Configuration

PyPSA-Eur has several configuration options which are documented in this section and are collected in a config.yaml file located in the root directory. Users should copy the provided default configuration (config.default.yaml) and amend their own modifications and assumptions in the user-specific configuration file (config.yaml); confer installation instructions at Set Up the Default Configuration.

Top-level configuration

version: 0.6.1
tutorial: false

logging:
  level: INFO
  format: '%(levelname)s:%(name)s:%(message)s'

run:
  clusters: [37, 128, 256, 512, 1024]
  prepare_links_p_nom: false
  retrieve_databundle: true
  retrieve_cost_data: true
  build_cutout: false
  retrieve_cutout: true
  build_natura_raster: false
  retrieve_natura_raster: true
  custom_busmap: false
  Unit Values Description
version 0.x.x Version of PyPSA-Eur
tutorial bool {true, false} Switch to retrieve the tutorial data set instead of the full data set.
logging      
– level Any of {‘INFO’, ‘WARNING’, ‘ERROR’} Restrict console outputs to all infos, warning or errors only
– format   Custom format for log messages. See LogRecord attributes.
summary_dir e.g. ‘results’ Directory into which results are written.
countries Subset of {‘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’} European countries defined by their Two-letter country codes (ISO 3166-1) which should be included in the energy system model.
focus_weights Keys should be two-digit country codes (e.g. DE) and values should range between 0 and 1 Ratio of total clusters for particular countries. the remaining weight is distributed according to mean load. An example: focus_weights: 'DE': 0.6 'FR': 0.2.
enable      
– prepare_links_p_nom bool {true, false} Switch to retrieve current HVDC projects from Wikipedia
– retrieve_databundle bool {true, false} Switch to retrieve databundle from zenodo via the rule retrieve_databundle or whether to keep a custom databundle located in the corresponding folder.
– build_cutout bool {true, false} Switch to enable the building of cutouts via the rule build_cutout.
– retrieve_cutout bool {true, false} Switch to enable the retrieval of cutouts from zenodo with retrieve_cutout.
– build_natura_raster bool {true, false} Switch to enable the creation of the raster natura.tiff via the rule build_natura_raster.
– retrieve_natura_raster bool {true, false} Switch to enable the retrieval of natura.tiff from zenodo with retrieve_natura_raster.
– custom_busmap bool {true, false} Switch to enable the use of custom busmaps in rule cluster_network. If activated the rule looks for provided busmaps at data/custom_busmap_elec_s{simpl}_{clusters}.csv which should have the same format as resources/busmap_elec_s{simpl}_{clusters}.csv, i.e. the index should contain the buses of networks/elec_s{simpl}.nc.

run

It is common conduct to analyse energy system optimisation models for multiple scenarios for a variety of reasons, e.g. assessing their sensitivity towards changing the temporal and/or geographical resolution or investigating how investment changes as more ambitious greenhouse-gas emission reduction targets are applied.

The run section is used for running and storing scenarios with different configurations which are not covered by Wildcards. It determines the path at which resources, networks and results are stored. Therefore the user can run different configurations within the same directory. If a run with a non-empty name should use cutouts shared across runs, set shared_cutouts to true.

run:
  name: "" # use this to keep track of runs with different settings
  shared_cutouts: false # set to true to share the default cutout(s) across runs

scenario

The scenario section is an extraordinary section of the config file that is strongly connected to the Wildcards and is designed to facilitate running multiple scenarios through a single command

snakemake -j 1 solve_all_networks

For each wildcard, a list of values is provided. The rule solve_all_networks will trigger the rules for creating results/networks/elec_s{simpl}_{clusters}_ec_l{ll}_{opts}.nc for all combinations of the provided wildcard values as defined by Python’s itertools.product(…) function that snakemake’s expand(…) function uses.

