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2
.github/workflows/ci.yaml vendored
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@ -64,7 +64,7 @@ jobs:
- name: Add solver to environment - name: Add solver to environment
run: | run: |
echo -e " - coincbc\n - ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml echo -e "- coincbc\n- ipopt<3.13.3" >> ../pypsa-eur/envs/environment.yaml
- name: Setup Mambaforge - name: Setup Mambaforge
uses: conda-incubator/setup-miniconda@v2 uses: conda-incubator/setup-miniconda@v2

2
.gitignore vendored
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@ -46,3 +46,5 @@ config.yaml
doc/_build doc/_build
*.xls *.xls
*.geojson

23
Snakefile
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@ -256,9 +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), 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 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"), regions_onshore=pypsaeur("resources/regions_onshore_elec_s{simpl}_{clusters}.geojson"),
nuts3_population="../pypsa-eur/data/bundle/nama_10r_3popgdp.tsv.gz", nuts3_population=pypsaeur("data/bundle/nama_10r_3popgdp.tsv.gz"),
swiss_cantons="../pypsa-eur/data/bundle/ch_cantons.csv", swiss_cantons=pypsaeur("data/bundle/ch_cantons.csv"),
swiss_population="../pypsa-eur/data/bundle/je-e-21.03.02.xls", swiss_population=pypsaeur("data/bundle/je-e-21.03.02.xls"),
country_shapes=pypsaeur('resources/country_shapes.geojson') country_shapes=pypsaeur('resources/country_shapes.geojson')
output: output:
biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv', biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',
@ -474,7 +474,7 @@ rule prepare_sector_network:
co2="data/eea/UNFCCC_v23.csv", co2="data/eea/UNFCCC_v23.csv",
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv', biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.csv',
heat_profile="data/heat_load_profile_BDEW.csv", heat_profile="data/heat_load_profile_BDEW.csv",
costs=CDIR + "costs_{planning_horizons}.csv", costs=CDIR + "costs_{}.csv".format(config['costs']['year']) if config["foresight"] == "overnight" else CDIR + "costs_{planning_horizons}.csv",
profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"), profile_offwind_ac=pypsaeur("resources/profile_offwind-ac.nc"),
profile_offwind_dc=pypsaeur("resources/profile_offwind-dc.nc"), profile_offwind_dc=pypsaeur("resources/profile_offwind-dc.nc"),
h2_cavern="resources/salt_cavern_potentials_s{simpl}_{clusters}.csv", h2_cavern="resources/salt_cavern_potentials_s{simpl}_{clusters}.csv",
@ -532,6 +532,14 @@ rule copy_config:
script: "scripts/copy_config.py" script: "scripts/copy_config.py"
rule copy_conda_env:
output: SDIR + '/configs/environment.yaml'
threads: 1
resources: mem_mb=500
benchmark: SDIR + "/benchmarks/copy_conda_env"
shell: "conda env export -f {output} --no-builds"
rule make_summary: rule make_summary:
input: input:
overrides="data/override_component_attrs", overrides="data/override_component_attrs",
@ -539,7 +547,7 @@ rule make_summary:
RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc", RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
**config['scenario'] **config['scenario']
), ),
costs=CDIR + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]), costs=CDIR + "costs_{}.csv".format(config['costs']['year']) if config["foresight"] == "overnight" else CDIR + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
plots=expand( plots=expand(
RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf", RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
**config['scenario'] **config['scenario']
@ -589,8 +597,9 @@ if config["foresight"] == "overnight":
input: input:
overrides="data/override_component_attrs", overrides="data/override_component_attrs",
network=RDIR + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc", network=RDIR + "/prenetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
costs=CDIR + "costs_{planning_horizons}.csv", costs=CDIR + "costs_{}.csv".format(config['costs']['year']),
config=SDIR + '/configs/config.yaml' config=SDIR + '/configs/config.yaml',
env=SDIR + '/configs/environment.yaml',
output: RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc" output: RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
shadow: "shallow" shadow: "shallow"
log: log:

119
config.default.yaml
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@ -33,15 +33,15 @@ scenario:
# A for agriculture, forestry and fishing # A for agriculture, forestry and fishing
# solar+c0.5 reduces the capital cost of solar to 50\% of reference value # solar+c0.5 reduces the capital cost of solar to 50\% of reference value
# solar+p3 multiplies the available installable potential by factor 3 # solar+p3 multiplies the available installable potential by factor 3
# co2 stored+e2 multiplies the potential of CO2 sequestration by a factor 2 # seq400 sets the potential of CO2 sequestration to 400 Mt CO2 per year
# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv # 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 # for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
# emissions throughout the transition path in the timeframe determined by the # emissions throughout the transition path in the timeframe determined by the
# planning_horizons), be:beta decay; ex:exponential decay # planning_horizons), be:beta decay; ex:exponential decay
# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential # cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
# decay with initial growth rate 0 # decay with initial growth rate 0
planning_horizons: # investment years for myopic and perfect; or costs year for overnight planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
- 2030 - 2050
# for example, set to # for example, set to
# - 2020 # - 2020
# - 2030 # - 2030
@ -154,11 +154,11 @@ sector:
potential: 0.6 # maximum fraction of urban demand which can be supplied by 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 # 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 = 0 means today's district heating share, progress = 1 means maximum fraction of urban demand is supplied by district heating
progress: 1 progress:
# 2020: 0.0 2020: 0.0
# 2030: 0.3 2030: 0.3
# 2040: 0.6 2040: 0.6
# 2050: 1.0 2050: 1.0
district_heating_loss: 0.15 district_heating_loss: 0.15
bev_dsm_restriction_value: 0.75 #Set to 0 for no restriction on BEV DSM 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 bev_dsm_restriction_time: 7 #Time at which SOC of BEV has to be dsm_restriction_value
@ -178,16 +178,16 @@ sector:
bev_avail_mean: 0.8 bev_avail_mean: 0.8
v2g: true #allows feed-in to grid from EV battery v2g: true #allows feed-in to grid from EV battery
#what is not EV or FCEV is oil-fuelled ICE #what is not EV or FCEV is oil-fuelled ICE
land_transport_fuel_cell_share: 0.15 # 1 means all FCEVs land_transport_fuel_cell_share: # 1 means all FCEVs
# 2020: 0 2020: 0
# 2030: 0.05 2030: 0.05
# 2040: 0.1 2040: 0.1
# 2050: 0.15 2050: 0.15
land_transport_electric_share: 0.85 # 1 means all EVs land_transport_electric_share: # 1 means all EVs
# 2020: 0 2020: 0
# 2030: 0.25 2030: 0.25
# 2040: 0.6 2040: 0.6
# 2050: 0.85 2050: 0.85
transport_fuel_cell_efficiency: 0.5 transport_fuel_cell_efficiency: 0.5
transport_internal_combustion_efficiency: 0.3 transport_internal_combustion_efficiency: 0.3
agriculture_machinery_electric_share: 0 agriculture_machinery_electric_share: 0
@ -195,29 +195,22 @@ sector:
agriculture_machinery_electric_efficiency: 0.3 # electricity 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_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_liquefaction: false # whether to consider liquefaction costs for shipping H2 demands
shipping_hydrogen_share: 1 # 1 means all hydrogen FC shipping_hydrogen_share: 0
# 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 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 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 # 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) 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 # this can represent e.g. building renovation, building demolition, or if
# the factor is negative: increasing floor area, increased thermal comfort, population growth # 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 reduce_space_heat_exogenously_factor: # per unit reduction in space heat demand
# the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221 # 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% 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 2025: 0.09 # first heat demand increases compared to 2020 because of larger floor area per capita
# 2030: 0.09 2030: 0.09
# 2035: 0.11 2035: 0.11
# 2040: 0.16 2040: 0.16
# 2045: 0.21 2045: 0.21
# 2050: 0.29 2050: 0.29
retrofitting : # co-optimises building renovation to reduce space heat demand retrofitting : # co-optimises building renovation to reduce space heat demand
retro_endogen: false # co-optimise space heat savings retro_endogen: false # co-optimise space heat savings
cost_factor: 1.0 # weight costs for building renovation cost_factor: 1.0 # weight costs for building renovation
@ -252,6 +245,7 @@ sector:
# - onshore # more than 50 km from sea # - onshore # more than 50 km from sea
- nearshore # within 50 km of sea - nearshore # within 50 km of sea
# - offshore # - offshore
ammonia: false # can be false (no NH3 carrier), true (copperplated NH3), "regional" (regionalised NH3 without network)
use_fischer_tropsch_waste_heat: true use_fischer_tropsch_waste_heat: true
use_fuel_cell_waste_heat: true use_fuel_cell_waste_heat: true
electricity_distribution_grid: true electricity_distribution_grid: true
@ -276,36 +270,38 @@ sector:
industry: industry:
St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6 St_primary_fraction: # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
# 2020: 0.6 2020: 0.6
# 2025: 0.55 2025: 0.55
# 2030: 0.5 2030: 0.5
# 2035: 0.45 2035: 0.45
# 2040: 0.4 2040: 0.4
# 2045: 0.35 2045: 0.35
# 2050: 0.3 2050: 0.3
DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF DRI_fraction: # fraction of the primary route converted to DRI + EAF
# 2020: 0 2020: 0
# 2025: 0 2025: 0
# 2030: 0.05 2030: 0.05
# 2035: 0.2 2035: 0.2
# 2040: 0.4 2040: 0.4
# 2045: 0.7 2045: 0.7
# 2050: 1 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 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 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 Al_primary_fraction: # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
# 2020: 0.4 2020: 0.4
# 2025: 0.375 2025: 0.375
# 2030: 0.35 2030: 0.35
# 2035: 0.325 2035: 0.325
# 2040: 0.3 2040: 0.3
# 2045: 0.25 2045: 0.25
# 2050: 0.2 2050: 0.2
MWh_NH3_per_tNH3: 5.166 # LHV
MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf MWh_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_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_H2_per_tNH3_electrolysis: 6.5 # from https://doi.org/10.1016/j.joule.2018.04.017, around 0.197 tH2/tHN3 (>3/17 since some H2 lost and used for energy)
MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB) MWh_elec_per_tNH3_electrolysis: 1.17 # from https://doi.org/10.1016/j.joule.2018.04.017 Table 13 (air separation and HB)
MWh_NH3_per_MWh_H2_cracker: 1.46 # https://github.com/euronion/trace/blob/44a5ff8401762edbef80eff9cfe5a47c8d3c8be4/data/efficiencies.csv
NH3_process_emissions: 24.5 # in MtCO2/a from SMR for H2 production for NH3 from UNFCCC for 2015 for EU28 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 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_primary_fraction: 1. # fraction of today's HVC produced via primary route
@ -327,6 +323,7 @@ industry:
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050 # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
costs: costs:
year: 2030
lifetime: 25 #default lifetime lifetime: 25 #default lifetime
# From a Lion Hirth paper, also reflects average of Noothout et al 2016 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
discountrate: 0.07 discountrate: 0.07
@ -585,6 +582,12 @@ plotting:
H2 pipeline retrofitted: '#ba99b5' H2 pipeline retrofitted: '#ba99b5'
H2 Fuel Cell: '#c251ae' H2 Fuel Cell: '#c251ae'
H2 Electrolysis: '#ff29d9' H2 Electrolysis: '#ff29d9'
# ammonia
NH3: '#46caf0'
ammonia: '#46caf0'
ammonia store: '#00ace0'
ammonia cracker: '#87d0e6'
Haber-Bosch: '#076987'
# syngas # syngas
Sabatier: '#9850ad' Sabatier: '#9850ad'
methanation: '#c44ce6' methanation: '#c44ce6'

