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cherry-pick from #282

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Co-authored-by: Fabian Hofmann <hofmann@fias.uni-frankfurt.de>
This commit is contained in:
Fabian Neumann 2023-02-16 18:42:19 +01:00
parent 8e74ee6c49
commit 35b8425b3b
8 changed files with 47 additions and 1059 deletions
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6
.github/workflows/ci.yaml vendored
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@ -102,7 +102,5 @@ jobs:
run: |
conda activate pypsa-eur
conda list
cp test/config.overnight.yaml config.yaml
snakemake -call
cp test/config.myopic.yaml config.yaml
snakemake -call
snakemake -call --configfile test/config.overnight.yaml
snakemake -call --configfile test/config.myopic.yaml

4
.gitignore vendored
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@ -27,6 +27,10 @@ gurobi.log
/data/retro/tabula-calculator-calcsetbuilding.csv
/data/nuts*
data/gas_network/scigrid-gas/
data/costs_*.csv
dask-worker-space
dask-worker-space/
publications.jrc.ec.europa.eu/

6
Snakefile
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@ -259,9 +259,9 @@ rule build_biomass_potentials:
enspreso_biomass=HTTP.remote("https://cidportal.jrc.ec.europa.eu/ftp/jrc-opendata/ENSPRESO/ENSPRESO_BIOMASS.xlsx", keep_local=True),
nuts2="data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson", # https://gisco-services.ec.europa.eu/distribution/v2/nuts/download/#nuts21
regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz",
swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv",
swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls",
nuts3_population=pypsaeur("data/bundle/nama_10r_3popgdp.tsv.gz"),
swiss_cantons=pypsaeur("data/bundle/ch_cantons.csv"),
swiss_population=pypsaeur("data/bundle/je-e-21.03.02.xls"),
country_shapes=pypsaeur('resources/country_shapes.geojson')
output:
biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',

3
scripts/add_brownfield.py
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@ -12,7 +12,6 @@ import numpy as np
from add_existing_baseyear import add_build_year_to_new_assets
from helper import override_component_attrs, update_config_with_sector_opts
from solve_network import basename
def add_brownfield(n, n_p, year):
@ -99,7 +98,7 @@ def add_brownfield(n, n_p, year):
pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom']
# already retrofitted capacity from gas -> H2
already_retrofitted = (n.links.loc[h2_retrofitted_fixed_i, 'p_nom']
.rename(lambda x: basename(x).replace(fr, to)).groupby(level=0).sum())
.rename(lambda x: x.split("-2")[0].replace(fr, to)).groupby(level=0).sum())
remaining_capacity = pipe_capacity - CH4_per_H2 * already_retrofitted.reindex(index=pipe_capacity.index).fillna(0)
n.links.loc[gas_pipes_i, "p_nom"] = remaining_capacity
else:

15
scripts/plot_network.py
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@ -62,6 +62,10 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
for comp in components:
df_c = getattr(n, comp)
if df_c.empty:
continue
df_c["nice_group"] = df_c.carrier.map(rename_techs_tyndp)
attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
@ -240,7 +244,7 @@ def group_pipes(df, drop_direction=False):
axis=1
)
# group pipe lines connecting the same buses and rename them for plotting
pipe_capacity = df["p_nom_opt"].groupby(level=0).sum()
pipe_capacity = df.groupby(level=0).agg({"p_nom_opt": sum, "bus0": "first", "bus1": "first"})
return pipe_capacity
@ -276,13 +280,16 @@ def plot_h2_map(network, regions):
# drop all links which are not H2 pipelines
n.links.drop(n.links.index[~n.links.carrier.str.contains("H2 pipeline")], inplace=True)
h2_new = n.links.loc[n.links.carrier=="H2 pipeline"]
h2_retro = n.links.loc[n.links.carrier=='H2 pipeline retrofitted']
h2_new = n.links[n.links.carrier=="H2 pipeline"]
h2_retro = n.links[n.links.carrier=='H2 pipeline retrofitted']
if snakemake.config['foresight'] == 'myopic':
# sum capacitiy for pipelines from different investment periods
h2_new = group_pipes(h2_new)
h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
if not h2_retro.empty:
h2_retro = group_pipes(h2_retro, drop_direction=True).reindex(h2_new.index).fillna(0)
if not h2_retro.empty:

41
scripts/prepare_sector_network.py
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@ -458,7 +458,9 @@ def remove_elec_base_techs(n):
for c in n.iterate_components(snakemake.config["pypsa_eur"]):
to_keep = snakemake.config["pypsa_eur"][c.name]
to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)
print("Removing", c.list_name, "with carrier", to_remove)
if to_remove.empty:
continue
logger.info(f"Removing {c.list_name} with carrier {list(to_remove)}")
names = c.df.index[c.df.carrier.isin(to_remove)]
n.mremove(c.name, names)
n.carriers.drop(to_remove, inplace=True, errors="ignore")
@ -469,8 +471,10 @@ def remove_non_electric_buses(n):
"""
remove buses from pypsa-eur with carriers which are not AC buses
"""
print("drop buses from PyPSA-Eur with carrier: ", n.buses[~n.buses.carrier.isin(["AC", "DC"])].carrier.unique())
n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]
to_drop = list(n.buses.query("carrier not in ['AC', 'DC']").carrier.unique())
if to_drop:
logger.info(f"Drop buses from PyPSA-Eur with carrier: {to_drop}")
n.buses = n.buses[n.buses.carrier.isin(["AC", "DC"])]
def patch_electricity_network(n):
@ -533,6 +537,8 @@ def add_co2_tracking(n, options):
bus=spatial.co2.nodes
)
n.add("Carrier", "co2 stored")
if options['co2_vent']:
n.madd("Link",
@ -821,12 +827,12 @@ def insert_electricity_distribution_grid(n, costs):
# TODO pop_layout?
# TODO options?
print("Inserting electricity distribution grid with investment cost factor of",
options['electricity_distribution_grid_cost_factor'])
cost_factor = options['electricity_distribution_grid_cost_factor']
logger.info(f"Inserting electricity distribution grid with investment cost factor of {cost_factor:.2f}")
nodes = pop_layout.index
cost_factor = options['electricity_distribution_grid_cost_factor']
n.madd("Bus",
nodes + " low voltage",
@ -941,7 +947,7 @@ def insert_gas_distribution_costs(n, costs):
f_costs = options['gas_distribution_grid_cost_factor']
print("Inserting gas distribution grid with investment cost factor of", f_costs)
logger.info(f"Inserting gas distribution grid with investment cost factor of {f_costs}")
capital_cost = costs.loc['electricity distribution grid']["fixed"] * f_costs
@ -1317,7 +1323,7 @@ def add_land_transport(n, costs):
total_share = fuel_cell_share + electric_share + ice_share
if total_share != 1:
logger.warning(f"Total land transport shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
logger.warning(f"Total land transport shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions.")
logger.info(f"FCEV share: {fuel_cell_share*100}%")
logger.info(f"EV share: {electric_share*100}%")
@ -1487,7 +1493,7 @@ def add_heat(n, costs):
# exogenously reduce space heat demand
if options["reduce_space_heat_exogenously"]:
dE = get(options["reduce_space_heat_exogenously_factor"], investment_year)
