Merge branch 'master' into nh3
This commit is contained in:
Fabian Neumann
GitHub
commit
1611d63a8a
4
.github/workflows/ci.yaml
vendored
4
.github/workflows/ci.yaml
vendored
@ -104,6 +104,6 @@ jobs:
|
||||
conda activate pypsa-eur
|
||||
conda list
|
||||
cp test/config.overnight.yaml config.yaml
|
||||
snakemake -call solve_all_networks
|
||||
snakemake -call
|
||||
cp test/config.myopic.yaml config.yaml
|
||||
snakemake -call solve_all_networks
|
||||
snakemake -call
|
||||
|
||||
18
README.md
18
README.md
@ -20,14 +20,16 @@ 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.
|
||||
[issues](https://github.com/PyPSA/pypsa-eur-sec/issues) collect known
|
||||
topics we are working on (please feel free to help or make suggestions).
|
||||
The [documentation](https://pypsa-eur-sec.readthedocs.io/) remains somewhat
|
||||
patchy.
|
||||
You can find showcases of the model's capabilities in the preprint
|
||||
[Benefits of a Hydrogen Network in Europe](https://arxiv.org/abs/2207.05816),
|
||||
a [paper in Joule with a description of the industry
|
||||
sector](https://arxiv.org/abs/2109.09563), or in [a 2021
|
||||
presentation at EMP-E](https://nworbmot.org/energy/brown-empe.pdf).
|
||||
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.
|
||||
|
||||
22
Snakefile
22
Snakefile
@ -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),
|
||||
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=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"),
|
||||
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",
|
||||
country_shapes=pypsaeur('resources/country_shapes.geojson')
|
||||
output:
|
||||
biomass_potentials_all='resources/biomass_potentials_all_s{simpl}_{clusters}.csv',
|
||||
@ -442,14 +442,14 @@ rule build_population_weighted_energy_totals:
|
||||
|
||||
|
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rule build_transport_demand:
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||||
input:
|
||||
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:
|
||||
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",
|
||||
@ -464,15 +464,17 @@ rule prepare_sector_network:
|
||||
overrides="data/override_component_attrs",
|
||||
network=pypsaeur('networks/elec_s{simpl}_{clusters}_ec_lv{lv}_{opts}.nc'),
|
||||
energy_totals_name='resources/energy_totals.csv',
|
||||
eurostat=input_eurostat,
|
||||
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',
|
||||
co2="data/eea/UNFCCC_v23.csv",
|
||||
biomass_potentials='resources/biomass_potentials_s{simpl}_{clusters}.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_dc=pypsaeur("resources/profile_offwind-dc.nc"),
|
||||
h2_cavern="resources/salt_cavern_potentials_s{simpl}_{clusters}.csv",
|
||||
@ -537,7 +539,7 @@ rule make_summary:
|
||||
RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc",
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**config['scenario']
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),
|
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costs=CDIR + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
|
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costs=CDIR + "costs_{}.csv".format(config['costs']['year']) if config["foresight"] == "overnight" else CDIR + "costs_{}.csv".format(config['scenario']['planning_horizons'][0]),
|
||||
plots=expand(
|
||||
RDIR + "/maps/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}-costs-all_{planning_horizons}.pdf",
|
||||
**config['scenario']
|
||||
@ -568,7 +570,9 @@ rule plot_summary:
|
||||
input:
|
||||
costs=SDIR + '/csvs/costs.csv',
|
||||
energy=SDIR + '/csvs/energy.csv',
|
||||
balances=SDIR + '/csvs/supply_energy.csv'
|
||||
balances=SDIR + '/csvs/supply_energy.csv',
|
||||
eurostat=input_eurostat,
|
||||
country_codes='data/Country_codes.csv',
|
||||
output:
|
||||
costs=SDIR + '/graphs/costs.pdf',
|
||||
energy=SDIR + '/graphs/energy.pdf',
|
||||
@ -585,7 +589,7 @@ if config["foresight"] == "overnight":
|
||||
input:
|
||||
overrides="data/override_component_attrs",
|
||||
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'
|
||||
output: RDIR + "/postnetworks/elec_s{simpl}_{clusters}_lv{lv}_{opts}_{sector_opts}_{planning_horizons}.nc"
|
||||
shadow: "shallow"
|
||||
|
||||
@ -33,16 +33,21 @@ scenario:
|
||||
# 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
|
||||
# seq400 sets the potential of CO2 sequestration to 400 Mt CO2 per year
|
||||
# dist{n} includes distribution grids with investment cost of n times cost in data/costs.csv
|
||||
# for myopic/perfect foresight cb states the carbon budget in GtCO2 (cumulative
|
||||
# emissions throughout the transition path in the timeframe determined by the
|
||||
# planning_horizons), be:beta decay; ex:exponential decay
|
||||
# cb40ex0 distributes a carbon budget of 40 GtCO2 following an exponential
|
||||
# decay with initial growth rate 0
|
||||
planning_horizons: # investment years for myopic and perfect; or costs year for overnight
|
||||
- 2030
|
||||
# for example, set to [2020, 2030, 2040, 2050] for myopic foresight
|
||||
planning_horizons: # investment years for myopic and perfect; for overnight, year of cost assumptions can be different and is defined under 'costs'
|
||||
- 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
|
||||
@ -134,7 +139,8 @@ solar_thermal:
|
||||
|
||||
# only relevant for foresight = myopic or perfect
|
||||
existing_capacities:
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grouping_years: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
|
||||
grouping_years_power: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2020, 2025, 2030]
|
||||
grouping_years_heat: [1980, 1985, 1990, 1995, 2000, 2005, 2010, 2015, 2019] # these should not extend 2020
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||||
threshold_capacity: 10
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||||
conventional_carriers:
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- lignite
|
||||
@ -148,11 +154,11 @@ sector:
|
||||
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
|
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# 2030: 0.3
|
