Configuration#

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

Top-level configuration#

“Private” refers to local, machine-specific settings or data meant for personal use, not to be shared. “Remote” indicates the address of a server used for data exchange, often for clusters and data pushing/pulling.

version: 0.8.1
tutorial: false

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

private:
  keys:
    entsoe_api:

remote:
  ssh: ""
  path: ""

Unit

Values

Description

version

–

0.x.x

Version of PyPSA-Eur. Descriptive only.

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.

private

– keys

– – entsoe_api

–

Optionally specify the ENTSO-E API key. See the guidelines to get ENTSO-E API key

remote

– ssh

–

Optionally specify the SSH of a remote cluster to be synchronized.

– path

–

Optionally specify the file path within the remote cluster to be synchronized.

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: ""
  disable_progressbar: false
  shared_resources: false
  shared_cutouts: true

Unit

Values

Description

name

–

any string

Specify a name for your run. Results will be stored under this name.

disable_progrssbar

bool

{true, false}

Switch to select whether progressbar should be disabled.

shared_resources

bool

{true, false}

Switch to select whether resources should be shared across runs.

shared_cutouts

bool

{true, false}

Switch to select whether cutouts should be shared across runs.

foresight#

foresight: overnight

Unit

Values

Description

foresight

string

{overnight, myopic, perfect}

See Foresight Options for detail explanations.

Note

If you use myopic or perfect foresight, the planning horizon in The {planning_horizons} wildcard in scenario has to be set.

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

# for electricity-only studies
snakemake -call solve_elec_networks

# for sector-coupling studies
snakemake -call solve_sector_networks

For each wildcard, a list of values is provided. The rule solve_all_elec_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:
  - v1.5
  clusters:
  - 37
  - 128
  - 256
  - 512
  - 1024
  opts:
  - ''
  sector_opts:
  - Co2L0-3H-T-H-B-I-A-solar+p3-dist1
  planning_horizons:
  # - 2020
  # - 2030
  # - 2040
  - 2050

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.

sector_opts

–

cf. The {sector_opts} wildcard

List of {sector_opts} wildcards to run.

planning_horizons

–

cf. The {planning_horizons} wildcard

List of {planning_horizon} wildcards to run.

countries#

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']

Unit

Values

Description

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.

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"
  inclusive: 'left'

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

inclusive

–

One of {‘neither’, ‘both’, ‘left’, ‘right’}

Make the time interval closed to the left, right, or both sides both or neither side None.

enable#

Switches for some rules and optional features.

enable:
  retrieve: auto
  prepare_links_p_nom: false
  retrieve_databundle: true
  retrieve_sector_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

enable

str or bool

{auto, true, false}

Switch to include (true) or exclude (false) the retrieve_* rules of snakemake into the workflow; ‘auto’ sets true|false based on availability of an internet connection to prevent issues with snakemake failing due to lack of internet connection.

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.

retrieve_sector_databundle

bool

{true, false}

Switch to retrieve sector databundle from zenodo via the rule retrieve_sector_databundle or whether to keep a custom databundle located in the corresponding folder.

retrieve_cost_data

bool

{true, false}

Switch to retrieve technology cost data from technology-data repository.

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.

co2 budget#

co2_budget:
  2020: 0.701
  2025: 0.524
  2030: 0.297
  2035: 0.150
  2040: 0.071
  2045: 0.032
  2050: 0.000

Unit

Values

Description

co2_budget

–

Dictionary with planning horizons as keys.

CO2 budget as a fraction of 1990 emissions. Overwritten if CO2Lx or cb are set in {sector_opts} wildcard”doc/configtables/othertoplevel.csv

Note

this parameter is over-ridden if CO2Lx or cb is set in sector_opts.

electricity#

electricity:
  voltages: [220., 300., 380.]
  gaslimit: false
  co2limit: 7.75e+7
  co2base: 1.487e+9
  agg_p_nom_limits: data/agg_p_nom_minmax.csv

  operational_reserve:
    activate: false
    epsilon_load: 0.02
    epsilon_vres: 0.02
    contingency: 4000

  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

  powerplants_filter: (DateOut >= 2022 or DateOut != DateOut)
  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
    from_opsd: true
    year: 2020
    expansion_limit: false
    technology_mapping:
      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 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 renewable capacities from OPSD database. The value is depreciated but still can be used.

– year

–

bool

Renewable capacities are based on existing capacities reported by IRENA (IRENASTAT) for the specified year

– expansion_limit

–

float or false

Artificially limit maximum IRENA capacities to a factor. For example, an expansion_limit: 1.1 means 110% of capacities . If false are chosen, the estimated renewable potentials determine by the workflow are used.

– technology_mapping

Mapping between PyPSA-Eur and powerplantmatching technology names

– – Offshore

–

Any subset of {offwind-ac, offwind-dc}

List of PyPSA-Eur carriers that is considered as (IRENA, OPSD) onshore technology.

– – Offshore

–

{onwind}

List of PyPSA-Eur carriers that is considered as (IRENA, OPSD) offshore technology.

– – PV

–

{solar}

List of PyPSA-Eur carriers that is considered as (IRENA, OPSD) PV technology.

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:
  default_cutout: europe-2013-era5
  nprocesses: 4
  show_progress: false
  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

default_cutout

–

str

Defines a default cutout.

nprocesses

–

int

Number of parallel processes in cutout preparation

show_progress

bool

true/false

Whether progressbar for atlite conversion processes should be shown. False saves time.

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.

– – dx

°

Larger than 0.25

Grid resolution for longitude

– – dy

°

Larger than 0.25

Grid resolution for latitude

– – 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
    # correction_factor: 0.93
    corine:
      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.

correction_factor

–

float

Correction factor for capacity factor time series.

excluder_resolution

m

float

Resolution on which to perform geographical elibility analysis.

Note

Notes on capacity_per_sqkm. 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.

Note

The default choice for corine grid_codes was based on Scholz, Y. (2012). Renewable energy based electricity supply at low costs development of the REMix model and application for Europe. ( p.42 / p.28)

offwind-ac#

  offwind-ac:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    capacity_per_sqkm: 2
    correction_factor: 0.8855
    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.

correction_factor

–

float

Correction factor for capacity factor time series.

excluder_resolution

m

float

Resolution on which to perform geographical elibility analysis.

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.

max_shore_distance

m

float

Maximum distance to the shore above 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.

Note

Notes on capacity_per_sqkm. 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.