4
doc/data.csv
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@ -23,7 +23,7 @@ Floor area missing in hotmaps building stock data,floor_area_missing.csv,unknown
Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment Comparative level investment,comparative_level_investment.csv,Eurostat,https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Comparative_price_levels_for_investment
Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en Electricity taxes,electricity_taxes_eu.csv,Eurostat,https://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nrg_pc_204&lang=en
Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx Building topologies and corresponding standard values,tabula-calculator-calcsetbuilding.csv,unknown,https://episcope.eu/fileadmin/tabula/public/calc/tabula-calculator.xlsx
Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unkown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/ Retrofitting thermal envelope costs for Germany,retro_cost_germany.csv,unknown,https://www.iwu.de/forschung/handlungslogiken/kosten-energierelevanter-bau-und-anlagenteile-bei-modernisierung/
District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,, District heating most countries,jrc-idees-2015/,CC BY 4.0,https://ec.europa.eu/jrc/en/potencia/jrc-idees,,
District heating missing countries,district_heat_share.csv,unkown,https://www.euroheat.org/knowledge-hub/country-profiles,, District heating missing countries,district_heat_share.csv,unknown,https://www.euroheat.org/knowledge-hub/country-profiles,,

Can't render this file because it has a wrong number of fields in line 27.

2
doc/installation.rst
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@ -54,7 +54,7 @@ The requirements are the same as `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>
xarray version >= 0.15.1, you will need the latest master branch of xarray version >= 0.15.1, you will need the latest master branch of
atlite version 0.0.2. atlite version 0.0.2.
You can create an enviroment using the environment.yaml file in pypsa-eur/envs: You can create an environment using the environment.yaml file in pypsa-eur/envs:
.. code:: bash .. code:: bash

15
doc/release_notes.rst
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@ -28,7 +28,7 @@ incorporates retrofitting options to hydrogen.
* New rule ``cluster_gas_network`` that clusters the gas transmission network * New rule ``cluster_gas_network`` that clusters the gas transmission network
data to the model resolution. Cross-regional pipeline capacities are aggregated data to the model resolution. Cross-regional pipeline capacities are aggregated
(while pressure and diameter compability is ignored), intra-regional pipelines (while pressure and diameter compatibility is ignored), intra-regional pipelines
are dropped. Lengths are recalculated based on the regions' centroids. are dropped. Lengths are recalculated based on the regions' centroids.
* With the option ``sector: gas_network:``, the existing gas network is * With the option ``sector: gas_network:``, the existing gas network is
@ -57,11 +57,13 @@ incorporates retrofitting options to hydrogen.
**New features and functionality** **New features and functionality**
* Add option to aggregate network temporally using representative snapshots or segments (with tsam package)
* Add option for biomass boilers (wood pellets) for decentral heating * Add option for biomass boilers (wood pellets) for decentral heating
* Add option for BioSNG (methane from biomass) with and without CC * Add option for BioSNG (methane from biomass) with and without CC
* Add option for BtL (Biomass to liquid fuel/oil) with and without CC * Add option for BtL (Biomass to liquid fuel/oil) with and without CC
* 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. * 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.
@ -71,10 +73,17 @@ incorporates retrofitting options to hydrogen.
* Add option to sweep the global CO2 sequestration potentials with keyword ``seq200`` in the ``{sector_opts}`` wildcard (for limit of 200 Mt CO2). * Add option to sweep the global CO2 sequestration potentials with keyword ``seq200`` in the ``{sector_opts}`` wildcard (for limit of 200 Mt CO2).
* Add option to resolve ammonia as separate energy carrier with Haber-Bosch
synthesis, ammonia cracking, storage and industrial demand. The ammonia
carrier can be nodally resolved or copperplated across Europe. This feature is
controlled by ``sector: ammonia:``.
* Updated `data bundle <https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_ that includes the hydrogan salt cavern storage potentials. * Updated `data bundle <https://zenodo.org/record/5824485/files/pypsa-eur-sec-data-bundle.tar.gz>`_ that includes the hydrogan salt cavern storage potentials.
* Updated and extended documentation in <https://pypsa-eur-sec.readthedocs.io/en/latest/> * Updated and extended documentation in <https://pypsa-eur-sec.readthedocs.io/en/latest/>
* Shipping demand now defaults to (synthetic) oil rather than liquefied hydrogen until 2050.
**Bugfixes** **Bugfixes**
* The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version) * The CO2 sequestration limit implemented as GlobalConstraint (introduced in the previous version)