An exemplary dependency graph (starting from the simplification rules) then looks like this:

_images/scenarios.png
scenario:
  simpl: ['']
  ll: ['copt']
  clusters: [37, 128, 256, 512, 1024]
  opts: [Co2L-3H]
  Unit Values Description
simpl cf. The {simpl} wildcard List of {simpl} wildcards to run.
clusters cf. The {clusters} wildcard List of {clusters} wildcards to run.
ll cf. The {ll} wildcard List of {ll} wildcards to run.
opts cf. The {opts} wildcard List of {opts} wildcards to run.

snapshots

Specifies the temporal range to build an energy system model for as arguments to pandas.date_range

snapshots:
  start: "2013-01-01"
  end: "2014-01-01"
  closed: 'left' # end is not inclusive
  Unit Values Description
start str or datetime-like; e.g. YYYY-MM-DD Left bound of date range
end str or datetime-like; e.g. YYYY-MM-DD Right bound of date range
closed One of {None, ‘left’, ‘right’} Make the time interval closed to the left, right, or open on both sides None.

electricity

electricity:
  voltages: [220., 300., 380.]
  gaslimit: false # global gas usage limit of X MWh_th
  co2limit: 7.75e+7 # 0.05 * 3.1e9*0.5
  co2base: 1.487e+9
  agg_p_nom_limits: data/agg_p_nom_minmax.csv

  operational_reserve: # like https://genxproject.github.io/GenX/dev/core/#Reserves
    activate: false
    epsilon_load: 0.02 # share of total load
    epsilon_vres: 0.02 # share of total renewable supply
    contingency: 4000 # fixed capacity in MW

  max_hours:
    battery: 6
    H2: 168

  extendable_carriers:
    Generator: [solar, onwind, offwind-ac, offwind-dc, OCGT]
    StorageUnit: [] # battery, H2
    Store: [battery, H2]
    Link: [] # H2 pipeline

  # use pandas query strings here, e.g. Country not in ['Germany']
  powerplants_filter: (DateOut >= 2022 or DateOut != DateOut)
  # use pandas query strings here, e.g. Country in ['Germany']
  custom_powerplants: false

  conventional_carriers: [nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass]
  renewable_carriers: [solar, onwind, offwind-ac, offwind-dc, hydro]

  estimate_renewable_capacities:
    enable: true
    # Add capacities from OPSD data
    from_opsd: true
    # Renewable capacities are based on existing capacities reported by IRENA
    year: 2020
    # Artificially limit maximum capacities to factor * (IRENA capacities),
    # i.e. 110% of <years>'s capacities => expansion_limit: 1.1
    # false: Use estimated renewable potentials determine by the workflow
    expansion_limit: false
    technology_mapping:
      # Wind is the Fueltype in powerplantmatching, onwind, offwind-{ac,dc} the carrier in PyPSA-Eur
      Offshore: [offwind-ac, offwind-dc]
      Onshore: [onwind]
      PV: [solar]
  Unit Values Description
voltages kV Any subset of {220., 300., 380.} Voltage levels to consider
gaslimit MWhth float or false Global gas usage limit
co2limit \(t_{CO_2-eq}/a\) float Cap on total annual system carbon dioxide emissions
co2base \(t_{CO_2-eq}/a\) float Reference value of total annual system carbon dioxide emissions if relative emission reduction target is specified in {opts} wildcard.
agg_p_nom_limits file path Reference to .csv file specifying per carrier generator nominal capacity constraints for individual countries if 'CCL' is in {opts} wildcard. Defaults to data/agg_p_nom_minmax.csv.
operational_reserve     Settings for reserve requirements following like GenX
– activate bool true or false Whether to take operational reserve requirements into account during optimisation
– epsilon_load float share of total load
– epsilon_vres float share of total renewable supply
– contingency MW float fixed reserve capacity
max_hours      
– battery h float Maximum state of charge capacity of the battery in terms of hours at full output capacity p_nom. Cf. PyPSA documentation.
– H2 h float Maximum state of charge capacity of the hydrogen storage in terms of hours at full output capacity p_nom. Cf. PyPSA documentation.
extendable_carriers      
– Generator Any extendable carrier Defines existing or non-existing conventional and renewable power plants to be extendable during the optimization. Conventional generators can only be built/expanded where already existent today. If a listed conventional carrier is not included in the conventional_carriers list, the lower limit of the capacity expansion is set to 0.
– StorageUnit Any subset of {‘battery’,’H2’} Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
– Store Any subset of {‘battery’,’H2’} Adds extendable storage units (battery and/or hydrogen) at every node/bus after clustering without capacity limits and with zero initial capacity.
– Link Any subset of {‘H2 pipeline’} Adds extendable links (H2 pipelines only) at every connection where there are lines or HVDC links without capacity limits and with zero initial capacity. Hydrogen pipelines require hydrogen storage to be modelled as Store.
powerplants_filter use pandas.query strings here, e.g. Country not in [‘Germany’] Filter query for the default powerplant database.
custom_powerplants use pandas.query strings here, e.g. Country in [‘Germany’] Filter query for the custom powerplant database.
conventional_carriers Any subset of {nuclear, oil, OCGT, CCGT, coal, lignite, geothermal, biomass} List of conventional power plants to include in the model from resources/powerplants.csv. If an included carrier is also listed in extendable_carriers, the capacity is taken as a lower bound.
renewable_carriers Any subset of {solar, onwind, offwind-ac, offwind-dc, hydro} List of renewable generators to include in the model.
estimate_renewable_capacities      
– enable   bool Activate routine to estimate renewable capacities
– from_opsd bool Add capacities from OPSD data
– year bool Renewable capacities are based on existing capacities reported by IRENA for the specified year
– expansion_limit float or false Artificially limit maximum capacities to factor * (IRENA capacities), i.e. 110% of <years>’s capacities => expansion_limit: 1.1 false: Use estimated renewable potentials determine by the workflow
– technology_mapping     Mapping between powerplantmatching and PyPSA-Eur technology names