Note

Notes on correction_factor. Correction due to proxy for wake losses from 10.1016/j.energy.2018.08.153 until done more rigorously in #153

offwind-dc#

  offwind-dc:
    cutout: europe-2013-era5
    resource:
      method: wind
      turbine: NREL_ReferenceTurbine_5MW_offshore
    capacity_per_sqkm: 2
    correction_factor: 0.8855
    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.

correction_factor

–

float

Correction factor for capacity factor time series.

excluder_resolution

m

float

Resolution on which to perform geographical elibility analysis.

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.

max_shore_distance

m

float

Maximum distance to the shore above which wind turbines cannot be build.

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.

Note

both offwind-ac and offwind-dc have the same assumption on capacity_per_sqkm and correction_factor.

solar#

  solar:
    cutout: europe-2013-sarah
    resource:
      method: pv
      panel: CSi
      orientation:
        slope: 35.
        azimuth: 180.
    capacity_per_sqkm: 1.7
    # 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.

excluder_resolution

m

float

Resolution on which to perform geographical elibility analysis.

Note

Notes on capacity_per_sqkm. 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 this <issue PyPSA/pypsa-eur#285>

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
    flatten_dispatch: false
    flatten_dispatch_buffer: 0.2
    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.

flatten_dispatch

bool

{true, false}

Consider an upper limit for the hydro dispatch. The limit is given by the average capacity factor plus the buffer given in flatten_dispatch_buffer

flatten_dispatch_buffer

–

float

If flatten_dispatch is true, specify the value added above the average capacity factor.

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.

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

Unit

Values

Description

unit_commitment

bool

{true, false}

Allow the overwrite of ramp_limit_up, ramp_limit_start_up, ramp_limit_shut_down, p_min_pu, min_up_time, min_down_time, and start_up_cost of conventional generators. Refer to the CSV file „unit_commitment.csv“.

dynamic_fuel_price

bool

{true, false}

Consider the monthly fluctuating fuel prices for each conventional generator. Refer to the CSV file “data/validation/monthly_fuel_price.csv”.

{name}

–

string

For any carrier/technology overwrite attributes as listed below.

– {attribute}

–

string or float

For any attribute, can specify a float or reference to a file path to a CSV file giving floats for each country (2-letter code).

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
  max_extension: .inf
  length_factor: 1.25
  under_construction: 'zero' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
  dynamic_line_rating:
    activate: false
    cutout: europe-2013-era5
    correction_factor: 0.95
    max_voltage_difference: false
    max_line_rating: false

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.

max_extension

MW

float

Upper limit for the extended 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.

dynamic_line_rating

– activate

bool

true or false

Whether to take dynamic line rating into account

– 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.

– correction_factor

–

float

Factor to compensate for overestimation of wind speeds in hourly averaged wind data

– max_voltage_difference

deg

float

Maximum voltage angle difference in degrees or ‘false’ to disable

– max_line_rating

–

float

Maximum line rating relative to nominal capacity without DLR, e.g. 1.3 or ‘false’ to disable

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#

Unit

Values

Description

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.

energy#

Note

Only used for sector-coupling studies.

energy:
  energy_totals_year: 2011
  base_emissions_year: 1990
  eurostat_report_year: 2016
  emissions: CO2

Unit

Values

Description

energy_totals_year

–

{1990,1995,2000,2005,2010,2011,…}

The year for the sector energy use. The year must be avaliable in the Eurostat report

base_emissions_year

–

YYYY; e.g. 1990

The base year for the sector emissions. See European Environment Agency (EEA).

eurostat_report_year

–

{2016,2017,2018}

The publication year of the Eurostat report. 2016 includes Bosnia and Herzegovina, 2017 does not

emissions

–

{CO2, All greenhouse gases - (CO2 equivalent)}

Specify which sectoral emissions are taken into account. Data derived from EEA. Currently only CO2 is implemented.

biomass#

Note

Only used for sector-coupling studies.

biomass:
  year: 2030
  scenario: ENS_Med
  classes:
    solid biomass:
    - Agricultural waste
    - Fuelwood residues
    - Secondary Forestry residues - woodchips
    - Sawdust
    - Residues from landscape care
    - Municipal waste
    not included:
    - Sugar from sugar beet
    - Rape seed
    - "Sunflower, soya seed "
    - Bioethanol barley, wheat, grain maize, oats, other cereals and rye
    - Miscanthus, switchgrass, RCG
    - Willow
    - Poplar
    - FuelwoodRW
    - C&P_RW
    biogas:
    - Manure solid, liquid
    - Sludge

Unit

Values

Description

year

–

{2010, 2020, 2030, 2040, 2050}

Year for which to retrieve biomass potential according to the assumptions of the JRC ENSPRESO .

scenario

–

{“ENS_Low”, “ENS_Med”, “ENS_High”}

Scenario for which to retrieve biomass potential. The scenario definition can be seen in ENSPRESO_BIOMASS

classes

– solid biomass

–

Array of biomass comodity

The comodity that are included as solid biomass

– not included

–

Array of biomass comodity

The comodity that are not included as a biomass potential

– biogas

–

Array of biomass comodity

The comodity that are included as biogas

The list of available biomass is given by the category in ENSPRESO_BIOMASS, namely:

  • Agricultural waste

  • Manure solid, liquid

  • Residues from landscape care

  • Bioethanol barley, wheat, grain maize, oats, other cereals and rye

  • Sugar from sugar beet

  • Miscanthus, switchgrass, RCG

  • Willow

  • Poplar

  • Sunflower, soya seed

  • Rape seed

  • Fuelwood residues

  • FuelwoodRW

  • C&P_RW

  • Secondary Forestry residues - woodchips

  • Sawdust

  • Municipal waste

  • Sludge

solar_thermal#

Note

Only used for sector-coupling studies.

solar_thermal:
  clearsky_model: simple  # should be "simple" or "enhanced"?
  orientation:
    slope: 45.
    azimuth: 180.