2
doc/spatial_resolution.rst
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@ -6,6 +6,8 @@ Spatial resolution
The default nodal resolution of the model follows the electricity generation and transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_, which clusters down the electricity transmission substations in each European country based on the k-means algorithm (See `cluster_network <https://pypsa-eur.readthedocs.io/en/latest/simplification/cluster_network.html#rule-cluster-network>`_ for a complete explanation). This gives nodes which correspond to major load and generation centres (typically cities). The default nodal resolution of the model follows the electricity generation and transmission model `PyPSA-Eur <https://github.com/PyPSA/pypsa-eur>`_, which clusters down the electricity transmission substations in each European country based on the k-means algorithm (See `cluster_network <https://pypsa-eur.readthedocs.io/en/latest/simplification/cluster_network.html#rule-cluster-network>`_ for a complete explanation). This gives nodes which correspond to major load and generation centres (typically cities).
The total number of nodes for Europe is set in the ``config.yaml`` file under ``clusters``. The number of nodes can vary between 37, the number of independent countries / synchronous areas, and several hundred. With 200-300 nodes the model needs 100-150 GB RAM to solve with a commercial solver like Gurobi.
Exemplary unsolved network clustered to 512 nodes: Exemplary unsolved network clustered to 512 nodes:
.. image:: ../graphics/elec_s_512.png .. image:: ../graphics/elec_s_512.png

6
scripts/add_existing_baseyear.py
View File

@ -165,11 +165,11 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout) df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
# drop assets which are already phased out / decomissioned # drop assets which are already phased out / decommissioned
phased_out = df_agg[df_agg["DateOut"]<baseyear].index phased_out = df_agg[df_agg["DateOut"]<baseyear].index
df_agg.drop(phased_out, inplace=True) df_agg.drop(phased_out, inplace=True)
# calculate remaining lifetime before phase-out (+1 because assumming # calculate remaining lifetime before phase-out (+1 because assuming
# phase out date at the end of the year) # phase out date at the end of the year)
df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1 df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
@ -251,7 +251,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
# existing capacities are split evenly among regions in every country # existing capacities are split evenly among regions in every country
inv_ind = [i for i in inv_busmap[ind]] inv_ind = [i for i in inv_busmap[ind]]
# for offshore the spliting only inludes coastal regions # for offshore the splitting only includes coastal regions
inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index] inv_ind = [i for i in inv_ind if (i + name_suffix) in n.generators.index]
p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]] p_max_pu = n.generators_t.p_max_pu[[i + name_suffix for i in inv_ind]]

11
scripts/build_industrial_energy_demand_per_country_today.py
View File

@ -65,6 +65,8 @@ def industrial_energy_demand_per_country(country):
df = df_dict[sheet][year].groupby(fuels).sum() df = df_dict[sheet][year].groupby(fuels).sum()
df["ammonia"] = 0.
df['other'] = df['all'] - df.loc[df.index != 'all'].sum() df['other'] = df['all'] - df.loc[df.index != 'all'].sum()
return df return df
@ -89,18 +91,21 @@ def add_ammonia_energy_demand(demand):
fn = snakemake.input.ammonia_production fn = snakemake.input.ammonia_production
ammonia = pd.read_csv(fn, index_col=0)[str(year)] / 1e3 ammonia = pd.read_csv(fn, index_col=0)[str(year)] / 1e3
def ammonia_by_fuel(x): def get_ammonia_by_fuel(x):
fuels = {'gas': config['MWh_CH4_per_tNH3_SMR'], fuels = {'gas': config['MWh_CH4_per_tNH3_SMR'],
'electricity': config['MWh_elec_per_tNH3_SMR']} 'electricity': config['MWh_elec_per_tNH3_SMR']}
return pd.Series({k: x*v for k,v in fuels.items()}) return pd.Series({k: x*v for k,v in fuels.items()})
ammonia = ammonia.apply(ammonia_by_fuel).T ammonia_by_fuel = ammonia.apply(get_ammonia_by_fuel).T
ammonia_by_fuel = ammonia_by_fuel.unstack().reindex(index=demand.index, fill_value=0.)
ammonia = pd.DataFrame({"ammonia": ammonia * config['MWh_NH3_per_tNH3']}).T
demand['Ammonia'] = ammonia.unstack().reindex(index=demand.index, fill_value=0.) demand['Ammonia'] = ammonia.unstack().reindex(index=demand.index, fill_value=0.)
demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - demand["Ammonia"] demand['Basic chemicals (without ammonia)'] = demand["Basic chemicals"] - ammonia_by_fuel
demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True) demand['Basic chemicals (without ammonia)'].clip(lower=0, inplace=True)

1
scripts/build_industrial_energy_demand_per_node.py
View File

@ -9,6 +9,7 @@ if __name__ == '__main__':
'build_industrial_energy_demand_per_node', 'build_industrial_energy_demand_per_node',
simpl='', simpl='',
clusters=48, clusters=48,
planning_horizons=2030,
) )
# import EU ratios df as csv # import EU ratios df as csv