atlite

Define and specify the atlite.Cutout used for calculating renewable potentials and time-series. All options except for features are directly used as cutout parameters.

atlite:
  nprocesses: 4
  cutouts:
    # use 'base' to determine geographical bounds and time span from config
    # base:
      # module: era5
    europe-2013-era5:
      module: era5 # in priority order
      x: [-12., 35.]
      y: [33., 72]
      dx: 0.3
      dy: 0.3
      time: ['2013', '2013']
    europe-2013-sarah:
      module: [sarah, era5] # in priority order
      x: [-12., 45.]
      y: [33., 65]
      dx: 0.2
      dy: 0.2
      time: ['2013', '2013']
      sarah_interpolate: false
      sarah_dir:
      features: [influx, temperature]
  Unit Values Description
nprocesses int Number of parallel processes in cutout preparation
cutouts      
– {name} Convention is to name cutouts like <region>-<year>-<source> (e.g. europe-2013-era5). Name of the cutout netcdf file. The user may specify multiple cutouts under configuration atlite: cutouts:. Reference is used in configuration renewable: {technology}: cutout:. The cutout base may be used to automatically calculate temporal and spatial bounds of the network.
– – module Subset of {‘era5’,’sarah’} Source of the reanalysis weather dataset (e.g. ERA5 or SARAH-2)
– – x ° Float interval within [-180, 180] Range of longitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes.
– – y ° Float interval within [-90, 90] Range of latitudes to download weather data for. If not defined, it defaults to the spatial bounds of all bus shapes.
– – time   Time interval within [‘1979’, ‘2018’] (with valid pandas date time strings) Time span to download weather data for. If not defined, it defaults to the time interval spanned by the snapshots.
– – features   String or list of strings with valid cutout features (‘inlfux’, ‘wind’). When freshly building a cutout, retrieve data only for those features. If not defined, it defaults to all available features.

renewable

onwind

renewable:
  onwind:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: Vestas_V112_3MW
    capacity_per_sqkm: 3 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 30% fraction of the already restricted
    # area is available for installation of wind generators due to competing land use and likely public
    # acceptance issues.
    # correction_factor: 0.93
    corine:
      # Scholz, Y. (2012). Renewable energy based electricity supply at low costs:
      #  development of the REMix model and application for Europe. ( p.42 / p.28)
      grid_codes: [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 31, 32]
      distance: 1000
      distance_grid_codes: [1, 2, 3, 4, 5, 6]
    natura: true
    excluder_resolution: 100
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  Unit Values Description
cutout Should be a folder listed in the configuration atlite: cutouts: (e.g. ‘europe-2013-era5’) or reference an existing folder in the directory cutouts. Source module must be ERA5. Specifies the directory where the relevant weather data ist stored.
resource      
– method Must be ‘wind’ A superordinate technology type.
– turbine One of turbine types included in atlite Specifies the turbine type and its characteristic power curve.
capacity_per_sqkm \(MW/km^2\) float Allowable density of wind turbine placement.
corine      
– grid_codes Any subset of the CORINE Land Cover code list Specifies areas according to CORINE Land Cover codes which are generally eligible for wind turbine placement.
– distance m float Distance to keep from areas specified in distance_grid_codes
– distance_grid_codes Any subset of the CORINE Land Cover code list Specifies areas according to CORINE Land Cover codes to which wind turbines must maintain a distance specified in the setting distance.
natura bool {true, false} Switch to exclude Natura 2000 natural protection areas. Area is excluded if true.
potential One of {‘simple’, ‘conservative’} Method to compute the maximal installable potential for a node; confer Rule build_renewable_profiles
clip_p_max_pu p.u. float To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.

