Merge branch 'master' into add_methanol_techs
This commit is contained in:
lisazeyen
GitHub
commit
8e068c8a67
41
data/biomass_transport_costs_supplychain1.csv
Normal file
41
data/biomass_transport_costs_supplychain1.csv
Normal file
@ -0,0 +1,41 @@
|
||||
country,Loading,Loading,Loading,Transport to plant,Transport to plant,Unloading,TOTAL,TOTAL,TOTAL
|
||||
country,Time,Machinery costs,Waiting time truck driver,60 average speed km/h,Trailer,Waiting time truck driver,Fix,per km,per km/ton
|
||||
country,h,EUR,EUR.1,EUR/km,EUR.2,EUR.3,EUR.4,EUR/km.1,EUR/km/ton
|
||||
BE,0.08,3.56,1.31,1.45,6.42,1.31,12.61,0.02,0.47
|
||||
BG,0.08,2.06,0.56,1.06,2.61,0.56,5.79,0.02,0.22
|
||||
CZ,0.08,2.34,0.62,1.1,4.08,0.62,7.65,0.02,0.29
|
||||
DK,0.08,5.14,2.54,1.85,7.91,2.54,18.13,0.03,0.68
|
||||
DE,0.08,3.41,1.31,1.37,5.99,1.31,12.01,0.02,0.45
|
||||
EE,0.08,2.26,0.5,1.1,4.14,0.5,7.38,0.02,0.28
|
||||
IE,0.08,3.29,1.03,1.4,6.29,1.03,11.64,0.02,0.44
|
||||
EL,0.08,2.83,0.85,1.25,5.17,0.85,9.7,0.02,0.37
|
||||
ES,0.08,3.19,1.22,1.32,5.28,1.22,10.91,0.02,0.41
|
||||
FR,0.08,3.52,1.35,1.38,6.49,1.35,12.71,0.02,0.48
|
||||
IT,0.08,3.59,1.26,1.56,5.82,1.26,11.94,0.03,0.45
|
||||
CY,0.08,2.82,0.85,1.25,5.09,0.85,9.6,0.02,0.36
|
||||
LV,0.08,2.11,0.43,1.05,3.87,0.43,6.84,0.02,0.26
|
||||
LT,0.08,1.99,0.39,1.02,3.49,0.39,6.25,0.02,0.24
|
||||
LU,0.08,3.38,1.21,1.31,6.97,1.21,12.78,0.02,0.48
|
||||
HU,0.08,2.12,0.42,1.12,3.33,0.42,6.28,0.02,0.24
|
||||
MT,0.08,2.44,0.62,1.16,4.32,0.62,7.99,0.02,0.3
|
||||
NL,0.08,3.83,1.57,1.51,6.37,1.57,13.33,0.03,0.5
|
||||
AT,0.08,3.33,1.18,1.35,6.37,1.18,12.06,0.02,0.45
|
||||
PL,0.08,2.27,0.68,1.08,3.35,0.68,6.98,0.02,0.27
|
||||
PT,0.08,2.3,0.37,1.16,4.67,0.37,7.71,0.02,0.29
|
||||
RO,0.08,1.88,0.4,0.99,2.8,0.4,5.49,0.02,0.21
|
||||
SI,0.08,2.47,0.58,1.18,4.65,0.58,8.29,0.02,0.32
|
||||
SK,0.08,2.24,0.48,1.12,3.93,0.48,7.13,0.02,0.27
|
||||
FI,0.08,3.59,1.2,1.48,7.0,1.2,12.98,0.02,0.49
|
||||
SE,0.08,3.84,1.26,1.57,7.71,1.26,14.07,0.03,0.53
|
||||
UK,0.08,3.65,1.21,1.56,6.56,1.21,12.64,0.03,0.48
|
||||
HR,0.08,2.12,0.46,1.05,3.79,0.46,6.84,0.02,0.26
|
||||
AL,0.08,1.67,0.19,0.96,2.55,0.19,4.61,0.02,0.18
|
||||
BA,0.08,1.66,0.23,0.91,2.81,0.23,4.92,0.02,0.19
|
||||
MK,0.08,1.5,0.22,0.84,2.32,0.22,4.27,0.01,0.17
|
||||
ME,0.08,1.71,0.24,0.94,2.83,0.24,5.02,0.02,0.2
|
||||
RS,0.08,1.69,0.22,0.96,2.64,0.22,4.77,0.02,0.19
|
||||
XK,0.08,1.62,0.21,0.89,2.81,0.21,4.85,0.01,0.19
|
||||
UA,0.08,1.25,0.13,0.65,2.8,0.13,4.32,0.01,0.16
|
||||
TR,0.08,2.35,0.52,1.24,3.44,0.52,6.82,0.02,0.26
|
||||
MD,0.08,1.33,0.09,0.74,2.8,0.09,4.31,0.01,0.17
|
||||
CH,0.08,3.79,1.18,1.47,8.9,1.18,15.06,0.02,0.56
|
||||
|
41
data/biomass_transport_costs_supplychain2.csv
Normal file
41
data/biomass_transport_costs_supplychain2.csv
Normal file
@ -0,0 +1,41 @@
|
||||