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# 2040: 0.6
|
||||
# 2050: 1.0
|
||||
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
|
||||
@ -172,16 +178,16 @@ sector:
|
||||
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
|
||||
land_transport_fuel_cell_share: # 1 means all FCEVs
|
||||
2020: 0
|
||||
2030: 0.05
|
||||
2040: 0.1
|
||||
2050: 0.15
|
||||
land_transport_electric_share: # 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
|
||||
@ -189,29 +195,29 @@ sector:
|
||||
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
|
||||
shipping_hydrogen_share: # 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
|
||||
reduce_space_heat_exogenously_factor: # per unit reduction in space heat demand
|
||||
# the default factors are determined by the LTS scenario from http://tool.european-calculator.eu/app/buildings/building-types-area/?levers=1ddd4444421213bdbbbddd44444ffffff11f411111221111211l212221
|
||||
# 2020: 0.10 # this results in a space heat demand reduction of 10%
|
||||
# 2025: 0.09 # first heat demand increases compared to 2020 because of larger floor area per capita
|
||||
# 2030: 0.09
|
||||
# 2035: 0.11
|
||||
# 2040: 0.16
|
||||
# 2045: 0.21
|
||||
# 2050: 0.29
|
||||
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
|
||||
@ -225,6 +231,7 @@ sector:
|
||||
central: 180
|
||||
boilers: true
|
||||
oil_boilers: false
|
||||
biomass_boiler: true
|
||||
chp: true
|
||||
micro_chp: false
|
||||
solar_thermal: true
|
||||
@ -232,8 +239,9 @@ sector:
|
||||
marginal_cost_storage: 0. #1e-4
|
||||
methanation: true
|
||||
helmeth: true
|
||||
coal_cc: false
|
||||
dac: true
|
||||
co2_vent: true
|
||||
co2_vent: false
|
||||
SMR: true
|
||||
co2_sequestration_potential: 200 #MtCO2/a sequestration potential for Europe
|
||||
co2_sequestration_cost: 10 #EUR/tCO2 for sequestration of CO2
|
||||
@ -263,35 +271,38 @@ sector:
|
||||
biomass_transport: false # biomass transport between nodes
|
||||
conventional_generation: # generator : carrier
|
||||
OCGT: gas
|
||||
biomass_to_liquid: false
|
||||
biosng: false
|
||||
|
||||
|
||||
|
||||
industry:
|
||||
St_primary_fraction: 0.3 # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
|
||||
# 2020: 0.6
|
||||
# 2025: 0.55
|
||||
# 2030: 0.5
|
||||
# 2035: 0.45
|
||||
# 2040: 0.4
|
||||
# 2045: 0.35
|
||||
# 2050: 0.3
|
||||
DRI_fraction: 1 # fraction of the primary route converted to DRI + EAF
|
||||
# 2020: 0
|
||||
# 2025: 0
|
||||
# 2030: 0.05
|
||||
# 2035: 0.2
|
||||
# 2040: 0.4
|
||||
# 2045: 0.7
|
||||
# 2050: 1
|
||||
St_primary_fraction: # fraction of steel produced via primary route versus secondary route (scrap+EAF); today fraction is 0.6
|
||||
2020: 0.6
|
||||
2025: 0.55
|
||||
2030: 0.5
|
||||
2035: 0.45
|
||||
2040: 0.4
|
||||
2045: 0.35
|
||||
2050: 0.3
|
||||
DRI_fraction: # fraction of the primary route converted to DRI + EAF
|
||||
2020: 0
|
||||
2025: 0
|
||||
2030: 0.05
|
||||
2035: 0.2
|
||||
2040: 0.4
|
||||
2045: 0.7
|
||||
2050: 1
|
||||
H2_DRI: 1.7 #H2 consumption in Direct Reduced Iron (DRI), MWh_H2,LHV/ton_Steel from 51kgH2/tSt in Vogl et al (2018) doi:10.1016/j.jclepro.2018.08.279
|
||||
elec_DRI: 0.322 #electricity consumption in Direct Reduced Iron (DRI) shaft, MWh/tSt HYBRIT brochure https://ssabwebsitecdn.azureedge.net/-/media/hybrit/files/hybrit_brochure.pdf
|
||||
Al_primary_fraction: 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
|
||||
Al_primary_fraction: # fraction of aluminium produced via the primary route versus scrap; today fraction is 0.4
|
||||
2020: 0.4
|
||||
2025: 0.375
|
||||
2030: 0.35
|
||||
2035: 0.325
|
||||
2040: 0.3
|
||||
2045: 0.25
|
||||
2050: 0.2
|
||||
MWh_NH3_per_tNH3: 5.166 # LHV
|
||||
MWh_CH4_per_tNH3_SMR: 10.8 # 2012's demand from https://ec.europa.eu/docsroom/documents/4165/attachments/1/translations/en/renditions/pdf
|
||||
MWh_elec_per_tNH3_SMR: 0.7 # same source, assuming 94-6% split methane-elec of total energy demand 11.5 MWh/tNH3
|
||||
@ -319,6 +330,7 @@ industry:
|
||||
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
|
||||
|
||||
costs:
|
||||
year: 2030
|
||||
lifetime: 25 #default lifetime
|
||||
# From a Lion Hirth paper, also reflects average of Noothout et al 2016
|
||||
discountrate: 0.07
|
||||
@ -510,6 +522,9 @@ plotting:
|
||||
solid biomass for industry CC: '#47411c'
|
||||
solid biomass for industry co2 from atmosphere: '#736412'
|
||||
solid biomass for industry co2 to stored: '#47411c'
|
||||
biomass boiler: '#8A9A5B'
|
||||
biomass to liquid: '#32CD32'
|
||||
BioSNG: '#123456'
|
||||
# power transmission
|
||||
lines: '#6c9459'
|
||||
transmission lines: '#6c9459'
|
||||
|
||||
@ -33,17 +33,20 @@ 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.
|
||||
|
||||
`issues <https://github.com/PyPSA/pypsa-eur-sec/issues>`_ collect known
|
||||
topics we are working on (please feel free to help or make suggestions).
|
||||
The `documentation <https://pypsa-eur-sec.readthedocs.io/>`_ remains somewhat
|
||||
patchy.
|
||||
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 following `paper in Joule with a description of the industry sector <https://arxiv.org/abs/2109.09563>`_.
|
||||
You can find showcases of the model's capabilities in the
|
||||
preprint `Benefits of a Hydrogen Network in Europe
|
||||
<https://arxiv.org/abs/2207.05816>`_, a `paper in Joule with a
|
||||
description of the industry sector
|
||||
<https://arxiv.org/abs/2109.09563>`_, or in `a 2021 presentation
|
||||
at EMP-E <https://nworbmot.org/energy/brown-empe.pdf>`_.
|
||||
|
||||
|
||||
This diagram gives an overview of the sectors and the links between
|
||||
them:
|
||||
|
||||
@ -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
|
||||
@ -56,6 +56,17 @@ incorporates retrofitting options to hydrogen.
|
||||
|
||||
**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 BioSNG (methane from biomass) 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.
|
||||
|
||||
* 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>`_.
|
||||
@ -89,7 +100,7 @@ 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.
|
||||
@ -207,19 +218,19 @@ 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.
