bjoern/pypsa-eur
1
0
Fork 0
Code Issues Pull Requests Actions 1 Packages Projects Releases Wiki Activity

merge master

Browse Source
This commit is contained in:
Fabian Neumann 2022-07-01 10:13:33 +02:00
commit 3986856282
38 changed files with 8088 additions and 433 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

109
.github/workflows/ci.yaml vendored Normal file
View File

@ -0,0 +1,109 @@
# SPDX-FileCopyrightText: : 2021 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: CC0-1.0
name: CI
# Caching method based on and described by:
# epassaro (2021): https://dev.to/epassaro/caching-anaconda-environments-in-github-actions-5hde
# and code in GitHub repo: https://github.com/epassaro/cache-conda-envs
on:
push:
branches:
- master
pull_request:
branches:
- master
schedule:
- cron: "0 5 * * TUE"
env:
CONDA_CACHE_NUMBER: 1 # Change this value to manually reset the environment cache
DATA_CACHE_NUMBER: 1
jobs:
build:
strategy:
matrix:
include:
# Matrix required to handle caching with Mambaforge
- os: ubuntu-latest
label: ubuntu-latest
prefix: /usr/share/miniconda3/envs/pypsa-eur
# - os: macos-latest
# label: macos-latest
# prefix: /Users/runner/miniconda3/envs/pypsa-eur
# - os: windows-latest
# label: windows-latest
# prefix: C:\Miniconda3\envs\pypsa-eur
name: ${{ matrix.label }}
runs-on: ${{ matrix.os }}
defaults:
run:
shell: bash -l {0}
steps:
- uses: actions/checkout@v2
- name: Clone pypsa-eur and technology-data repositories
run: |
git clone https://github.com/pypsa/pypsa-eur ../pypsa-eur
git clone https://github.com/pypsa/technology-data ../technology-data
cp ../pypsa-eur/test/config.test1.yaml ../pypsa-eur/config.yaml
- name: Setup secrets
run: |
echo -ne "url: ${CDSAPI_URL}\nkey: ${CDSAPI_TOKEN}\n" > ~/.cdsapirc
- name: Add solver to environment
run: |
echo -e " - coincbc\n - ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml
- name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2
with:
miniforge-variant: Mambaforge
miniforge-version: latest
activate-environment: pypsa-eur
use-mamba: true
- name: Set cache dates
run: |
echo "DATE=$(date +'%Y%m%d')" >> $GITHUB_ENV
echo "WEEK=$(date +'%Y%U')" >> $GITHUB_ENV
- name: Cache data and cutouts folders
uses: actions/cache@v3
with:
path: |
data
../pypsa-eur/cutouts
../pypsa-eur/data
key: data-cutouts-${{ env.WEEK }}-${{ env.DATA_CACHE_NUMBER }}
- name: Create environment cache
uses: actions/cache@v2
id: cache
with:
path: ${{ matrix.prefix }}
key: ${{ matrix.label }}-conda-${{ env.DATE }}-${{ env.CONDA_CACHE_NUMBER }}
- name: Update environment due to outdated or unavailable cache
run: mamba env update -n pypsa-eur -f ../pypsa-eur/envs/environment.yaml
if: steps.cache.outputs.cache-hit != 'true'
- name: Test snakemake workflow
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

27
README.md
View File

@ -7,19 +7,8 @@
# PyPSA-Eur-Sec: A Sector-Coupled Open Optimisation Model of the European Energy System
**WARNING**: This model is under construction and contains serious problems that
distort the results. See the github repository
[issues](https://github.com/PyPSA/pypsa-eur-sec/issues) for some of the problems
(please feel free to help or make suggestions). There is neither a full
documentation nor a paper yet, but we hope to have a preprint out by the end of 2021.
You can find out more about the model capabilities in [a recent
presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf) or the
following [preprint with a description of the industry
sector](https://arxiv.org/abs/2109.09563). We cannot support this model if you
choose to use it.
PyPSA-Eur-Sec is an open model dataset of the European energy system at the
transmission network level that covers the full ENTSO-E area.
PyPSA-Eur-Sec builds on the electricity generation and transmission
model [PyPSA-Eur](https://github.com/PyPSA/pypsa-eur) to add demand
@ -28,6 +17,18 @@ heating, biomass, industry and industrial feedstocks, agriculture,
forestry and fishing. This completes the energy system and includes
all greenhouse gas emitters except waste management and land use.
**WARNING**: PyPSA-Eur-Sec is under active development and has several
[limitations](https://pypsa-eur-sec.readthedocs.io/en/latest/limitations.html) which
you should understand before using the model. The github repository
[issues](https://github.com/PyPSA/pypsa-eur-sec/issues) collects known
topics we are working on (please feel free to help or make suggestions). There is neither a full
documentation nor a paper yet, but we hope to have a preprint out by mid-2022.
You can find out more about the model capabilities in [a recent
presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf) or the
following [paper in Joule with a description of the industry
sector](https://arxiv.org/abs/2109.09563). We cannot support this model if you
choose to use it.
Please see the [documentation](https://pypsa-eur-sec.readthedocs.io/)
for installation instructions and other useful information about the snakemake workflow.

62
Snakefile
View File

@ -45,18 +45,22 @@ rule prepare_sector_networks:
**config['scenario'])
datafiles = [
"eea/UNFCCC_v23.csv",
"switzerland-sfoe/switzerland-new_format.csv",
"nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
"myb1-2017-nitro.xls",
"Industrial_Database.csv",
"emobility/KFZ__count",
"emobility/Pkw__count",
"data/eea/UNFCCC_v23.csv",
"data/switzerland-sfoe/switzerland-new_format.csv",
"data/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
"data/myb1-2017-nitro.xls",
"data/Industrial_Database.csv",
"data/emobility/KFZ__count",
"data/emobility/Pkw__count",
"data/h2_salt_caverns_GWh_per_sqkm.geojson",
directory("data/eurostat-energy_balances-june_2016_edition"),
directory("data/eurostat-energy_balances-may_2018_edition"),
directory("data/jrc-idees-2015"),
]
if config.get('retrieve_sector_databundle', True):
rule retrieve_sector_databundle:
output: expand('data/{file}', file=datafiles)
output: *datafiles
log: "logs/retrieve_sector_databundle.log"
script: 'scripts/retrieve_sector_databundle.py'
@ -252,9 +256,9 @@ 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',
@ -427,15 +431,45 @@ else:
build_retro_cost_output = {}
rule build_population_weighted_energy_totals:
input:
energy_totals='resources/energy_totals.csv',
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv"
output: "resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=2000
script: "scripts/build_population_weighted_energy_totals.py"
rule build_transport_demand:
input:
clustered_pop_layout="resources/pop_layout_elec_s{simpl}_{clusters}.csv",
pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
transport_data='resources/transport_data.csv',
traffic_data_KFZ="data/emobility/KFZ__count",
traffic_data_Pkw="data/emobility/Pkw__count",
temp_air_total="resources/temp_air_total_elec_s{simpl}_{clusters}.nc",
output:
transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv"
threads: 1
resources: mem_mb=2000
script: "scripts/build_transport_demand.py"
rule prepare_sector_network:
input:
overrides="data/override_component_attrs",
network=pypsaeur('networks/elec_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
energy_totals_name='resources/energy_totals.csv',
pop_weighted_energy_totals="resources/pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
transport_demand="resources/transport_demand_s{simpl}_{clusters}.csv",
transport_data="resources/transport_data_s{simpl}_{clusters}.csv",
avail_profile="resources/avail_profile_s{simpl}_{clusters}.csv",
dsm_profile="resources/dsm_profile_s{simpl}_{clusters}.csv",
co2_totals_name='resources/co2_totals.csv',
transport_name='resources/transport_data.csv',
traffic_data_KFZ="data/emobility/KFZ__count",
traffic_data_Pkw="data/emobility/Pkw__count",
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
heat_profile="data/heat_load_profile_BDEW.csv",
costs=CDIR + "costs_{planning_horizons}.csv",

595
config.co2seq.yaml Normal file
View File

@ -0,0 +1,595 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-co2seq # 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
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq50
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq100
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq200
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq400
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq600
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq800
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5-seq1000
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

594
config.cost.yaml Normal file
View File

@ -0,0 +1,594 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-cost # 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
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-solar-c0.75
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-wind-c0.75
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-Electrolysis-c0.75
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-SMR-c0.75
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-battery-c0.75
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5-DAC-c0.75
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

592
config.decentral.yaml Normal file
View File

@ -0,0 +1,592 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-decentral # 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"
- opt
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral-noH2network
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral-onwind+p0
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-decentral-noH2network-onwind+p0
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

6
config.default.yaml
View File

@ -134,7 +134,7 @@ solar_thermal:
# only relevant for foresight = myopic or perfect
existing_capacities:
grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019]
grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
threshold_capacity: 10
conventional_carriers:
- lignite
@ -232,6 +232,7 @@ sector:
marginal_cost_storage: 0. #1e-4
methanation: true
helmeth: true
coal_cc: false
dac: true
co2_vent: true
SMR: true
@ -388,6 +389,9 @@ plotting:
color_geomap:
ocean: white
land: white
eu_node_location:
x: -5.5
y: 46.
costs_max: 1000
costs_threshold: 1
energy_max: 20000

590
config.gas.yaml Normal file
View File

@ -0,0 +1,590 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-gas # 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"
- 1.0
- 1.5
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 170000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

596
config.h2.yaml Normal file
View File

@ -0,0 +1,596 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-h2 # 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"
- 1.0
#- 1.25
- opt
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-noH2network
#- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-noH2network-onwind+p0.25
#- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.25
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-noH2network-onwind+p0
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

626
config.import.yaml Normal file
View File

@ -0,0 +1,626 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-import # 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
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-import
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
# 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: [2030] # investment years for myopic and perfect; or costs year for overnight
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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: 3 # 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
import:
capacity_boost: 2
options:
- pipeline-h2
- shipping-lh2
- shipping-lch4
- shipping-ftfuel
- hvdc
# limit: 100 # TWh
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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
import:
capacity_boost: 2
options:
- pipeline-h2
- shipping-lh2
- shipping-lch4
- shipping-ftfuel
- hvdc
# limit: 100 # TWh
costs:
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: gurobi
threads: 8
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 160000 #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
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'
import pipeline-h2: '#fff6e0'
import shipping-lh2: '#ebe1ca'
import shipping-lch4: '#d6cbb2'
import shipping-ftfuel: '#bdb093'
import hvdc: '#91856a'

594
config.lv.yaml Normal file
View File

@ -0,0 +1,594 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-lv # 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"
- 1.0
- 1.125
- 1.25
- 1.5
- 1.75
- 2.0
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

594
config.onw.yaml Normal file
View File

@ -0,0 +1,594 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-onw # 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"
- 1.25
clusters: # number of nodes in Europe, any integer between 37 (1 node per country-zone) and several hundred
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.125
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.25
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.5
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p0.75
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind+p1
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

593
config.spatial.yaml Normal file
View File

@ -0,0 +1,593 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-spatial # 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
- 37
- 50
- 100
- 150
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