country,Loading,Loading,Loading,Transport to plant,Transport to plant,Unloading,TOTAL,TOTAL,TOTAL
|
||||
country,Time,Machinery costs,Waiting time truck driver,60 average speed km/h,Trailer,Waiting time truck driver,Fix,per km,per km/ton
|
||||
country,h,EUR,EUR.1,EUR/km,EUR.2,EUR.3,EUR.4,EUR/km.1,EUR/km/ton
|
||||
BE,0.05,3.06,0.88,1.49,16.57,0.88,21.39,0.02,0.88
|
||||
BG,0.05,1.77,0.37,1.07,6.74,0.37,9.26,0.02,0.39
|
||||
CZ,0.05,2.05,0.41,1.13,10.52,0.41,13.4,0.02,0.55
|
||||
DK,0.05,4.24,1.7,1.89,20.4,1.7,28.04,0.03,1.15
|
||||
DE,0.05,2.91,0.87,1.41,15.45,0.87,20.11,0.02,0.83
|
||||
EE,0.05,2.01,0.33,1.13,10.67,0.33,13.34,0.02,0.55
|
||||
IE,0.05,2.87,0.69,1.44,16.23,0.69,20.48,0.02,0.84
|
||||
EL,0.05,2.47,0.57,1.28,13.34,0.57,16.95,0.02,0.7
|
||||
ES,0.05,2.71,0.82,1.35,13.63,0.82,17.98,0.02,0.74
|
||||
FR,0.05,3.02,0.91,1.42,16.74,0.91,21.56,0.02,0.89
|
||||
IT,0.05,3.07,0.85,1.59,15.0,0.85,19.77,0.03,0.82
|
||||
CY,0.05,2.46,0.57,1.28,13.12,0.57,16.71,0.02,0.69
|
||||
LV,0.05,1.89,0.29,1.08,9.98,0.29,12.44,0.02,0.52
|
||||
LT,0.05,1.78,0.26,1.04,9.01,0.26,11.3,0.02,0.47
|
||||
LU,0.05,2.94,0.81,1.35,17.98,0.81,22.54,0.02,0.92
|
||||
HU,0.05,1.88,0.28,1.14,8.59,0.28,11.03,0.02,0.46
|
||||
MT,0.05,2.15,0.41,1.19,11.15,0.41,14.12,0.02,0.58
|
||||
NL,0.05,3.23,1.05,1.55,16.42,1.05,21.76,0.03,0.9
|
||||
AT,0.05,2.88,0.79,1.38,16.42,0.79,20.88,0.02,0.86
|
||||
PL,0.05,1.96,0.46,1.1,8.64,0.46,11.51,0.02,0.48
|
||||
PT,0.05,2.09,0.25,1.18,12.04,0.25,14.63,0.02,0.6
|
||||
RO,0.05,1.66,0.27,1.01,7.23,0.27,9.43,0.02,0.39
|
||||
SI,0.05,2.2,0.39,1.2,12.0,0.39,14.98,0.02,0.62
|
||||
SK,0.05,1.99,0.32,1.15,10.13,0.32,12.77,0.02,0.53
|
||||
FI,0.05,3.12,0.8,1.52,18.05,0.8,22.78,0.03,0.94
|
||||
SE,0.05,3.35,0.85,1.62,19.89,0.85,24.93,0.03,1.02
|
||||
UK,0.05,3.16,0.81,1.6,16.93,0.81,21.71,0.03,0.9
|
||||
HR,0.05,1.89,0.31,1.07,9.77,0.31,12.28,0.02,0.51
|
||||
AL,0.05,1.5,0.13,0.98,6.59,0.13,8.35,0.02,0.35
|
||||
BA,0.05,1.5,0.15,0.93,7.25,0.15,9.05,0.02,0.38
|
||||
MK,0.05,1.35,0.15,0.85,5.98,0.15,7.62,0.01,0.32
|
||||
ME,0.05,1.54,0.16,0.96,7.31,0.16,9.17,0.02,0.38
|
||||
RS,0.05,1.52,0.15,0.97,6.8,0.15,8.62,0.02,0.36
|
||||
XK,0.05,1.46,0.14,0.91,7.25,0.14,8.99,0.02,0.37
|
||||
UA,0.05,1.16,0.09,0.66,7.23,0.09,8.57,0.01,0.35
|
||||
TR,0.05,2.07,0.35,1.26,8.88,0.35,11.63,0.02,0.49
|
||||
MD,0.05,1.24,0.06,0.75,7.23,0.06,8.59,0.01,0.36
|
||||
CH,0.05,3.37,0.79,1.52,22.97,0.79,27.92,0.03,1.14
|
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|
@ -10,15 +10,23 @@ Release Notes
|
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.. Upcoming Release
|
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|
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|
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* Add technology options for methanol, like electricity production from methanol, biomass to methanol, methanol to kerosene, ...