|
||||
|
||||
@ -230,7 +241,7 @@ 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
|
||||
|
||||
@ -11,7 +11,7 @@ import yaml
|
||||
import numpy as np
|
||||
|
||||
from add_existing_baseyear import add_build_year_to_new_assets
|
||||
from helper import override_component_attrs
|
||||
from helper import override_component_attrs, update_config_with_sector_opts
|
||||
from solve_network import basename
|
||||
|
||||
|
||||
@ -123,6 +123,8 @@ if __name__ == "__main__":
|
||||
planning_horizons=2030,
|
||||
)
|
||||
|
||||
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
|
||||
|
||||
print(snakemake.input.network_p)
|
||||
logging.basicConfig(level=snakemake.config['logging_level'])
|
||||
|
||||
@ -137,4 +139,5 @@ if __name__ == "__main__":
|
||||
|
||||
add_brownfield(n, n_p, year)
|
||||
|
||||
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
|
||||
n.export_to_netcdf(snakemake.output[0])
|
||||
|
||||
@ -13,7 +13,7 @@ import pypsa
|
||||
import yaml
|
||||
|
||||
from prepare_sector_network import prepare_costs, define_spatial
|
||||
from helper import override_component_attrs
|
||||
from helper import override_component_attrs, update_config_with_sector_opts
|
||||
|
||||
from types import SimpleNamespace
|
||||
spatial = SimpleNamespace()
|
||||
@ -131,7 +131,8 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
'Oil': 'oil',
|
||||
'OCGT': 'OCGT',
|
||||
'CCGT': 'CCGT',
|
||||
'Natural Gas': 'gas'
|
||||
'Natural Gas': 'gas',
|
||||
'Bioenergy': 'urban central solid biomass CHP',
|
||||
}
|
||||
|
||||
fueltype_to_drop = [
|
||||
@ -139,7 +140,6 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
'Wind',
|
||||
'Solar',
|
||||
'Geothermal',
|
||||
'Bioenergy',
|
||||
'Waste',
|
||||
'Other',
|
||||
'CCGT, Thermal'
|
||||
@ -150,10 +150,29 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
'Storage Technologies'
|
||||
]
|
||||
|
||||
# drop unused fueltyps and technologies
|
||||
df_agg.drop(df_agg.index[df_agg.Fueltype.isin(fueltype_to_drop)], inplace=True)
|
||||
df_agg.drop(df_agg.index[df_agg.Technology.isin(technology_to_drop)], inplace=True)
|
||||
df_agg.Fueltype = df_agg.Fueltype.map(rename_fuel)
|
||||
|
||||
# Intermediate fix for DateIn & DateOut
|
||||
# Fill missing DateIn
|
||||
biomass_i = df_agg.loc[df_agg.Fueltype=='urban central solid biomass CHP'].index
|
||||
mean = df_agg.loc[biomass_i, 'DateIn'].mean()
|
||||
df_agg.loc[biomass_i, 'DateIn'] = df_agg.loc[biomass_i, 'DateIn'].fillna(int(mean))
|
||||
# Fill missing DateOut
|
||||
dateout = df_agg.loc[biomass_i, 'DateIn'] + snakemake.config['costs']['lifetime']
|
||||
df_agg.loc[biomass_i, 'DateOut'] = df_agg.loc[biomass_i, 'DateOut'].fillna(dateout)
|
||||
|
||||
|
||||
# drop assets which are already phased out / decomissioned
|
||||
phased_out = df_agg[df_agg["DateOut"]<baseyear].index
|
||||
df_agg.drop(phased_out, inplace=True)
|
||||
|
||||
# calculate remaining lifetime before phase-out (+1 because assumming
|
||||
# phase out date at the end of the year)
|
||||
df_agg["lifetime"] = df_agg.DateOut - df_agg.DateIn + 1
|
||||
|
||||
# assign clustered bus
|
||||
busmap_s = pd.read_csv(snakemake.input.busmap_s, index_col=0).squeeze()
|
||||
busmap = pd.read_csv(snakemake.input.busmap, index_col=0).squeeze()
|
||||
@ -182,35 +201,52 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
aggfunc='sum'
|
||||
)
|
||||
|
||||
lifetime = df_agg.pivot_table(
|
||||
index=["grouping_year", 'Fueltype'],
|
||||
columns='cluster_bus',
|
||||
values='lifetime',
|
||||
aggfunc='mean' # currently taken mean for clustering lifetimes
|
||||
)
|
||||
|
||||
carrier = {
|
||||
"OCGT": "gas",
|
||||
"CCGT": "gas",
|
||||
"coal": "coal",
|
||||
"oil": "oil",
|
||||
"lignite": "lignite",
|
||||
"nuclear": "uranium"
|
||||
"nuclear": "uranium",
|
||||
'urban central solid biomass CHP': "biomass",
|
||||
}
|
||||
|
||||
for grouping_year, generator in df.index:
|
||||
|
||||
|
||||
# capacity is the capacity in MW at each node for this
|
||||
capacity = df.loc[grouping_year, generator]
|
||||
capacity = capacity[~capacity.isna()]
|
||||
capacity = capacity[capacity > snakemake.config['existing_capacities']['threshold_capacity']]
|
||||
|
||||
suffix = '-ac' if generator == 'offwind' else ''
|
||||
name_suffix = f' {generator}{suffix}-{grouping_year}'
|
||||
asset_i = capacity.index + name_suffix
|
||||
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()
|
||||
|
||||
# check if assets are already in network (e.g. for 2020)
|
||||
already_build = n.generators.index.intersection(asset_i)
|
||||
new_build = asset_i.difference(n.generators.index)
|
||||
|
||||
# this is for the year 2020
|
||||
if not already_build.empty:
|
||||
n.generators.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
|
||||
new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
|
||||
|
||||
if 'm' in snakemake.wildcards.clusters:
|
||||
|
||||
for ind in capacity.index:
|
||||
for ind in new_capacity.index:
|
||||
|
||||
# existing capacities are split evenly among regions in every country
|
||||
inv_ind = [i for i in inv_busmap[ind]]
|
||||
@ -225,7 +261,7 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
[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
|
||||
p_nom=new_capacity[ind] / len(inv_ind), # split among regions in a country
|
||||
marginal_cost=costs.at[generator,'VOM'],
|
||||
capital_cost=capital_cost,
|
||||
efficiency=costs.at[generator, 'efficiency'],
|
||||
@ -236,42 +272,72 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
|
||||
else:
|
||||
|
||||
p_max_pu = n.generators_t.p_max_pu[capacity.index + name_suffix]
|
||||
p_max_pu = n.generators_t.p_max_pu[capacity.index + f' {generator}{suffix}-{baseyear}']
|
||||
|
||||
n.madd("Generator",
|
||||
capacity.index,
|
||||
suffix=' ' + generator +"-"+ str(grouping_year),
|
||||
bus=capacity.index,
|
||||
carrier=generator,
|
||||
p_nom=capacity,
|
||||
marginal_cost=costs.at[generator, 'VOM'],
|
||||
capital_cost=capital_cost,
|
||||
efficiency=costs.at[generator, 'efficiency'],
|
||||
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
|
||||
build_year=grouping_year,
|
||||
lifetime=costs.at[generator, 'lifetime']
|
||||
)
|
||||
if not new_build.empty:
|
||||
n.madd("Generator",
|
||||
new_capacity.index,
|
||||
suffix=' ' + name_suffix,
|
||||
bus=new_capacity.index,
|
||||
carrier=generator,
|
||||
p_nom=new_capacity,