12
scripts/build_industry_sector_ratios.py
View File

@ -60,6 +60,7 @@ index = [
"hydrogen", "hydrogen",
"heat", "heat",
"naphtha", "naphtha",
"ammonia",
"process emission", "process emission",
"process emission from feedstock", "process emission from feedstock",
] ]
@ -432,8 +433,11 @@ def chemicals_industry():
sector = "Ammonia" sector = "Ammonia"
df[sector] = 0.0 df[sector] = 0.0
df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"] if snakemake.config["sector"].get("ammonia", False):
df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"] df.loc["ammonia", sector] = config["MWh_NH3_per_tNH3"]
else:
df.loc["hydrogen", sector] = config["MWh_H2_per_tNH3_electrolysis"]
df.loc["elec", sector] = config["MWh_elec_per_tNH3_electrolysis"]
# Chlorine # Chlorine
@ -614,7 +618,7 @@ def nonmetalic_mineral_products():
# (c) clinker production (kilns), # (c) clinker production (kilns),
# (d) Grinding, packaging. # (d) Grinding, packaging.
# (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c). # (b)+(c) represent 94% of fec. So (a) is joined to (b) and (d) is joined to (c).
# Temperatures above 1400C are required for procesing limestone and sand into clinker. # Temperatures above 1400C are required for processing limestone and sand into clinker.
# Everything (except current electricity and heat consumption and existing biomass) # Everything (except current electricity and heat consumption and existing biomass)
# is transformed into methane for high T. # is transformed into methane for high T.
@ -1106,7 +1110,7 @@ def non_ferrous_metals():
# Aluminium secondary route # Aluminium secondary route
# All is coverted into secondary route fully electrified. # All is converted into secondary route fully electrified.
sector = "Aluminium - secondary production" sector = "Aluminium - secondary production"

16
scripts/build_retro_cost.py
View File

@ -33,7 +33,7 @@ The basic equations:
E_space = H_losses - H_gains E_space = H_losses - H_gains
Heat losses constitute from the losses through heat trasmission (H_tr [W/m²K]) Heat losses constitute from the losses through heat transmission (H_tr [W/m²K])
(this includes heat transfer through building elements and thermal bridges) (this includes heat transfer through building elements and thermal bridges)
and losses by ventilation (H_ve [W/m²K]): and losses by ventilation (H_ve [W/m²K]):
@ -71,7 +71,7 @@ import xarray as xr
# thermal conductivity standard value # thermal conductivity standard value
k = 0.035 k = 0.035
# strenght of relative retrofitting depending on the component # strength of relative retrofitting depending on the component
# determined by historical data of insulation thickness for retrofitting # determined by historical data of insulation thickness for retrofitting
l_weight = pd.DataFrame({"weight": [1.95, 1.48, 1.]}, l_weight = pd.DataFrame({"weight": [1.95, 1.48, 1.]},
index=["Roof", "Wall", "Floor"]) index=["Roof", "Wall", "Floor"])
@ -89,8 +89,8 @@ tau_H_0 = 30
# constant parameter alpha_H_0 [-] according to EN 13790 seasonal method # constant parameter alpha_H_0 [-] according to EN 13790 seasonal method
alpha_H_0 = 0.8 alpha_H_0 = 0.8
# paramter for solar heat load during heating season ------------------------- # parameter for solar heat load during heating season -------------------------
# tabular standard values table p.8 in documenation # tabular standard values table p.8 in documentation
external_shading = 0.6 # vertical orientation: fraction of window area shaded [-] external_shading = 0.6 # vertical orientation: fraction of window area shaded [-]
frame_area_fraction = 0.3 # fraction of frame area of window [-] frame_area_fraction = 0.3 # fraction of frame area of window [-]
non_perpendicular = 0.9 # reduction factor, considering radiation non perpendicular to the glazing[-] non_perpendicular = 0.9 # reduction factor, considering radiation non perpendicular to the glazing[-]
@ -279,7 +279,7 @@ def prepare_building_stock_data():
def prepare_building_topology(u_values, same_building_topology=True): def prepare_building_topology(u_values, same_building_topology=True):
""" """
reads in typical building topologies (e.g. average surface of building elements) reads in typical building topologies (e.g. average surface of building elements)
and typical losses trough thermal bridging and air ventilation and typical losses through thermal bridging and air ventilation
""" """
data_tabula = pd.read_csv(snakemake.input.data_tabula, data_tabula = pd.read_csv(snakemake.input.data_tabula,
@ -585,7 +585,7 @@ def map_to_lstrength(l_strength, df):
def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor): def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor):
""" """
calculates total annual heat losses Q_ht for different insulation thiknesses calculates total annual heat losses Q_ht for different insulation thicknesses
(l_strength), depening on current insulation state (u_values), standard (l_strength), depening on current insulation state (u_values), standard
building topologies and air ventilation from TABULA (data_tabula) and building topologies and air ventilation from TABULA (data_tabula) and
the accumulated difference between internal and external temperature the accumulated difference between internal and external temperature
@ -790,7 +790,7 @@ def sample_dE_costs_area(area, area_tot, costs, dE_space, countries,
# drop not considered countries # drop not considered countries
cost_dE = cost_dE.reindex(countries,level=0) cost_dE = cost_dE.reindex(countries,level=0)
# get share of residential and sevice floor area # get share of residential and service floor area
sec_w = area_tot.value / area_tot.value.groupby(level=0).sum() sec_w = area_tot.value / area_tot.value.groupby(level=0).sum()
# get the total cost-energy-savings weight by sector area # get the total cost-energy-savings weight by sector area
tot = (cost_dE.mul(sec_w, axis=0).groupby(level="country_code").sum() tot = (cost_dE.mul(sec_w, axis=0).groupby(level="country_code").sum()
@ -863,7 +863,7 @@ if __name__ == "__main__":
data_tabula = prepare_building_topology(u_values) data_tabula = prepare_building_topology(u_values)
# costs for retrofitting ------------------------------------------------- # costs for retrofitting -------------------------------------------------
cost_retro, window_assumptions, cost_w, tax_w = prepare_cost_retro(country_iso_dic) cost_retro, window_assumptions, cost_w, tax_w = prepare_cost_retro(country_iso_dic)
# temperature dependend parameters # temperature dependent parameters
d_heat, temperature_factor = prepare_temperature_data() d_heat, temperature_factor = prepare_temperature_data()

