakemake.config['electricity']['co2base']
|
||||
add_co2limit(n, Nyears, co2limit)
|
||||
else:
|
||||
add_co2limit(n, Nyears)
|
||||
add_co2limit(n, Nyears, snakemake.config['electricity']['co2limit'])
|
||||
break
|
||||
|
||||
for o in opts:
|
||||
@ -267,12 +256,13 @@ if __name__ == "__main__":
|
||||
c.df.loc[sel,attr] *= factor
|
||||
|
||||
if 'Ep' in opts:
|
||||
add_emission_prices(n)
|
||||
add_emission_prices(n, emission_prices=snakemake.config['costs']['emission_prices'])
|
||||
|
||||
ll_type, factor = snakemake.wildcards.ll[0], snakemake.wildcards.ll[1:]
|
||||
set_transmission_limit(n, ll_type, factor, Nyears)
|
||||
set_transmission_limit(n, ll_type, factor, costs, Nyears)
|
||||
|
||||
set_line_nom_max(n)
|
||||
set_line_nom_max(n, s_nom_max_set=snakemake.config["lines"].get("s_nom_max,", np.inf),
|
||||
p_nom_max_set=snakemake.config["links"].get("p_nom_max,", np.inf))
|
||||
|
||||
if "ATK" in opts:
|
||||
enforce_autarky(n)
|
||||
|
||||
@ -138,13 +138,9 @@ def simplify_network_to_380(n):
|
||||
return n, trafo_map
|
||||
|
||||
|
||||
def _prepare_connection_costs_per_link(n):
|
||||
def _prepare_connection_costs_per_link(n, costs):
|
||||
if n.links.empty: return {}
|
||||
|
||||
Nyears = n.snapshot_weightings.objective.sum() / 8760
|
||||
costs = load_costs(Nyears, snakemake.input.tech_costs,
|
||||
snakemake.config['costs'], snakemake.config['electricity'])
|
||||
|
||||
connection_costs_per_link = {}
|
||||
|
||||
for tech in snakemake.config['renewable']:
|
||||
@ -158,9 +154,9 @@ def _prepare_connection_costs_per_link(n):
|
||||
return connection_costs_per_link
|
||||
|
||||
|
||||
def _compute_connection_costs_to_bus(n, busmap, connection_costs_per_link=None, buses=None):
|
||||
def _compute_connection_costs_to_bus(n, busmap, costs, connection_costs_per_link=None, buses=None):
|
||||
if connection_costs_per_link is None:
|
||||
connection_costs_per_link = _prepare_connection_costs_per_link(n)
|
||||
connection_costs_per_link = _prepare_connection_costs_per_link(n, costs)
|
||||
|
||||
if buses is None:
|
||||
buses = busmap.index[busmap.index != busmap.values]
|
||||
@ -217,7 +213,7 @@ def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, aggregate
|
||||
n.mremove(c, df.index[df.bus0.isin(buses_to_del) | df.bus1.isin(buses_to_del)])
|
||||
|
||||
|
||||
def simplify_links(n):
|
||||
def simplify_links(n, costs):
|
||||
## Complex multi-node links are folded into end-points
|
||||
logger.info("Simplifying connected link components")
|
||||
|
||||
@ -264,7 +260,7 @@ def simplify_links(n):
|
||||
|
||||
busmap = n.buses.index.to_series()
|
||||
|
||||
connection_costs_per_link = _prepare_connection_costs_per_link(n)
|
||||
connection_costs_per_link = _prepare_connection_costs_per_link(n, costs)
|
||||
connection_costs_to_bus = pd.DataFrame(0., index=n.buses.index, columns=list(connection_costs_per_link))
|
||||
|
||||
for lbl in labels.value_counts().loc[lambda s: s > 2].index:
|
||||
@ -278,7 +274,7 @@ def simplify_links(n):
|
||||
m = sp.spatial.distance_matrix(n.buses.loc[b, ['x', 'y']],
|
||||
n.buses.loc[buses[1:-1], ['x', 'y']])
|
||||
busmap.loc[buses] = b[np.r_[0, m.argmin(axis=0), 1]]
|
||||
connection_costs_to_bus.loc[buses] += _compute_connection_costs_to_bus(n, busmap, connection_costs_per_link, buses)
|
||||
connection_costs_to_bus.loc[buses] += _compute_connection_costs_to_bus(n, busmap, costs, connection_costs_per_link, buses)
|
||||
|
||||
all_links = [i for _, i in sum(links, [])]
|
||||
|
||||
@ -312,12 +308,12 @@ def simplify_links(n):
|
||||
_aggregate_and_move_components(n, busmap, connection_costs_to_bus)
|
||||
return n, busmap
|
||||
|
||||
def remove_stubs(n):
|
||||
def remove_stubs(n, costs):
|
||||
logger.info("Removing stubs")
|
||||
|
||||
busmap = busmap_by_stubs(n) # ['country'])
|
||||
|
||||
connection_costs_to_bus = _compute_connection_costs_to_bus(n, busmap)
|
||||
connection_costs_to_bus = _compute_connection_costs_to_bus(n, busmap, costs)
|
||||
|
||||
_aggregate_and_move_components(n, busmap, connection_costs_to_bus)
|
||||
|
||||
@ -394,9 +390,14 @@ if __name__ == "__main__":
|
||||
|
||||
n, trafo_map = simplify_network_to_380(n)
|
||||
|
||||
n, simplify_links_map = simplify_links(n)
|
||||
Nyears = n.snapshot_weightings.objective.sum() / 8760
|
||||
technology_costs = load_costs(tech_costs = snakemake.input.tech_costs,
|
||||
config = snakemake.config['costs'],
|
||||
elec_config = snakemake.config['electricity'], Nyears = Nyears)
|
||||
|
||||
n, stub_map = remove_stubs(n)
|
||||
n, simplify_links_map = simplify_links(n, technology_costs)
|
||||
|
||||
n, stub_map = remove_stubs(n, technology_costs)
|
||||
|
||||
busmaps = [trafo_map, simplify_links_map, stub_map]
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user