offwind-ac

  offwind-ac:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    capacity_per_sqkm: 2 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 20% fraction of the already restricted
    # area is available for installation of wind generators due to competing land use and likely public
    # acceptance issues.
    correction_factor: 0.8855
    # proxy for wake losses
    # from 10.1016/j.energy.2018.08.153
    # until done more rigorously in #153
    corine: [44, 255]
    natura: true
    ship_threshold: 400
    max_depth: 50
    max_shore_distance: 30000
    excluder_resolution: 200
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  Unit Values Description
cutout Should be a folder listed in the configuration atlite: cutouts: (e.g. ‘europe-2013-era5’) or reference an existing folder in the directory cutouts. Source module must be ERA5. Specifies the directory where the relevant weather data ist stored.
resource      
– method Must be ‘wind’ A superordinate technology type.
– turbine One of turbine types included in atlite Specifies the turbine type and its characteristic power curve.
capacity_per_sqkm \(MW/km^2\) float Allowable density of wind turbine placement.
corine Any realistic subset of the CORINE Land Cover code list Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement.
natura bool {true, false} Switch to exclude Natura 2000 natural protection areas. Area is excluded if true.
ship_threshold float Ship density threshold from which areas are excluded.
max_depth m float Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential.
min_shore_distance m float Minimum distance to the shore below which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential.
potential One of {‘simple’, ‘conservative’} Method to compute the maximal installable potential for a node; confer Rule build_renewable_profiles
clip_p_max_pu p.u. float To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.

offwind-dc

  offwind-dc:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    capacity_per_sqkm: 2 # ScholzPhd Tab 4.3.1: 10MW/km^2 and assuming 20% fraction of the already restricted
    # area is available for installation of wind generators due to competing land use and likely public
    # acceptance issues.
    correction_factor: 0.8855
    # proxy for wake losses
    # from 10.1016/j.energy.2018.08.153
    # until done more rigorously in #153
    corine: [44, 255]
    natura: true
    ship_threshold: 400
    max_depth: 50
    min_shore_distance: 30000
    excluder_resolution: 200
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  Unit Values Description
cutout Should be a folder listed in the configuration atlite: cutouts: (e.g. ‘europe-2013-era5’) or reference an existing folder in the directory cutouts. Source module must be ERA5. Specifies the directory where the relevant weather data ist stored.
resource      
– method Must be ‘wind’ A superordinate technology type.
– turbine One of turbine types included in atlite Specifies the turbine type and its characteristic power curve.
capacity_per_sqkm \(MW/km^2\) float Allowable density of wind turbine placement.
corine Any realistic subset of the CORINE Land Cover code list Specifies areas according to CORINE Land Cover codes which are generally eligible for AC-connected offshore wind turbine placement.
natura bool {true, false} Switch to exclude Natura 2000 natural protection areas. Area is excluded if true.
ship_threshold float Ship density threshold from which areas are excluded.
max_depth m float Maximum sea water depth at which wind turbines can be build. Maritime areas with deeper waters are excluded in the process of calculating the AC-connected offshore wind potential.
min_shore_distance m float Minimum distance to the shore below which wind turbines cannot be build. Such areas close to the shore are excluded in the process of calculating the AC-connected offshore wind potential.
potential One of {‘simple’, ‘conservative’} Method to compute the maximal installable potential for a node; confer Rule build_renewable_profiles
clip_p_max_pu p.u. float To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.