Unit

Values

Description

clearsky_model

–

{‘simple’, ‘enhanced’}

Type of clearsky model for diffuse irradiation

orientation

–

{units of degrees, ‘latitude_optimal’}

Panel orientation with slope and azimuth

– azimuth

float

units of degrees

The angle between the North and the sun with panels on the local horizon

– slope

float

units of degrees

The angle between the ground and the panels

existing_capacities#

Note

Only used for sector-coupling studies. The value for grouping years are only used in myopic or perfect foresight scenarios.

existing_capacities:
  grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
  grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020
  threshold_capacity: 10
  conventional_carriers:
  - lignite
  - coal
  - oil
  - uranium

Unit

Values

Description

grouping_years_power

–

A list of years

Intervals to group existing capacities for power

grouping_years_heat

–

A list of years below 2020

Intervals to group existing capacities for heat

threshold_capacity

MW

float

Capacities generators and links of below threshold are removed during add_existing_capacities

conventional_carriers

–

Any subset of {uranium, coal, lignite, oil}

List of conventional power plants to include in the sectoral network

sector#

Note

Only used for sector-coupling studies.

sector:
  district_heating:
    potential: 0.6
    progress:
      2020: 0.0
      2030: 0.3
      2040: 0.6
      2050: 1.0
    district_heating_loss: 0.15
  cluster_heat_buses: false
  bev_dsm_restriction_value: 0.75
  bev_dsm_restriction_time: 7
  transport_heating_deadband_upper: 20.
  transport_heating_deadband_lower: 15.
  ICE_lower_degree_factor: 0.375
  ICE_upper_degree_factor: 1.6
  EV_lower_degree_factor: 0.98
  EV_upper_degree_factor: 0.63
  bev_dsm: true
  bev_availability: 0.5
  bev_energy: 0.05
  bev_charge_efficiency: 0.9
  bev_plug_to_wheel_efficiency: 0.2
  bev_charge_rate: 0.011
  bev_avail_max: 0.95
  bev_avail_mean: 0.8
  v2g: true
  land_transport_fuel_cell_share:
    2020: 0
    2030: 0.05
    2040: 0.1
    2050: 0.15
  land_transport_electric_share:
    2020: 0
    2030: 0.25
    2040: 0.6
    2050: 0.85
  land_transport_ice_share:
    2020: 1
    2030: 0.7
    2040: 0.3
    2050: 0
  transport_fuel_cell_efficiency: 0.5
  transport_internal_combustion_efficiency: 0.3
  agriculture_machinery_electric_share: 0
  agriculture_machinery_oil_share: 1
  agriculture_machinery_fuel_efficiency: 0.7
  agriculture_machinery_electric_efficiency: 0.3
  MWh_MeOH_per_MWh_H2: 0.8787
  MWh_MeOH_per_tCO2: 4.0321
  MWh_MeOH_per_MWh_e: 3.6907
  shipping_hydrogen_liquefaction: false
  shipping_hydrogen_share:
    2020: 0
    2030: 0
    2040: 0
    2050: 0
  shipping_methanol_share:
    2020: 0
    2030: 0.3
    2040: 0.7
    2050: 1
  shipping_oil_share:
    2020: 1
    2030: 0.7
    2040: 0.3
    2050: 0
  shipping_methanol_efficiency: 0.46
  shipping_oil_efficiency: 0.40
  aviation_demand_factor: 1.
  HVC_demand_factor: 1.
  time_dep_hp_cop: true
  heat_pump_sink_T: 55.
  reduce_space_heat_exogenously: true
  reduce_space_heat_exogenously_factor:
    2020: 0.10  # this results in a space heat demand reduction of 10%
    2025: 0.09  # first heat demand increases compared to 2020 because of larger floor area per capita
    2030: 0.09
    2035: 0.11
    2040: 0.16
    2045: 0.21
    2050: 0.29
  retrofitting:
    retro_endogen: false
    cost_factor: 1.0
    interest_rate: 0.04
    annualise_cost: true
    tax_weighting: false
    construction_index: true
  tes: true
  tes_tau:
    decentral: 3
    central: 180
  boilers: true
  oil_boilers: false
  biomass_boiler: true
  chp: true
  micro_chp: false
  solar_thermal: true
  solar_cf_correction: 0.788457  # =  >>> 1/1.2683
  marginal_cost_storage: 0. #1e-4
  methanation: true
  helmeth: false
  coal_cc: false
  dac: true
  co2_vent: false
  allam_cycle: false
  hydrogen_fuel_cell: true
  hydrogen_turbine: false
  SMR: true
  regional_co2_sequestration_potential:
    enable: false
    attribute: 'conservative estimate Mt'
    include_onshore: false
    min_size: 3
    max_size: 25
    years_of_storage: 25
  co2_sequestration_potential: 200
  co2_sequestration_cost: 10
  co2_spatial: false
  co2network: false
  cc_fraction: 0.9
  hydrogen_underground_storage: true
  hydrogen_underground_storage_locations:
    # - onshore  # more than 50 km from sea
  - nearshore    # within 50 km of sea
    # - offshore
  ammonia: false
  min_part_load_fischer_tropsch: 0.9
  min_part_load_methanolisation: 0.5
  use_fischer_tropsch_waste_heat: true
  use_fuel_cell_waste_heat: true
  use_electrolysis_waste_heat: false
  electricity_distribution_grid: true
  electricity_distribution_grid_cost_factor: 1.0
  electricity_grid_connection: true
  H2_network: true
  gas_network: false
  H2_retrofit: false
  H2_retrofit_capacity_per_CH4: 0.6
  gas_network_connectivity_upgrade: 1
  gas_distribution_grid: true
  gas_distribution_grid_cost_factor: 1.0
  biomass_spatial: false
  biomass_transport: false
  conventional_generation:
    OCGT: gas
  biomass_to_liquid: false
  biosng: false

Unit

Values

Description

district_heating

–

prepare_sector_network.py

– potential

–

float

maximum fraction of urban demand which can be supplied by district heating

– progress

–

Dictionary with planning horizons as keys.

Increase of today’s district heating demand to potential maximum district heating share. Progress = 0 means today’s district heating share. Progress = 1 means maximum fraction of urban demand is supplied by district heating

– district_heating_loss

–

float

Share increase in district heat demand in urban central due to heat losses

cluster_heat_buses

–

{true, false}

Cluster residential and service heat buses in prepare_sector_network.py to one to save memory.

bev_dsm_restriction _value

–

float

Adds a lower state of charge (SOC) limit for battery electric vehicles (BEV) to manage its own energy demand (DSM). Located in build_transport_demand.py. Set to 0 for no restriction on BEV DSM

bev_dsm_restriction _time

–

float

Time at which SOC of BEV has to be dsm_restriction_value

transport_heating _deadband_upper

°C

float

The maximum temperature in the vehicle. At higher temperatures, the energy required for cooling in the vehicle increases.

transport_heating _deadband_lower

°C

float

The minimum temperature in the vehicle. At lower temperatures, the energy required for heating in the vehicle increases.

ICE_lower_degree_factor

–

float

Share increase in energy demand in internal combustion engine (ICE) for each degree difference between the cold environment and the minimum temperature.

ICE_upper_degree_factor

–

float

Share increase in energy demand in internal combustion engine (ICE) for each degree difference between the hot environment and the maximum temperature.