590
config.temporal.yaml Normal file
View File

@ -0,0 +1,590 @@
version: 0.6.0
logging_level: INFO
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
run: 20211218-181-temporal # 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
- 181
opts: # only relevant for PyPSA-Eur
- ''
sector_opts: # this is where the main scenario settings are
- Co2L0-3H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5
- Co2L0-4H-T-H-B-I-A-solar+p3-linemaxext10-onwind-p0.5
# 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: "2013-01-01"
end: "2014-01-01"
closed: left # end is not inclusive
atlite:
cutout: ../pypsa-eur/cutouts/europe-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: true # 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: false
dac: true
co2_vent: false
SMR: true
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 20 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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: 0.45 # fraction of today's HVC produced via primary route
HVC_mechanical_recycling_fraction: 0.30 # fraction of today's HVC produced via mechanical recycling
HVC_chemical_recycling_fraction: 0.15 # 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:
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: gurobi
threads: 4
method: 2 # barrier
crossover: 0
BarConvTol: 1.e-4
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
mem: 126000 #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
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'
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'
# 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'
Lignite: '#826837'
lignite: '#826837'
Lignite marginal: '#826837'
# biomass
biogas: '#e3d37d'
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: '#c78536'
retrofitting: '#8487e8'
building retrofitting: '#8487e8'
# hydrogen
H2 for industry: "#f073da"
H2 for shipping: "#ebaee0"
H2: '#bf13a0'
SMR: '#870c71'
SMR CC: '#4f1745'
H2 liquefaction: '#d647bd'
hydrogen storage: '#bf13a0'
land transport fuel cell: '#6b3161'
H2 pipeline: '#f081dc'
H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9'
# syngas
Sabatier: '#9850ad'
methanation: '#c44ce6'
helmeth: '#e899ff'
# synfuels
Fischer-Tropsch: '#25c49a'
kerosene for aviation: '#a1ffe6'
naphtha for industry: '#57ebc4'
# co2
CC: '#9e132c'
CO2 sequestration: '#9e132c'
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: '#cc1f1f'
power-to-gas: '#c44ce6'
power-to-liquid: '#25c49a'
gas-to-power/heat: '#ee8340'

5
data/override_component_attrs/generators.csv
View File

@ -1,3 +1,4 @@
attribute,type,unit,default,description,status
build_year,integer,year,n/a,build year,Input (optional)
lifetime,float,years,n/a,lifetime,Input (optional)
carrier,string,n/a,n/a,carrier,Input (optional)
lifetime,float,years,inf,lifetime,Input (optional)
build_year,int,year ,0,build year,Input (optional)

1 attribute type unit default description status
2 build_year carrier integer string year n/a n/a build year carrier Input (optional)
3 lifetime float years n/a inf lifetime Input (optional) Input (optional)
4 build_year int year 0 build year Input (optional)

16
data/override_component_attrs/links.csv
View File

@ -2,12 +2,12 @@ attribute,type,unit,default,description,status
bus2,string,n/a,n/a,2nd bus,Input (optional)
bus3,string,n/a,n/a,3rd bus,Input (optional)
bus4,string,n/a,n/a,4th bus,Input (optional)
efficiency2,static or series,per unit,1.,2nd bus efficiency,Input (optional)
efficiency3,static or series,per unit,1.,3rd bus efficiency,Input (optional)
efficiency4,static or series,per unit,1.,4th bus efficiency,Input (optional)
p2,series,MW,0.,2nd bus output,Output
p3,series,MW,0.,3rd bus output,Output
p4,series,MW,0.,4th bus output,Output
build_year,integer,year,n/a,build year,Input (optional)
lifetime,float,years,n/a,lifetime,Input (optional)
efficiency2,static or series,per unit,1,2nd bus efficiency,Input (optional)
efficiency3,static or series,per unit,1,3rd bus efficiency,Input (optional)
efficiency4,static or series,per unit,1,4th bus efficiency,Input (optional)
p2,series,MW,0,2nd bus output,Output
p3,series,MW,0,3rd bus output,Output
p4,series,MW,0,4th bus output,Output
carrier,string,n/a,n/a,carrier,Input (optional)
lifetime,float,years,inf,lifetime,Input (optional)
build_year,int,year ,0,build year,Input (optional)

1 attribute type unit default description status
2 bus2 string n/a n/a 2nd bus Input (optional)
3 bus3 string n/a n/a 3rd bus Input (optional)
4 bus4 string n/a n/a 4th bus Input (optional)
5 efficiency2 static or series per unit 1. 1 2nd bus efficiency Input (optional)
6 efficiency3 static or series per unit 1. 1 3rd bus efficiency Input (optional)
7 efficiency4 static or series per unit 1. 1 4th bus efficiency Input (optional)
8 p2 series MW 0. 0 2nd bus output Output
9 p3 series MW 0. 0 3rd bus output Output
10 p4 series MW 0. 0 4th bus output Output
build_year integer year n/a build year Input (optional)
lifetime float years n/a lifetime Input (optional)
11 carrier string n/a n/a carrier Input (optional)
12 lifetime float years inf lifetime Input (optional)
13 build_year int year 0 build year Input (optional)

4
data/override_component_attrs/stores.csv
View File

@ -1,4 +1,4 @@
attribute,type,unit,default,description,status
build_year,integer,year,n/a,build year,Input (optional)
lifetime,float,years,n/a,lifetime,Input (optional)
carrier,string,n/a,n/a,carrier,Input (optional)
lifetime,float,years,inf,lifetime,Input (optional)
build_year,int,year ,0,build year,Input (optional)

1 attribute type unit default description status
build_year integer year n/a build year Input (optional)
lifetime float years n/a lifetime Input (optional)
2 carrier string n/a n/a carrier Input (optional)
3 lifetime float years inf lifetime Input (optional)
4 build_year int year 0 build year Input (optional)

27
doc/index.rst
View File

@ -29,10 +29,21 @@ heating, biomass, industry and industrial feedstocks. This completes
the energy system and includes all greenhouse gas emitters except
waste management, agriculture, forestry and land use.
**WARNING**: PyPSA-Eur-Sec is under active development and has several
`limitations <https://pypsa-eur-sec.readthedocs.io/en/latest/limitations.html>`_ which
you should understand before using the model. The github repository
`issues <https://github.com/PyPSA/pypsa-eur-sec/issues>`_ collects known
topics we are working on (please feel free to help or make suggestions). There is neither a full
documentation nor a paper yet, but we hope to have a preprint out by mid-2022.
We cannot support this model if you
choose to use it.
.. note::
More about the current model capabilities and preliminary results
can be found in `a recent presentation at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_
and the the following `preprint with a description of the industry sector <https://arxiv.org/abs/2109.09563>`_.
and the following `paper in Joule with a description of the industry sector <https://arxiv.org/abs/2109.09563>`_.
This diagram gives an overview of the sectors and the links between
them:
@ -131,6 +142,7 @@ Documentation
**References**
* :doc:`release_notes`
* :doc:`limitations`
.. toctree::
:hidden:
@ -138,18 +150,7 @@ Documentation
:caption: References
release_notes
Warnings
========
**WARNING**: This model is under construction and contains serious
problems that distort the results. See the github repository
`issues <https://github.com/PyPSA/pypsa-eur-sec/issues>`_ for some of
the problems (please feel free to help or make suggestions). There is
neither documentation nor a paper yet, but we hope to have a preprint
out by summer 2020. We cannot support this model if you choose to use
it.
limitations
Licence

4
doc/installation.rst
View File

@ -68,14 +68,14 @@ Data requirements
=================
Small data files are included directly in the git repository, while
larger ones are archived in a data bundle on zenodo (`10.5281/zenodo.5546517 <https://doi.org/10.5281/zenodo.5546517>`_).
larger ones are archived in a data bundle on zenodo (`10.5281/zenodo.5824485 <https://doi.org/10.5281/zenodo.5824485>`_).
The data bundle's size is around 640 MB.
To download and extract the data bundle on the command line:
.. code:: bash
projects/pypsa-eur-sec/data % wget "https://zenodo.org/record/5546517/files/pypsa-eur-sec-data-bundle.tar.gz"
projects/pypsa-eur-sec/data % wget "https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz"
projects/pypsa-eur-sec/data % tar -xvzf pypsa-eur-sec-data-bundle.tar.gz

61
doc/limitations.rst Normal file
View File

@ -0,0 +1,61 @@
##########################################
Limitations
##########################################
While the benefit of an openly available, functional and partially validated
model of the European energy system is high, many approximations have
been made due to missing data.
The limitations of the dataset are listed below,
both as a warning to the user and as an encouragement to assist in
improving the approximations.
This list of limitations is incomplete and will be added to over time.
See also the `GitHub repository issues <https://github.com/PyPSA/pypsa-eur-sec/issues>`_.
- **Electricity transmission network topology:**
The grid data is based on a map of the ENTSO-E area that is known
to contain small distortions to improve readability. Since the exact impedances
of the lines are unknown, approximations based on line lengths and standard
line parameters were made that ignore specific conductoring choices for
particular lines. There is no openly available data on busbar configurations, switch
locations, transformers or reactive power compensation assets.
- **Assignment of electricity demand to transmission nodes:**
Using Voronoi cells to aggregate load and generator data to transmission
network substations ignores the topology of the underlying distribution network,
meaning that assets may be connected to the wrong substation.
- **Incomplete information on existing assets:** Approximations have
been made for missing data, including: existing distribution grid
capacities and costs, existing space and water heating supply,
existing industry facilities, existing transport vehicle fleets.
- **Exogenous pathways for transformation of transport and industry:**
To avoid penny-switching the transformation of transport and
industry away from fossil fuels is determined exogenously.
- **Energy demand distribution within countries:**
Assumptions
have been made about the distribution of demand in each country proportional to
population and GDP that may not reflect local circumstances.
Openly available
data on load time series may not correspond to the true vertical load and is
not spatially disaggregated; assuming, as we have done, that the load time series
shape is the same at each node within each country ignores local differences.
- **Hydro-electric power plants:**
The database of hydro-electric power plants does not include plant-specific
energy storage information, so that blanket values based on country storage
totals have been used. Inflow time series are based on country-wide approximations,
ignoring local topography and basin drainage; in principle a full
hydrological model should be used.
- **International interactions:**
Border connections and power flows to Russia,
Belarus, Ukraine, Turkey and Morocco have not been taken into account;
islands which are not connected to the main European system, such as Malta,
Crete and Cyprus, are also excluded from the model.
- **Demand sufficiency:** Further measures of demand reduction may be
possible beyond the assumptions made here.