|
||||
marginal_cost=costs.at[generator, 'VOM'],
|
||||
capital_cost=capital_cost,
|
||||
efficiency=costs.at[generator, 'efficiency'],
|
||||
p_max_pu=p_max_pu.rename(columns=n.generators.bus),
|
||||
build_year=grouping_year,
|
||||
lifetime=costs.at[generator, 'lifetime']
|
||||
)
|
||||
|
||||
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=bus0,
|
||||
bus1=capacity.index,
|
||||
bus2="co2 atmosphere",
|
||||
carrier=generator,
|
||||
marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
|
||||
capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
|
||||
p_nom=capacity / costs.at[generator, 'efficiency'],
|
||||
efficiency=costs.at[generator, 'efficiency'],
|
||||
efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
|
||||
build_year=grouping_year,
|
||||
lifetime=costs.at[generator, 'lifetime']
|
||||
)
|
||||
already_build = n.links.index.intersection(asset_i)
|
||||
new_build = asset_i.difference(n.links.index)
|
||||
lifetime_assets = lifetime.loc[grouping_year,generator].dropna()
|
||||
|
||||
# this is for the year 2020
|
||||
if not already_build.empty:
|
||||
n.links.loc[already_build, "p_nom_min"] = capacity.loc[already_build.str.replace(name_suffix, "")].values
|
||||
|
||||
if not new_build.empty:
|
||||
new_capacity = capacity.loc[new_build.str.replace(name_suffix, "")]
|
||||
|
||||
if generator!="urban central solid biomass CHP":
|
||||
n.madd("Link",
|
||||
new_capacity.index,
|
||||
suffix= name_suffix,
|
||||
bus0=bus0,
|
||||
bus1=new_capacity.index,
|
||||
bus2="co2 atmosphere",
|
||||
carrier=generator,
|
||||
marginal_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'VOM'], #NB: VOM is per MWel
|
||||
capital_cost=costs.at[generator, 'efficiency'] * costs.at[generator, 'fixed'], #NB: fixed cost is per MWel
|
||||
p_nom=new_capacity / costs.at[generator, 'efficiency'],
|
||||
efficiency=costs.at[generator, 'efficiency'],
|
||||
efficiency2=costs.at[carrier[generator], 'CO2 intensity'],
|
||||
build_year=grouping_year,
|
||||
lifetime=lifetime_assets.loc[new_capacity.index],
|
||||
)
|
||||
else:
|
||||
key = 'central solid biomass CHP'
|
||||
n.madd("Link",
|
||||
new_capacity.index,
|
||||
suffix= name_suffix,
|
||||
bus0=spatial.biomass.df.loc[new_capacity.index]["nodes"].values,
|
||||
bus1=new_capacity.index,
|
||||
bus2=new_capacity.index + " urban central heat",
|
||||
carrier=generator,
|
||||
p_nom=new_capacity / costs.at[key, 'efficiency'],
|
||||
capital_cost=costs.at[key, 'fixed'] * costs.at[key, 'efficiency'],
|
||||
marginal_cost=costs.at[key, 'VOM'],
|
||||
efficiency=costs.at[key, 'efficiency'],
|
||||
build_year=grouping_year,
|
||||
efficiency2=costs.at[key, 'efficiency-heat'],
|
||||
lifetime=lifetime_assets.loc[new_capacity.index]
|
||||
)
|
||||
|
||||
|
||||
def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime):
|
||||
@ -376,10 +442,10 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
|
||||
for i, grouping_year in enumerate(grouping_years):
|
||||
|
||||
if int(grouping_year) + default_lifetime <= int(baseyear):
|
||||
ratio = 0
|
||||
else:
|
||||
# installation is assumed to be linear for the past 25 years (default lifetime)
|
||||
ratio = (int(grouping_year) - int(grouping_years[i-1])) / default_lifetime
|
||||
continue
|
||||
|
||||
# installation is assumed to be linear for the past 25 years (default lifetime)
|
||||
ratio = (int(grouping_year) - int(grouping_years[i-1])) / default_lifetime
|
||||
|
||||
n.madd("Link",
|
||||
nodes[name],
|
||||
@ -443,7 +509,7 @@ def add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years
|
||||
# delete links with p_nom=nan corresponding to extra nodes in country
|
||||
n.mremove("Link", [index for index in n.links.index.to_list() if str(grouping_year) in index and np.isnan(n.links.p_nom[index])])
|
||||
|
||||
# delete links if their lifetime is over and p_nom=0
|
||||
# delete links with capacities below threshold
|
||||
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])
|
||||
|
||||
@ -454,19 +520,21 @@ if __name__ == "__main__":
|
||||
snakemake = mock_snakemake(
|
||||
'add_existing_baseyear',
|
||||
simpl='',
|
||||
clusters="37",
|
||||
clusters="45",
|
||||
lv=1.0,
|
||||
opts='',
|
||||
sector_opts='168H-T-H-B-I-solar+p3-dist1',
|
||||
planning_horizons=2020,
|
||||
sector_opts='365H-T-H-B-I-A-solar+p3-dist1',
|
||||
planning_horizons=2030,
|
||||
)
|
||||
|
||||
logging.basicConfig(level=snakemake.config['logging_level'])
|
||||
|
||||
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
|
||||
|
||||
options = snakemake.config["sector"]
|
||||
opts = snakemake.wildcards.sector_opts.split('-')
|
||||
|
||||
baseyear= snakemake.config['scenario']["planning_horizons"][0]
|
||||
baseyear = snakemake.config['scenario']["planning_horizons"][0]
|
||||
|
||||
overrides = override_component_attrs(snakemake.input.overrides)
|
||||
n = pypsa.Network(snakemake.input.network, override_component_attrs=overrides)
|
||||
@ -483,14 +551,17 @@ if __name__ == "__main__":
|
||||
snakemake.config['costs']['lifetime']
|
||||
)
|
||||
|
||||
grouping_years = snakemake.config['existing_capacities']['grouping_years']
|
||||
add_power_capacities_installed_before_baseyear(n, grouping_years, costs, baseyear)
|
||||
grouping_years_power = snakemake.config['existing_capacities']['grouping_years_power']
|
||||
grouping_years_heat = snakemake.config['existing_capacities']['grouping_years_heat']
|
||||
add_power_capacities_installed_before_baseyear(n, grouping_years_power, costs, baseyear)
|
||||
|
||||
if "H" in opts:
|
||||
time_dep_hp_cop = options["time_dep_hp_cop"]
|
||||
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)
|
||||
default_lifetime = snakemake.config['costs']['lifetime']
|
||||
add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years, ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
|
||||
add_heating_capacities_installed_before_baseyear(n, baseyear, grouping_years_heat,
|
||||
ashp_cop, gshp_cop, time_dep_hp_cop, costs, default_lifetime)
|
||||
|
||||
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
|
||||
n.export_to_netcdf(snakemake.output[0])
|
||||
|
||||
@ -127,17 +127,16 @@ to_ipcc = {
|
||||
}
|
||||
|
||||
|
||||
def build_eurostat(countries, year):
|
||||
def build_eurostat(input_eurostat, countries, report_year, year):
|
||||
"""Return multi-index for all countries' energy data in TWh/a."""