EV_lower_degree_factor

–

float

Share increase in energy demand in electric vehicles (EV) for each degree difference between the cold environment and the minimum temperature.

EV_upper_degree_factor

–

float

Share increase in energy demand in electric vehicles (EV) for each degree difference between the hot environment and the maximum temperature.

bev_dsm

–

{true, false}

Add the option for battery electric vehicles (BEV) to participate in demand-side management (DSM)

bev_availability

–

float

The share for battery electric vehicles (BEV) that are able to do demand side management (DSM)

bev_energy

–

float

The average size of battery electric vehicles (BEV) in MWh

bev_charge_efficiency

–

float

Battery electric vehicles (BEV) charge and discharge efficiency

bev_plug_to_wheel _efficiency

km/kWh

float

The distance battery electric vehicles (BEV) can travel in km per kWh of energy charge in battery. Base value comes from Tesla Model S

bev_charge_rate

MWh

float

The power consumption for one electric vehicle (EV) in MWh. Value derived from 3-phase charger with 11 kW.

bev_avail_max

–

float

The maximum share plugged-in availability for passenger electric vehicles.

bev_avail_mean

–

float

The average share plugged-in availability for passenger electric vehicles.

v2g

–

{true, false}

Allows feed-in to grid from EV battery

land_transport_fuel_cell _share

–

Dictionary with planning horizons as keys.

The share of vehicles that uses fuel cells in a given year

land_transport_electric _share

–

Dictionary with planning horizons as keys.

The share of vehicles that uses electric vehicles (EV) in a given year

land_transport_ice _share

–

Dictionary with planning horizons as keys.

The share of vehicles that uses internal combustion engines (ICE) in a given year. What is not EV or FCEV is oil-fuelled ICE.

transport_fuel_cell _efficiency

–

float

The H2 conversion efficiencies of fuel cells in transport

transport_internal _combustion_efficiency

–

float

The oil conversion efficiencies of internal combustion engine (ICE) in transport

agriculture_machinery _electric_share

–

float

The share for agricultural machinery that uses electricity

agriculture_machinery _oil_share

–

float

The share for agricultural machinery that uses oil

agriculture_machinery _fuel_efficiency

–

float

The efficiency of electric-powered machinery in the conversion of electricity to meet agricultural needs.

agriculture_machinery _electric_efficiency

–

float

The efficiency of oil-powered machinery in the conversion of oil to meet agricultural needs.

Mwh_MeOH_per_MWh_H2

LHV

float

The energy amount of the produced methanol per energy amount of hydrogen. From DECHEMA (2017), page 64.

MWh_MeOH_per_tCO2

LHV

float

The energy amount of the produced methanol per ton of CO2. From DECHEMA (2017), page 64.

MWh_MeOH_per_MWh_e

LHV

float

The energy amount of the produced methanol per energy amount of electricity. From DECHEMA (2017), page 64.

shipping_hydrogen _liquefaction

–

{true, false}

Whether to include liquefaction costs for hydrogen demand in shipping.

shipping_hydrogen_share

–

Dictionary with planning horizons as keys.

The share of ships powered by hydrogen in a given year

shipping_methanol_share

–

Dictionary with planning horizons as keys.

The share of ships powered by methanol in a given year

shipping_oil_share

–

Dictionary with planning horizons as keys.

The share of ships powered by oil in a given year

shipping_methanol _efficiency

–

float

The efficiency of methanol-powered ships in the conversion of methanol to meet shipping needs (propulsion). The efficiency increase from oil can be 10-15% higher according to the IEA

shipping_oil_efficiency

–

float

The efficiency of oil-powered ships in the conversion of oil to meet shipping needs (propulsion). Base value derived from 2011

aviation_demand_factor

–

float

The proportion of demand for aviation compared to today’s consumption

HVC_demand_factor

–

float

The proportion of demand for high-value chemicals compared to today’s consumption

time_dep_hp_cop

–

{true, false}

Consider the time dependent coefficient of performance (COP) of the heat pump

heat_pump_sink_T

°C

float

The temperature heat sink used in heat pumps based on DTU / large area radiators. The value is conservatively high to cover hot water and space heating in poorly-insulated buildings

reduce_space_heat _exogenously

–

{true, false}

Influence on space heating demand by a certain factor (applied before losses in district heating).

reduce_space_heat _exogenously_factor

–

Dictionary with planning horizons as keys.

A positive factor can mean renovation or demolition of a building. If the factor is negative, it can mean an increase in floor area, increased thermal comfort, population growth. The default factors are determined by the Eurocalc Homes and buildings decarbonization scenario

retrofitting

– retro_endogen

–

{true, false}

Add retrofitting as an endogenous system which co-optimise space heat savings.

– cost_factor

–

float

Weight costs for building renovation

– interest_rate

–

float

The interest rate for investment in building components

– annualise_cost

–

{true, false}

Annualise the investment costs of retrofitting

– tax_weighting

–

{true, false}

Weight the costs of retrofitting depending on taxes in countries

– construction_index

–

{true, false}

Weight the costs of retrofitting depending on labour/material costs per country

tes

–

{true, false}

Add option for storing thermal energy in large water pits associated with district heating systems and individual thermal energy storage (TES)

tes_tau

The time constant used to calculate the decay of thermal energy in thermal energy storage (TES): 1- \(e^{-1/24τ}\).

– decentral

days

float

The time constant in decentralized thermal energy storage (TES)

– central

days

float

The time constant in centralized thermal energy storage (TES)

boilers

–

{true, false}

Add option for transforming electricity into heat using resistive heater

oil_boilers

–

{true, false}

Add option for transforming oil into heat using boilers

biomass_boiler

–

{true, false}

Add option for transforming biomass into heat using boilers

chp

–

{true, false}

Add option for using Combined Heat and Power (CHP)

micro_chp

–

{true, false}

Add option for using Combined Heat and Power (CHP) for decentral areas.

solar_thermal

–

{true, false}

Add option for using solar thermal to generate heat.

solar_cf_correction

–

float

The correction factor for the value provided by the solar thermal profile calculations

marginal_cost_storage

currency/MWh

float

The marginal cost of discharging batteries in distributed grids

methanation

–

{true, false}

Add option for transforming hydrogen and CO2 into methane using methanation.

helmeth

–

{true, false}

Add option for transforming power into gas using HELMETH (Integrated High-Temperature ELectrolysis and METHanation for Effective Power to Gas Conversion)

coal_cc

–

{true, false}

Add option for coal CHPs with carbon capture

dac

–

{true, false}

Add option for Direct Air Capture (DAC)

co2_vent

–

{true, false}

Add option for vent out CO2 from storages to the atmosphere.

allam_cycle

–

{true, false}

Add option to include Allam cycle gas power plants

hydrogen_fuel_cell

–

{true, false}

Add option to include hydrogen fuel cell for re-electrification. Assuming OCGT technology costs

hydrogen_turbine

–

{true, false}

Add option to include hydrogen turbine for re-electrification. Assuming OCGT technology costs

SMR

–

{true, false}

Add option for transforming natural gas into hydrogen and CO2 using Steam Methane Reforming (SMR)

regional_co2 _sequestration_potential

– enable

–

{true, false}

Add option for regionally-resolved geological carbon dioxide sequestration potentials based on CO2StoP.