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|
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* Change the heating demand from final energy which includes losses in legacy equipment to thermal energy service based on JRC-IDEES. Efficiencies of existing heating capacities are lowered according to the conversion of final energy to thermal energy service. For overnight scenarios or future planning horizon this change leads to a reduction in heat supply.
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|
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* Updated district heating supply temperatures based on `Euroheat's DHC Market Outlook 2024<https://api.euroheat.org/uploads/Market_Outlook_2024_beeecd62d4.pdf>`__ and `AGFW-Hauptbericht 2022 <https://www.agfw.de/securedl/sdl-eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJpYXQiOjE3MjU2MjI2MTUsImV4cCI6MTcyNTcxMjYxNSwidXNlciI6MCwiZ3JvdXBzIjpbMCwtMV0sImZpbGUiOiJmaWxlYWRtaW4vdXNlcl91cGxvYWQvWmFobGVuX3VuZF9TdGF0aXN0aWtlbi9IYXVwdGJlcmljaHRfMjAyMi9BR0ZXX0hhdXB0YmVyaWNodF8yMDIyLnBkZiIsInBhZ2UiOjQzNn0.Bhma3PKg9uJnC57Ixi2p9STW5-II9VXPTDXS544M208/AGFW_Hauptbericht_2022.pdf>`__. `min_forward_temperature` and `return_temperature` (not given by Euroheat) are extrapolated based on German values.
|
||||
|
||||
* Made the overdimensioning factor for heating systems specific for central/decentral heating, defaults to no overdimensionining for central heating and no changes to decentral heating compared to previous version.
|
||||
|
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* bugfix: The carrier of stores was silently overwritten by their bus_carrier as a side effect when building the co2 constraints
|
||||
|
||||
* bugfix: The oil generator was incorrectly dropped when the config `oil_refining_emissions` was greater than zero. This was the default behaviour in 0.12.0.
|
||||
|
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* Uses of Snakemake's ``storage()`` function are integrated into retrieval
|
||||
rules. This simplifies the use of ``mock_snakemake`` and places downloaded
|
||||
data more transparently into the ``data`` directory.
|
||||
|
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PyPSA-Eur 0.12.0 (30th August 2024)
|
||||
===================================
|
||||
|
||||
|
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@ -24,7 +24,7 @@ dependencies:
|
||||
- yaml
|
||||
- pytables
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- lxml
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||||
- powerplantmatching>=0.5.15
|
||||
- powerplantmatching>=0.5.15,<0.6
|
||||
- numpy
|
||||
- pandas>=2.1
|
||||
- geopandas>=1
|
||||
@ -64,4 +64,3 @@ dependencies:
|
||||
- snakemake-executor-plugin-slurm
|
||||
- snakemake-executor-plugin-cluster-generic
|
||||
- highspy
|
||||
- tabula-py
|
||||
|
||||
@ -366,6 +366,7 @@ rule build_energy_totals:
|
||||
co2_name=resources("co2_totals.csv"),
|
||||
transport_name=resources("transport_data.csv"),
|
||||
district_heat_share=resources("district_heat_share.csv"),
|
||||
heating_efficiencies=resources("heating_efficiencies.csv"),
|
||||
threads: 16
|
||||
resources:
|
||||
mem_mb=10000,
|
||||
@ -402,10 +403,7 @@ rule build_biomass_potentials:
|
||||
params:
|
||||
biomass=config_provider("biomass"),
|
||||
input:
|
||||
enspreso_biomass=storage(
|
||||
"https://zenodo.org/records/10356004/files/ENSPRESO_BIOMASS.xlsx",
|
||||
keep_local=True,
|
||||
),
|
||||
enspreso_biomass="data/ENSPRESO_BIOMASS.xlsx",
|
||||
eurostat="data/eurostat/Balances-April2023",
|
||||
nuts2="data/bundle/nuts/NUTS_RG_10M_2013_4326_LEVL_2.geojson",
|
||||
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
@ -435,10 +433,8 @@ rule build_biomass_potentials:
|
||||
|
||||
rule build_biomass_transport_costs:
|
||||
input:
|
||||
transport_cost_data=storage(
|
||||
"https://publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass potentials in europe_web rev.pdf",
|
||||
keep_local=True,
|
||||
),
|
||||
sc1="data/biomass_transport_costs_supplychain1.csv",
|
||||
sc2="data/biomass_transport_costs_supplychain2.csv",
|
||||
output:
|
||||
biomass_transport_costs=resources("biomass_transport_costs.csv"),
|
||||
threads: 1
|
||||
@ -460,10 +456,7 @@ rule build_sequestration_potentials:
|
||||
"sector", "regional_co2_sequestration_potential"
|
||||
),
|
||||
input:
|
||||
sequestration_potential=storage(
|
||||
"https://raw.githubusercontent.com/ericzhou571/Co2Storage/main/resources/complete_map_2020_unit_Mt.geojson",
|
||||
keep_local=True,
|
||||
),
|
||||
sequestration_potential="data/complete_map_2020_unit_Mt.geojson",
|
||||
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
regions_offshore=resources("regions_offshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
output:
|
||||
@ -505,9 +498,7 @@ rule build_salt_cavern_potentials:
|
||||
|
||||
rule build_ammonia_production:
|
||||
input:
|
||||
usgs=storage(
|
||||
"https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/media/files/myb1-2022-nitro-ert.xlsx"
|
||||
),
|
||||
usgs="data/myb1-2022-nitro-ert.xlsx",
|
||||
output:
|
||||
ammonia_production=resources("ammonia_production.csv"),
|
||||
threads: 1
|
||||
@ -636,10 +627,7 @@ rule build_industrial_distribution_key:
|
||||
input:
|
||||
regions_onshore=resources("regions_onshore_elec_s{simpl}_{clusters}.geojson"),
|
||||
clustered_pop_layout=resources("pop_layout_elec_s{simpl}_{clusters}.csv"),
|
||||
hotmaps=storage(
|
||||
"https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database/-/raw/master/data/Industrial_Database.csv",
|
||||
keep_local=True,
|
||||
),
|
||||
hotmaps="data/Industrial_Database.csv",
|
||||
gem_gspt="data/gem/Global-Steel-Plant-Tracker-April-2024-Standard-Copy-V1.xlsx",
|
||||
ammonia="data/ammonia_plants.csv",
|
||||
cement_supplement="data/cement-plants-noneu.csv",
|
||||
@ -1028,6 +1016,7 @@ rule prepare_sector_network:
|
||||
RDIR=RDIR,
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
heat_systems=config_provider("sector", "heat_systems"),
|
||||
energy_totals_year=config_provider("energy", "energy_totals_year"),
|
||||
input:
|
||||
unpack(input_profile_offwind),
|
||||
**rules.cluster_gas_network.output,
|
||||
@ -1105,6 +1094,7 @@ rule prepare_sector_network:
|
||||
district_heat_share=resources(
|
||||
"district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||
),
|
||||
heating_efficiencies=resources("heating_efficiencies.csv"),
|
||||
temp_soil_total=resources("temp_soil_total_elec_s{simpl}_{clusters}.nc"),
|
||||
temp_air_total=resources("temp_air_total_elec_s{simpl}_{clusters}.nc"),
|
||||
cop_profiles=resources("cop_profiles_elec_s{simpl}_{clusters}.nc"),
|
||||
|
||||
@ -191,6 +191,65 @@ if config["enable"]["retrieve"]:
|
||||
validate_checksum(output[0], input[0])
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
|
||||
rule retrieve_jrc_enspreso_biomass:
|
||||
input:
|
||||
storage(
|
||||
"https://zenodo.org/records/10356004/files/ENSPRESO_BIOMASS.xlsx",
|
||||
keep_local=True,
|
||||
),
|
||||
output:
|
||||
"data/ENSPRESO_BIOMASS.xlsx",
|
||||
retries: 1
|
||||
run:
|
||||
move(input[0], output[0])
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
|
||||
rule retrieve_hotmaps_industrial_sites:
|
||||
input:
|
||||
storage(
|
||||
"https://gitlab.com/hotmaps/industrial_sites/industrial_sites_Industrial_Database/-/raw/master/data/Industrial_Database.csv",
|
||||
keep_local=True,
|
||||
),
|
||||
output:
|
||||
"data/Industrial_Database.csv",
|
||||
retries: 1
|
||||
run:
|
||||
move(input[0], output[0])