print(f"assumed space heat reduction of {dE*100} %")
logger.info(f"assumed space heat reduction of {dE:.2%}")
for sector in sectors:
heat_demand[sector + " space"] = (1 - dE) * heat_demand[sector + " space"]
@ -1847,10 +1853,9 @@ def create_nodes_for_heat_sector():
diff = (urban_fraction * central_fraction) - dist_fraction_node
progress = get(options["district_heating"]["progress"], investment_year)
dist_fraction_node += diff * progress
print(
"The current district heating share compared to the maximum",
f"possible is increased by a progress factor of\n{progress}",
f"resulting in a district heating share of\n{dist_fraction_node}"
logger.info(
f"Increase district heating share by a progress factor of {progress:.2%} "
f"resulting in new average share of {dist_fraction_node.mean():.2%}"
)
return nodes, dist_fraction_node, urban_fraction
@ -2188,7 +2193,7 @@ def add_industry(n, costs):
total_share = shipping_hydrogen_share + shipping_methanol_share + shipping_oil_share
if total_share != 1:
logger.warning(f"Total shipping shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
logger.warning(f"Total shipping shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions.")
domestic_navigation = pop_weighted_energy_totals.loc[nodes, "total domestic navigation"].squeeze()
international_navigation = pd.read_csv(snakemake.input.shipping_demand, index_col=0).squeeze()
@ -2557,7 +2562,7 @@ def add_agriculture(n, costs):
total_share = electric_share + oil_share
if total_share != 1:
logger.warning(f"Total agriculture machinery shares sum up to {total_share*100}%, corresponding to increased or decreased demand assumptions.")
logger.warning(f"Total agriculture machinery shares sum up to {total_share:.2%}, corresponding to increased or decreased demand assumptions.")
machinery_nodal_energy = pop_weighted_energy_totals.loc[nodes, "total agriculture machinery"]
@ -2635,7 +2640,7 @@ def maybe_adjust_costs_and_potentials(n, opts):
else:
sel = c.df.carrier.str.contains(carrier)
c.df.loc[sel,attr] *= factor
print("changing", attr , "for", carrier, "by factor", factor)
logger.info(f"changing {attr} for {carrier} by factor {factor}")
# TODO this should rather be a config no wildcard
@ -2880,7 +2885,7 @@ if __name__ == "__main__":
for o in opts:
if o[:4] == "wave":
wave_cost_factor = float(o[4:].replace("p", ".").replace("m", "-"))
print("Including wave generators with cost factor of", wave_cost_factor)
logger.info(f"Including wave generators with cost factor of {wave_cost_factor}")
add_wave(n, wave_cost_factor)
if o[:4] == "dist":
options['electricity_distribution_grid'] = True
@ -2949,7 +2954,7 @@ if __name__ == "__main__":
limit = o[o.find("Co2L")+4:]
limit = float(limit.replace("p", ".").replace("m", "-"))
break
print("Add CO2 limit from", limit_type)
logger.info(f"Add CO2 limit from {limit_type}")
add_co2limit(n, Nyears, limit)
for o in opts:

468
test/config.myopic.yaml
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@ -1,62 +1,18 @@
version: 0.6.0
logging_level: INFO
retrieve_sector_databundle: true
retrieve_cost_data: true
results_dir: results/
summary_dir: results
run: test-myopic # use this to keep track of runs with different settings
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: # only relevant for PyPSA-Eur
- ''
lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
- 1.5
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 5
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- 191H-T-H-B-I-A-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
# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
# 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; or costs year for overnight
- 2030
- 2040
- 2050
# for example, set to [2020, 2030, 2040, 2050] for myopic foresight
# 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: 0.7011648746
2025: 0.5241935484
2030: 0.2970430108
2035: 0.1500896057
2040: 0.0712365591
2045: 0.0322580645
2050: 0
# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
snapshots:
# arguments to pd.date_range
start: "2013-03-01"
@ -66,173 +22,10 @@ snapshots:
atlite:
cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
# this information is NOT used but needed as an argument for
# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
electricity:
max_hours:
battery: 6
H2: 168
# 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: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
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
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: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
threshold_capacity: 10
conventional_carriers:
- lignite
- coal
- oil
- uranium
sector:
district_heating:
potential: 0.6 # 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: 0.3
2040: 0.6
2050: 1.0
district_heating_loss: 0.15
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: true #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: 0.05
2040: 0.1
2050: 0.15
land_transport_electric_share:
2020: 0
2030: 0.25
2040: 0.6
2050: 0.85
transport_fuel_cell_efficiency: 0.5
transport_internal_combustion_efficiency: 0.3
agriculture_machinery_electric_share: 0
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
shipping_hydrogen_share:
2020: 0
2025: 0
2030: 0.05
2035: 0.15
2040: 0.3
2045: 0.6
2050: 1
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: # 0.29 # 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
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
dac: true
co2_vent: true
SMR: true
regional_co2_sequestration_potential:
@ -348,267 +141,8 @@ costs:
solving:
#tmpdir: "path/to/tmp"
options:
formulation: kirchhoff
clip_p_max_pu: 1.e-2
load_shedding: false
noisy_costs: true
skip_iterations: true
track_iterations: false
min_iterations: 4
max_iterations: 6
keep_shadowprices:
- Bus
- Line
- Link
- Transformer
- GlobalConstraint
- Generator
- Store
- StorageUnit
solver:
name: cbc
# threads: 4
# method: 2 # barrier
# crossover: 0
# BarConvTol: 1.e-6
# Seed: 123
# AggFill: 0
# PreDual: 0
# GURO_PAR_BARDENSETHRESH: 200
#FeasibilityTol: 1.e-6
#name: cplex
#threads: 4
#lpmethod: 4 # barrier
#solutiontype: 2 # non basic solution, ie no crossover
#barrier_convergetol: 1.e-5
#feasopt_tolerance: 1.e-6
options: cbc-default
mem: 4000 #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: whitesmoke
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
tech_colors:
# wind
onwind: "#235ebc"
onshore wind: "#235ebc"
offwind: "#6895dd"
offshore wind: "#6895dd"
offwind-ac: "#6895dd"
offshore wind (AC): "#6895dd"
offwind-dc: "#74c6f2"
offshore wind (DC): "#74c6f2"
# water
hydro: '#298c81'
hydro reservoir: '#298c81'
ror: '#3dbfb0'
run of river: '#3dbfb0'
hydroelectricity: '#298c81'
PHS: '#51dbcc'
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'
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'
# 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'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
methane: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
liquid: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# 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"
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'