22
doc/release_notes.rst
View File

@ -24,7 +24,7 @@ incorporates retrofitting options to hydrogen.
* New rule ``build_gas_input_locations`` compiles the LNG import capacities
(including planned projects from gem.wiki), pipeline entry capacities and
local production capacities for each region of the model. These are the
regions where fossil gas can eventually enter the model.
regions where fossil gas can eventually enter the model.
* New rule ``cluster_gas_network`` that clusters the gas transmission network
data to the model resolution. Cross-regional pipeline capacities are aggregated
@ -47,8 +47,8 @@ incorporates retrofitting options to hydrogen.
H2_retrofit_capacity_per_CH4`` units are made available as hydrogen pipeline
capacity in the corresponding corridor. These repurposed hydrogen pipelines
have lower costs than new hydrogen pipelines. Both new and repurposed pipelines
can be built simultaneously. The retrofitting option ``sector: H2_retrofit:`` also works
with a copperplated methane infrastructure, i.e. when ``sector: gas_network: false``.
can be built simultaneously. The retrofitting option ``sector: H2_retrofit:`` also works
with a copperplated methane infrastructure, i.e. when ``sector: gas_network: false``.
* New hydrogen pipelines can now be built where there are already power or gas
transmission routes. Previously, only the electricity transmission routes were
@ -56,12 +56,16 @@ incorporates retrofitting options to hydrogen.
**New features and functionality**
* Units are assigned to the buses. These only provide a better understanding. The specifications of the units are not taken into account in the optimisation, which means that no automatic conversion of units takes place.
* Option ``retrieve_sector_databundle`` to automatically retrieve and extract data bundle.
* Add regionalised hydrogen salt cavern storage potentials from `Technical Potential of Salt Caverns for Hydrogen Storage in Europe <https://doi.org/10.20944/preprints201910.0187.v1>`_.
* Add option to sweep the global CO2 sequestration potentials with keyword ``seq200`` in the ``{sector_opts}`` wildcard (for limit of 200 Mt CO2).
* Updated `data bundle <https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_ that includes the hydrogan salt cavern storage potentials.
**Bugfixes**
* The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version)
@ -82,7 +86,7 @@ besides many performance improvements.
This release is known to work with `PyPSA-Eur
<https://github.com/PyPSA/pypsa-eur>`_ Version 0.4.0, `Technology Data
<https://github.com/PyPSA/technology-data>`_ Version 0.3.0 and
<https://github.com/PyPSA/technology-data>`_ Version 0.3.0 and
`PyPSA <https://github.com/PyPSA/PyPSA>`_ Version 0.18.0.
Please note that the data bundle has also been updated.
@ -200,19 +204,19 @@ Please note that the data bundle has also been updated.
A function ``helper.override_component_attrs`` was added that loads this data
and can pass the overridden component attributes into ``pypsa.Network()``.
* Add various parameters to ``config.default.yaml`` which were previously hardcoded inside the scripts
* Add various parameters to ``config.default.yaml`` which were previously hardcoded inside the scripts
(e.g. energy reference years, BEV settings, solar thermal collector models, geomap colours).
* Removed stale industry demand rules ``build_industrial_energy_demand_per_country``
and ``build_industrial_demand``. These are superseded with more regionally resolved rules.
* Use simpler and shorter ``gdf.sjoin()`` function to allocate industrial sites
from the Hotmaps database to onshore regions.
from the Hotmaps database to onshore regions.
This change also fixes a bug:
The previous version allocated sites to the closest bus,
but at country borders (where Voronoi cells are distorted by the borders),
this had resulted in e.g. a Spanish site close to the French border
being wrongly allocated to the French bus if the bus center was closer.
being wrongly allocated to the French bus if the bus center was closer.
* Retrofitting rule is now only triggered if endogeneously optimised.
@ -223,7 +227,7 @@ Please note that the data bundle has also been updated.
* Improve legibility of ``config.default.yaml`` and remove unused options.
* Use the country-specific time zone mappings from ``pytz`` rather than a manual mapping.
* A function ``add_carrier_buses()`` was added to the ``prepare_network`` rule to reduce code duplication.
* In the ``prepare_network`` rule the cost and potential adjustment was moved into an
@ -433,4 +437,4 @@ To make a new release of the data bundle, make an archive of the files in ``data
.. code:: bash
data % tar pczf pypsa-eur-sec-data-bundle.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv retro/tabula-calculator-calcsetbuilding.csv nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson
data % tar pczf pypsa-eur-sec-data-bundle.tar.gz eea/UNFCCC_v23.csv switzerland-sfoe biomass eurostat-energy_balances-* jrc-idees-2015 emobility WindWaveWEC_GLTB.xlsx myb1-2017-nitro.xls Industrial_Database.csv retro/tabula-calculator-calcsetbuilding.csv nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson h2_salt_caverns_GWh_per_sqkm.geojson

45
scripts/add_brownfield.py
View File

@ -8,15 +8,22 @@ idx = pd.IndexSlice
import pypsa
import yaml
import numpy as np
from add_existing_baseyear import add_build_year_to_new_assets
from helper import override_component_attrs
from solve_network import basename
def add_brownfield(n, n_p, year):
print("adding brownfield")
# electric transmission grid set optimised capacities of previous as minimum
n.lines.s_nom_min = n_p.lines.s_nom_opt
dc_i = n.links[n.links.carrier=="DC"].index
n.links.loc[dc_i, "p_nom_min"] = n_p.links.loc[dc_i, "p_nom_opt"]
for c in n_p.iterate_components(["Link", "Generator", "Store"]):
attr = "e" if c.name == "Store" else "p"
@ -25,7 +32,7 @@ def add_brownfield(n, n_p, year):
# CO2 or global EU values since these are already in n
n_p.mremove(
c.name,
c.df.index[c.df.lifetime.isna()]
c.df.index[c.df.lifetime==np.inf]
)
# remove assets whose build_year + lifetime < year
@ -44,7 +51,7 @@ def add_brownfield(n, n_p, year):
)]
threshold = snakemake.config['existing_capacities']['threshold_capacity']
if not chp_heat.empty:
threshold_chp_heat = (threshold
* c.df.efficiency[chp_heat.str.replace("heat", "electric")].values
@ -55,7 +62,7 @@ def add_brownfield(n, n_p, year):
c.name,
chp_heat[c.df.loc[chp_heat, attr + "_nom_opt"] < threshold_chp_heat]
)
n_p.mremove(
c.name,
c.df.index[c.df[attr + "_nom_extendable"] & ~c.df.index.isin(chp_heat) & (c.df[attr + "_nom_opt"] < threshold)]
@ -75,16 +82,44 @@ def add_brownfield(n, n_p, year):
for tattr in n.component_attrs[c.name].index[selection]:
n.import_series_from_dataframe(c.pnl[tattr], c.name, tattr)
# deal with gas network
pipe_carrier = ['gas pipeline']
if snakemake.config["sector"]['H2_retrofit']:
# drop capacities of previous year to avoid duplicating
to_drop = n.links.carrier.isin(pipe_carrier) & (n.links.build_year!=year)
n.mremove("Link", n.links.loc[to_drop].index)
# subtract the already retrofitted from today's gas grid capacity
h2_retrofitted_fixed_i = n.links[(n.links.carrier=='H2 pipeline retrofitted') & (n.links.build_year!=year)].index
gas_pipes_i = n.links[n.links.carrier.isin(pipe_carrier)].index
CH4_per_H2 = 1 / snakemake.config["sector"]["H2_retrofit_capacity_per_CH4"]
fr = "H2 pipeline retrofitted"
to = "gas pipeline"
# today's pipe capacity
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())
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:
new_pipes = n.links.carrier.isin(pipe_carrier) & (n.links.build_year==year)
n.links.loc[new_pipes, "p_nom"] = 0.
n.links.loc[new_pipes, "p_nom_min"] = 0.
#%%
if __name__ == "__main__":
if 'snakemake' not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'add_brownfield',
simpl='',
clusters=48,
clusters="37",
opts="",
lv=1.0,
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
sector_opts='168H-T-H-B-I-solar+p3-dist1',
planning_horizons=2030,
)

46
scripts/add_existing_baseyear.py
View File

@ -12,9 +12,11 @@ import xarray as xr
import pypsa
import yaml
from prepare_sector_network import prepare_costs
from prepare_sector_network import prepare_costs, define_spatial
from helper import override_component_attrs
from types import SimpleNamespace
spatial = SimpleNamespace()
def add_build_year_to_new_assets(n, baseyear):
"""
@ -28,7 +30,7 @@ def add_build_year_to_new_assets(n, baseyear):
# Give assets with lifetimes and no build year the build year baseyear
for c in n.iterate_components(["Link", "Generator", "Store"]):
assets = c.df.index[~c.df.lifetime.isna() & c.df.build_year==0]
assets = c.df.index[(c.df.lifetime!=np.inf) & (c.df.build_year==0)]
c.df.loc[assets, "build_year"] = baseyear
# add -baseyear to name
@ -153,13 +155,13 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
# assign clustered bus
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0, squeeze=True)
busmap = pd.read_csv(snakemake.input.busmap, index_col=0, squeeze=True)
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
inv_busmap = {}
for k, v in busmap.iteritems():
inv_busmap[v] = inv_busmap.get(v, []) + [k]
clustermaps = busmap_s.map(busmap)
clustermaps.index = clustermaps.index.astype(int)
@ -197,10 +199,15 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
if generator in ['solar', 'onwind', 'offwind']:
suffix = '-ac' if generator == 'offwind' else ''
name_suffix = f' {generator}{suffix}-{baseyear}'
# to consider electricity grid connection costs or a split between
# solar utility and rooftop as well, rather take cost assumptions
# from existing network than from the cost database
capital_cost = n.generators.loc[n.generators.carrier==generator+suffix, "capital_cost"].mean()
if 'm' in snakemake.wildcards.clusters:
for ind in capacity.index:
@ -213,14 +220,14 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]
p_max_pu.columns=[i + name_suffix for i in inv_ind ]
n.madd("Generator",
[i + name_suffix for i in inv_ind],
bus=ind,
carrier=generator,
p_nom=capacity[ind] / len(inv_ind), # split among regions in a country
marginal_cost=costs.at[generator,'VOM'],
capital_cost=costs.at[generator,'fixed'],
capital_cost=capital_cost,
efficiency=costs.at[generator, 'efficiency'],
p_max_pu=p_max_pu,
build_year=grouping_year,
@ -238,7 +245,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
carrier=generator,
p_nom=capacity,
marginal_cost=costs.at[generator, 'VOM'],
capital_cost=costs.at[generator, 'fixed'],
capital_cost=capital_cost,
efficiency=costs.at[generator, 'efficiency'],
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
build_year=grouping_year,
@ -246,11 +253,14 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
)
else:
bus0 = vars(spatial)[carrier[generator]].nodes
if "EU" not in vars(spatial)[carrier[generator]].locations:
bus0 = bus0.intersection(capacity.index + " gas")
n.madd("Link",
capacity.index,
suffix= " " + generator +"-" + str(grouping_year),
bus0="EU " + carrier[generator],
bus0=bus0,
bus1=capacity.index,
bus2="co2 atmosphere",
carrier=generator,
@ -399,10 +409,11 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
lifetime=costs.at[costs_name, 'lifetime']
)
n.madd("Link",
nodes[name],
suffix= f" {name} gas boiler-{grouping_year}",
bus0="EU gas",
bus0=spatial.gas.nodes,
bus1=nodes[name] + " " + name + " heat",
bus2="co2 atmosphere",
carrier=name + " gas boiler",
@ -417,7 +428,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
n.madd("Link",
nodes[name],
suffix=f" {name} oil boiler-{grouping_year}",
bus0="EU oil",
bus0=spatial.oil.nodes,
bus1=nodes[name] + " " + name + " heat",
bus2="co2 atmosphere",
carrier=name + " oil boiler",
@ -436,17 +447,17 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
threshold = snakemake.config['existing_capacities']['threshold_capacity']
n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and n.links.p_nom[index] < threshold])
#%%
if __name__ == "__main__":
if 'snakemake' not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'add_existing_baseyear',
simpl='',
clusters=45,
clusters="37",
lv=1.0,
opts='',
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
sector_opts='168H-T-H-B-I-solar+p3-dist1',
planning_horizons=2020,
)
@ -459,7 +470,8 @@ if __name__ == "__main__":
overrides = override_component_attrs(snakemake.input.overrides)
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
# define spatial resolution of carriers
spatial = define_spatial(n.buses[n.buses.carrier=="AC"].index, options)
add_build_year_to_new_assets(n, baseyear)
Nyears = n.snapshot_weightings.generators.sum() / 8760.
@ -471,7 +483,7 @@ if __name__ == "__main__":
snakemake.config['costs']['lifetime']
)
grouping_years=snakemake.config['existing_capacities']['grouping_years']
grouping_years = snakemake.config['existing_capacities']['grouping_years']
add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear)
if "H" in opts:

6
scripts/build_biomass_potentials.py
View File

@ -144,10 +144,12 @@ def build_nuts2_shapes():
nuts2 = gpd.GeoDataFrame(gpd.read_file(snakemake.input.nuts2).set_index('id').geometry)
countries = gpd.read_file(snakemake.input.country_shapes).set_index('name')
missing = countries.loc[["AL", "RS", "BA"]]
missing_iso2 = countries.index.intersection(["AL", "RS", "BA"])
missing = countries.loc[missing_iso2]
nuts2.rename(index={"ME00": "ME", "MK00": "MK"}, inplace=True)
return nuts2.append(missing)
return pd.concat([nuts2, missing])
def area(gdf):

2
scripts/build_gas_input_locations.py
View File

@ -26,7 +26,7 @@ def build_gas_input_locations(lng_fn, planned_lng_fn, entry_fn, prod_fn, countri
planned_lng = pd.read_csv(planned_lng_fn)
planned_lng.geometry = planned_lng.geometry.apply(wkt.loads)
planned_lng = gpd.GeoDataFrame(planned_lng, crs=4326)
lng = lng.append(planned_lng, ignore_index=True)
lng = pd.concat([lng, planned_lng], ignore_index=True)
# Entry points from outside the model scope
entry = read_scigrid_gas(entry_fn)

6
scripts/build_industrial_production_per_country.py
View File

@ -115,14 +115,14 @@ def get_energy_ratio(country):
# estimate physical output, energy consumption in the sector and country
fn = f"{eurostat_dir}/{eb_names[country]}.XLSX"
df = pd.read_excel(fn, sheet_name='2016', index_col=2,
header=0, skiprows=1, squeeze=True)
header=0, skiprows=1).squeeze('columns')
e_country = df.loc[eb_sectors.keys(
), 'Total all products'].rename(eb_sectors)
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_EU28.xlsx'
df = pd.read_excel(fn, sheet_name='Ind_Summary',
index_col=0, header=0, squeeze=True)
index_col=0, header=0).squeeze('columns')
assert df.index[48] == "by sector"
year_i = df.columns.get_loc(year)
@ -142,7 +142,7 @@ def industry_production_per_country(country):
fn = f'{jrc_dir}/JRC-IDEES-2015_Industry_{jrc_country}.xlsx'
sheet = sub_sheet_name_dict[sector]
df = pd.read_excel(fn, sheet_name=sheet,
index_col=0, header=0, squeeze=True)
index_col=0, header=0).squeeze('columns')
year_i = df.columns.get_loc(year)
df = df.iloc[find_physical_output(df), year_i]

3
scripts/build_industry_sector_ratios.py
View File

@ -78,12 +78,11 @@ def load_idees_data(sector, country="EU28"):
sheet_name=list(sheets.values()),
index_col=0,
header=0,
squeeze=True,
usecols=usecols,
)
for k, v in sheets.items():
idees[k] = idees.pop(v)
idees[k] = idees.pop(v).squeeze()
return idees

2
scripts/build_population_layouts.py
View File

@ -33,7 +33,7 @@ if __name__ == '__main__':
urban_fraction = pd.read_csv(snakemake.input.urban_percent,
header=None, index_col=0,
names=['fraction'], squeeze=True) / 100.
names=['fraction']).squeeze() / 100.
# fill missing Balkans values
missing = ["AL", "ME", "MK"]

22
scripts/build_population_weighted_energy_totals.py Normal file
View File

@ -0,0 +1,22 @@
"""Build population-weighted energy totals."""
import pandas as pd
if __name__ == '__main__':
if 'snakemake' not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
'build_population_weighted_energy_totals',
simpl='',
clusters=48,
)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
energy_totals = pd.read_csv(snakemake.input.energy_totals, index_col=0)
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
nodal_energy_totals.index = pop_layout.index
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
nodal_energy_totals.to_csv(snakemake.output[0])

201
scripts/build_transport_demand.py Normal file
View File

@ -0,0 +1,201 @@
"""Build transport demand."""
import pandas as pd
import numpy as np
import xarray as xr
from helper import generate_periodic_profiles
def build_nodal_transport_data(fn, pop_layout):
transport_data = pd.read_csv(fn, index_col=0)
nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.0)
nodal_transport_data.index = pop_layout.index
nodal_transport_data["number cars"] = (
pop_layout["fraction"] * nodal_transport_data["number cars"]
)
nodal_transport_data.loc[
nodal_transport_data["average fuel efficiency"] == 0.0,
"average fuel efficiency",
] = transport_data["average fuel efficiency"].mean()
return nodal_transport_data
def build_transport_demand(traffic_fn, airtemp_fn, nodes, nodal_transport_data):
## Get overall demand curve for all vehicles
traffic = pd.read_csv(
traffic_fn, skiprows=2, usecols=["count"], squeeze=True
)
transport_shape = generate_periodic_profiles(
dt_index=snapshots,
nodes=nodes,
weekly_profile=traffic.values,
)
transport_shape = transport_shape / transport_shape.sum()
# electric motors are more efficient, so alter transport demand
plug_to_wheels_eta = options["bev_plug_to_wheel_efficiency"]
battery_to_wheels_eta = plug_to_wheels_eta * options["bev_charge_efficiency"]
efficiency_gain = (
nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta
)
# get heating demand for correction to demand time series
temperature = xr.open_dataarray(airtemp_fn).to_pandas()
# correction factors for vehicle heating
dd_ICE = transport_degree_factor(
temperature,
options["transport_heating_deadband_lower"],
options["transport_heating_deadband_upper"],
options["ICE_lower_degree_factor"],
options["ICE_upper_degree_factor"],
)
dd_EV = transport_degree_factor(
temperature,
options["transport_heating_deadband_lower"],
options["transport_heating_deadband_upper"],
options["EV_lower_degree_factor"],
options["EV_upper_degree_factor"],
)
# divide out the heating/cooling demand from ICE totals
# and multiply back in the heating/cooling demand for EVs
ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()
energy_totals_transport = (
pop_weighted_energy_totals["total road"]
+ pop_weighted_energy_totals["total rail"]
- pop_weighted_energy_totals["electricity rail"]
)
transport = (
(transport_shape.multiply(energy_totals_transport) * 1e6 * Nyears)
.divide(efficiency_gain * ice_correction)
.multiply(1 + dd_EV)
)
return transport
def transport_degree_factor(
temperature,
deadband_lower=15,
deadband_upper=20,
lower_degree_factor=0.5,
upper_degree_factor=1.6,
):
"""
Work out how much energy demand in vehicles increases due to heating and cooling.
There is a deadband where there is no increase.
Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
Returns per unit increase in demand for each place and time
"""
dd = temperature.copy()
dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.0
dT_lower = deadband_lower - temperature[temperature < deadband_lower]
dd[temperature < deadband_lower] = lower_degree_factor / 100 * dT_lower
dT_upper = temperature[temperature > deadband_upper] - deadband_upper
dd[temperature > deadband_upper] = upper_degree_factor / 100 * dT_upper
return dd
def bev_availability_profile(fn, snapshots, nodes, options):
"""
Derive plugged-in availability for passenger electric vehicles.
"""
traffic = pd.read_csv(fn, skiprows=2, usecols=["count"], squeeze=True)
avail_max = options["bev_avail_max"]
avail_mean = options["bev_avail_mean"]
avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (
traffic.mean() - traffic.min()
)
avail_profile = generate_periodic_profiles(
dt_index=snapshots,
nodes=nodes,
weekly_profile=avail.values,
)
return avail_profile
def bev_dsm_profile(snapshots, nodes, options):
dsm_week = np.zeros((24 * 7,))
dsm_week[(np.arange(0, 7, 1) * 24 + options["bev_dsm_restriction_time"])] = options[
"bev_dsm_restriction_value"
]
dsm_profile = generate_periodic_profiles(
dt_index=snapshots,
nodes=nodes,
weekly_profile=dsm_week,
)
return dsm_profile
if __name__ == "__main__":
if "snakemake" not in globals():
from helper import mock_snakemake
snakemake = mock_snakemake(
"build_transport_demand",
simpl="",
clusters=48,
)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
nodes = pop_layout.index
pop_weighted_energy_totals = pd.read_csv(
snakemake.input.pop_weighted_energy_totals, index_col=0
)
options = snakemake.config["sector"]
snapshots = pd.date_range(freq='h', **snakemake.config["snapshots"], tz="UTC")
Nyears = 1
nodal_transport_data = build_nodal_transport_data(
snakemake.input.transport_data,
pop_layout
)
transport_demand = build_transport_demand(
snakemake.input.traffic_data_KFZ,
snakemake.input.temp_air_total,
nodes, nodal_transport_data
)
avail_profile = bev_availability_profile(
snakemake.input.traffic_data_Pkw,
snapshots, nodes, options
)
dsm_profile = bev_dsm_profile(snapshots, nodes, options)
nodal_transport_data.to_csv(snakemake.output.transport_data)
transport_demand.to_csv(snakemake.output.transport_demand)
avail_profile.to_csv(snakemake.output.avail_profile)
dsm_profile.to_csv(snakemake.output.dsm_profile)

14
scripts/copy_config.py
View File

@ -1,5 +1,6 @@
from shutil import copy
import yaml
files = {
"config.yaml": "config.yaml",
@ -14,5 +15,16 @@ if __name__ == '__main__':
from helper import mock_snakemake
snakemake = mock_snakemake('copy_config')
basepath = snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/configs/'
for f, name in files.items():
copy(f,snakemake.config['summary_dir'] + '/' + snakemake.config['run'] + '/configs/' + name)
copy(f, basepath + name)
with open(basepath + 'config.snakemake.yaml', 'w') as yaml_file:
yaml.dump(
snakemake.config,
yaml_file,
default_flow_style=False,
allow_unicode=True,
sort_keys=False
)