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
|
||||
rule retrieve_usgs_ammonia_production:
|
||||
input:
|
||||
storage(
|
||||
"https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/media/files/myb1-2022-nitro-ert.xlsx"
|
||||
),
|
||||
output:
|
||||
"data/myb1-2022-nitro-ert.xlsx",
|
||||
retries: 1
|
||||
run:
|
||||
move(input[0], output[0])
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
|
||||
rule retrieve_geological_co2_storage_potential:
|
||||
input:
|
||||
storage(
|
||||
"https://raw.githubusercontent.com/ericzhou571/Co2Storage/main/resources/complete_map_2020_unit_Mt.geojson",
|
||||
keep_local=True,
|
||||
),
|
||||
output:
|
||||
"data/complete_map_2020_unit_Mt.geojson",
|
||||
retries: 1
|
||||
run:
|
||||
move(input[0], output[0])
|
||||
|
||||
|
||||
if config["enable"]["retrieve"]:
|
||||
|
||||
# Downloading Copernicus Global Land Cover for land cover and land use:
|
||||
|
||||
@ -10,6 +10,7 @@ rule add_existing_baseyear:
|
||||
existing_capacities=config_provider("existing_capacities"),
|
||||
costs=config_provider("costs"),
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
energy_totals_year=config_provider("energy", "energy_totals_year"),
|
||||
input:
|
||||
network=RESULTS
|
||||
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
@ -26,6 +27,7 @@ rule add_existing_baseyear:
|
||||
existing_heating_distribution=resources(
|
||||
"existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||
),
|
||||
heating_efficiencies=resources("heating_efficiencies.csv"),
|
||||
output:
|
||||
RESULTS
|
||||
+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
|
||||
@ -8,6 +8,7 @@ rule add_existing_baseyear:
|
||||
existing_capacities=config_provider("existing_capacities"),
|
||||
costs=config_provider("costs"),
|
||||
heat_pump_sources=config_provider("sector", "heat_pump_sources"),
|
||||
energy_totals_year=config_provider("energy", "energy_totals_year"),
|
||||
input:
|
||||
network=RESULTS
|
||||
+ "prenetworks/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
@ -25,6 +26,7 @@ rule add_existing_baseyear:
|
||||
"existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv"
|
||||
),
|
||||
existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
|
||||
heating_efficiencies=resources("heating_efficiencies.csv"),
|
||||
output:
|
||||
RESULTS
|
||||
+ "prenetworks-brownfield/elec_s{simpl}_{clusters}_l{ll}_{opts}_{sector_opts}_{planning_horizons}.nc",
|
||||
|
||||
@ -418,6 +418,50 @@ def add_power_capacities_installed_before_baseyear(n, grouping_years, costs, bas
|
||||
]
|
||||
|
||||
|
||||
def get_efficiency(heat_system, carrier, nodes, heating_efficiencies, costs):
|
||||
"""
|
||||
Computes the heating system efficiency based on the sector and carrier
|
||||
type.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
heat_system : object
|
||||
carrier : str
|
||||
The type of fuel or energy carrier (e.g., 'gas', 'oil').
|
||||
nodes : pandas.Series
|
||||
A pandas Series containing node information used to match the heating efficiency data.
|
||||
heating_efficiencies : dict
|
||||
A dictionary containing efficiency values for different carriers and sectors.
|
||||
costs : pandas.DataFrame
|
||||
A DataFrame containing boiler cost and efficiency data for different heating systems.
|
||||
|
||||
Returns:
|
||||
--------
|
||||
efficiency : pandas.Series or float
|
||||
A pandas Series mapping the efficiencies based on nodes for residential and services sectors, or a single
|
||||
efficiency value for other heating systems (e.g., urban central).
|
||||
|
||||
Notes:
|
||||
------
|
||||
- For residential and services sectors, efficiency is mapped based on the nodes.
|
||||
- For other sectors, the default boiler efficiency is retrieved from the `costs` database.
