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config.default.yaml
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# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
version: 0.8.0
tutorial: false
logging:
level: INFO
format: '%(levelname)s:%(name)s:%(message)s'
run:
name: "run_name" # use this to keep track of runs with different settings
disable_progressbar: false # set to true to disable the progressbar
shared_resources: true # set to true to share the default resources across runs
shared_cutouts: true # set to true to share the default cutout(s) across runs
foresight: myopic # options are overnight, myopic, perfect (perfect is not yet implemented)
# if you use myopic or perfect foresight, set the investment years in "planning_horizons" below
scenario:
simpl:
- ''
ll: # allowed transmission line volume expansion, can be any float >= 1.0 with a prefix v|c (today) or "copt"
- v1.0
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 44
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0.45-3H-T-H-B-I-solar+p3-dist1
# to really understand the options here, look in scripts/prepare_sector_network.py
# Co2Lx specifies the CO2 target in x% of the 1990 values; default will give default (5%);
# Co2L0p25 will give 25% CO2 emissions; Co2Lm0p05 will give 5% negative emissions
# xH is the temporal resolution; 3H is 3-hourly, i.e. one snapshot every 3 hours
# single letters are sectors: T for land transport, H for building heating,
# B for biomass supply, I for industry, shipping and aviation,
# A for agriculture, forestry and fishing
# solar+c0.5 reduces the capital cost of solar to 50\% of reference value
# solar+p3 multiplies the available installable potential by factor 3
# seq400 sets the potential of CO2 sequestration to 400 Mt CO2 per year
# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
# emissions throughout the transition path in the timeframe determined by the
# planning_horizons), be:beta decay; ex:exponential decay
# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
# decay with initial growth rate 0
planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
- 2030
# for example, set to
# - 2020
# - 2030
# - 2040
# - 2050
# for myopic foresight
focus_weights:
'AL': 0.023
'AT': 0.023
'BA': 0.023
'BE': 0.023
'BG': 0.023
'CH': 0.023
'CZ': 0.023
'DE': 0.023
'EE': 0.023
'ES': 0.046
'FI': 0.023
'FR': 0.023
'GB': 0.046
'GR': 0.023
'HR': 0.023
'HU': 0.023
'IE': 0.023
'IT': 0.046
'LT': 0.023
'LU': 0.023
'LV': 0.023
'ME': 0.023
'MK': 0.023
'NL': 0.023
'NO': 0.023
'PL': 0.023
'PT': 0.023
'RO': 0.023
'RS': 0.023
'SE': 0.023
'SI': 0.023
'SK': 0.023
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']
snapshots:
start: "2013-01-01"
end: "2014-01-01"
inclusive: 'left' # include start, not end
enable:
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: true
save_duals: true ### OBS ###
land_transport_const:
ev_share_const: True
ev_const_country: 'DK'
land_transport_electric_share: 0.21
dk_transmission_expansion:
trans_exp_const: True
trans_exp_coeff: 1.0
# CO2 budget as a fraction of 1990 emissions
# this is over-ridden if CO2Lx is set in sector_opts
# this is also over-ridden if cb is set in sector_opts
co2_budget:
2020: 0.701
2025: 0.524
2030: 0.297
2035: 0.150
2040: 0.071
2045: 0.032
2050: 0.000
# set to true in case CO2 atmosphere is local (i.e. each node has a separated (local) CO2 atmosphere from remaining nodes) or false in case CO2 atmosphere is not local (i.e. all nodes share the same (global) CO2 atmosphere)
co2_local_atmosphere: true
# CO2 budget as a fraction of 1990 emissions per country
co2_budget_per_country:
'AL': 0.60
'AT': 0.82
'BA': 0.63
'BE': 0.68
'BG': 0.59
'CH': 0.50
'CZ': 0.52
'DE': 0.35
'DK': 0.21 #VAR
'EE': 0.30
'ES': 0.68
'FI': 0.40
'FR': 0.45
'GB': 0.32
'GR': 0.45
'HR': 0.55
'HU': 0.60
'IE': 0.49
'IT': 0.40
'LT': 0.60
'LU': 0.45
'LV': 0.62
'ME': 0.65
'MK': 0.18
'NL': 0.40
'NO': 0.45
'PL': 0.61
'PT': 0.70
'RO': 0.60
'RS': 0.67
'SE': 0.37
'SI': 0.60
'SK': 0.45
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]
atlite:
default_cutout: europe-2013-era5
nprocesses: 4
show_progress: false # false saves time
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]
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
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
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
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
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
conventional:
nuclear:
p_max_pu: "data/nuclear_p_max_pu.csv" # float of file name
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: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
links:
p_max_pu: 1.0
p_nom_max: .inf
include_tyndp: true
under_construction: 'keep' # 'zero': set capacity to zero, 'remove': remove, 'keep': with full capacity
transformers:
x: 0.1
s_nom: 2000.