2
scripts/helper.py
View File

@ -25,7 +25,7 @@ def override_component_attrs(directory):
Returns Returns
------- -------
Dictionary of overriden component attributes. Dictionary of overridden component attributes.
""" """
attrs = Dict({k : v.copy() for k,v in component_attrs.items()}) attrs = Dict({k : v.copy() for k,v in component_attrs.items()})

4
scripts/make_summary.py
View File

@ -273,7 +273,7 @@ def calculate_supply(n, label, supply):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]: for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + end].map(bus_map, na_action=False)] items = c.df.index[c.df["bus" + end].map(bus_map, na_action=None)]
if len(items) == 0: if len(items) == 0:
continue continue
@ -318,7 +318,7 @@ def calculate_supply_energy(n, label, supply_energy):
for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]: for end in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
items = c.df.index[c.df["bus" + str(end)].map(bus_map, na_action=False)] items = c.df.index[c.df["bus" + str(end)].map(bus_map, na_action=None)]
if len(items) == 0: if len(items) == 0:
continue continue

2
scripts/plot_network.py
View File

@ -23,6 +23,8 @@ def rename_techs_tyndp(tech):
return "power-to-gas" return "power-to-gas"
elif tech == "H2": elif tech == "H2":
return "H2 storage" return "H2 storage"
elif tech in ["NH3", "Haber-Bosch", "ammonia cracker", "ammonia store"]:
return "ammonia"
elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]: elif tech in ["OCGT", "CHP", "gas boiler", "H2 Fuel Cell"]:
return "gas-to-power/heat" return "gas-to-power/heat"
elif "solar" in tech: elif "solar" in tech:

8
scripts/plot_summary.py
View File

@ -52,6 +52,7 @@ def rename_techs(label):
"ror": "hydroelectricity", "ror": "hydroelectricity",
"hydro": "hydroelectricity", "hydro": "hydroelectricity",
"PHS": "hydroelectricity", "PHS": "hydroelectricity",
"NH3": "ammonia",
"co2 Store": "DAC", "co2 Store": "DAC",
"co2 stored": "CO2 sequestration", "co2 stored": "CO2 sequestration",
"AC": "transmission lines", "AC": "transmission lines",
@ -107,6 +108,7 @@ preferred_order = pd.Index([
"natural gas", "natural gas",
"helmeth", "helmeth",
"methanation", "methanation",
"ammonia",
"hydrogen storage", "hydrogen storage",
"power-to-gas", "power-to-gas",
"power-to-liquid", "power-to-liquid",
@ -255,7 +257,7 @@ def plot_balances():
df = df / 1e6 df = df / 1e6
#remove trailing link ports #remove trailing link ports
df.index = [i[:-1] if ((i != "co2") and (i[-1:] in ["0","1","2","3"])) else i for i in df.index] df.index = [i[:-1] if ((i not in ["co2", "NH3"]) and (i[-1:] in ["0","1","2","3"])) else i for i in df.index]
df = df.groupby(df.index.map(rename_techs)).sum() df = df.groupby(df.index.map(rename_techs)).sum()
@ -399,7 +401,7 @@ def plot_carbon_budget_distribution(input_eurostat):
ax1.plot(emissions, color='black', linewidth=3, label=None) ax1.plot(emissions, color='black', linewidth=3, label=None)
#plot commited and uder-discussion targets #plot committed and uder-discussion targets
#(notice that historical emissions include all countries in the #(notice that historical emissions include all countries in the
# network, but targets refer to EU) # network, but targets refer to EU)
ax1.plot([2020],[0.8*emissions[1990]], ax1.plot([2020],[0.8*emissions[1990]],
@ -425,7 +427,7 @@ def plot_carbon_budget_distribution(input_eurostat):
ax1.plot([2050],[0.125*emissions[1990]],'ro', ax1.plot([2050],[0.125*emissions[1990]],'ro',
marker='*', markersize=12, markerfacecolor='black', marker='*', markersize=12, markerfacecolor='black',
markeredgecolor='black', label='EU commited target') markeredgecolor='black', label='EU committed target')
ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01), ax1.legend(fancybox=True, fontsize=18, loc=(0.01,0.01),
facecolor='white', frameon=True) facecolor='white', frameon=True)