|
||||
"""
|
||||
|
||||
if heat_system.value == "urban central":
|
||||
boiler_costs_name = getattr(heat_system, f"{carrier}_boiler_costs_name")
|
||||
efficiency = costs.at[boiler_costs_name, "efficiency"]
|
||||
elif heat_system.sector.value == "residential":
|
||||
key = f"{carrier} residential space efficiency"
|
||||
efficiency = nodes.str[:2].map(heating_efficiencies[key])
|
||||
elif heat_system.sector.value == "services":
|
||||
key = f"{carrier} services space efficiency"
|
||||
efficiency = nodes.str[:2].map(heating_efficiencies[key])
|
||||
else:
|
||||
logger.warning(f"{heat_system} not defined.")
|
||||
|
||||
return efficiency
|
||||
|
||||
|
||||
def add_heating_capacities_installed_before_baseyear(
|
||||
n: pypsa.Network,
|
||||
baseyear: int,
|
||||
@ -546,6 +590,10 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
lifetime=costs.at[heat_system.resistive_heater_costs_name, "lifetime"],
|
||||
)
|
||||
|
||||
efficiency = get_efficiency(
|
||||
heat_system, "gas", nodes, heating_efficiencies, costs
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
@ -554,7 +602,7 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
bus1=nodes + " " + heat_system.value + " heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=heat_system.value + " gas boiler",
|
||||
efficiency=costs.at[heat_system.gas_boiler_costs_name, "efficiency"],
|
||||
efficiency=efficiency,
|
||||
efficiency2=costs.at["gas", "CO2 intensity"],
|
||||
capital_cost=(
|
||||
costs.at[heat_system.gas_boiler_costs_name, "efficiency"]
|
||||
@ -569,6 +617,10 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
lifetime=costs.at[heat_system.gas_boiler_costs_name, "lifetime"],
|
||||
)
|
||||
|
||||
efficiency = get_efficiency(
|
||||
heat_system, "oil", nodes, heating_efficiencies, costs
|
||||
)
|
||||
|
||||
n.madd(
|
||||
"Link",
|
||||
nodes,
|
||||
@ -577,7 +629,7 @@ def add_heating_capacities_installed_before_baseyear(
|
||||
bus1=nodes + " " + heat_system.value + " heat",
|
||||
bus2="co2 atmosphere",
|
||||
carrier=heat_system.value + " oil boiler",
|
||||
efficiency=costs.at[heat_system.oil_boiler_costs_name, "efficiency"],
|
||||
efficiency=efficiency,
|
||||
efficiency2=costs.at["oil", "CO2 intensity"],
|
||||
capital_cost=costs.at[heat_system.oil_boiler_costs_name, "efficiency"]
|
||||
* costs.at[heat_system.oil_boiler_costs_name, "fixed"],
|
||||
@ -621,12 +673,12 @@ if __name__ == "__main__":
|
||||
|
||||
snakemake = mock_snakemake(
|
||||
"add_existing_baseyear",
|
||||
configfiles="config/config.yaml",
|
||||
configfiles="config/test/config.myopic.yaml",
|
||||
simpl="",
|
||||
clusters="20",
|
||||
clusters="5",
|
||||
ll="v1.5",
|
||||
opts="",
|
||||
sector_opts="none",
|
||||
sector_opts="",
|
||||
planning_horizons=2030,
|
||||
)
|
||||
|
||||
@ -660,6 +712,11 @@ if __name__ == "__main__":
|
||||
|
||||
if options["heating"]:
|
||||
|
||||
# one could use baseyear here instead (but dangerous if no data)
|
||||
fn = snakemake.input.heating_efficiencies
|
||||
year = int(snakemake.params["energy_totals_year"])
|
||||
heating_efficiencies = pd.read_csv(fn, index_col=[1, 0]).loc[year]
|
||||
|
||||
add_heating_capacities_installed_before_baseyear(
|
||||
n=n,
|
||||
baseyear=baseyear,
|
||||
|
||||
@ -16,33 +16,51 @@ assuming as an approximation energy content of wood pellets
|
||||
|
||||
@author: bw0928
|
||||
"""
|
||||
|
||||
import platform
|
||||
|
||||
import pandas as pd
|
||||
import tabula as tbl
|
||||
|
||||
ENERGY_CONTENT = 4.8 # unit MWh/t (wood pellets)
|
||||
system = platform.system()
|
||||
encoding = "cp1252" if system == "Windows" else "utf-8"
|
||||
|
||||
|
||||
def get_countries():
|
||||
pandas_options = dict(
|
||||
def get_cost_per_tkm(pdf, datapage, countrypage):
|
||||
"""
|
||||
Extracts the cost tables from the JRC report PDF.