solar

  solar:
    cutout: europe-2013-sarah
    resource:
      method: pv
      panel: CSi
      orientation:
        slope: 35.
        azimuth: 180.
    capacity_per_sqkm: 1.7 # ScholzPhd Tab 4.3.1: 170 MW/km^2 and assuming 1% of the area can be used for solar PV panels
    # Correction factor determined by comparing uncorrected area-weighted full-load hours to those
    # published in Supplementary Data to
    # Pietzcker, Robert Carl, et al. "Using the sun to decarbonize the power
    # sector: The economic potential of photovoltaics and concentrating solar
    # power." Applied Energy 135 (2014): 704-720.
    # This correction factor of 0.854337 may be in order if using reanalysis data.
    # for discussion refer to https://github.com/PyPSA/pypsa-eur/pull/304
    # correction_factor: 0.854337
    corine: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 26, 31, 32]
    natura: true
    excluder_resolution: 100
    potential: simple # or conservative
    clip_p_max_pu: 1.e-2
  Unit Values Description
cutout Should be a folder listed in the configuration atlite: cutouts: (e.g. ‘europe-2013-era5’) or reference an existing folder in the directory cutouts. Source module can be ERA5 or SARAH-2. Specifies the directory where the relevant weather data ist stored that is specified at atlite/cutouts configuration. Both sarah and era5 work.
resource      
– method Must be ‘pv’ A superordinate technology type.
– panel One of {‘Csi’, ‘CdTe’, ‘KANENA’} as defined in atlite Specifies the solar panel technology and its characteristic attributes.
– orientation      
– – slope ° Realistically any angle in [0., 90.] Specifies the tilt angle (or slope) of the solar panel. A slope of zero corresponds to the face of the panel aiming directly overhead. A positive tilt angle steers the panel towards the equator.
– – azimuth ° Any angle in [0., 360.] Specifies the azimuth orientation of the solar panel. South corresponds to 180.°.
capacity_per_sqkm \(MW/km^2\) float Allowable density of solar panel placement.
correction_factor float A correction factor for the capacity factor (availability) time series.
corine Any subset of the CORINE Land Cover code list Specifies areas according to CORINE Land Cover codes which are generally eligible for solar panel placement.
natura bool {true, false} Switch to exclude Natura 2000 natural protection areas. Area is excluded if true.
potential One of {‘simple’, ‘conservative’} Method to compute the maximal installable potential for a node; confer Rule build_renewable_profiles
clip_p_max_pu p.u. float To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.

hydro

  hydro:
    cutout: europe-2013-era5
    carriers: [ror, PHS, hydro]
    PHS_max_hours: 6
    hydro_max_hours: "energy_capacity_totals_by_country" # one of energy_capacity_totals_by_country, estimate_by_large_installations or a float
    clip_min_inflow: 1.0
  Unit Values Description
cutout Must be ‘europe-2013-era5’ Specifies the directory where the relevant weather data ist stored.
carriers Any subset of {‘ror’, ‘PHS’, ‘hydro’} Specifies the types of hydro power plants to build per-unit availability time series for. ‘ror’ stands for run-of-river plants, ‘PHS’ represents pumped-hydro storage, and ‘hydro’ stands for hydroelectric dams.
PHS_max_hours h float Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity p_nom. Cf. PyPSA documentation.
hydro_max_hours h Any of {float, ‘energy_capacity_totals_by_country’, ‘estimate_by_large_installations’} Maximum state of charge capacity of the pumped-hydro storage (PHS) in terms of hours at full output capacity p_nom or heuristically determined. Cf. PyPSA documentation.
clip_min_inflow MW float To avoid too small values in the inflow time series, values below this threshold are set to zero.

conventional

Define additional generator attribute for conventional carrier types. If a scalar value is given it is applied to all generators. However if a string starting with “data/” is given, the value is interpreted as a path to a csv file with country specific values. Then, the values are read in and applied to all generators of the given carrier in the given country. Note that the value(s) overwrite the existing values in the corresponding section of the generators dataframe.