– attribute

–

string

Name of the attribute for the sequestration potential

– include_onshore

–

{true, false}

Add options for including onshore sequestration potentials

– min_size

Gt

float

Any sites with lower potential than this value will be excluded

– max_size

Gt

float

The maximum sequestration potential for any one site.

– years_of_storage

years

float

The years until potential exhausted at optimised annual rate

co2_sequestration_potential

MtCO2/a

float

The potential of sequestering CO2 in Europe per year

co2_sequestration_cost

currency/tCO2

float

The cost of sequestering a ton of CO2

co2_spatial

–

{true, false}

Add option to spatially resolve carrier representing stored carbon dioxide. This allows for more detailed modelling of CCUTS, e.g. regarding the capturing of industrial process emissions, usage as feedstock for electrofuels, transport of carbon dioxide, and geological sequestration sites.

co2network

–

{true, false}

Add option for planning a new carbon dioxide transmission network

cc_fraction

–

float

The default fraction of CO2 captured with post-combustion capture

hydrogen_underground _storage

–

{true, false}

Add options for storing hydrogen underground. Storage potential depends regionally.

hydrogen_underground _storage_locations

{onshore, nearshore, offshore}

The location where hydrogen underground storage can be located. Onshore, nearshore, offshore means it must be located more than 50 km away from the sea, within 50 km of the sea, or within the sea itself respectively.

ammonia

–

{true, false, regional}

Add ammonia as a carrrier. It can be either true (copperplated NH3), false (no NH3 carrier) or “regional” (regionalised NH3 without network)

min_part_load_fischer _tropsch

per unit of p_nom

float

The minimum unit dispatch (p_min_pu) for the Fischer-Tropsch process

min_part_load _methanolisation

per unit of p_nom

float

The minimum unit dispatch (p_min_pu) for the methanolisation process

use_fischer_tropsch _waste_heat

–

{true, false}

Add option for using waste heat of Fischer Tropsch in district heating networks

use_fuel_cell_waste_heat

–

{true, false}

Add option for using waste heat of fuel cells in district heating networks

use_electrolysis_waste _heat

–

{true, false}

Add option for using waste heat of electrolysis in district heating networks

electricity_distribution _grid

–

{true, false}

Add a simplified representation of the exchange capacity between transmission and distribution grid level through a link.

electricity_distribution _grid_cost_factor

Multiplies the investment cost of the electricity distribution grid

electricity_grid _connection

–

{true, false}

Add the cost of electricity grid connection for onshore wind and solar

H2_network

–

{true, false}

Add option for new hydrogen pipelines

gas_network

–

{true, false}

Add existing natural gas infrastructure, incl. LNG terminals, production and entry-points. The existing gas network is added with a lossless transport model. A length-weighted k-edge augmentation algorithm can be run to add new candidate gas pipelines such that all regions of the model can be connected to the gas network. When activated, all the gas demands are regionally disaggregated as well.

H2_retrofit

–

{true, false}

Add option for retrofiting existing pipelines to transport hydrogen.

H2_retrofit_capacity _per_CH4

–

float

The ratio for H2 capacity per original CH4 capacity of retrofitted pipelines. The European Hydrogen Backbone (April, 2020) p.15 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity.

gas_network_connectivity _upgrade

–

float

The number of desired edge connectivity (k) in the length-weighted k-edge augmentation algorithm used for the gas network

gas_distribution_grid

–

{true, false}

Add a gas distribution grid

gas_distribution_grid _cost_factor

Multiplier for the investment cost of the gas distribution grid

biomass_spatial

–

{true, false}

Add option for resolving biomass demand regionally

biomass_transport

–

{true, false}

Add option for transporting solid biomass between nodes

conventional_generation

Add a more detailed description of conventional carriers. Any power generation requires the consumption of fuel from nodes representing that fuel.

biomass_to_liquid

–

{true, false}

Add option for transforming solid biomass into liquid fuel with the same properties as oil

biosng

–

{true, false}

Add option for transforming solid biomass into synthesis gas with the same properties as natural gas

industry#

Note

Only used for sector-coupling studies.

industry:
  St_primary_fraction:
    2020: 0.6
    2025: 0.55
    2030: 0.5
    2035: 0.45
    2040: 0.4
    2045: 0.35
    2050: 0.3
  DRI_fraction:
    2020: 0
    2025: 0
    2030: 0.05
    2035: 0.2
    2040: 0.4
    2045: 0.7
    2050: 1
  H2_DRI: 1.7
  elec_DRI: 0.322
  Al_primary_fraction:
    2020: 0.4
    2025: 0.375
    2030: 0.35
    2035: 0.325
    2040: 0.3
    2045: 0.25
    2050: 0.2
  MWh_NH3_per_tNH3: 5.166
  MWh_CH4_per_tNH3_SMR: 10.8
  MWh_elec_per_tNH3_SMR: 0.7
  MWh_H2_per_tNH3_electrolysis: 6.5
  MWh_elec_per_tNH3_electrolysis: 1.17
  MWh_NH3_per_MWh_H2_cracker: 1.46 # https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
  NH3_process_emissions: 24.5
  petrochemical_process_emissions: 25.5
  HVC_primary_fraction: 1.
  HVC_mechanical_recycling_fraction: 0.
  HVC_chemical_recycling_fraction: 0.
  HVC_production_today: 52.
  MWh_elec_per_tHVC_mechanical_recycling: 0.547
  MWh_elec_per_tHVC_chemical_recycling: 6.9
  chlorine_production_today: 9.58
  MWh_elec_per_tCl: 3.6
  MWh_H2_per_tCl: -0.9372
  methanol_production_today: 1.5
  MWh_elec_per_tMeOH: 0.167
  MWh_CH4_per_tMeOH: 10.25
  hotmaps_locate_missing: false
  reference_year: 2015

Unit

Values

Description

St_primary_fraction

–

Dictionary with planning horizons as keys.