28
scripts/helper.py
View File

@ -1,4 +1,5 @@
import os
import pytz
import pandas as pd
from pathlib import Path
from pypsa.descriptors import Dict
@ -55,6 +56,7 @@ def mock_snakemake(rulename, **wildcards):
import os
from pypsa.descriptors import Dict
from snakemake.script import Snakemake
from packaging.version import Version, parse
script_dir = Path(__file__).parent.resolve()
assert Path.cwd().resolve() == script_dir, \
@ -64,7 +66,8 @@ def mock_snakemake(rulename, **wildcards):
if os.path.exists(p):
snakefile = p
break
workflow = sm.Workflow(snakefile)
kwargs = dict(rerun_triggers=[]) if parse(sm.__version__) > Version("7.7.0") else {}
workflow = sm.Workflow(snakefile, overwrite_configfiles=[], **kwargs)
workflow.include(snakefile)
workflow.global_resources = {}
rule = workflow.get_rule(rulename)
@ -98,4 +101,25 @@ def progress_retrieve(url, file):
def dlProgress(count, blockSize, totalSize):
pbar.update( int(count * blockSize * 100 / totalSize) )
urllib.request.urlretrieve(url, file, reporthook=dlProgress)
urllib.request.urlretrieve(url, file, reporthook=dlProgress)
def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
"""
Give a 24*7 long list of weekly hourly profiles, generate this for each
country for the period dt_index, taking account of time zones and summer time.
"""
weekly_profile = pd.Series(weekly_profile, range(24*7))
week_df = pd.DataFrame(index=dt_index, columns=nodes)
for node in nodes:
timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
tz_dt_index = dt_index.tz_convert(timezone)
week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
week_df[node] = week_df[node].map(weekly_profile)
week_df = week_df.tz_localize(localize)
return week_df

56
scripts/plot_network.py
View File

@ -154,7 +154,9 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
costs = costs.stack() # .sort_index()
# hack because impossible to drop buses...
n.buses.loc["EU gas", ["x", "y"]] = n.buses.loc["DE0 0", ["x", "y"]]
eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
n.buses.loc["EU gas", "x"] = eu_location["x"]
n.buses.loc["EU gas", "y"] = eu_location["y"]
n.links.drop(n.links.index[(n.links.carrier != "DC") & (
n.links.carrier != "B2B")], inplace=True)
@ -287,6 +289,26 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
bbox_inches="tight"
)
def group_pipes(df, drop_direction=False):
"""Group pipes which connect same buses and return overall capacity.
"""
if drop_direction:
positive_order = df.bus0 < df.bus1
df_p = df[positive_order]
swap_buses = {"bus0": "bus1", "bus1": "bus0"}
df_n = df[~positive_order].rename(columns=swap_buses)
df = pd.concat([df_p, df_n])
# there are pipes for each investment period rename to AC buses name for plotting
df.index = df.apply(
lambda x: f"H2 pipeline {x.bus0.replace(' H2', '')} -> {x.bus1.replace(' H2', '')}",
axis=1
)
# group pipe lines connecting the same buses and rename them for plotting
pipe_capacity = df["p_nom_opt"].groupby(level=0).sum()
return pipe_capacity
def plot_h2_map(network, regions):
@ -305,7 +327,7 @@ def plot_h2_map(network, regions):
bus_size_factor = 1e5
linewidth_factor = 1e4
# MW below which not drawn
line_lower_threshold = 1e3
line_lower_threshold = 1e2
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
@ -318,12 +340,14 @@ def plot_h2_map(network, regions):
# make a fake MultiIndex so that area is correct for legend
bus_sizes.rename(index=lambda x: x.replace(" H2", ""), level=0, inplace=True)
# 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']
# 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:
@ -354,6 +378,9 @@ def plot_h2_map(network, regions):
h2_total = h2_new
link_widths_total = h2_total / linewidth_factor
n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
n.links = n.links.groupby(level=0).first()
link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.)
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.
@ -498,7 +525,7 @@ def plot_ch4_map(network):
# Drop non-electric buses so they don't clutter the plot
n.buses.drop(n.buses.index[n.buses.carrier != "AC"], inplace=True)
fossil_gas_i = n.generators[n.generators.carrier=="gas"].index
fossil_gas_i = n.generators[n.generators.carrier=="gas"].index
fossil_gas = n.generators_t.p.loc[:,fossil_gas_i].mul(n.snapshot_weightings.generators, axis=0).sum().groupby(n.generators.loc[fossil_gas_i,"bus"]).sum() / bus_size_factor
fossil_gas.rename(index=lambda x: x.replace(" gas", ""), inplace=True)
fossil_gas = fossil_gas.reindex(n.buses.index).fillna(0)
@ -523,10 +550,10 @@ def plot_ch4_map(network):
to_remove = n.links.index[~n.links.carrier.str.contains("gas pipeline")]
n.links.drop(to_remove, inplace=True)
link_widths_rem = n.links.p_nom_opt / linewidth_factor
link_widths_rem = n.links.p_nom_opt / linewidth_factor
link_widths_rem[n.links.p_nom_opt < line_lower_threshold] = 0.
link_widths_orig = n.links.p_nom / linewidth_factor
link_widths_orig = n.links.p_nom / linewidth_factor
link_widths_orig[n.links.p_nom < line_lower_threshold] = 0.
max_usage = n.links_t.p0.abs().max(axis=0)
@ -561,8 +588,7 @@ def plot_ch4_map(network):
link_colors=pipe_colors['gas pipeline (2020)'],
link_widths=link_widths_orig,
branch_components=["Link"],
ax=ax,
geomap=True,
ax=ax,
**map_opts
)
@ -682,7 +708,9 @@ def plot_map_without(network):
# hack because impossible to drop buses...
if "EU gas" in n.buses.index:
n.buses.loc["EU gas", ["x", "y"]] = n.buses.loc["DE0 0", ["x", "y"]]
eu_location = snakemake.config["plotting"].get("eu_node_location", dict(x=-5.5, y=46))
n.buses.loc["EU gas", "x"] = eu_location["x"]
n.buses.loc["EU gas", "y"] = eu_location["y"]
to_drop = n.links.index[(n.links.carrier != "DC") & (n.links.carrier != "B2B")]
n.links.drop(to_drop, inplace=True)
@ -865,11 +893,11 @@ if __name__ == "__main__":
snakemake = mock_snakemake(
'plot_network',
simpl='',
clusters=45,
lv=1.5,
clusters="45",
lv=1.0,
opts='',
sector_opts='Co2L0-168H-T-H-B-I-solar+p3-dist1',
planning_horizons=2030,
sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
planning_horizons="2050",
)
overrides = override_component_attrs(snakemake.input.overrides)