conventional:
  nuclear:
    p_max_pu: "data/nuclear_p_max_pu.csv" # float of file name

lines

lines:
  types:
    220.: "Al/St 240/40 2-bundle 220.0"
    300.: "Al/St 240/40 3-bundle 300.0"
    380.: "Al/St 240/40 4-bundle 380.0"
  s_max_pu: 0.7
  s_nom_max: .inf
  length_factor: 1.25
  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
  Unit Values Description
types Values should specify a line type in PyPSA. Keys should specify the corresponding voltage level (e.g. 220., 300. and 380. kV) Specifies line types to assume for the different voltage levels of the ENTSO-E grid extraction. Should normally handle voltage levels 220, 300, and 380 kV
s_max_pu Value in [0.,1.] Correction factor for line capacities (s_nom) to approximate \(N-1\) security and reserve capacity for reactive power flows
s_nom_max MW float Global upper limit for the maximum capacity of each extendable line.
length_factor float Correction factor to account for the fact that buses are not connected by lines through air-line distance.
under_construction One of {‘zero’: set capacity to zero, ‘remove’: remove completely, ‘keep’: keep with full capacity} Specifies how to handle lines which are currently under construction.

transformers

transformers:
  x: 0.1
  s_nom: 2000.
  type: ''
  Unit Values Description
x p.u. float Series reactance (per unit, using s_nom as base power of the transformer. Overwritten if type is specified.
s_nom MVA float Limit of apparent power which can pass through branch. Overwritten if type is specified.
type A transformer type in PyPSA. Specifies transformer types to assume for the transformers of the ENTSO-E grid extraction.

load

  length_factor: 1.25
  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity

links:
  p_max_pu: 1.0
  p_nom_max: .inf
  Unit Values Description
url string Link to open power system data time series data.
power_statistics bool {true, false} Whether to load the electricity consumption data of the ENTSOE power statistics (only for files from 2019 and before) or from the ENTSOE transparency data (only has load data from 2015 onwards).
interpolate_limit hours integer Maximum gap size (consecutive nans) which interpolated linearly.
time_shift_for_large_gaps string string Periods which are used for copying time-slices in order to fill large gaps of nans. Have to be valid pandas period strings.
manual_adjustments bool {true, false} Whether to adjust the load data manually according to the function in manual_adjustment().
scaling_factor float Global correction factor for the load time series.

costs

costs:
  year: 2030
  version: v0.4.0
  rooftop_share: 0.14  # based on the potentials, assuming  (0.1 kW/m2 and 10 m2/person)
  fill_values:
    FOM: 0
    VOM: 0
    efficiency: 1
    fuel: 0
    investment: 0
    lifetime: 25
    "CO2 intensity": 0
    "discount rate": 0.07
  marginal_cost:
    solar: 0.01
    onwind: 0.015
    offwind: 0.015
    hydro: 0.
    H2: 0.
    electrolysis: 0.
    fuel cell: 0.
    battery: 0.
    battery inverter: 0.
  emission_prices: # in currency per tonne emission, only used with the option Ep
    co2: 0.
  Unit Values Description
year YYYY; e.g. ‘2030’ Year for which to retrieve cost assumptions of resources/costs.csv.
version vX.X.X; e.g. ‘v0.1.0’ Version of technology-data repository to use.
rooftop_share float Share of rooftop PV when calculating capital cost of solar (joint rooftop and utility-scale PV).
fill_values float Default values if not specified for a technology in resources/costs.csv.
capital_cost EUR/MW Keys should be in the ‘technology’ column of resources/costs.csv. Values can be any float. For the given technologies, assumptions about their capital investment costs are set to the corresponding value. Optional; overwrites cost assumptions from resources/costs.csv.
marginal_cost EUR/MWh Keys should be in the ‘technology’ column of resources/costs.csv. Values can be any float. For the given technologies, assumptions about their marginal operating costs are set to the corresponding value. Optional; overwrites cost assumptions from resources/costs.csv.
emission_prices     Specify exogenous prices for emission types listed in network.carriers to marginal costs.
– co2 EUR/t float Exogenous price of carbon-dioxide added to the marginal costs of fossil-fuelled generators according to their carbon intensity. Added through the keyword Ep in the {opts} wildcard only in the rule prepare_network`.