type: ''
load:
power_statistics: true # only for files from <2019; set false in order to get ENTSOE transparency data
interpolate_limit: 3 # data gaps up until this size are interpolated linearly
time_shift_for_large_gaps: 1w # data gaps up until this size are copied by copying from
manual_adjustments: true # false
scaling_factor: 1.0
# regulate what components with which carriers are kept from PyPSA-Eur;
# some technologies are removed because they are implemented differently
# (e.g. battery or H2 storage) or have different year-dependent costs
# in PyPSA-Eur-Sec
pypsa_eur:
Bus:
- AC
Link:
- DC
Generator:
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
StorageUnit:
- PHS
- hydro
Store: []
energy:
energy_totals_year: 2011
base_emissions_year: 1990
eurostat_report_year: 2016
emissions: All greenhouse gases - (CO2 equivalent) # "CO2" or "All greenhouse gases - (CO2 equivalent)"
biomass:
year: 2030
scenario: ENS_High # VAR
classes:
solid biomass:
- Agricultural waste
- Fuelwood residues
- Secondary Forestry residues - woodchips
- Sawdust
- Residues from landscape care
- Municipal waste
- Willow # VAR
- Poplar # VAR
- FuelwoodRW # VAR
- C&P_RW # VAR
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 # VAR
# - Poplar # VAR
# - FuelwoodRW # VAR
# - C&P_RW # VAR
biogas:
- Manure solid, liquid
- Sludge
solar_thermal:
clearsky_model: simple # should be "simple" or "enhanced"?
orientation:
slope: 45.
azimuth: 180.
# only relevant for foresight = myopic or perfect
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
sector:
district_heating: # OBS Investigate
potential: 0.68 # maximum fraction of urban demand which can be supplied by district heating
# 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
progress:
# 2020: 0.0
2030: 1.0
# 2040: 0.6
# 2050: 1.0
district_heating_loss: 0.15
cluster_heat_buses: false # cluster residential and service heat buses to one to save memory
bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM
bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
transport_heating_deadband_upper: 20.
transport_heating_deadband_lower: 15.
ICE_lower_degree_factor: 0.375 #in per cent increase in fuel consumption per degree above deadband
ICE_upper_degree_factor: 1.6
EV_lower_degree_factor: 0.98
EV_upper_degree_factor: 0.63
bev_dsm: true #turns on EV battery
bev_availability: 0.5 #How many cars do smart charging
bev_energy: 0.05 #average battery size in MWh
bev_charge_efficiency: 0.9 #BEV (dis-)charging efficiency
bev_plug_to_wheel_efficiency: 0.2 #kWh/km from EPA https://www.fueleconomy.gov/feg/ for Tesla Model S
bev_charge_rate: 0.011 #3-phase charger with 11 kW
bev_avail_max: 0.95
bev_avail_mean: 0.8
v2g: false #allows feed-in to grid from EV battery
#what is not EV or FCEV is oil-fuelled ICE
land_transport_fuel_cell_share:
2020: 0
2030: 0
2040: 0.1
2050: 0.15
land_transport_electric_share:
2020: 0
2030: 0.11
2040: 0.6
2050: 0.85
land_transport_ice_share:
2020: 1
2030: 0.89
2040: 0.3
2050: 0
transport_fuel_cell_efficiency: 0.5
transport_internal_combustion_efficiency: 0.265 #OBS Changed efficiency from 0.3 to 0.265 (factor of 0.87) to reach KF emissions on ICE land transport
agriculture_machinery_electric_share: 0
agriculture_machinery_oil_share: 1
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
MWh_MeOH_per_MWh_H2: 0.8787 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
MWh_MeOH_per_tCO2: 4.0321 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
MWh_MeOH_per_MWh_e: 3.6907 # in LHV, source: DECHEMA (2017): Low carbon energy and feedstock for the European chemical industry , pg. 64.