|
||||
|
||||
report_year = snakemake.config["energy"]["eurostat_report_year"]
|
||||
filenames = {
|
||||
2016: f"/{year}-Energy-Balances-June2016edition.xlsx",
|
||||
2017: f"/{year}-ENERGY-BALANCES-June2017edition.xlsx"
|
||||
}
|
||||
|
||||
dfs = pd.read_excel(
|
||||
snakemake.input.eurostat + filenames[report_year],
|
||||
input_eurostat + filenames[report_year],
|
||||
sheet_name=None,
|
||||
skiprows=1,
|
||||
index_col=list(range(4)),
|
||||
@ -563,18 +562,18 @@ def build_energy_totals(countries, eurostat, swiss, idees):
|
||||
return df
|
||||
|
||||
|
||||
def build_eea_co2(year=1990):
|
||||
def build_eea_co2(input_co2, year=1990, emissions_scope="CO2"):
|
||||
|
||||
# https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16
|
||||
# downloaded 201228 (modified by EEA last on 201221)
|
||||
df = pd.read_csv(snakemake.input.co2, encoding="latin-1")
|
||||
df = pd.read_csv(input_co2, encoding="latin-1")
|
||||
|
||||
df.replace(dict(Year="1985-1987"), 1986, inplace=True)
|
||||
df.Year = df.Year.astype(int)
|
||||
index_col = ["Country_code", "Pollutant_name", "Year", "Sector_name"]
|
||||
df = df.set_index(index_col).sort_index()
|
||||
|
||||
emissions_scope = snakemake.config["energy"]["emissions"]
|
||||
emissions_scope = emissions_scope
|
||||
|
||||
cts = ["CH", "EUA", "NO"] + eu28_eea
|
||||
|
||||
@ -611,9 +610,9 @@ def build_eea_co2(year=1990):
|
||||
return emissions / 1e3
|
||||
|
||||
|
||||
def build_eurostat_co2(countries, year=1990):
|
||||
def build_eurostat_co2(input_eurostat, countries, report_year, year=1990):
|
||||
|
||||
eurostat = build_eurostat(countries, year)
|
||||
eurostat = build_eurostat(input_eurostat, countries, report_year, year)
|
||||
|
||||
specific_emissions = pd.Series(index=eurostat.columns, dtype=float)
|
||||
|
||||
@ -702,7 +701,9 @@ if __name__ == "__main__":
|
||||
idees_countries = countries.intersection(eu28)
|
||||
|
||||
data_year = config["energy_totals_year"]
|
||||
eurostat = build_eurostat(countries, data_year)
|
||||
report_year = snakemake.config["energy"]["eurostat_report_year"]
|
||||
input_eurostat = snakemake.input.eurostat
|
||||
eurostat = build_eurostat(input_eurostat, countries, report_year, data_year)
|
||||
swiss = build_swiss(data_year)
|
||||
idees = build_idees(idees_countries, data_year)
|
||||
|
||||
@ -710,8 +711,9 @@ if __name__ == "__main__":
|
||||
energy.to_csv(snakemake.output.energy_name)
|
||||
|
||||
base_year_emissions = config["base_emissions_year"]
|
||||
eea_co2 = build_eea_co2(base_year_emissions)
|
||||
eurostat_co2 = build_eurostat_co2(countries, base_year_emissions)
|
||||
emissions_scope = snakemake.config["energy"]["emissions"]
|
||||
eea_co2 = build_eea_co2(snakemake.input.co2, base_year_emissions, emissions_scope)
|
||||
eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, base_year_emissions)
|
||||
|
||||
co2 = build_co2_totals(countries, eea_co2, eurostat_co2)
|
||||
co2.to_csv(snakemake.output.co2_name)
|
||||
|
||||
@ -5,9 +5,7 @@ import pandas as pd
|
||||
import geopandas as gpd
|
||||
|
||||
from itertools import product
|
||||
from distutils.version import StrictVersion
|
||||
|
||||
gpd_version = StrictVersion(gpd.__version__)
|
||||
from packaging.version import Version, parse
|
||||
|
||||
|
||||
def locate_missing_industrial_sites(df):
|
||||
@ -73,7 +71,7 @@ def prepare_hotmaps_database(regions):
|
||||
|
||||
gdf = gpd.GeoDataFrame(df, geometry='coordinates', crs="EPSG:4326")
|
||||
|
||||
kws = dict(op="within") if gpd_version < '0.10' else dict(predicate="within")
|
||||
kws = dict(op="within") if parse(gpd.__version__) < Version('0.10') else dict(predicate="within")
|
||||
gdf = gpd.sjoin(gdf, regions, how="inner", **kws)
|
||||
|
||||
gdf.rename(columns={"index_right": "bus"}, inplace=True)
|
||||
|
||||
@ -8,9 +8,8 @@ import geopandas as gpd
|
||||
|
||||
from shapely import wkt
|
||||
from pypsa.geo import haversine_pts
|
||||
from distutils.version import StrictVersion
|
||||
from packaging.version import Version, parse
|
||||
|
||||
gpd_version = StrictVersion(gpd.__version__)
|
||||
|
||||
def concat_gdf(gdf_list, crs='EPSG:4326'):
|
||||
"""Concatenate multiple geopandas dataframes with common coordinate reference system (crs)."""
|
||||
@ -34,7 +33,7 @@ def build_clustered_gas_network(df, bus_regions, length_factor=1.25):
|
||||
|
||||
gdf = gpd.GeoDataFrame(geometry=df[f"point{i}"], crs="EPSG:4326")
|
||||
|
||||
kws = dict(op="within") if gpd_version < '0.10' else dict(predicate="within")
|
||||
kws = dict(op="within") if parse(gpd.__version__) < Version('0.10') else dict(predicate="within")
|
||||
bus_mapping = gpd.sjoin(gdf, bus_regions, how="left", **kws).index_right
|
||||
bus_mapping = bus_mapping.groupby(bus_mapping.index).first()
|
||||
|
||||
|
||||
@ -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
|
||||
)
|
||||
@ -1,7 +1,9 @@
|
||||
import os
|
||||
import yaml
|
||||
import pytz
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
from snakemake.utils import update_config
|
||||
from pypsa.descriptors import Dict
|
||||
from pypsa.components import components, component_attrs
|
||||
|
||||
@ -58,6 +60,7 @@ 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, \
|
||||
@ -67,7 +70,8 @@ def mock_snakemake(rulename, **wildcards):
|
||||
if os.path.exists(p):
|
||||
snakefile = p
|
||||
break
|
||||
workflow = sm.Workflow(snakefile, overwrite_configfiles=[])
|
||||
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)
|
||||
@ -122,4 +126,18 @@ def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
|
||||
|
||||
week_df = week_df.tz_localize(localize)
|
||||
|
||||
return week_df
|
||||
return week_df
|
||||
|
||||
|
||||
def parse(l):
|
||||
if len(l) == 1:
|
||||
return yaml.safe_load(l[0])
|
||||
else:
|
||||
return {l.pop(0): parse(l)}
|
||||
|
||||
|
||||
def update_config_with_sector_opts(config, sector_opts):
|
||||
for o in sector_opts.split("-"):
|
||||
if o.startswith("CF:"):
|
||||
l = o.split("+")[1:]
|
||||
update_config(config, parse(l))
|
||||
@ -309,7 +309,6 @@ def plot_h2_map(network):
|
||||
)
|
||||
|
||||
n.plot(
|
||||
# geomap=False,
|
||||
bus_sizes=0,
|
||||
link_colors='#72d3d6',
|
||||
link_widths=link_widths_retro,
|
||||
@ -443,7 +442,6 @@ def plot_ch4_map(network):
|
||||
)
|
||||
|
||||
n.plot(
|
||||
# geomap=False,
|
||||
ax=ax,
|
||||
bus_sizes=0.,
|
||||
link_colors='#e8d1d1',
|
||||
@ -453,7 +451,6 @@ def plot_ch4_map(network):
|
||||
)
|
||||
|
||||
n.plot(
|
||||
# geomap=False,
|
||||
ax=ax,
|
||||
bus_sizes=0.,
|
||||
link_colors=link_color_used,
|
||||
|
||||
@ -7,6 +7,7 @@ import matplotlib.pyplot as plt
|
||||
plt.style.use('ggplot')
|
||||
|
||||
from prepare_sector_network import co2_emissions_year
|
||||
from helper import update_config_with_sector_opts
|
||||
|
||||
#consolidate and rename
|
||||
def rename_techs(label):
|
||||
@ -203,7 +204,7 @@ def plot_energy():
|
||||
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
|
||||
|
||||
new_columns = df.columns.sort_values()
|
||||
|
||||
|
||||
fig, ax = plt.subplots(figsize=(12,8))
|
||||
|
||||
print(df.loc[new_index, new_columns])
|
||||
@ -364,7 +365,7 @@ def historical_emissions(cts):
|
||||
|
||||
|
||||
|
||||
def plot_carbon_budget_distribution():
|
||||
def plot_carbon_budget_distribution(input_eurostat):
|
||||
"""
|
||||
Plot historical carbon emissions in the EU and decarbonization path
|
||||
"""
|
||||
@ -386,9 +387,9 @@ def plot_carbon_budget_distribution():
|
||||
ax1.set_xlim([1990,snakemake.config['scenario']['planning_horizons'][-1]+1])
|
||||
|
||||
path_cb = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/'
|
||||
countries=pd.read_csv(path_cb + 'countries.csv', index_col=1)
|
||||
cts=countries.index.to_list()
|
||||
e_1990 = co2_emissions_year(cts, opts, year=1990)
|
||||
countries = pd.read_csv(snakemake.input.country_codes, index_col=1)
|
||||
cts = countries.index.to_list()
|
||||
e_1990 = co2_emissions_year(cts, input_eurostat, opts, year=1990)
|
||||
CO2_CAP=pd.read_csv(path_cb + 'carbon_budget_distribution.csv',
|
||||
index_col=0)
|
||||
|
||||
@ -439,7 +440,8 @@ if __name__ == "__main__":
|
||||
if 'snakemake' not in globals():
|
||||
from helper import mock_snakemake
|
||||
snakemake = mock_snakemake('plot_summary')
|
||||
|
||||
|
||||
|
||||
n_header = 4
|
||||
|
||||
plot_costs()
|
||||
@ -452,4 +454,4 @@ if __name__ == "__main__":
|
||||
opts=sector_opts.split('-')
|
||||
for o in opts:
|
||||
if "cb" in o:
|
||||
plot_carbon_budget_distribution()
|
||||
plot_carbon_budget_distribution(snakemake.input.eurostat)
|
||||
|
||||
@ -14,7 +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, generate_periodic_profiles
|
||||
from helper import override_component_attrs, generate_periodic_profiles, update_config_with_sector_opts
|
||||
|
||||
from networkx.algorithms.connectivity.edge_augmentation import k_edge_augmentation
|
||||
from networkx.algorithms import complement
|
||||
@ -171,21 +171,22 @@ def get(item, investment_year=None):
|
||||
return item
|
||||
|
||||
|
||||
def co2_emissions_year(countries, opts, year):
|
||||
def co2_emissions_year(countries, input_eurostat, opts, emissions_scope, report_year, year):
|
||||
"""
|
||||
Calculate CO2 emissions in one specific year (e.g. 1990 or 2018).