|
||||
|
||||
https://publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass%20potentials%20in%20europe_web%20rev.pdf
|
||||
- pdf (str): The filepath of the PDF file containing the data.
|
||||
- datapage (int): The page number of the data table in the PDF.
|
||||
- countrypage (int): The page number of the table containing the country indices in the PDF.
|
||||
|
||||
Returns:
|
||||
- pandas.DataFrame: The data table with the cost per tkm for biomass transport, indexed by country.
|
||||
|
||||
Raises:
|
||||
- ImportError: If tabula-py and platform are not installed.
|
||||
"""
|
||||
try:
|
||||
import platform
|
||||
|
||||
import tabula as tbl
|
||||
except:
|
||||
ImportError("Please install tabula-py and platform")
|
||||
|
||||
system = platform.system()
|
||||
encoding = "cp1252" if system == "Windows" else "utf-8"
|
||||
|
||||
# Obtain countries:
|
||||
pandas_options_country = dict(
|
||||
skiprows=range(6), header=None, index_col=0, encoding=encoding
|
||||
)
|
||||
|
||||
return tbl.read_pdf(
|
||||
str(snakemake.input.transport_cost_data),
|
||||
pages="145",
|
||||
countries = tbl.read_pdf(
|
||||
pdf,
|
||||
pages=countrypage,
|
||||
multiple_tables=False,
|
||||
pandas_options=pandas_options,
|
||||
pandas_options=pandas_options_country,
|
||||
encoding=encoding,
|
||||
)[0].index
|
||||
|
||||
|
||||
def get_cost_per_tkm(page, countries):
|
||||
pandas_options = dict(
|
||||
# Obtain data tables
|
||||
pandas_options_data = dict(
|
||||
skiprows=range(6),
|
||||
header=0,
|
||||
sep=" |,",
|
||||
@ -52,10 +70,10 @@ def get_cost_per_tkm(page, countries):
|
||||
)
|
||||
|
||||
sc = tbl.read_pdf(
|
||||
str(snakemake.input.transport_cost_data),
|
||||
pages=page,
|
||||
pdf,
|
||||
pages=datapage,
|
||||
multiple_tables=False,
|
||||
pandas_options=pandas_options,
|
||||
pandas_options=pandas_options_data,
|
||||
encoding=encoding,
|
||||
)[0]
|
||||
sc.index = countries
|
||||
@ -65,10 +83,16 @@ def get_cost_per_tkm(page, countries):
|
||||
|
||||
|
||||
def build_biomass_transport_costs():
|
||||
countries = get_countries()
|
||||
# Optional build from JRC report pdf, requires tabula and java dependencies.
|
||||
# Update `pdf` path to the JRC report if needed.
|
||||
# sc1 = get_cost_per_tkm(pdf = "report.pdf", datapage=146, countrypage=145)
|
||||
# sc2 = get_cost_per_tkm(pdf = "report.pdf", datapage=147, countrypage=145)
|
||||
|
||||
sc1 = get_cost_per_tkm(146, countries)
|
||||
sc2 = get_cost_per_tkm(147, countries)
|
||||
# Use extracted csv from JRC report
|
||||
# https://publications.jrc.ec.europa.eu/repository/bitstream/JRC98626/biomass%20potentials%20in%20europe_web%20rev.pdf
|
||||
# Pages 146 (144) for supply chain 1 and 147 (145) for supply chain 2
|
||||
sc1 = pd.read_csv(snakemake.input.sc1, index_col=0, skiprows=2)
|
||||
sc2 = pd.read_csv(snakemake.input.sc2, index_col=0, skiprows=2)
|
||||
|
||||
# take mean of both supply chains
|
||||
to_concat = [sc1["EUR/km/ton"], sc2["EUR/km/ton"]]
|
||||
|
||||
@ -367,6 +367,24 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
|
||||
assert df.index[43] == "Thermal uses"
|
||||
ct_totals["thermal uses residential"] = df.iloc[43]
|
||||
|
||||
df = pd.read_excel(fn_residential, "RES_hh_eff", index_col=0)
|
||||
|
||||
ct_totals["total residential space efficiency"] = df.loc["Space heating"]
|
||||
|
||||
assert df.index[5] == "Diesel oil"
|
||||
ct_totals["oil residential space efficiency"] = df.iloc[5]
|
||||
|
||||
assert df.index[6] == "Natural gas"
|
||||
ct_totals["gas residential space efficiency"] = df.iloc[6]
|
||||
|
||||
ct_totals["total residential water efficiency"] = df.loc["Water heating"]
|
||||
|
||||
assert df.index[18] == "Diesel oil"