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
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 # 10-15% higher https://www.iea-amf.org/app/webroot/files/file/Annex%20Reports/AMF_Annex_56.pdf, https://users.ugent.be/~lsileghe/documents/extended_abstract.pdf
shipping_oil_efficiency: 0.40 #For conversion of fuel oil to propulsion in 2011
aviation_demand_factor: 1. # relative aviation demand compared to today
HVC_demand_factor: 1. # relative HVC demand compared to today
time_dep_hp_cop: true #time dependent heat pump coefficient of performance
heat_pump_sink_T: 55. # Celsius, based on DTU / large area radiators; used in build_cop_profiles.py
# conservatively high to cover hot water and space heating in poorly-insulated buildings
reduce_space_heat_exogenously: true # reduces space heat demand by a given factor (applied before losses in DH)
# this can represent e.g. building renovation, building demolition, or if
# the factor is negative: increasing floor area, increased thermal comfort, population growth
reduce_space_heat_exogenously_factor: # per unit reduction in space heat demand
# the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
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: # co-optimises building renovation to reduce space heat demand
retro_endogen: false # co-optimise space heat savings
cost_factor: 1.0 # weight costs for building renovation
interest_rate: 0.04 # for investment in building components
annualise_cost: true # annualise the investment costs
tax_weighting: false # weight costs depending on taxes in countries
construction_index: true # weight costs depending on labour/material costs per country
tes: true
tes_tau: # 180 day time constant for centralised, 3 day for decentralised
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: true
coal_cc: false
dac: true
co2_vent: false
allam_cycle: false
SMR: true
regional_co2_sequestration_potential:
enable: false # enable regionally resolved geological co2 storage potential
attribute: 'conservative estimate Mt'
include_onshore: false # include onshore sequestration potentials
min_size: 3 # Gt, sites with lower potential will be excluded
max_size: 25 # Gt, max sequestration potential for any one site, TODO research suitable value
years_of_storage: 25 # years until potential exhausted at optimised annual rate
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
co2_spatial: false
co2network: false
cc_fraction: 0.9 # default fraction of CO2 captured with post-combustion capture
hydrogen_underground_storage: true
hydrogen_underground_storage_locations:
# - onshore # more than 50 km from sea
- nearshore # within 50 km of sea
# - offshore
ammonia: false # can be false (no NH3 carrier), true (copperplated NH3), "regional" (regionalised NH3 without network)
min_part_load_fischer_tropsch: 0.9 # p_min_pu
min_part_load_methanolisation: 0.5 # p_min_pu
use_fischer_tropsch_waste_heat: true
use_fuel_cell_waste_heat: true
use_electrolysis_waste_heat: true
electricity_distribution_grid: true
electricity_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
electricity_grid_connection: true # only applies to onshore wind and utility PV
H2_network: true
gas_network: false
H2_retrofit: false # if set to True existing gas pipes can be retrofitted to H2 pipes
# according to hydrogen backbone strategy (April, 2020) p.15
# https://gasforclimate2050.eu/wp-content/uploads/2020/07/2020_European-Hydrogen-Backbone_Report.pdf
# 60% of original natural gas capacity could be used in cost-optimal case as H2 capacity
H2_retrofit_capacity_per_CH4: 0.6 # ratio for H2 capacity per original CH4 capacity of retrofitted pipelines
gas_network_connectivity_upgrade: 1 # https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation.html#networkx.algorithms.connectivity.edge_augmentation.k_edge_augmentation
gas_distribution_grid: true
gas_distribution_grid_cost_factor: 1.0 #multiplies cost in data/costs.csv
biomass_spatial: true # regionally resolve biomass (e.g. potentials)
biomass_transport: false # allow transport of solid biomass between nodes
conventional_generation: # generator : carrier
OCGT: gas
biomass_to_liquid: true
biosng: false
industry:
St_primary_fraction: # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
2020: 0.6
2025: 0.55
2030: 0.5
2035: 0.45
2040: 0.4
2045: 0.35
2050: 0.3
DRI_fraction: # fraction of the primary route converted to DRI + EAF
2020: 0
2025: 0
2030: 0.05
2035: 0.2
2040: 0.4
2045: 0.7
2050: 1
H2_DRI: 1.7 #H2 consumption in Direct Reduced Iron (DRI), MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
elec_DRI: 0.322 #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
Al_primary_fraction: # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
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 # LHV
MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
MWh_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
MWh_NH3_per_MWh_H2_cracker: 1.46 # https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28
petrochemical_process_emissions: 25.5 # in MtCO2/a for petrochemical and other from UNFCCC for 2015 for EU28
HVC_primary_fraction: 1. # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0. # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0. # fraction of today's HVC produced via chemical recycling
HVC_production_today: 52. # MtHVC/a from DECHEMA (2017), Figure 16, page 107; includes ethylene, propylene and BTX
MWh_elec_per_tHVC_mechanical_recycling: 0.547 # from SI of https://doi.org/10.1016/j.resconrec.2020.105010, Table S5, for HDPE, PP, PS, PET. LDPE would be 0.756.