The fraction of steel produced via primary route versus secondary route (scrap+EAF). Current fraction is 0.6

DRI_fraction

–

Dictionary with planning horizons as keys.

The fraction of the primary route DRI + EAF

H2_DRI

–

float

The hydrogen consumption in Direct Reduced Iron (DRI) Mwh_H2 LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018)

elec_DRI

MWh/tSt

float

The electricity consumed in Direct Reduced Iron (DRI) shaft. From HYBRIT brochure

Al_primary_fraction

–

Dictionary with planning horizons as keys.

The fraction of aluminium produced via the primary route versus scrap. Current fraction is 0.4

MWh_NH3_per_tNH3

LHV

float

The energy amount per ton of ammonia.

MWh_CH4_per_tNH3_SMR

–

float

The energy amount of methane needed to produce a ton of ammonia using steam methane reforming (SMR). Value derived from 2012’s demand from Center for European Policy Studies (2008)

MWh_elec_per_tNH3_SMR

–

float

The energy amount of electricity needed to produce a ton of ammonia using steam methane reforming (SMR). same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3

Mwh_H2_per_tNH3 _electrolysis

–

float

The energy amount of hydrogen needed to produce a ton of ammonia using Haber–Bosch process. From Wang et al (2018), Base value assumed around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)

Mwh_elec_per_tNH3 _electrolysis

–

float

The energy amount of electricity needed to produce a ton of ammonia using Haber–Bosch process. From Wang et al (2018), Table 13 (air separation and HB)

Mwh_NH3_per_MWh _H2_cracker

–

float

The energy amount of amonia needed to produce an energy amount hydrogen using ammonia cracker

NH3_process_emissions

MtCO2/a

float

The emission of ammonia production from steam methane reforming (SMR). From UNFCCC for 2015 for EU28

petrochemical_process _emissions

MtCO2/a

float

The emission of petrochemical production. From UNFCCC for 2015 for EU28

HVC_primary_fraction

–

float

The fraction of high value chemicals (HVC) produced via primary route

HVC_mechanical_recycling _fraction

–

float

The fraction of high value chemicals (HVC) produced using mechanical recycling

HVC_chemical_recycling _fraction

–

float

The fraction of high value chemicals (HVC) produced using chemical recycling

HVC_production_today

MtHVC/a

float

The amount of high value chemicals (HVC) produced. This includes ethylene, propylene and BTX. From DECHEMA (2017), Figure 16, page 107

Mwh_elec_per_tHVC _mechanical_recycling

MWh/tHVC

float

The energy amount of electricity needed to produce a ton of high value chemical (HVC) using mechanical recycling. From SI of Meys et al (2020), Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.

Mwh_elec_per_tHVC _chemical_recycling

MWh/tHVC

float

The energy amount of electricity needed to produce a ton of high value chemical (HVC) using chemical recycling. The default value is based on pyrolysis and electric steam cracking. From Material Economics (2019), page 125

chlorine_production _today

MtCl/a

float

The amount of chlorine produced. From DECHEMA (2017), Table 7, page 43

MWh_elec_per_tCl

MWh/tCl

float

The energy amount of electricity needed to produce a ton of chlorine. From DECHEMA (2017), Table 6 page 43

MWh_H2_per_tCl

MWhH2/tCl

float

The energy amount of hydrogen needed to produce a ton of chlorine. The value is negative since hydrogen produced in chloralkali process. From DECHEMA (2017), page 43

methanol_production _today

MtMeOH/a

float

The amount of methanol produced. From DECHEMA (2017), page 62

MWh_elec_per_tMeOH

MWh/tMeOH

float

The energy amount of electricity needed to produce a ton of methanol. From DECHEMA (2017), Table 14, page 65

MWh_CH4_per_tMeOH

MWhCH4/tMeOH

float

The energy amount of methane needed to produce a ton of methanol. From DECHEMA (2017), Table 14, page 65

hotmaps_locate_missing

–

{true,false}

Locate industrial sites without valid locations based on city and countries.

reference_year

year

YYYY

The year used as the baseline for industrial energy demand and production. Data extracted from JRC-IDEES 2015

costs#

costs:
  year: 2030
  version: v0.6.0
  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 and capital costs can be overwritten
  # capital_cost:
  #   onwind: 500
  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:
    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. ‘v0.5.0’

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

rooftop_share: are based on the potentials, assuming (0.1 kW/m2 and 10 m2/person)

clustering#

clustering:
  simplify_network:
    to_substations: false
    algorithm: kmeans # choose from: [hac, kmeans]
    feature: solar+onwind-time
    exclude_carriers: []
    remove_stubs: true
    remove_stubs_across_borders: true
  cluster_network:
    algorithm: kmeans
    feature: solar+onwind-time
    exclude_carriers: []
    consider_efficiency_classes: false
  aggregation_strategies:
    generators:
      committable: any
      ramp_limit_up: max
      ramp_limit_down: max

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.

– remove stubs

bool

{‘true’,’false’}

Controls whether radial parts of the network should be recursively aggregated. Defaults to true.

– remove_stubs_across_borders

bool

{‘true’,’false’}

Controls whether radial parts of the network should be recursively aggregated across borders. Defaults to true.

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.

– consider_efficiency_classes

bool

{‘true’,’false’}

Aggregated each carriers into the top 10-quantile (high), the bottom 90-quantile (low), and everything in between (medium).

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.

Note

feature: in simplify_network: are only relevant if hac were chosen in algorithm.

Tip

use min in p_nom_max: for more ` conservative assumptions.

solving#

solving:
  #tmpdir: "path/to/tmp"
  options:
    clip_p_max_pu: 1.e-2
    load_shedding: false
    noisy_costs: true
    skip_iterations: true
    rolling_horizon: false
    seed: 123
    # options that go into the optimize function
    track_iterations: false
    min_iterations: 4
    max_iterations: 6
    transmission_losses: 0
    linearized_unit_commitment: true
    horizon: 365