563
test/config.overnight.yaml
View File

@ -1,60 +1,17 @@
version: 0.6.0
logging_level: INFO
retrieve_sector_databundle: true
retrieve_cost_data: true
results_dir: results/
summary_dir: results
run: test-overnight # use this to keep track of runs with different settings
foresight: overnight # 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: # only relevant for PyPSA-Eur
- ''
lv: # allowed transmission line volume expansion, can be any float >= 1.0 (today) or "opt"
- 1.5
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 5
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- CO2L0-191H-T-H-B-I-A-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
# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2
# 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; or costs year for overnight
- 2030
# for example, set to [2020, 2030, 2040, 2050] for myopic foresight
# 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: 0.7011648746
2025: 0.5241935484
2030: 0.2970430108
2035: 0.1500896057
2040: 0.0712365591
2045: 0.0322580645
2050: 0
# snapshots are originally set in PyPSA-Eur/config.yaml but used again by PyPSA-Eur-Sec
snapshots:
# arguments to pd.date_range
start: "2013-03-01"
@ -64,173 +21,7 @@ snapshots:
atlite:
cutout: ../pypsa-eur/cutouts/be-03-2013-era5.nc
# this information is NOT used but needed as an argument for
# pypsa-eur/scripts/add_electricity.py/load_costs in make_summary.py
electricity:
max_hours:
battery: 6
H2: 168
# 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: CO2 # "CO2" or "All greenhouse gases - (CO2 equivalent)"
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
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: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
threshold_capacity: 10
conventional_carriers:
- lignite
- coal
- oil
- uranium
sector:
district_heating:
potential: 0.6 # 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: 1
# 2020: 0.0
# 2030: 0.3
# 2040: 0.6
# 2050: 1.0
district_heating_loss: 0.15
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: true #allows feed-in to grid from EV battery
#what is not EV or FCEV is oil-fuelled ICE
land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs
# 2020: 0
# 2030: 0.05
# 2040: 0.1
# 2050: 0.15
land_transport_electric_share: 0.85 # 1 means all EVs
# 2020: 0
# 2030: 0.25
# 2040: 0.6
# 2050: 0.85
transport_fuel_cell_efficiency: 0.5
transport_internal_combustion_efficiency: 0.3
agriculture_machinery_electric_share: 0
agriculture_machinery_fuel_efficiency: 0.7 # fuel oil per use
agriculture_machinery_electric_efficiency: 0.3 # electricity per use
shipping_average_efficiency: 0.4 #For conversion of fuel oil to propulsion in 2011
shipping_hydrogen_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
shipping_hydrogen_share: 1 # 1 means all hydrogen FC
# 2020: 0
# 2025: 0
# 2030: 0.05
# 2035: 0.15
# 2040: 0.3
# 2045: 0.6
# 2050: 1
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: 0.29 # 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
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
dac: true
co2_vent: true
SMR: true
regional_co2_sequestration_potential:
@ -251,362 +42,12 @@ sector:
electricity_grid_connection: true # only applies to onshore wind and utility PV
H2_network: true
gas_network: true
H2_retrofit: true # 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_transport: false # biomass transport between nodes
conventional_generation: # generator : carrier
OCGT: gas
H2_retrofit: true # if set to True existing gas pipes can be retrofitted to H2 pipes
biomass_boiler: false
biomass_to_liquid: false
biosng: false
industry:
St_primary_fraction: 0.3 # 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: 1 # 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: 0.2 # 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_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)
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
lifetime: 25 #default lifetime
# From a Lion Hirth paper, also reflects average of Noothout et al 2016
discountrate: 0.07
# [EUR/USD] ECB: https://www.ecb.europa.eu/stats/exchange/eurofxref/html/eurofxref-graph-usd.en.html # noqa: E501
USD2013_to_EUR2013: 0.7532
# 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.
battery: 0.
emission_prices: # only used with the option Ep (emission prices)
co2: 0.
lines:
length_factor: 1.25 #to estimate offwind connection costs
solving:
#tmpdir: "path/to/tmp"
options:
formulation: kirchhoff
clip_p_max_pu: 1.e-2
load_shedding: false
noisy_costs: true
skip_iterations: true
track_iterations: false
min_iterations: 4
max_iterations: 6
keep_shadowprices:
- Bus
- Line
- Link
- Transformer
- GlobalConstraint
- Generator
- Store
- StorageUnit
solver:
name: cbc
# threads: 4
# method: 2 # barrier
# crossover: 0
# BarConvTol: 1.e-6
# Seed: 123
# AggFill: 0
# PreDual: 0
# GURO_PAR_BARDENSETHRESH: 200
#FeasibilityTol: 1.e-6
#name: cplex
#threads: 4
#lpmethod: 4 # barrier
#solutiontype: 2 # non basic solution, ie no crossover
#barrier_convergetol: 1.e-5
#feasopt_tolerance: 1.e-6
options: cbc-default
mem: 4000 #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: whitesmoke
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
tech_colors:
# wind
onwind: "#235ebc"
onshore wind: "#235ebc"
offwind: "#6895dd"
offshore wind: "#6895dd"
offwind-ac: "#6895dd"
offshore wind (AC): "#6895dd"
offwind-dc: "#74c6f2"
offshore wind (DC): "#74c6f2"
# water
hydro: '#298c81'
hydro reservoir: '#298c81'
ror: '#3dbfb0'
run of river: '#3dbfb0'
hydroelectricity: '#298c81'
PHS: '#51dbcc'
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'
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'
# 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'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
methane: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
liquid: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# 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"
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'