178
scripts/prepare_sector_network.py
View File

@ -93,6 +93,19 @@ def define_spatial(nodes, options):
spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes) spatial.gas.df = pd.DataFrame(vars(spatial.gas), index=nodes)
# ammonia
if options.get('ammonia'):
spatial.ammonia = SimpleNamespace()
if options.get("ammonia") == "regional":
spatial.ammonia.nodes = nodes + " NH3"
spatial.ammonia.locations = nodes
else:
spatial.ammonia.nodes = ["EU NH3"]
spatial.ammonia.locations = ["EU"]
spatial.ammonia.df = pd.DataFrame(vars(spatial.ammonia), index=nodes)
# oil # oil
spatial.oil = SimpleNamespace() spatial.oil = SimpleNamespace()
spatial.oil.nodes = ["EU oil"] spatial.oil.nodes = ["EU oil"]
@ -664,6 +677,61 @@ def add_generation(n, costs):
) )
def add_ammonia(n, costs):
logger.info("adding ammonia carrier with synthesis, cracking and storage")
nodes = pop_layout.index
cf_industry = snakemake.config["industry"]
n.add("Carrier", "NH3")
n.madd("Bus",
spatial.ammonia.nodes,
location=spatial.ammonia.locations,
carrier="NH3"
)
n.madd("Link",
nodes,
suffix=" Haber-Bosch",
bus0=nodes,
bus1=spatial.ammonia.nodes,
bus2=nodes + " H2",
p_nom_extendable=True,
carrier="Haber-Bosch",
efficiency=1 / (cf_industry["MWh_elec_per_tNH3_electrolysis"] / cf_industry["MWh_NH3_per_tNH3"]), # output: MW_NH3 per MW_elec
efficiency2=-cf_industry["MWh_H2_per_tNH3_electrolysis"] / cf_industry["MWh_elec_per_tNH3_electrolysis"], # input: MW_H2 per MW_elec
capital_cost=costs.at["Haber-Bosch synthesis", "fixed"],
lifetime=costs.at["Haber-Bosch synthesis", 'lifetime']
)
n.madd("Link",
nodes,
suffix=" ammonia cracker",
bus0=spatial.ammonia.nodes,
bus1=nodes + " H2",
p_nom_extendable=True,
carrier="ammonia cracker",
efficiency=1 / cf_industry["MWh_NH3_per_MWh_H2_cracker"],
capital_cost=costs.at["Ammonia cracker", "fixed"] / cf_industry["MWh_NH3_per_MWh_H2_cracker"], # given per MW_H2
lifetime=costs.at['Ammonia cracker', 'lifetime']
)
# Ammonia Storage
n.madd("Store",
spatial.ammonia.nodes,
suffix=" ammonia store",
bus=spatial.ammonia.nodes,
e_nom_extendable=True,
e_cyclic=True,
carrier="ammonia store",
capital_cost=costs.at["NH3 (l) storage tank incl. liquefaction", "fixed"],
lifetime=costs.at['NH3 (l) storage tank incl. liquefaction', 'lifetime']
)
def add_wave(n, wave_cost_factor): def add_wave(n, wave_cost_factor):
# TODO: handle in Snakefile # TODO: handle in Snakefile
@ -1314,7 +1382,7 @@ def add_land_transport(n, costs):
def build_heat_demand(n): def build_heat_demand(n):
# copy forward the daily average heat demand into each hour, so it can be multipled by the intraday profile # copy forward the daily average heat demand into each hour, so it can be multiplied by the intraday profile
daily_space_heat_demand = xr.open_dataarray(snakemake.input.heat_demand_total).to_pandas().reindex(index=n.snapshots, method="ffill") 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) intraday_profiles = pd.read_csv(snakemake.input.heat_profile, index_col=0)
@ -1656,7 +1724,7 @@ def add_heat(n, costs):
# minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1) # minimum heat demand 'dE' after retrofitting in units of original heat demand (values between 0-1)
dE = retro_data.loc[(ct, sec), ("dE")] dE = retro_data.loc[(ct, sec), ("dE")]
# get addtional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node # get additional energy savings 'dE_diff' between the different retrofitting strengths/generators at one node
dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0]) dE_diff = abs(dE.diff()).fillna(1-dE.iloc[0])
# convert costs Euro/m^2 -> Euro/MWh # convert costs Euro/m^2 -> Euro/MWh
capital_cost = retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \ capital_cost = retro_data.loc[(ct, sec), ("cost")] * floor_area_node / \
@ -2278,6 +2346,20 @@ def add_industry(n, costs):
lifetime=costs.at['cement capture', 'lifetime'] lifetime=costs.at['cement capture', 'lifetime']
) )
if options.get("ammonia"):
if options["ammonia"] == 'regional':
p_set = industrial_demand.loc[spatial.ammonia.locations, "ammonia"].rename(index=lambda x: x + " NH3") / 8760
else:
p_set = industrial_demand["ammonia"].sum() / 8760
n.madd("Load",
spatial.ammonia.nodes,
bus=spatial.ammonia.nodes,
carrier="NH3",
p_set=p_set
)
def add_waste_heat(n): def add_waste_heat(n):
# TODO options? # TODO options?
@ -2417,6 +2499,88 @@ def limit_individual_line_extension(n, maxext):
hvdc = n.links.index[n.links.carrier == 'DC'] hvdc = n.links.index[n.links.carrier == 'DC']
n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext n.links.loc[hvdc, 'p_nom_max'] = n.links.loc[hvdc, 'p_nom'] + maxext
def apply_time_segmentation(n, segments, solver_name="cbc",
overwrite_time_dependent=True):
"""Aggregating time series to segments with different lengths
Input:
n: pypsa Network
segments: (int) number of segments in which the typical period should be
subdivided
solver_name: (str) name of solver
overwrite_time_dependent: (bool) overwrite time dependent data of pypsa network
with typical time series created by tsam
"""
try:
import tsam.timeseriesaggregation as tsam
except:
raise ModuleNotFoundError("Optional dependency 'tsam' not found."
"Install via 'pip install tsam'")
# get all time-dependent data
columns = pd.MultiIndex.from_tuples([],names=['component', 'key', 'asset'])
raw = pd.DataFrame(index=n.snapshots,columns=columns)
for c in n.iterate_components():
for attr, pnl in c.pnl.items():
# exclude e_min_pu which is used for SOC of EVs in the morning
if not pnl.empty and attr != 'e_min_pu':
df = pnl.copy()
df.columns = pd.MultiIndex.from_product([[c.name], [attr], df.columns])
raw = pd.concat([raw, df], axis=1)
# normalise all time-dependent data
annual_max = raw.max().replace(0,1)
raw = raw.div(annual_max, level=0)
# get representative segments
agg = tsam.TimeSeriesAggregation(raw, hoursPerPeriod=len(raw),
noTypicalPeriods=1, noSegments=int(segments),
segmentation=True, solver=solver_name)
segmented = agg.createTypicalPeriods()
weightings = segmented.index.get_level_values("Segment Duration")
offsets = np.insert(np.cumsum(weightings[:-1]), 0, 0)
timesteps = [raw.index[0] + pd.Timedelta(f"{offset}h") for offset in offsets]
snapshots = pd.DatetimeIndex(timesteps)
sn_weightings = pd.Series(weightings, index=snapshots, name="weightings", dtype="float64")
n.set_snapshots(sn_weightings.index)
n.snapshot_weightings = n.snapshot_weightings.mul(sn_weightings, axis=0)
# overwrite time-dependent data with timeseries created by tsam
if overwrite_time_dependent:
values_t = segmented.mul(annual_max).set_index(snapshots)
for component, key in values_t.columns.droplevel(2).unique():
n.pnl(component)[key] = values_t[component, key]
return n
def set_temporal_aggregation(n, opts, solver_name):
"""Aggregate network temporally."""
for o in opts:
# temporal averaging
m = re.match(r"^\d+h$", o, re.IGNORECASE)
if m is not None:
n = average_every_nhours(n, m.group(0))
break
# representative snapshots
m = re.match(r"(^\d+)sn$", o, re.IGNORECASE)
if m is not None:
sn = int(m[1])
logger.info(f"use every {sn} snapshot as representative")
n.set_snapshots(n.snapshots[::sn])
n.snapshot_weightings *= sn
break
# segments with package tsam
m = re.match(r"^(\d+)seg$", o, re.IGNORECASE)
if m is not None:
segments = int(m[1])
logger.info(f"use temporal segmentation with {segments} segments")
n = apply_time_segmentation(n, segments, solver_name=solver_name)
break
return n
#%% #%%
if __name__ == "__main__": if __name__ == "__main__":
if 'snakemake' not in globals(): if 'snakemake' not in globals():
@ -2509,6 +2673,9 @@ if __name__ == "__main__":
if options['dac']: if options['dac']:
add_dac(n, costs) add_dac(n, costs)
if options['ammonia']:
add_ammonia(n, costs)
if "decentral" in opts: if "decentral" in opts:
decentral(n) decentral(n)
@ -2518,11 +2685,8 @@ if __name__ == "__main__":
if options["co2_network"]: if options["co2_network"]:
add_co2_network(n, costs) add_co2_network(n, costs)
for o in opts: solver_name = snakemake.config["solving"]["solver"]["name"]
m = re.match(r'^\d+h$', o, re.IGNORECASE) n = set_temporal_aggregation(n, opts, solver_name)
if m is not None:
n = average_every_nhours(n, m.group(0))
break
limit_type = "config" limit_type = "config"
limit = get(snakemake.config["co2_budget"], investment_year) limit = get(snakemake.config["co2_budget"], investment_year)