  solver:
    name: gurobi
    options: gurobi-default

  solver_options:
    highs-default:
      # refer to https://ergo-code.github.io/HiGHS/options/definitions.html#solver
      threads: 4
      solver: "ipm"
      run_crossover: "off"
      small_matrix_value: 1e-6
      large_matrix_value: 1e9
      primal_feasibility_tolerance: 1e-5
      dual_feasibility_tolerance: 1e-5
      ipm_optimality_tolerance: 1e-4
      parallel: "on"
      random_seed: 123
    gurobi-default:
      threads: 4
      method: 2 # barrier
      crossover: 0
      BarConvTol: 1.e-6
      Seed: 123
      AggFill: 0
      PreDual: 0
      GURO_PAR_BARDENSETHRESH: 200
    gurobi-numeric-focus:
      name: gurobi
      NumericFocus: 3       # Favour numeric stability over speed
      method: 2             # barrier
      crossover: 0          # do not use crossover
      BarHomogeneous: 1     # Use homogeneous barrier if standard does not converge
      BarConvTol: 1.e-5
      FeasibilityTol: 1.e-4
      OptimalityTol: 1.e-4
      ObjScale: -0.5
      threads: 8
      Seed: 123
    gurobi-fallback:        # Use gurobi defaults
      name: gurobi
      crossover: 0
      method: 2             # barrier
      BarHomogeneous: 1     # Use homogeneous barrier if standard does not converge
      BarConvTol: 1.e-5
      FeasibilityTol: 1.e-5
      OptimalityTol: 1.e-5
      Seed: 123
      threads: 8
    cplex-default:
      threads: 4
      lpmethod: 4 # barrier
      solutiontype: 2 # non basic solution, ie no crossover
      barrier.convergetol: 1.e-5
      feasopt.tolerance: 1.e-6
    cbc-default: {} # Used in CI
    glpk-default: {} # Used in CI

  mem: 30000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
  walltime: "12:00:00"

Unit

Values

Description

options

– 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.

– load_shedding

bool/float

{‘true’,’false’, float}

Add generators with very high marginal cost to simulate load shedding and avoid problem infeasibilities. If load shedding is a float, it denotes the marginal cost in EUR/kWh.

– 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 \(\mathcal{U}(0.09,0,11)\).

– skip_iterations

bool

{‘true’,’false’}

Skip iterating, do not update impedances of branches. Defaults to true.

– rolling_horizon

bool

{‘true’,’false’}

Whether to optimize the network in a rolling horizon manner, where the snapshot range is split into slices of size horizon which are solved consecutively.

– seed

–

int

Random seed for increased deterministic behaviour.

– 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)

– 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.

– transmission_losses

int

[0-9]

Add piecewise linear approximation of transmission losses based on n tangents. Defaults to 0, which means losses are ignored.

– linearized_unit_commitment

bool

{‘true’,’false’}

Whether to optimise using the linearized unit commitment formulation.

– horizon

–

int

Number of snapshots to consider in each iteration. Defaults to 100.

solver

– 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.

– options

–

Key listed under solver_options.

Link to specific parameter settings.

solver_options

dict

Dictionaries with solver-specific parameter settings.

mem

MB

int

Estimated maximum memory requirement for solving networks.

plotting#

Warning

More comprehensive documentation for this segment will be released soon.

plotting:
  map:
    boundaries: [-11, 30, 34, 71]
    color_geomap:
      ocean: white
      land: white
  eu_node_location:
    x: -5.5
    y: 46.
  costs_max: 1000
  costs_threshold: 1
  energy_max: 20000
  energy_min: -20000
  energy_threshold: 50.

  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"
    ac: "AC"
    dc: "DC"