|
||||
"""
|
||||
|
||||
eea_co2 = build_eea_co2(year)
|
||||
emissions_scope = snakemake.config["energy"]["emissions"]
|
||||
eea_co2 = build_eea_co2(snakemake.input.co2, year, emissions_scope)
|
||||
|
||||
# TODO: read Eurostat data from year > 2014
|
||||
# this only affects the estimation of CO2 emissions for BA, RS, AL, ME, MK
|
||||
report_year = snakemake.config["energy"]["eurostat_report_year"]
|
||||
if year > 2014:
|
||||
eurostat_co2 = build_eurostat_co2(year=2014)
|
||||
eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, year=2014)
|
||||
else:
|
||||
eurostat_co2 = build_eurostat_co2(year)
|
||||
eurostat_co2 = build_eurostat_co2(input_eurostat, countries, report_year, year)
|
||||
|
||||
co2_totals = build_co2_totals(eea_co2, eurostat_co2)
|
||||
co2_totals = build_co2_totals(countries, eea_co2, eurostat_co2)
|
||||
|
||||
sectors = emission_sectors_from_opts(opts)
|
||||
|
||||
@ -198,7 +199,7 @@ def co2_emissions_year(countries, opts, year):
|
||||
|
||||
|
||||
# TODO: move to own rule with sector-opts wildcard?
|
||||
def build_carbon_budget(o, fn):
|
||||
def build_carbon_budget(o, input_eurostat, fn, emissions_scope, report_year):
|
||||
"""
|
||||
Distribute carbon budget following beta or exponential transition path.
|
||||
"""
|
||||
@ -215,10 +216,12 @@ def build_carbon_budget(o, fn):
|
||||
|
||||
countries = n.buses.country.dropna().unique()
|
||||
|
||||
e_1990 = co2_emissions_year(countries, opts, year=1990)
|
||||
e_1990 = co2_emissions_year(countries, input_eurostat, opts, emissions_scope,
|
||||
report_year, year=1990)
|
||||
|
||||
#emissions at the beginning of the path (last year available 2018)
|
||||
e_0 = co2_emissions_year(countries, opts, year=2018)
|
||||
e_0 = co2_emissions_year(countries, input_eurostat, opts, emissions_scope,
|
||||
report_year,year=2018)
|
||||
|
||||
planning_horizons = snakemake.config['scenario']['planning_horizons']
|
||||
t_0 = planning_horizons[0]
|
||||
@ -246,8 +249,9 @@ def build_carbon_budget(o, fn):
|
||||
co2_cap = pd.Series({t: exponential_decay(t) for t in planning_horizons}, name=o)
|
||||
|
||||
# TODO log in Snakefile
|
||||
if not os.path.exists(fn):
|
||||
os.makedirs(fn)
|
||||
csvs_folder = fn.rsplit("/", 1)[0]
|
||||
if not os.path.exists(csvs_folder):
|
||||
os.makedirs(csvs_folder)
|
||||
co2_cap.to_csv(fn, float_format='%.3f')
|
||||
|
||||
|
||||
@ -398,10 +402,13 @@ 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=location,
|
||||
carrier=carrier
|
||||
carrier=carrier,
|
||||
unit=unit
|
||||
)
|
||||
|
||||
#capital cost could be corrected to e.g. 0.2 EUR/kWh * annuity and O&M
|
||||
@ -452,6 +459,7 @@ 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)
|
||||
@ -468,7 +476,8 @@ def add_co2_tracking(n, options):
|
||||
n.add("Bus",
|
||||
"co2 atmosphere",
|
||||
location="EU",
|
||||
carrier="co2"
|
||||
carrier="co2",
|
||||
unit="t_co2"
|
||||
)
|
||||
|
||||
# can also be negative
|
||||
@ -484,7 +493,8 @@ 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",
|
||||
@ -771,7 +781,8 @@ 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",
|
||||
@ -838,7 +849,8 @@ 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",
|
||||
@ -913,7 +925,8 @@ def add_storage_and_grids(n, costs):
|
||||
n.madd("Bus",
|
||||
nodes + " H2",
|
||||
location=nodes,
|
||||
carrier="H2"
|
||||
carrier="H2",
|
||||
unit="MWh_LHV"
|
||||
)
|
||||
|
||||
n.madd("Link",
|
||||
@ -1119,7 +1132,8 @@ def add_storage_and_grids(n, costs):
|
||||
n.madd("Bus",
|
||||
nodes + " battery",
|
||||
location=nodes,
|
||||
carrier="battery"
|
||||
carrier="battery",
|
||||
unit="MWh_el"
|
||||
)
|
||||
|
||||
n.madd("Store",
|
||||
@ -1186,6 +1200,24 @@ 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']:
|
||||
|
||||
@ -1249,7 +1281,8 @@ 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
|
||||
@ -1323,7 +1356,8 @@ def add_land_transport(n, costs):
|
||||
n.madd("Bus",
|
||||
spatial.oil.nodes,
|
||||
location=spatial.oil.locations,
|
||||
carrier="oil"
|
||||
carrier="oil",
|
||||
unit="MWh_LHV"
|
||||
)
|
||||
|
||||
ice_efficiency = options['transport_internal_combustion_efficiency']
|
||||
@ -1431,7 +1465,8 @@ 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
|
||||
@ -1488,7 +1523,8 @@ 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",
|
||||
@ -1517,9 +1553,6 @@ def add_heat(n, costs):
|
||||
"for 'decentral' and 'central' separately.")