MWh_elec_per_tHVC_chemical_recycling: 6.9 # Material Economics (2019), page 125; based on pyrolysis and electric steam cracking
chlorine_production_today: 9.58 # MtCl/a from DECHEMA (2017), Table 7, page 43
MWh_elec_per_tCl: 3.6 # DECHEMA (2017), Table 6, page 43
MWh_H2_per_tCl: -0.9372 # DECHEMA (2017), page 43; negative since hydrogen produced in chloralkali process
methanol_production_today: 1.5 # MtMeOH/a from DECHEMA (2017), page 62
MWh_elec_per_tMeOH: 0.167 # DECHEMA (2017), Table 14, page 65
MWh_CH4_per_tMeOH: 10.25 # DECHEMA (2017), Table 14, page 65
hotmaps_locate_missing: false
reference_year: 2015
# references:
# DECHEMA (2017): https://dechema.de/dechema_media/Downloads/Positionspapiere/Technology_study_Low_carbon_energy_and_feedstock_for_the_European_chemical_industry-p-20002750.pdf
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
costs:
year: 2030
version: v0.5.0
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: # in currency per tonne emission, only used with the option Ep
co2: 0.
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: []
remove_stubs: true
remove_stubs_across_borders: true
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
solving:
#tmpdir: "path/to/tmp"
options:
formulation: kirchhoff
clip_p_max_pu: 1.e-2
load_shedding: false
noisy_costs: false
skip_iterations: true
track_iterations: false
min_iterations: 4
max_iterations: 6
seed: 123
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
seed: 10 # Consistent seed for all plattforms
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: 70000 #memory in MB; 20 GB enough for 50+B+I+H2; 100 GB for 181+B+I+H2
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.
vre_techs:
- onwind
- offwind-ac
- offwind-dc
- solar
- ror
renewable_storage_techs:
- PHS
- hydro
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
heat_links:
- heat pump
- resistive heater
- CHP heat
- CHP electric
- gas boiler
- central heat pump
- central resistive heater
- central CHP heat
- central CHP electric
- central gas boiler
heat_generators:
- gas boiler
- central gas boiler
- solar thermal collector
- central solar thermal collector
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"
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'
solar rooftop: '#ffea80'
# gas
OCGT: '#e0986c'
OCGT marginal: '#e0986c'
OCGT-heat: '#e0986c'
gas boiler: '#db6a25'
gas boilers: '#db6a25'
gas boiler marginal: '#db6a25'
gas: '#e05b09'
fossil gas: '#e05b09'
natural gas: '#e05b09'
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'
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'
biomass boiler: '#8A9A5B'
biomass to liquid: '#32CD32'
BioSNG: '#123456'
# power transmission
lines: '#6c9459'
transmission lines: '#6c9459'
electricity distribution grid: '#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'
home battery: '#80c944'
home battery storage: '#80c944'
BEV charger: '#baf238'
V2G: '#e5ffa8'
land transport EV: '#baf238'
Li ion: '#baf238'
# hot water storage
water tanks: '#e69487'
hot water storage: '#e69487'
hot water charging: '#e69487'
hot water discharging: '#e69487'
# heat demand
Heat load: '#cc1f1f'
heat: '#cc1f1f'
heat demand: '#cc1f1f'
rural heat: '#ff5c5c'
central heat: '#cc1f1f'
decentral heat: '#750606'
low-temperature heat for industry: '#8f2727'
process heat: '#ff0000'
agriculture heat: '#d9a5a5'
# heat supply
heat pumps: '#2fb537'
heat pump: '#2fb537'
air heat pump: '#36eb41'
ground heat pump: '#2fb537'
Ambient: '#98eb9d'
CHP: '#8a5751'
CHP CC: '#634643'
CHP heat: '#8a5751'
CHP electric: '#8a5751'
district heating: '#e8beac'
resistive heater: '#d8f9b8'
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 storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 pipeline retrofitted: '#ba99b5'
H2 Fuel Cell: '#c251ae'
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'