2
scripts/solve_network.py
View File

@ -227,7 +227,7 @@ def add_co2_sequestration_limit(n, sns):
limit = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6 limit = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
for o in opts: for o in opts:
if not "seq" in o: continue if not "seq" in o: continue
limit = float(o[o.find("seq")+3:]) limit = float(o[o.find("seq")+3:]) * 1e6
break break
name = 'co2_sequestration_limit' name = 'co2_sequestration_limit'

4
test/config.myopic.yaml
View File

@ -262,6 +262,9 @@ sector:
biomass_transport: false # biomass transport between nodes biomass_transport: false # biomass transport between nodes
conventional_generation: # generator : carrier conventional_generation: # generator : carrier
OCGT: gas OCGT: gas
biomass_boiler: false
biomass_to_liquid: false
biosng: false
industry: industry:
@ -316,6 +319,7 @@ industry:
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050 # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
costs: costs:
year: 2030
lifetime: 25 #default lifetime lifetime: 25 #default lifetime
# From a Lion Hirth paper, also reflects average of Noothout et al 2016 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
discountrate: 0.07 discountrate: 0.07

4
test/config.overnight.yaml
View File

@ -260,6 +260,9 @@ sector:
biomass_transport: false # biomass transport between nodes biomass_transport: false # biomass transport between nodes
conventional_generation: # generator : carrier conventional_generation: # generator : carrier
OCGT: gas OCGT: gas
biomass_boiler: false
biomass_to_liquid: false
biosng: false
industry: industry:
@ -314,6 +317,7 @@ industry:
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050 # Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
costs: costs:
year: 2030
lifetime: 25 #default lifetime lifetime: 25 #default lifetime
# From a Lion Hirth paper, also reflects average of Noothout et al 2016 # From a Lion Hirth paper, also reflects average of Noothout et al 2016
discountrate: 0.07 discountrate: 0.07