247-cfe/config_BackgroundSystem.yaml at main · PyPSA/247-cfe · GitHub

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# SPDX-FileCopyrightText: 2023-2024 Iegor Riepin, Tom Brown
#
# SPDX-License-Identifier: MIT

# Retrieve technology data <version> for <year>
# Source https://github.com/PyPSA/technology-data
retrieve_cost_data: true
technology_data:
  year: 2030
  version: v0.6.2

###################
# Scenario controls

run: BackgroundSystem

#wildcards
scenario:
  policy: ["ref"]  # "ref", "res100", "cfe90", "cfe95", "cfe98",
  palette: ["p4"]
  zone: ["DE" #37-node network of ENTSO-E area | possible toy zones: "IE", "DE", "IEDK", "DKDE"
]
  participation: ['100', '75', '50', '25'] # participation share of CI load per bidding zone that commit to voluntary CFE procurement [percentage]
  year: ["2030"] # controls both the brownfield fleet and tech costs projection

# CI consumer settings
ci:
  datacenters: {"DE1 0": "Germany"} # {"PT1 0": "Portugal"}
  profile_shape: "baseload" #"baseload", "datacenter", "industry"
  #load: 100. # nominal load in MW -> now scaled to match participation share
  res_techs: ["onwind", "solar"]
  excess_share: 0.  # share of excess CI generation that can be sold to the grid [percentage]
  flexibility: 0. # possible deviation of requested load from nominal level [percentage of nominal load]
  spatial_shifting: false #turns on/off spatial flexibiilty  (aka virtual links)
  temporal_shifting: false #turns on/off temporal flexibiilty (aka DSM)

# learning settings
### a workaround to keep same wildcards in all the scenarios
### if policy == "ref", CI node is not added, so we can use <participation>*10 as reduction steps of the learning coefficient
learning_coef: 1 # cost reduction coefficient for advanced tech in 2030
system_res_constraint: false #turns on/off constraint on RES expansion following NECP targets with *equality* sign


###################
# Time-series management

time_sampling: "3H" # one of ["1H", "3H"]
plot_timeseries: false


###################
# Fixed settings

node_suffixes_to_keep: ["H2", "battery"] #"low voltage","home battery"]
carrier_to_keep: ["offwind", "offwind-ac", "offwind-dc", "solar", "onwind", "ror", "PHS", "hydro", "AC", "DC", "nuclear", "uranium", "gas", "OCGT", "CCGT", "oil", "lignite", "coal", "battery charger", "battery discharger", "urban central solid biomass CHP", "co2", "co2 stored", "battery", "solid biomass", "electricity"]
additional_nodes: ["EU oil", "EU gas", "EU lignite", "EU coal", "EU uranium", "EU solid biomass", "co2 atmosphere", "co2 stored"]


costs:
  lifetime: 25 #default lifetime
  discountrate: 0.07  # From a Lion Hirth paper, also reflects average of Noothout et al 2016
  USD2013_to_EUR2013: 0.7532 # [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
  USD2021_to_EUR2021: 0.8460 # [EUR/USD] ECB
  USD2023_to_EUR2023: 0.9248 # [EUR/USD] ECB
  adv_nuclear_overnight: 7138 # 2021$/kW CAPEX https://www.eia.gov/outlooks/aeo/ 2022
  price_green_hydrogen: 3 #EUR/kg
  price_lignite: 6.5 # EUR/MWhth from TYNDP2020 -> default is 2.9
  price_coal: 8.9 # EUR/MWhth from TYNDP2020 -> default is 8.15
  price_gas: 35 # EUR/MWhth -> https://energy.ec.europa.eu/system/files/2022-05/SWD_2022_230_1_EN_autre_document_travail_service_part1_v3.pdf
  adv_geo_overnight_2025: 10000 # 2022$/kW CAPEX
  adv_geo_overnight_2030: 10000  # 2022$/kW CAPEX
  allam_ccs_overnight_2025: 2760 # 2022$/kW CAPEX, 2300*1.2
  allam_ccs_overnight_2030: 2600 # 2022$/kW CAPEX, 2300*1.2 (!)
  ironair_capacity_2025: 1700 # 2023$/kW CAPEX (+ 500 $/kW for energy part = 2200 $/kW)
  ironair_energy_2025: 5 # 2023$/kWh energy (5 $/kWh * 100h = 500 $/kW)
  ironair_capacity_2030: 1700 # dummy -> value is endogenised via learning curve
  ironair_energy_2030: 5 # dummy -> value is endogenised via learning curve
  ironair_duration: 100 # P/E ratio of iron-air battery