|
||||
tes_time_constant_days = options["tes_tau"] if name_type == "decentral" else 180.
|
||||
|
||||
# conversion from EUR/m^3 to EUR/MWh for 40 K diff and 1.17 kWh/m^3/K
|
||||
capital_cost = costs.at[name_type + ' water tank storage', 'fixed'] / 0.00117 / 40
|
||||
|
||||
n.madd("Store",
|
||||
nodes[name] + f" {name} water tanks",
|
||||
bus=nodes[name] + f" {name} water tanks",
|
||||
@ -1527,7 +1560,7 @@ def add_heat(n, costs):
|
||||
e_nom_extendable=True,
|
||||
carrier=name + " water tanks",
|
||||
standing_loss=1 - np.exp(- 1 / 24 / tes_time_constant_days),
|
||||
capital_cost=capital_cost,
|
||||
capital_cost=costs.at[name_type + ' water tank storage', 'fixed'],
|
||||
lifetime=costs.at[name_type + ' water tank storage', 'lifetime']
|
||||
)
|
||||
|
||||
@ -1561,6 +1594,7 @@ def add_heat(n, costs):
|
||||
lifetime=costs.at[key, 'lifetime']
|
||||
)
|
||||
|
||||
|
||||
if options["solar_thermal"]:
|
||||
|
||||
n.add("Carrier", name + " solar thermal")
|
||||
@ -1793,13 +1827,15 @@ 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",
|
||||
@ -1897,6 +1933,95 @@ def add_biomass(n, costs):
|
||||
lifetime=costs.at[key, 'lifetime']
|
||||
)
|
||||
|
||||
if options["biomass_boiler"]:
|
||||
#TODO: Add surcharge for pellets
|
||||
nodes_heat = create_nodes_for_heat_sector()[0]
|
||||
for name in ["residential rural", "services rural",
|
||||
"residential urban decentral", "services urban decentral"]:
|
||||
|
||||
n.madd("Link",
|
||||
nodes_heat[name] + f" {name} biomass boiler",
|
||||
p_nom_extendable=True,
|
||||
bus0=spatial.biomass.df.loc[nodes_heat[name], "nodes"].values,
|
||||
bus1=nodes_heat[name] + f" {name} heat",
|
||||
carrier=name + " biomass boiler",
|
||||
efficiency=costs.at['biomass boiler', 'efficiency'],
|
||||
capital_cost=costs.at['biomass boiler', 'efficiency'] * costs.at['biomass boiler', 'fixed'],
|
||||
lifetime=costs.at['biomass boiler', 'lifetime']
|
||||
)
|
||||
|
||||
#Solid biomass to liquid fuel
|
||||
if options["biomass_to_liquid"]:
|
||||
n.madd("Link",
|
||||
spatial.biomass.nodes,
|
||||
suffix=" biomass to liquid",
|
||||
bus0=spatial.biomass.nodes,
|
||||
bus1=spatial.oil.nodes,
|
||||
bus2="co2 atmosphere",
|
||||
carrier="biomass to liquid",
|
||||
lifetime=costs.at['BtL', 'lifetime'],
|
||||
efficiency=costs.at['BtL', 'efficiency'],
|
||||
efficiency2=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'],
|
||||
p_nom_extendable=True,
|
||||
capital_cost=costs.at['BtL', 'fixed'],
|
||||
marginal_cost=costs.at['BtL', 'efficiency']*costs.loc["BtL", "VOM"]
|
||||
)
|
||||
|
||||
#TODO: Update with energy penalty
|
||||
n.madd("Link",
|
||||
spatial.biomass.nodes,
|
||||
suffix=" biomass to liquid CC",
|
||||
bus0=spatial.biomass.nodes,
|
||||
bus1=spatial.oil.nodes,
|
||||
bus2="co2 atmosphere",
|
||||
bus3=spatial.co2.nodes,
|
||||
carrier="biomass to liquid",
|
||||
lifetime=costs.at['BtL', 'lifetime'],
|
||||
efficiency=costs.at['BtL', 'efficiency'],
|
||||
efficiency2=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BtL', 'CO2 stored'] * (1 - costs.at['BtL', 'capture rate']),
|
||||
efficiency3=costs.at['BtL', 'CO2 stored'] * costs.at['BtL', 'capture rate'],
|
||||
p_nom_extendable=True,
|
||||
capital_cost=costs.at['BtL', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
|
||||
"BtL", "CO2 stored"],
|
||||
marginal_cost=costs.at['BtL', 'efficiency'] * costs.loc["BtL", "VOM"])
|
||||
|
||||
#BioSNG from solid biomass
|
||||
if options["biosng"]:
|
||||
n.madd("Link",
|
||||
spatial.biomass.nodes,
|
||||
suffix=" solid biomass to gas",
|
||||
bus0=spatial.biomass.nodes,
|
||||
bus1=spatial.gas.nodes,
|
||||
bus3="co2 atmosphere",
|
||||
carrier="BioSNG",
|
||||
lifetime=costs.at['BioSNG', 'lifetime'],
|
||||
efficiency=costs.at['BioSNG', 'efficiency'],
|
||||
efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'],
|
||||
p_nom_extendable=True,
|
||||
capital_cost=costs.at['BioSNG', 'fixed'],
|
||||
marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
|
||||
)
|
||||
|
||||
#TODO: Update with energy penalty for CC
|
||||
n.madd("Link",
|
||||
spatial.biomass.nodes,
|
||||
suffix=" solid biomass to gas CC",
|
||||
bus0=spatial.biomass.nodes,
|
||||
bus1=spatial.gas.nodes,
|
||||
bus2=spatial.co2.nodes,
|
||||
bus3="co2 atmosphere",
|
||||
carrier="BioSNG",
|
||||
lifetime=costs.at['BioSNG', 'lifetime'],
|
||||
efficiency=costs.at['BioSNG', 'efficiency'],
|
||||
efficiency2=costs.at['BioSNG', 'CO2 stored'] * costs.at['BioSNG', 'capture rate'],
|
||||
efficiency3=-costs.at['solid biomass', 'CO2 intensity'] + costs.at['BioSNG', 'CO2 stored'] * (1 - costs.at['BioSNG', 'capture rate']),
|
||||
p_nom_extendable=True,
|
||||
capital_cost=costs.at['BioSNG', 'fixed'] + costs.at['biomass CHP capture', 'fixed'] * costs.at[
|
||||
"BioSNG", "CO2 stored"],
|
||||
marginal_cost=costs.at['BioSNG', 'efficiency']*costs.loc["BioSNG", "VOM"]
|
||||
|
||||
)
|
||||
|
||||
|
||||
def add_industry(n, costs):
|
||||
|
||||
@ -1910,7 +2035,8 @@ 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"]:
|
||||
@ -1952,7 +2078,8 @@ 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.