Note

To change cost assumptions in more detail (i.e. other than marginal_cost and capital_cost), consider modifying cost assumptions directly in resources/costs.csv as this is not yet supported through the config file. You can also build multiple different cost databases. Make a renamed copy of resources/costs.csv (e.g. data/costs-optimistic.csv) and set the variable COSTS=data/costs-optimistic.csv in the Snakefile.

clustering

clustering:
  simplify_network:
    to_substations: false # network is simplified to nodes with positive or negative power injection (i.e. substations or offwind connections)
    algorithm: kmeans # choose from: [hac, kmeans]
    feature: solar+onwind-time # only for hac. choose from: [solar+onwind-time, solar+onwind-cap, solar-time, solar-cap, solar+offwind-cap] etc.
    exclude_carriers: []
  cluster_network:
    algorithm: kmeans
    feature: solar+onwind-time
    exclude_carriers: []
  aggregation_strategies:
    generators:
      p_nom_max: sum # use "min" for more conservative assumptions
      p_nom_min: sum
      p_min_pu: mean
      marginal_cost: mean
      committable: any
      ramp_limit_up: max
      ramp_limit_down: max
      efficiency: mean
  Unit Values Description
simplify_network      
– to_substations bool {‘true’,’false’} Aggregates all nodes without power injection (positive or negative, i.e. demand or generation) to electrically closest ones
– algorithm str One of {‘kmeans’, ‘hac’, ‘modularity‘}  
– feature str Str in the format ‘carrier1+carrier2+…+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.  
– exclude_carriers list List of Str like [ ‘solar’, ‘onwind’] or empy list [] List of carriers which will not be aggregated. If empty, all carriers will be aggregated.
cluster_network      
– algorithm str One of {‘kmeans’, ‘hac’}  
– feature str Str in the format ‘carrier1+carrier2+…+carrierN-X’, where CarrierI can be from {‘solar’, ‘onwind’, ‘offwind’, ‘ror’} and X is one of {‘cap’, ‘time’}.  
– exclude_carriers list List of Str like [ ‘solar’, ‘onwind’] or empy list [] List of carriers which will not be aggregated. If empty, all carriers will be aggregated.
aggregation_strategies      
– generators      
– – {key} str {key} can be any of the component of the generator (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}. Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new generator.
– buses      
– – {key} str {key} can be any of the component of the bus (str). It’s value can be any that can be converted to pandas.Series using getattr(). For example one of {min, max, sum}. Aggregates the component according to the given strategy. For example, if sum, then all values within each cluster are summed to represent the new bus.

solving

options

solving:
  options:
    formulation: kirchhoff
    load_shedding: false
    noisy_costs: true
    min_iterations: 4
    max_iterations: 6
    clip_p_max_pu: 0.01
    skip_iterations: false
    track_iterations: false
    #nhours: 10
  Unit Values Description
formulation Any of {‘angles’, ‘kirchhoff’, ‘cycles’, ‘ptdf’} Specifies which variant of linearized power flow formulations to use in the optimisation problem. Recommended is ‘kirchhoff’. Explained in this article.
load_shedding bool {‘true’,’false’} Add generators with a prohibitively high marginal cost to simulate load shedding and avoid problem infeasibilities.
noisy_costs bool {‘true’,’false’} Add random noise to marginal cost of generators by \(\mathcal{U}(0.009,0,011)\) and capital cost of lines and links by \(\mathcal{U}(0.09,0,11)\).
min_iterations int Minimum number of solving iterations in between which resistance and reactence (x/r) are updated for branches according to s_nom_opt of the previous run.
max_iterations int Maximum number of solving iterations in between which resistance and reactence (x/r) are updated for branches according to s_nom_opt of the previous run.
nhours int Specifies the \(n\) first snapshots to take into account. Must be less than the total number of snapshots. Rather recommended only for debugging.
clip_p_max_pu p.u. float To avoid too small values in the renewables` per-unit availability time series values below this threshold are set to zero.
skip_iterations bool {‘true’,’false’} Skip iterating, do not update impedances of branches.
track_iterations bool {‘true’,’false’} Flag whether to store the intermediate branch capacities and objective function values are recorded for each iteration in network.lines['s_nom_opt_X'] (where X labels the iteration)

solver

  solver:
    name: gurobi
    threads: 4
    method: 2 # barrier
    crossover: 0
    BarConvTol: 1.e-5
    FeasibilityTol: 1.e-6
    AggFill: 0
    PreDual: 0
    GURO_PAR_BARDENSETHRESH: 200
  # solver:
  #   name: cplex
  #   threads: 4
  #   lpmethod: 4 # barrier
  #   solutiontype: 2 # non basic solution, ie no crossover
  #   barrier.convergetol: 1.e-5
  #   feasopt.tolerance: 1.e-6
  Unit Values Description
name One of {‘gurobi’, ‘cplex’, ‘cbc’, ‘glpk’, ‘ipopt’}; potentially more possible Solver to use for optimisation problems in the workflow; e.g. clustering and linear optimal power flow.
opts Parameter list for Gurobi and CPLEX Solver specific parameter settings.