  tech_colors:
    # wind
    onwind: "#235ebc"
    onshore wind: "#235ebc"
    offwind: "#6895dd"
    offshore wind: "#6895dd"
    offwind-ac: "#6895dd"
    offshore wind (AC): "#6895dd"
    offshore wind ac: "#6895dd"
    offwind-dc: "#74c6f2"
    offshore wind (DC): "#74c6f2"
    offshore wind dc: "#74c6f2"
    # water
    hydro: '#298c81'
    hydro reservoir: '#298c81'
    ror: '#3dbfb0'
    run of river: '#3dbfb0'
    hydroelectricity: '#298c81'
    PHS: '#51dbcc'
    hydro+PHS: "#08ad97"
    wave: '#a7d4cf'
    # solar
    solar: "#f9d002"
    solar PV: "#f9d002"
    solar thermal: '#ffbf2b'
    residential rural solar thermal: '#f1c069'
    services rural solar thermal: '#eabf61'
    residential urban decentral solar thermal: '#e5bc5a'
    services urban decentral solar thermal: '#dfb953'
    urban central solar thermal: '#d7b24c'
    solar rooftop: '#ffea80'
    # gas
    OCGT: '#e0986c'
    OCGT marginal: '#e0986c'
    OCGT-heat: '#e0986c'
    gas boiler: '#db6a25'
    gas boilers: '#db6a25'
    gas boiler marginal: '#db6a25'
    residential rural gas boiler: '#d4722e'
    residential urban decentral gas boiler: '#cb7a36'
    services rural gas boiler: '#c4813f'
    services urban decentral gas boiler: '#ba8947'
    urban central gas boiler: '#b0904f'
    gas: '#e05b09'
    fossil gas: '#e05b09'
    natural gas: '#e05b09'
    biogas to gas: '#e36311'
    CCGT: '#a85522'
    CCGT marginal: '#a85522'
    allam: '#B98F76'
    gas for industry co2 to atmosphere: '#692e0a'
    gas for industry co2 to stored: '#8a3400'
    gas for industry: '#853403'
    gas for industry CC: '#692e0a'
    gas pipeline: '#ebbca0'
    gas pipeline new: '#a87c62'
    # oil
    oil: '#c9c9c9'
    oil boiler: '#adadad'
    residential rural oil boiler: '#a9a9a9'
    services rural oil boiler: '#a5a5a5'
    residential urban decentral oil boiler: '#a1a1a1'
    urban central oil boiler: '#9d9d9d'
    services urban decentral oil boiler: '#999999'
    agriculture machinery oil: '#949494'
    shipping oil: "#808080"
    land transport oil: '#afafaf'
    # nuclear
    Nuclear: '#ff8c00'
    Nuclear marginal: '#ff8c00'
    nuclear: '#ff8c00'
    uranium: '#ff8c00'
    # coal
    Coal: '#545454'
    coal: '#545454'
    Coal marginal: '#545454'
    solid: '#545454'
    Lignite: '#826837'
    lignite: '#826837'
    Lignite marginal: '#826837'
    # biomass
    biogas: '#e3d37d'
    biomass: '#baa741'
    solid biomass: '#baa741'
    solid biomass transport: '#baa741'
    solid biomass for industry: '#7a6d26'
    solid biomass for industry CC: '#47411c'
    solid biomass for industry co2 from atmosphere: '#736412'
    solid biomass for industry co2 to stored: '#47411c'
    urban central solid biomass CHP: '#9d9042'
    urban central solid biomass CHP CC: '#6c5d28'
    biomass boiler: '#8A9A5B'
    residential rural biomass boiler: '#a1a066'
    residential urban decentral biomass boiler: '#b0b87b'
    services rural biomass boiler: '#c6cf98'
    services urban decentral biomass boiler: '#dde5b5'
    biomass to liquid: '#32CD32'
    BioSNG: '#123456'
    # power transmission
    lines: '#6c9459'
    transmission lines: '#6c9459'
    electricity distribution grid: '#97ad8c'
    low voltage: '#97ad8c'
    # electricity demand
    Electric load: '#110d63'
    electric demand: '#110d63'
    electricity: '#110d63'
    industry electricity: '#2d2a66'
    industry new electricity: '#2d2a66'
    agriculture electricity: '#494778'
    # battery + EVs
    battery: '#ace37f'
    battery storage: '#ace37f'
    battery charger: '#88a75b'
    battery discharger: '#5d4e29'
    home battery: '#80c944'
    home battery storage: '#80c944'
    home battery charger: '#5e8032'
    home battery discharger: '#3c5221'
    BEV charger: '#baf238'
    V2G: '#e5ffa8'
    land transport EV: '#baf238'
    Li ion: '#baf238'
    # hot water storage
    water tanks: '#e69487'
    residential rural water tanks: '#f7b7a3'
    services rural water tanks: '#f3afa3'
    residential urban decentral water tanks: '#f2b2a3'
    services urban decentral water tanks: '#f1b4a4'
    urban central water tanks: '#e9977d'
    hot water storage: '#e69487'
    hot water charging: '#e8998b'
    urban central water tanks charger: '#b57a67'
    residential rural water tanks charger: '#b4887c'
    residential urban decentral water tanks charger: '#b39995'
    services rural water tanks charger: '#b3abb0'
    services urban decentral water tanks charger: '#b3becc'
    hot water discharging: '#e99c8e'
    urban central water tanks discharger: '#b9816e'
    residential rural water tanks discharger: '#ba9685'
    residential urban decentral water tanks discharger: '#baac9e'
    services rural water tanks discharger: '#bbc2b8'
    services urban decentral water tanks discharger: '#bdd8d3'
    # heat demand
    Heat load: '#cc1f1f'
    heat: '#cc1f1f'
    heat demand: '#cc1f1f'
    rural heat: '#ff5c5c'
    residential rural heat: '#ff7c7c'
    services rural heat: '#ff9c9c'
    central heat: '#cc1f1f'
    urban central heat: '#d15959'
    decentral heat: '#750606'
    residential urban decentral heat: '#a33c3c'
    services urban decentral heat: '#cc1f1f'
    low-temperature heat for industry: '#8f2727'
    process heat: '#ff0000'
    agriculture heat: '#d9a5a5'
    # heat supply
    heat pumps: '#2fb537'
    heat pump: '#2fb537'
    air heat pump: '#36eb41'
    residential urban decentral air heat pump: '#48f74f'
    services urban decentral air heat pump: '#5af95d'
    urban central air heat pump: '#6cfb6b'
    ground heat pump: '#2fb537'
    residential rural ground heat pump: '#48f74f'
    services rural ground heat pump: '#5af95d'
    Ambient: '#98eb9d'
    CHP: '#8a5751'
    urban central gas CHP: '#8d5e56'
    CHP CC: '#634643'
    urban central gas CHP CC: '#6e4e4c'
    CHP heat: '#8a5751'
    CHP electric: '#8a5751'
    district heating: '#e8beac'
    resistive heater: '#d8f9b8'
    residential rural resistive heater: '#bef5b5'
    residential urban decentral resistive heater: '#b2f1a9'
    services rural resistive heater: '#a5ed9d'
    services urban decentral resistive heater: '#98e991'
    urban central resistive heater: '#8cdf85'
    retrofitting: '#8487e8'
    building retrofitting: '#8487e8'
    # hydrogen
    H2 for industry: "#f073da"
    H2 for shipping: "#ebaee0"
    H2: '#bf13a0'
    hydrogen: '#bf13a0'
    SMR: '#870c71'
    SMR CC: '#4f1745'
    H2 liquefaction: '#d647bd'
    hydrogen storage: '#bf13a0'
    H2 Store: '#bf13a0'
    H2 storage: '#bf13a0'
    land transport fuel cell: '#6b3161'
    H2 pipeline: '#f081dc'
    H2 pipeline retrofitted: '#ba99b5'
    H2 Fuel Cell: '#c251ae'
    H2 fuel cell: '#c251ae'
    H2 turbine: '#991f83'
    H2 Electrolysis: '#ff29d9'
    H2 electrolysis: '#ff29d9'
    # ammonia
    NH3: '#46caf0'
    ammonia: '#46caf0'
    ammonia store: '#00ace0'
    ammonia cracker: '#87d0e6'
    Haber-Bosch: '#076987'
    # syngas
    Sabatier: '#9850ad'
    methanation: '#c44ce6'
    methane: '#c44ce6'
    helmeth: '#e899ff'
    # synfuels
    Fischer-Tropsch: '#25c49a'
    liquid: '#25c49a'
    kerosene for aviation: '#a1ffe6'
    naphtha for industry: '#57ebc4'
    methanolisation: '#83d6d5'
    methanol: '#468c8b'
    shipping methanol: '#468c8b'
    # co2
    CC: '#f29dae'
    CCS: '#f29dae'
    CO2 sequestration: '#f29dae'
    DAC: '#ff5270'
    co2 stored: '#f2385a'
    co2: '#f29dae'
    co2 vent: '#ffd4dc'
    CO2 pipeline: '#f5627f'
    # emissions
    process emissions CC: '#000000'
    process emissions: '#222222'
    process emissions to stored: '#444444'
    process emissions to atmosphere: '#888888'
    oil emissions: '#aaaaaa'
    shipping oil emissions: "#555555"
    shipping methanol emissions: '#666666'
    land transport oil emissions: '#777777'
    agriculture machinery oil emissions: '#333333'
    # other
    shipping: '#03a2ff'
    power-to-heat: '#2fb537'
    power-to-gas: '#c44ce6'
    power-to-H2: '#ff29d9'
    power-to-liquid: '#25c49a'
    gas-to-power/heat: '#ee8340'
    waste: '#e3d37d'
    other: '#000000'
    geothermal: '#ba91b1'
    AC: "#70af1d"
    AC-AC: "#70af1d"
    AC line: "#70af1d"
    links: "#8a1caf"
    HVDC links: "#8a1caf"
    DC: "#8a1caf"
    DC-DC: "#8a1caf"
    DC link: "#8a1caf"

Unit

Values

Description

map

– boundaries

°

[x1,x2,y1,y2]

Boundaries of the map plots in degrees latitude (y) and longitude (x)

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.