|
||||
|
||||
@ -2008,7 +2135,8 @@ def add_industry(n, costs):
|
||||
nodes,
|
||||
suffix=" H2 liquid",
|
||||
carrier="H2 liquid",
|
||||
location=nodes
|
||||
location=nodes,
|
||||
unit="MWh_LHV"
|
||||
)
|
||||
|
||||
n.madd("Link",
|
||||
@ -2066,7 +2194,8 @@ def add_industry(n, costs):
|
||||
n.madd("Bus",
|
||||
spatial.oil.nodes,
|
||||
location=spatial.oil.locations,
|
||||
carrier="oil"
|
||||
carrier="oil",
|
||||
unit="MWh_LHV"
|
||||
)
|
||||
|
||||
if "oil" not in n.stores.carrier.unique():
|
||||
@ -2180,7 +2309,8 @@ 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
|
||||
@ -2369,6 +2499,88 @@ def limit_individual_line_extension(n, maxext):
|
||||
hvdc = n.links.index[n.links.carrier == 'DC']
|
||||
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$", o, re.IGNORECASE)
|
||||
if m is not None:
|
||||
n = average_every_nhours(n, m.group(0))
|
||||
break
|
||||
# representive snapshots
|
||||
m = re.match(r"(^\d+)sn$", 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$", o, re.IGNORECASE)
|
||||
if m is not None:
|
||||
segments = int(m[1])
|
||||
logger.info(f"use temporal segmentation with {segments} segments")
|
||||
n = apply_time_segmentation(n, segments, solver_name=solver_name)
|
||||
break
|
||||
return n
|
||||
#%%
|
||||
if __name__ == "__main__":
|
||||
if 'snakemake' not in globals():
|
||||
@ -2378,13 +2590,15 @@ if __name__ == "__main__":
|
||||
simpl='',
|
||||
opts="",
|
||||
clusters="37",
|
||||
lv=1.0,
|
||||
sector_opts='Co2L0-168H-T-H-B-I-solar3-dist1',
|
||||
lv=1.5,
|
||||
sector_opts='cb40ex0-365H-T-H-B-I-A-solar+p3-dist1',
|
||||
planning_horizons="2020",
|
||||
)
|
||||
|
||||
logging.basicConfig(level=snakemake.config['logging_level'])
|
||||
|
||||
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
|
||||
|
||||
options = snakemake.config["sector"]
|
||||
|
||||
opts = snakemake.wildcards.sector_opts.split('-')
|
||||
@ -2471,11 +2685,8 @@ if __name__ == "__main__":
|
||||
if options["co2_network"]:
|
||||
add_co2_network(n, costs)
|
||||
|
||||
for o in opts:
|
||||
m = re.match(r'^\d+h$', o, re.IGNORECASE)
|
||||
if m is not None:
|
||||
n = average_every_nhours(n, m.group(0))
|
||||
break
|
||||
solver_name = snakemake.config["solving"]["solver"]["name"]
|
||||
n = set_temporal_aggregation(n, opts, solver_name)
|
||||
|
||||
limit_type = "config"
|
||||
limit = get(snakemake.config["co2_budget"], investment_year)
|
||||
@ -2484,9 +2695,11 @@ if __name__ == "__main__":
|
||||
limit_type = "carbon budget"
|
||||
fn = snakemake.config['results_dir'] + snakemake.config['run'] + '/csvs/carbon_budget_distribution.csv'
|
||||
if not os.path.exists(fn):
|
||||
build_carbon_budget(o, fn)
|
||||
emissions_scope = snakemake.config["energy"]["emissions"]
|
||||
report_year = snakemake.config["energy"]["eurostat_report_year"]
|
||||
build_carbon_budget(o, snakemake.input.eurostat, fn, emissions_scope, report_year)
|
||||
co2_cap = pd.read_csv(fn, index_col=0).squeeze()
|
||||
limit = co2_cap[investment_year]
|
||||
limit = co2_cap.loc[investment_year]
|
||||
break
|
||||
for o in opts:
|
||||
if not "Co2L" in o: continue
|
||||
@ -2514,4 +2727,5 @@ if __name__ == "__main__":
|
||||
if options['electricity_grid_connection']:
|
||||
add_electricity_grid_connection(n, costs)
|
||||
|
||||
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
|
||||
n.export_to_netcdf(snakemake.output[0])
|
||||
|
||||
@ -11,7 +11,7 @@ from pypsa.linopf import network_lopf, ilopf
|
||||
|
||||
from vresutils.benchmark import memory_logger
|
||||
|
||||
from helper import override_component_attrs
|
||||
from helper import override_component_attrs, update_config_with_sector_opts
|
||||
|
||||
import logging
|
||||
logger = logging.getLogger(__name__)
|
||||
@ -227,7 +227,7 @@ def add_co2_sequestration_limit(n, sns):
|
||||
limit = n.config["sector"].get("co2_sequestration_potential", 200) * 1e6
|
||||
for o in opts:
|
||||
if not "seq" in o: continue
|
||||
limit = float(o[o.find("seq")+3:])
|
||||
limit = float(o[o.find("seq")+3:]) * 1e6
|
||||
break
|
||||
|
||||
name = 'co2_sequestration_limit'
|
||||
@ -290,11 +290,13 @@ if __name__ == "__main__":
|
||||
logging.basicConfig(filename=snakemake.log.python,
|
||||
level=snakemake.config['logging_level'])
|
||||
|
||||
update_config_with_sector_opts(snakemake.config, snakemake.wildcards.sector_opts)
|
||||
|
||||
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('-')
|
||||
opts = snakemake.wildcards.sector_opts.split('-')
|
||||
solve_opts = snakemake.config['solving']['options']
|
||||
|
||||
fn = getattr(snakemake.log, 'memory', None)
|
||||
@ -313,6 +315,7 @@ if __name__ == "__main__":
|
||||
n.line_volume_limit = n.global_constraints.at["lv_limit", "constant"]
|
||||
n.line_volume_limit_dual = n.global_constraints.at["lv_limit", "mu"]
|
||||
|
||||
n.meta = dict(snakemake.config, **dict(wildcards=dict(snakemake.wildcards)))
|
||||
n.export_to_netcdf(snakemake.output[0])
|
||||
|
||||
logger.info("Maximum memory usage: {}".format(mem.mem_usage))
|
||||
|
||||
@ -262,6 +262,9 @@ sector:
|
||||
biomass_transport: false # biomass transport between nodes
|
||||
conventional_generation: # generator : carrier
|
||||
OCGT: gas
|
||||
biomass_boiler: false
|
||||
biomass_to_liquid: false
|
||||
biosng: false
|
||||
|
||||
|
||||
industry:
|
||||
@ -316,6 +319,7 @@ industry:
|
||||
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
|
||||
|
||||
costs:
|
||||
year: 2030
|
||||
lifetime: 25 #default lifetime
|
||||
# From a Lion Hirth paper, also reflects average of Noothout et al 2016
|
||||
discountrate: 0.07
|
||||
|
||||
@ -260,6 +260,9 @@ sector:
|
||||
biomass_transport: false # biomass transport between nodes
|
||||
conventional_generation: # generator : carrier
|
||||
OCGT: gas
|
||||
biomass_boiler: false
|
||||
biomass_to_liquid: false
|
||||
biosng: false
|
||||
|
||||
|
||||
industry:
|
||||
@ -314,6 +317,7 @@ industry:
|
||||
# Material Economics (2019): https://materialeconomics.com/latest-updates/industrial-transformation-2050
|
||||
|
||||
costs:
|
||||
year: 2030
|
||||
lifetime: 25 #default lifetime
|
||||
# From a Lion Hirth paper, also reflects average of Noothout et al 2016
|
||||
discountrate: 0.07
|
||||
|
||||
Loading…
Reference in New Issue
Block a user