524
scripts/prepare_sector_network.py
View File

@ -3,7 +3,6 @@
import pypsa
import re
import os
import pytz
import pandas as pd
import numpy as np
@ -15,7 +14,7 @@ from scipy.stats import beta
from vresutils.costdata import annuity
from build_energy_totals import build_eea_co2, build_eurostat_co2, build_co2_totals
from helper import override_component_attrs
from helper import override_component_attrs, generate_periodic_profiles
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
from networkx.algorithms import complement
@ -28,7 +27,7 @@ from types import SimpleNamespace
spatial = SimpleNamespace()
def define_spatial(nodes):
def define_spatial(nodes, options):
"""
Namespace for spatial
@ -38,7 +37,6 @@ def define_spatial(nodes):
"""
global spatial
global options
spatial.nodes = nodes
@ -73,7 +71,7 @@ def define_spatial(nodes):
spatial.co2.vents = ["co2 vent"]
spatial.co2.df = pd.DataFrame(vars(spatial.co2), index=nodes)
# gas
spatial.gas = SimpleNamespace()
@ -95,6 +93,28 @@ def define_spatial(nodes):
spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
# oil
spatial.oil = SimpleNamespace()
spatial.oil.nodes = ["EU oil"]
spatial.oil.locations = ["EU"]
# uranium
spatial.uranium = SimpleNamespace()
spatial.uranium.nodes = ["EU uranium"]
spatial.uranium.locations = ["EU"]
# coal
spatial.coal = SimpleNamespace()
spatial.coal.nodes = ["EU coal"]
spatial.coal.locations = ["EU"]
# lignite
spatial.lignite = SimpleNamespace()
spatial.lignite.nodes = ["EU lignite"]
spatial.lignite.locations = ["EU"]
return spatial
from types import SimpleNamespace
spatial = SimpleNamespace()
@ -252,6 +272,7 @@ def create_network_topology(n, prefix, carriers=["DC"], connector=" -> ", bidire
ln_attrs = ["bus0", "bus1", "length"]
lk_attrs = ["bus0", "bus1", "length", "underwater_fraction"]
lk_attrs = n.links.columns.intersection(lk_attrs)
candidates = pd.concat([
n.lines[ln_attrs],
@ -278,7 +299,7 @@ def create_network_topology(n, prefix, carriers=["DC"], connector=" -> ", bidire
topo_reverse = topo.copy()
topo_reverse.rename(columns=swap_buses, inplace=True)
topo_reverse.index = topo_reverse.apply(make_index, axis=1)
topo = topo.append(topo_reverse)
topo = pd.concat([topo, topo_reverse])
return topo
@ -352,7 +373,8 @@ def add_carrier_buses(n, carrier, nodes=None):
"""
if nodes is None:
nodes = ["EU " + carrier]
nodes = vars(spatial)[carrier].nodes
location = vars(spatial)[carrier].locations
# skip if carrier already exists
if carrier in n.carriers.index:
@ -363,10 +385,13 @@ def add_carrier_buses(n, carrier, nodes=None):
n.add("Carrier", carrier)
unit = "MWh_LHV" if carrier == "gas" else "MWh_th"
n.madd("Bus",
nodes,
location=nodes.str.replace(" " + carrier, ""),
carrier=carrier
location=location,
carrier=carrier,
unit=unit
)
#capital cost could be corrected to e.g. 0.2 EUR/kWh * annuity and O&M
@ -417,6 +442,7 @@ def patch_electricity_network(n):
update_wind_solar_costs(n, costs)
n.loads["carrier"] = "electricity"
n.buses["location"] = n.buses.index
n.buses["unit"] = "MWh_el"
# remove trailing white space of load index until new PyPSA version after v0.18.
n.loads.rename(lambda x: x.strip(), inplace=True)
n.loads_t.p_set.rename(lambda x: x.strip(), axis=1, inplace=True)
@ -433,7 +459,8 @@ def add_co2_tracking(n, options):
n.add("Bus",
"co2 atmosphere",
location="EU",
carrier="co2"
carrier="co2",
unit="t_co2"
)
# can also be negative
@ -449,7 +476,8 @@ def add_co2_tracking(n, options):
n.madd("Bus",
spatial.co2.nodes,
location=spatial.co2.locations,
carrier="co2 stored"
carrier="co2 stored",
unit="t_co2"
)
n.madd("Store",
@ -565,27 +593,6 @@ def average_every_nhours(n, offset):
return m
def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
"""
Give a 24*7 long list of weekly hourly profiles, generate this for each
country for the period dt_index, taking account of time zones and summer time.
"""
weekly_profile = pd.Series(weekly_profile, range(24*7))
week_df = pd.DataFrame(index=dt_index, columns=nodes)
for node in nodes:
timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
tz_dt_index = dt_index.tz_convert(timezone)
week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
week_df[node] = week_df[node].map(weekly_profile)
week_df = week_df.tz_localize(localize)
return week_df
def cycling_shift(df, steps=1):
"""Cyclic shift on index of pd.Series|pd.DataFrame by number of steps"""
df = df.copy()
@ -594,179 +601,6 @@ def cycling_shift(df, steps=1):
return df
def transport_degree_factor(
temperature,
deadband_lower=15,
deadband_upper=20,
lower_degree_factor=0.5,
upper_degree_factor=1.6):
"""
Work out how much energy demand in vehicles increases due to heating and cooling.
There is a deadband where there is no increase.
Degree factors are % increase in demand compared to no heating/cooling fuel consumption.
Returns per unit increase in demand for each place and time
"""
dd = temperature.copy()
dd[(temperature > deadband_lower) & (temperature < deadband_upper)] = 0.
dT_lower = deadband_lower - temperature[temperature < deadband_lower]
dd[temperature < deadband_lower] = lower_degree_factor / 100 * dT_lower
dT_upper = temperature[temperature > deadband_upper] - deadband_upper
dd[temperature > deadband_upper] = upper_degree_factor / 100 * dT_upper
return dd
# TODO separate sectors and move into own rules
def prepare_data(n):
##############
#Heating
##############
ashp_cop = xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots)
gshp_cop = xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
energy_totals = pd.read_csv(snakemake.input.energy_totals_name, index_col=0)
nodal_energy_totals = energy_totals.loc[pop_layout.ct].fillna(0.)
nodal_energy_totals.index = pop_layout.index
# district heat share not weighted by population
district_heat_share = nodal_energy_totals["district heat share"].round(2)
nodal_energy_totals = nodal_energy_totals.multiply(pop_layout.fraction, axis=0)
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
sectors = ["residential", "services"]
uses = ["water", "space"]
heat_demand = {}
electric_heat_supply = {}
for sector, use in product(sectors, uses):
weekday = list(intraday_profiles[f"{sector} {use} weekday"])
weekend = list(intraday_profiles[f"{sector} {use} weekend"])
weekly_profile = weekday * 5 + weekend * 2
intraday_year_profile = generate_periodic_profiles(
daily_space_heat_demand.index.tz_localize("UTC"),
nodes=daily_space_heat_demand.columns,
weekly_profile=weekly_profile
)
if use == "space":
heat_demand_shape = daily_space_heat_demand * intraday_year_profile
else:
heat_demand_shape = intraday_year_profile
heat_demand[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"total {sector} {use}"]) * 1e6
electric_heat_supply[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(nodal_energy_totals[f"electricity {sector} {use}"]) * 1e6
heat_demand = pd.concat(heat_demand, axis=1)
electric_heat_supply = pd.concat(electric_heat_supply, axis=1)
# subtract from electricity load since heat demand already in heat_demand
electric_nodes = n.loads.index[n.loads.carrier == "electricity"]
n.loads_t.p_set[electric_nodes] = n.loads_t.p_set[electric_nodes] - electric_heat_supply.groupby(level=1, axis=1).sum()[electric_nodes]
##############
#Transport
##############
## Get overall demand curve for all vehicles
traffic = pd.read_csv(snakemake.input.traffic_data_KFZ, skiprows=2, usecols=["count"], squeeze=True)
#Generate profiles
transport_shape = generate_periodic_profiles(
dt_index=n.snapshots.tz_localize("UTC"),
nodes=pop_layout.index,
weekly_profile=traffic.values
)
transport_shape = transport_shape / transport_shape.sum()
transport_data = pd.read_csv(snakemake.input.transport_name, index_col=0)
nodal_transport_data = transport_data.loc[pop_layout.ct].fillna(0.)
nodal_transport_data.index = pop_layout.index
nodal_transport_data["number cars"] = pop_layout["fraction"] * nodal_transport_data["number cars"]
nodal_transport_data.loc[nodal_transport_data["average fuel efficiency"] == 0., "average fuel efficiency"] = transport_data["average fuel efficiency"].mean()
# electric motors are more efficient, so alter transport demand
plug_to_wheels_eta = options.get("bev_plug_to_wheel_efficiency", 0.2)
battery_to_wheels_eta = plug_to_wheels_eta * options.get("bev_charge_efficiency", 0.9)
efficiency_gain = nodal_transport_data["average fuel efficiency"] / battery_to_wheels_eta
#get heating demand for correction to demand time series
temperature = xr.open_dataarray(snakemake.input.temp_air_total).to_pandas()
# correction factors for vehicle heating
dd_ICE = transport_degree_factor(
temperature,
options['transport_heating_deadband_lower'],
options['transport_heating_deadband_upper'],
options['ICE_lower_degree_factor'],
options['ICE_upper_degree_factor']
)
dd_EV = transport_degree_factor(
temperature,
options['transport_heating_deadband_lower'],
options['transport_heating_deadband_upper'],
options['EV_lower_degree_factor'],
options['EV_upper_degree_factor']
)
# divide out the heating/cooling demand from ICE totals
# and multiply back in the heating/cooling demand for EVs
ice_correction = (transport_shape * (1 + dd_ICE)).sum() / transport_shape.sum()
energy_totals_transport = nodal_energy_totals["total road"] + nodal_energy_totals["total rail"] - nodal_energy_totals["electricity rail"]
transport = (transport_shape.multiply(energy_totals_transport) * 1e6 * Nyears).divide(efficiency_gain * ice_correction).multiply(1 + dd_EV)
## derive plugged-in availability for PKW's (cars)
traffic = pd.read_csv(snakemake.input.traffic_data_Pkw, skiprows=2, usecols=["count"], squeeze=True)
avail_max = options.get("bev_avail_max", 0.95)
avail_mean = options.get("bev_avail_mean", 0.8)
avail = avail_max - (avail_max - avail_mean) * (traffic - traffic.min()) / (traffic.mean() - traffic.min())
avail_profile = generate_periodic_profiles(
dt_index=n.snapshots.tz_localize("UTC"),
nodes=pop_layout.index,
weekly_profile=avail.values
)
dsm_week = np.zeros((24*7,))
dsm_week[(np.arange(0,7,1) * 24 + options['bev_dsm_restriction_time'])] = options['bev_dsm_restriction_value']
dsm_profile = generate_periodic_profiles(
dt_index=n.snapshots.tz_localize("UTC"),
nodes=pop_layout.index,
weekly_profile=dsm_week
)
return nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share
# TODO checkout PyPSA-Eur script
def prepare_costs(cost_file, USD_to_EUR, discount_rate, Nyears, lifetime):
@ -806,10 +640,8 @@ def add_generation(n, costs):
for generator, carrier in conventionals.items():
if carrier == 'gas':
carrier_nodes = spatial.gas.nodes
else:
carrier_nodes = ["EU " + carrier]
carrier_nodes = vars(spatial)[carrier].nodes
add_carrier_buses(n, carrier, carrier_nodes)
@ -877,7 +709,8 @@ def insert_electricity_distribution_grid(n, costs):
n.madd("Bus",
nodes + " low voltage",
location=nodes,
carrier="low voltage"
carrier="low voltage",
unit="MWh_el"
)
n.madd("Link",
@ -944,7 +777,8 @@ def insert_electricity_distribution_grid(n, costs):
n.madd("Bus",
nodes + " home battery",
location=nodes,
carrier="home battery"
carrier="home battery",
unit="MWh_el"
)
n.madd("Store",
@ -1019,7 +853,8 @@ def add_storage_and_grids(n, costs):
n.madd("Bus",
nodes + " H2",
location=nodes,
carrier="H2"
carrier="H2",
unit="MWh_LHV"
)
n.madd("Link",
@ -1045,18 +880,20 @@ def add_storage_and_grids(n, costs):
)
cavern_types = snakemake.config["sector"]["hydrogen_underground_storage_locations"]
h2_caverns = pd.read_csv(snakemake.input.h2_cavern, index_col=0)[cavern_types].sum(axis=1)
h2_caverns = pd.read_csv(snakemake.input.h2_cavern, index_col=0)
# only use sites with at least 2 TWh potential
h2_caverns = h2_caverns[h2_caverns > 2]
# convert TWh to MWh
h2_caverns = h2_caverns * 1e6
if not h2_caverns.empty and options['hydrogen_underground_storage']:
# clip at 1000 TWh for one location
h2_caverns.clip(upper=1e9, inplace=True)
h2_caverns = h2_caverns[cavern_types].sum(axis=1)
if options['hydrogen_underground_storage']:
# only use sites with at least 2 TWh potential
h2_caverns = h2_caverns[h2_caverns > 2]
# convert TWh to MWh
h2_caverns = h2_caverns * 1e6
# clip at 1000 TWh for one location
h2_caverns.clip(upper=1e9, inplace=True)
logger.info("Add hydrogen underground storage")
@ -1069,7 +906,8 @@ def add_storage_and_grids(n, costs):
e_nom_max=h2_caverns.values,
e_cyclic=True,
carrier="H2 Store",
capital_cost=h2_capital_cost
capital_cost=h2_capital_cost,
lifetime=costs.at["hydrogen storage underground", "lifetime"]
)
# hydrogen stored overground (where not already underground)
@ -1119,7 +957,7 @@ def add_storage_and_grids(n, costs):
carrier="gas pipeline",
lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
)
# remove fossil generators where there is neither
# production, LNG terminal, nor entry-point beyond system scope
@ -1154,24 +992,27 @@ def add_storage_and_grids(n, costs):
# apply k_edge_augmentation weighted by length of complement edges
k_edge = options.get("gas_network_connectivity_upgrade", 3)
augmentation = k_edge_augmentation(G, k_edge, avail=complement_edges.values)
new_gas_pipes = pd.DataFrame(augmentation, columns=["bus0", "bus1"])
new_gas_pipes["length"] = new_gas_pipes.apply(haversine, axis=1)
augmentation = list(k_edge_augmentation(G, k_edge, avail=complement_edges.values))
new_gas_pipes.index = new_gas_pipes.apply(
lambda x: f"gas pipeline new {x.bus0} <-> {x.bus1}", axis=1)
if augmentation:
n.madd("Link",
new_gas_pipes.index,
bus0=new_gas_pipes.bus0 + " gas",
bus1=new_gas_pipes.bus1 + " gas",
p_min_pu=-1, # new gas pipes are bidirectional
p_nom_extendable=True,
length=new_gas_pipes.length,
capital_cost=new_gas_pipes.length * costs.at['CH4 (g) pipeline', 'fixed'],
carrier="gas pipeline new",
lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
)
new_gas_pipes = pd.DataFrame(augmentation, columns=["bus0", "bus1"])
new_gas_pipes["length"] = new_gas_pipes.apply(haversine, axis=1)
new_gas_pipes.index = new_gas_pipes.apply(
lambda x: f"gas pipeline new {x.bus0} <-> {x.bus1}", axis=1)
n.madd("Link",
new_gas_pipes.index,
bus0=new_gas_pipes.bus0 + " gas",
bus1=new_gas_pipes.bus1 + " gas",
p_min_pu=-1, # new gas pipes are bidirectional
p_nom_extendable=True,
length=new_gas_pipes.length,
capital_cost=new_gas_pipes.length * costs.at['CH4 (g) pipeline', 'fixed'],