plotting

plotting:
  map:
    figsize: [7, 7]
    boundaries: [-10.2, 29, 35, 72]
    p_nom:
      bus_size_factor: 5.e+4
      linewidth_factor: 3.e+3

  costs_max: 800
  costs_threshold: 1

  energy_max: 15000.
  energy_min: -10000.
  energy_threshold: 50.

  vre_techs: ["onwind", "offwind-ac", "offwind-dc", "solar", "ror"]
  conv_techs: ["OCGT", "CCGT", "Nuclear", "Coal"]
  storage_techs: ["hydro+PHS", "battery", "H2"]
  load_carriers: ["AC load"]
  AC_carriers: ["AC line", "AC transformer"]
  link_carriers: ["DC line", "Converter AC-DC"]
  tech_colors:
    "onwind": "#235ebc"
    "onshore wind": "#235ebc"
    'offwind': "#6895dd"
    'offwind-ac': "#6895dd"
    'offshore wind': "#6895dd"
    'offshore wind ac': "#6895dd"
    'offwind-dc': "#74c6f2"
    'offshore wind dc': "#74c6f2"
    "hydro": "#08ad97"
    "hydro+PHS": "#08ad97"
    "PHS": "#08ad97"
    "hydro reservoir": "#08ad97"
    'hydroelectricity': '#08ad97'
    "ror": "#4adbc8"
    "run of river": "#4adbc8"
    'solar': "#f9d002"
    'solar PV': "#f9d002"
    'solar thermal': '#ffef60'
    'biomass': '#0c6013'
    'solid biomass': '#06540d'
    'biogas': '#23932d'
    'waste': '#68896b'
    'geothermal': '#ba91b1'
    "OCGT": "#d35050"
    "gas": "#d35050"
    "natural gas": "#d35050"
    "CCGT": "#b20101"
    "nuclear": "#ff9000"
    "coal": "#707070"
    "lignite": "#9e5a01"
    "oil": "#262626"
    "H2": "#ea048a"
    "hydrogen storage": "#ea048a"
    "battery": "#b8ea04"
    "Electric load": "#f9d002"
    "electricity": "#f9d002"
    "lines": "#70af1d"
    "transmission lines": "#70af1d"
    "AC-AC": "#70af1d"
    "AC line": "#70af1d"
    "links": "#8a1caf"
    "HVDC links": "#8a1caf"
    "DC-DC": "#8a1caf"
    "DC link": "#8a1caf"
  nice_names:
    OCGT: "Open-Cycle Gas"
    CCGT: "Combined-Cycle Gas"
    offwind-ac: "Offshore Wind (AC)"
    offwind-dc: "Offshore Wind (DC)"
    onwind: "Onshore Wind"
    solar: "Solar"
    PHS: "Pumped Hydro Storage"
    hydro: "Reservoir & Dam"
    battery: "Battery Storage"
    H2: "Hydrogen Storage"
    lines: "Transmission Lines"
    ror: "Run of River"
  Unit Values Description
map      
– figsize [width, height]; e.g. [7,7] Figure size in inches.
– boundaries ° [x1,x2,y1,y2] Boundaries of the map plots in degrees latitude (y) and longitude (x)
– p_nom      
– – bus_size_factor float Factor by which values determining bus sizes are scaled to fit well in the plot.
– – linewidth_factor float Factor by which values determining bus sizes are scaled to fit well in the plot.
costs_max bn Euro float Upper y-axis limit in cost bar plots.
costs_threshold bn Euro float Threshold below which technologies will not be shown in cost bar plots.
energy_max TWh float Upper y-axis limit in energy bar plots.
energy_min TWh float Lower y-axis limit in energy bar plots.
energy_threshold TWh float Threshold below which technologies will not be shown in energy bar plots.
tech_colors carrier -> HEX colour code Mapping from network carrier to a colour (HEX colour code).
nice_names str -> str Mapping from network carrier to a more readable name.