carrier="gas pipeline new",
lifetime=costs.at['CH4 (g) pipeline', 'lifetime']
)
if options["H2_retrofit"]:
@ -1219,7 +1060,8 @@ def add_storage_and_grids(n, costs):
n.madd("Bus",
nodes + " battery",
location=nodes,
carrier="battery"
carrier="battery",
unit="MWh_el"
)
n.madd("Store",
@ -1286,6 +1128,24 @@ def add_storage_and_grids(n, costs):
lifetime=costs.at['helmeth', 'lifetime']
)
if options.get('coal_cc'):
n.madd("Link",
spatial.nodes,
suffix=" coal CC",
bus0=spatial.coal.nodes,
bus1=spatial.nodes,
bus2="co2 atmosphere",
bus3="co2 stored",
marginal_cost=costs.at['coal', 'efficiency'] * costs.at['coal', 'VOM'], #NB: VOM is per MWel
capital_cost=costs.at['coal', 'efficiency'] * costs.at['coal', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at['coal', 'CO2 intensity'], #NB: fixed cost is per MWel
p_nom_extendable=True,
carrier="coal",
efficiency=costs.at['coal', 'efficiency'],
efficiency2=costs.at['coal', 'CO2 intensity'] * (1 - costs.at['biomass CHP capture','capture_rate']),
efficiency3=costs.at['coal', 'CO2 intensity'] * costs.at['biomass CHP capture','capture_rate'],
lifetime=costs.at['coal','lifetime']
)
if options['SMR']:
@ -1324,6 +1184,11 @@ def add_land_transport(n, costs):
logger.info("Add land transport")
transport = pd.read_csv(snakemake.input.transport_demand, index_col=0, parse_dates=True)
number_cars = pd.read_csv(snakemake.input.transport_data, index_col=0)["number cars"]
avail_profile = pd.read_csv(snakemake.input.avail_profile, index_col=0, parse_dates=True)
dsm_profile = pd.read_csv(snakemake.input.dsm_profile, index_col=0, parse_dates=True)
fuel_cell_share = get(options["land_transport_fuel_cell_share"], investment_year)
electric_share = get(options["land_transport_electric_share"], investment_year)
ice_share = 1 - fuel_cell_share - electric_share
@ -1344,7 +1209,8 @@ def add_land_transport(n, costs):
nodes,
location=nodes,
suffix=" EV battery",
carrier="Li ion"
carrier="Li ion",
unit="MWh_el"
)
p_set = electric_share * (transport[nodes] + cycling_shift(transport[nodes], 1) + cycling_shift(transport[nodes], 2)) / 3
@ -1357,8 +1223,7 @@ def add_land_transport(n, costs):
p_set=p_set
)
p_nom = nodal_transport_data["number cars"] * options.get("bev_charge_rate", 0.011) * electric_share
p_nom = number_cars * options.get("bev_charge_rate", 0.011) * electric_share
n.madd("Link",
nodes,
@ -1390,7 +1255,7 @@ def add_land_transport(n, costs):
if electric_share > 0 and options["bev_dsm"]:
e_nom = nodal_transport_data["number cars"] * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
e_nom = number_cars * options.get("bev_energy", 0.05) * options["bev_availability"] * electric_share
n.madd("Store",
nodes,
@ -1415,11 +1280,12 @@ def add_land_transport(n, costs):
if ice_share > 0:
if "EU oil" not in n.buses.index:
n.add("Bus",
"EU oil",
location="EU",
carrier="oil"
if "oil" not in n.buses.carrier.unique():
n.madd("Bus",
spatial.oil.nodes,
location=spatial.oil.locations,
carrier="oil",
unit="MWh_LHV"
)
ice_efficiency = options['transport_internal_combustion_efficiency']
@ -1427,7 +1293,7 @@ def add_land_transport(n, costs):
n.madd("Load",
nodes,
suffix=" land transport oil",
bus="EU oil",
bus=spatial.oil.nodes,
carrier="land transport oil",
p_set=ice_share / ice_efficiency * transport[nodes]
)
@ -1442,12 +1308,53 @@ def add_land_transport(n, costs):
)
def build_heat_demand(n):
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile
daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill")
intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
sectors = ["residential", "services"]
uses = ["water", "space"]
heat_demand = {}
electric_heat_supply = {}
for sector, use in product(sectors, uses):
weekday = list(intraday_profiles[f"{sector} {use} weekday"])
weekend = list(intraday_profiles[f"{sector} {use} weekend"])
weekly_profile = weekday * 5 + weekend * 2
intraday_year_profile = generate_periodic_profiles(
daily_space_heat_demand.index.tz_localize("UTC"),
nodes=daily_space_heat_demand.columns,
weekly_profile=weekly_profile
)
if use == "space":
heat_demand_shape = daily_space_heat_demand * intraday_year_profile
else:
heat_demand_shape = intraday_year_profile
heat_demand[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
electric_heat_supply[f"{sector} {use}"] = (heat_demand_shape/heat_demand_shape.sum()).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
heat_demand = pd.concat(heat_demand, axis=1)
electric_heat_supply = pd.concat(electric_heat_supply, axis=1)
# subtract from electricity load since heat demand already in heat_demand
electric_nodes = n.loads.index[n.loads.carrier == "electricity"]
n.loads_t.p_set[electric_nodes] = n.loads_t.p_set[electric_nodes] - electric_heat_supply.groupby(level=1, axis=1).sum()[electric_nodes]
return heat_demand
def add_heat(n, costs):
logger.info("Add heat sector")
sectors = ["residential", "services"]
heat_demand = build_heat_demand(n)
nodes, dist_fraction, urban_fraction = create_nodes_for_heat_sector()
@ -1468,6 +1375,15 @@ def add_heat(n, costs):
"urban central"
]
cop = {
"air": xr.open_dataarray(snakemake.input.cop_air_total).to_pandas().reindex(index=n.snapshots),
"ground": xr.open_dataarray(snakemake.input.cop_soil_total).to_pandas().reindex(index=n.snapshots)
}
solar_thermal = xr.open_dataarray(snakemake.input.solar_thermal_total).to_pandas().reindex(index=n.snapshots)
# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
solar_thermal = options['solar_cf_correction'] * solar_thermal / 1e3
for name in heat_systems:
name_type = "central" if name == "urban central" else "decentral"
@ -1477,7 +1393,8 @@ def add_heat(n, costs):
n.madd("Bus",
nodes[name] + f" {name} heat",
location=nodes[name],
carrier=name + " heat"
carrier=name + " heat",
unit="MWh_th"
)
## Add heat load
@ -1513,7 +1430,6 @@ def add_heat(n, costs):
heat_pump_type = "air" if "urban" in name else "ground"
costs_name = f"{name_type} {heat_pump_type}-sourced heat pump"
cop = {"air" : ashp_cop, "ground" : gshp_cop}
efficiency = cop[heat_pump_type][nodes[name]] if options["time_dep_hp_cop"] else costs.at[costs_name, 'efficiency']
n.madd("Link",
@ -1535,7 +1451,8 @@ def add_heat(n, costs):
n.madd("Bus",
nodes[name] + f" {name} water tanks",
location=nodes[name],
carrier=name + " water tanks"
carrier=name + " water tanks",
unit="MWh_th"
)
n.madd("Link",
@ -1792,6 +1709,8 @@ def create_nodes_for_heat_sector():
nodes[sector + " rural"] = pop_layout.index
nodes[sector + " urban decentral"] = pop_layout.index
district_heat_share = pop_weighted_energy_totals["district heat share"]
# maximum potential of urban demand covered by district heating
central_fraction = options['district_heating']["potential"]
# district heating share at each node
@ -1838,13 +1757,15 @@ def add_biomass(n, costs):
n.madd("Bus",
spatial.gas.biogas,
location=spatial.gas.locations,
carrier="biogas"
carrier="biogas",
unit="MWh_LHV"
)
n.madd("Bus",
spatial.biomass.nodes,
location=spatial.biomass.locations,
carrier="solid biomass"
carrier="solid biomass",
unit="MWh_LHV"
)
n.madd("Store",
@ -1882,8 +1803,7 @@ def add_biomass(n, costs):
transport_costs = pd.read_csv(
snakemake.input.biomass_transport_costs,
index_col=0,
squeeze=True
)
).squeeze()
# add biomass transport
biomass_transport = create_network_topology(n, "biomass transport ", bidirectional=False)
@ -1956,7 +1876,8 @@ def add_industry(n, costs):
n.madd("Bus",
spatial.biomass.industry,
location=spatial.biomass.locations,
carrier="solid biomass for industry"
carrier="solid biomass for industry",
unit="MWh_LHV"
)
if options["biomass_transport"]:
@ -1998,7 +1919,8 @@ def add_industry(n, costs):
n.madd("Bus",
spatial.gas.industry,
location=spatial.gas.locations,
carrier="gas for industry")
carrier="gas for industry",
unit="MWh_LHV")
gas_demand = industrial_demand.loc[nodes, "methane"] / 8760.
@ -2054,7 +1976,8 @@ def add_industry(n, costs):
nodes,
suffix=" H2 liquid",
carrier="H2 liquid",
location=nodes
location=nodes,
unit="MWh_LHV"
)
n.madd("Link",
@ -2075,7 +1998,7 @@ def add_industry(n, costs):
all_navigation = ["total international navigation", "total domestic navigation"]
efficiency = options['shipping_average_efficiency'] / costs.at["fuel cell", "efficiency"]
shipping_hydrogen_share = get(options['shipping_hydrogen_share'], investment_year)
p_set = shipping_hydrogen_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
p_set = shipping_hydrogen_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 * efficiency / 8760
n.madd("Load",
nodes,
@ -2089,17 +2012,17 @@ def add_industry(n, costs):
shipping_oil_share = 1 - shipping_hydrogen_share
p_set = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
p_set = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum(axis=1) * 1e6 / 8760.
n.madd("Load",
nodes,
suffix=" shipping oil",
bus="EU oil",
bus=spatial.oil.nodes,
carrier="shipping oil",
p_set=p_set
)
co2 = shipping_oil_share * nodal_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
co2 = shipping_oil_share * pop_weighted_energy_totals.loc[nodes, all_navigation].sum().sum() * 1e6 / 8760 * costs.at["oil", "CO2 intensity"]
n.add("Load",
"shipping oil emissions",
@ -2108,30 +2031,30 @@ def add_industry(n, costs):
p_set=-co2
)
if "EU oil" not in n.buses.index:
n.add("Bus",
"EU oil",
location="EU",
carrier="oil"
if "oil" not in n.buses.carrier.unique():
n.madd("Bus",
spatial.oil.nodes,
location=spatial.oil.locations,
carrier="oil",
unit="MWh_LHV"
)
if "EU oil Store" not in n.stores.index:
if "oil" not in n.stores.carrier.unique():
#could correct to e.g. 0.001 EUR/kWh * annuity and O&M
n.add("Store",
"EU oil Store",
bus="EU oil",
n.madd("Store",
[oil_bus + " Store" for oil_bus in spatial.oil.nodes],
bus=spatial.oil.nodes,
e_nom_extendable=True,
e_cyclic=True,
carrier="oil",
)
if "EU oil" not in n.generators.index:
if "oil" not in n.generators.carrier.unique():
n.add("Generator",
"EU oil",
bus="EU oil",
n.madd("Generator",
spatial.oil.nodes,
bus=spatial.oil.nodes,
p_nom_extendable=True,
carrier="oil",
marginal_cost=costs.at["oil", 'fuel']
@ -2146,7 +2069,7 @@ def add_industry(n, costs):
n.madd("Link",
nodes_heat[name] + f" {name} oil boiler",
p_nom_extendable=True,
bus0="EU oil",
bus0=spatial.oil.nodes,
bus1=nodes_heat[name] + f" {name} heat",
bus2="co2 atmosphere",
carrier=f"{name} oil boiler",
@ -2159,7 +2082,7 @@ def add_industry(n, costs):
n.madd("Link",
nodes + " Fischer-Tropsch",
bus0=nodes + " H2",
bus1="EU oil",
bus1=spatial.oil.nodes,
bus2=spatial.co2.nodes,
carrier="Fischer-Tropsch",
efficiency=costs.at["Fischer-Tropsch", 'efficiency'],
@ -2169,19 +2092,19 @@ def add_industry(n, costs):
lifetime=costs.at['Fischer-Tropsch', 'lifetime']
)
n.add("Load",
"naphtha for industry",
bus="EU oil",
n.madd("Load",
["naphtha for industry"],
bus=spatial.oil.nodes,
carrier="naphtha for industry",
p_set=industrial_demand.loc[nodes, "naphtha"].sum() / 8760
)
all_aviation = ["total international aviation", "total domestic aviation"]
p_set = nodal_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
p_set = pop_weighted_energy_totals.loc[nodes, all_aviation].sum(axis=1).sum() * 1e6 / 8760
n.add("Load",
"kerosene for aviation",
bus="EU oil",
n.madd("Load",
["kerosene for aviation"],
bus=spatial.oil.nodes,
carrier="kerosene for aviation",
p_set=p_set
)
@ -2227,7 +2150,8 @@ def add_industry(n, costs):
n.add("Bus",
"process emissions",
location="EU",
carrier="process emissions"
carrier="process emissions",
unit="t_co2"
)
# this should be process emissions fossil+feedstock
@ -2297,7 +2221,7 @@ def add_agriculture(n, costs):
suffix=" agriculture electricity",
bus=nodes,
carrier='agriculture electricity',
p_set=nodal_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture electricity"] * 1e6 / 8760
)
# heat
@ -2307,7 +2231,7 @@ def add_agriculture(n, costs):
suffix=" agriculture heat",
bus=nodes + " services rural heat",
carrier="agriculture heat",
p_set=nodal_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
p_set=pop_weighted_energy_totals.loc[nodes, "total agriculture heat"] * 1e6 / 8760
)
# machinery
@ -2316,7 +2240,7 @@ def add_agriculture(n, costs):
assert electric_share <= 1.
ice_share = 1 - electric_share
machinery_nodal_energy = nodal_energy_totals.loc[nodes, "total agriculture machinery"]
machinery_nodal_energy = pop_weighted_energy_totals.loc[nodes, "total agriculture machinery"]
if electric_share > 0:
@ -2332,9 +2256,9 @@ def add_agriculture(n, costs):
if ice_share > 0:
n.add("Load",
"agriculture machinery oil",
bus="EU oil",
n.madd("Load",
["agriculture machinery oil"],
bus=spatial.oil.nodes,
carrier="agriculture machinery oil",
p_set=ice_share * machinery_nodal_energy.sum() * 1e6 / 8760
)
@ -2357,7 +2281,7 @@ def decentral(n):
def remove_h2_network(n):
n.links.drop(n.links.index[n.links.carrier == "H2 pipeline"], inplace=True)
n.links.drop(n.links.index[n.links.carrier.str.contains("H2 pipeline")], inplace=True)
if "EU H2 Store" in n.stores.index:
n.stores.drop("EU H2 Store", inplace=True)
@ -2411,7 +2335,7 @@ if __name__ == "__main__":
simpl='',
opts="",
clusters="37",
lv=1.0,
lv=1.5,
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
planning_horizons="2020",
)
@ -2436,9 +2360,11 @@ if __name__ == "__main__":
Nyears,
snakemake.config['costs']['lifetime'])
pop_weighted_energy_totals = pd.read_csv(snakemake.input.pop_weighted_energy_totals, index_col=0)
patch_electricity_network(n)
define_spatial(pop_layout.index)
spatial = define_spatial(pop_layout.index, options)
if snakemake.config["foresight"] == 'myopic':
@ -2466,8 +2392,6 @@ if __name__ == "__main__":
if o == "biomasstransport":
options["biomass_transport"] = True
nodal_energy_totals, heat_demand, ashp_cop, gshp_cop, solar_thermal, transport, avail_profile, dsm_profile, nodal_transport_data, district_heat_share = prepare_data(n)
if "nodistrict" in opts:
options["district_heating"]["progress"] = 0.0
@ -2515,7 +2439,7 @@ if __name__ == "__main__":
fn = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/carbon_budget_distribution.csv'
if not os.path.exists(fn):
build_carbon_budget(o, fn)
co2_cap = pd.read_csv(fn, index_col=0, squeeze=True)
co2_cap = pd.read_csv(fn, index_col=0).squeeze()
limit = co2_cap[investment_year]
break
for o in opts:

2
scripts/retrieve_sector_databundle.py
View File

@ -19,7 +19,7 @@ from _helpers import progress_retrieve, configure_logging
if __name__ == "__main__":
configure_logging(snakemake)
url = "https://zenodo.org/record/5546517/files/pypsa-eur-sec-data-bundle.tar.gz"
url = "https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz"
tarball_fn = Path("sector-bundle.tar.gz")
to_fn = Path("data")

45
scripts/solve_network.py
View File

@ -33,14 +33,14 @@ def _add_land_use_constraint(n):
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"].groupby(n.generators.bus.map(n.buses.location)).sum()
existing.index += " " + carrier + "-" + snakemake.wildcards.planning_horizons
n.generators.loc[existing.index,"p_nom_max"] -= existing
n.generators.p_nom_max.clip(lower=0, inplace=True)
def _add_land_use_constraint_m(n):
# if generators clustering is lower than network clustering, land_use accounting is at generators clusters
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
planning_horizons = snakemake.config["scenario"]["planning_horizons"]
grouping_years = snakemake.config["existing_capacities"]["grouping_years"]
current_horizon = snakemake.wildcards.planning_horizons
@ -48,9 +48,9 @@ def _add_land_use_constraint_m(n):
existing = n.generators.loc[n.generators.carrier==carrier,"p_nom"]
ind = list(set([i.split(sep=" ")[0] + ' ' + i.split(sep=" ")[1] for i in existing.index]))
previous_years = [
str(y) for y in
str(y) for y in
planning_horizons + grouping_years
if y < int(snakemake.wildcards.planning_horizons)
]
@ -59,13 +59,13 @@ def _add_land_use_constraint_m(n):
ind2 = [i for i in ind if i + " " + carrier + "-" + p_year in existing.index]
sel_current = [i + " " + carrier + "-" + current_horizon for i in ind2]
sel_p_year = [i + " " + carrier + "-" + p_year for i in ind2]
n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[sel_p_year].rename(lambda x: x[:-4] + current_horizon)
n.generators.loc[sel_current, "p_nom_max"] -= existing.loc[sel_p_year].rename(lambda x: x[:-4] + current_horizon)
n.generators.p_nom_max.clip(lower=0, inplace=True)
def prepare_network(n, solve_opts=None):
if 'clip_p_max_pu' in solve_opts:
for df in (n.generators_t.p_max_pu, n.generators_t.p_min_pu, n.storage_units_t.inflow):
df.where(df>solve_opts['clip_p_max_pu'], other=0., inplace=True)
@ -185,40 +185,43 @@ def add_chp_constraints(n):
define_constraints(n, lhs, "<=", 0, 'chplink', 'backpressure')
def basename(x):
return x.split("-2")[0]
def add_pipe_retrofit_constraint(n):
"""Add constraint for retrofitting existing CH4 pipelines to H2 pipelines."""
gas_pipes_i = n.links[n.links.carrier=="gas pipeline"].index
h2_retrofitted_i = n.links[n.links.carrier=='H2 pipeline retrofitted'].index
gas_pipes_i = n.links.query("carrier == 'gas pipeline' and p_nom_extendable").index
h2_retrofitted_i = n.links.query("carrier == 'H2 pipeline retrofitted' and p_nom_extendable").index
if h2_retrofitted_i.empty or gas_pipes_i.empty: return
link_p_nom = get_var(n, "Link", "p_nom")
pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom']
CH4_per_H2 = 1 / n.config["sector"]["H2_retrofit_capacity_per_CH4"]
fr = "H2 pipeline retrofitted"
to = "gas pipeline"
pipe_capacity = n.links.loc[gas_pipes_i, 'p_nom'].rename(basename)
lhs = linexpr(
(CH4_per_H2, link_p_nom.loc[h2_retrofitted_i].rename(index=lambda x: x.replace(fr, to))),
(1, link_p_nom.loc[gas_pipes_i])
)
lhs.rename(basename, inplace=True)
define_constraints(n, lhs, "=", pipe_capacity, 'Link', 'pipe_retrofit')
def add_co2_sequestration_limit(n, sns):
co2_stores = n.stores.loc[n.stores.carrier=='co2 stored'].index
if co2_stores.empty or ('Store', 'e') not in n.variables.index:
return
vars_final_co2_stored = get_var(n, 'Store', 'e').loc[sns[-1], co2_stores]
lhs = linexpr((1, vars_final_co2_stored)).sum()
limit = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
@ -226,7 +229,7 @@ def add_co2_sequestration_limit(n, sns):
if not "seq" in o: continue
limit = float(o[o.find("seq")+3:])
break
name = 'co2_sequestration_limit'
sense = "<="
@ -258,7 +261,7 @@ def solve_network(n, config, opts='', **kwargs):
if cf_solving.get('skip_iterations', False):
network_lopf(n, solver_name=solver_name, solver_options=solver_options,
extra_functionality=extra_functionality,
extra_functionality=extra_functionality,
keep_shadowprices=keep_shadowprices, **kwargs)
else:
ilopf(n, solver_name=solver_name, solver_options=solver_options,
@ -277,10 +280,11 @@ if __name__ == "__main__":
snakemake = mock_snakemake(
'solve_network',
simpl='',
clusters=48,
opts="",
clusters="37",
lv=1.0,
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
planning_horizons=2050,
sector_opts='168H-T-H-B-I-A-solar+p3-dist1',
planning_horizons="2030",
)
logging.basicConfig(filename=snakemake.log.python,
@ -288,6 +292,7 @@ if __name__ == "__main__":
tmpdir = snakemake.config['solving'].get('tmpdir')
if tmpdir is not None:
from pathlib import Path
Path(tmpdir).mkdir(parents=True, exist_ok=True)
opts = snakemake.wildcards.opts.split('-')
solve_opts = snakemake.config['solving']['options']

607
test/config.myopic.yaml Normal file
View File

@ -0,0 +1,607 @@
version: 0.6.0
logging_level: INFO
retrieve_sector_databundle: true
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
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"
end: "2013-04-01"
closed: left # end is not inclusive
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
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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_transport: false # biomass transport between nodes
conventional_generation: # generator : carrier
OCGT: gas
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:
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
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'

605
test/config.overnight.yaml Normal file
View File

@ -0,0 +1,605 @@
version: 0.6.0
logging_level: INFO
retrieve_sector_databundle: true
results_dir: results/
summary_dir: results
costs_dir: ../technology-data/outputs/
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"
end: "2013-04-01"
closed: left # end is not inclusive
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
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
co2_network: 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
use_fischer_tropsch_waste_heat: true
use_fuel_cell_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: 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
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:
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
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'