|
||||
ct_totals["oil residential water efficiency"] = df.iloc[18]
|
||||
|
||||
assert df.index[19] == "Natural gas"
|
||||
ct_totals["gas residential water efficiency"] = df.iloc[19]
|
||||
|
||||
# services
|
||||
|
||||
df = pd.read_excel(fn_tertiary, "SER_hh_fec", index_col=0)
|
||||
@ -400,6 +418,24 @@ def idees_per_country(ct: str, base_dir: str) -> pd.DataFrame:
|
||||
assert df.index[46] == "Thermal uses"
|
||||
ct_totals["thermal uses services"] = df.iloc[46]
|
||||
|
||||
df = pd.read_excel(fn_tertiary, "SER_hh_eff", index_col=0)
|
||||
|
||||
ct_totals["total services space efficiency"] = df.loc["Space heating"]
|
||||
|
||||
assert df.index[5] == "Diesel oil"
|
||||
ct_totals["oil services space efficiency"] = df.iloc[5]
|
||||
|
||||
assert df.index[7] == "Conventional gas heaters"
|
||||
ct_totals["gas services space efficiency"] = df.iloc[7]
|
||||
|
||||
ct_totals["total services water efficiency"] = df.loc["Hot water"]
|
||||
|
||||
assert df.index[20] == "Diesel oil"
|
||||
ct_totals["oil services water efficiency"] = df.iloc[20]
|
||||
|
||||
assert df.index[21] == "Natural gas"
|
||||
ct_totals["gas services water efficiency"] = df.iloc[21]
|
||||
|
||||
# agriculture, forestry and fishing
|
||||
|
||||
start = "Detailed split of energy consumption (ktoe)"
|
||||
@ -576,7 +612,8 @@ def build_idees(countries: List[str]) -> pd.DataFrame:
|
||||
# efficiency kgoe/100km -> ktoe/100km so that after conversion TWh/100km
|
||||
totals.loc[:, "passenger car efficiency"] /= 1e6
|
||||
# convert ktoe to TWh
|
||||
exclude = totals.columns.str.fullmatch("passenger cars")
|
||||
patterns = ["passenger cars", ".*space efficiency", ".*water efficiency"]
|
||||
exclude = totals.columns.str.fullmatch("|".join(patterns))
|
||||
totals = totals.copy()
|
||||
totals.loc[:, ~exclude] *= 11.63 / 1e3
|
||||
|
||||
@ -654,11 +691,14 @@ def build_energy_totals(
|
||||
eurostat_countries = eurostat.index.unique(0)
|
||||
eurostat_years = eurostat.index.unique(1)
|
||||
|
||||
to_drop = ["passenger cars", "passenger car efficiency"]
|
||||
new_index = pd.MultiIndex.from_product(
|
||||
[countries, eurostat_years], names=["country", "year"]
|
||||
)
|
||||
|
||||
efficiency_keywords = ["space efficiency", "water efficiency"]
|
||||
to_drop = idees.columns[idees.columns.str.contains("|".join(efficiency_keywords))]
|
||||
to_drop = to_drop.append(pd.Index(["passenger cars", "passenger car efficiency"]))
|
||||
|
||||
df = idees.reindex(new_index).drop(to_drop, axis=1)
|
||||
|
||||
in_eurostat = df.index.levels[0].intersection(eurostat_countries)
|
||||
@ -1501,6 +1541,59 @@ def build_transformation_output_coke(eurostat, fn):
|
||||
df.to_csv(fn)
|
||||
|
||||
|
||||
def build_heating_efficiencies(
|
||||
countries: List[str], idees: pd.DataFrame
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
Build heating efficiencies for a set of countries based on IDEES data.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
countries : List[str]
|
||||
List of country codes.
|
||||
idees : pd.DataFrame
|
||||
DataFrame with IDEES data.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
DataFrame with heating efficiencies.
|
||||
|
||||
|
||||
Notes
|
||||
-----
|
||||
- It fills missing data with average data.
|
||||
"""
|
||||
|
||||
years = np.arange(2000, 2022)
|
||||
|
||||
cols = idees.columns[
|
||||
idees.columns.str.contains("space efficiency")
|
||||
^ idees.columns.str.contains("water efficiency")
|
||||
]
|
||||
|
||||
heating_efficiencies = pd.DataFrame(idees[cols])
|
||||
|
||||
new_index = pd.MultiIndex.from_product(
|
||||
[countries, heating_efficiencies.index.unique(1)],
|
||||
names=["country", "year"],
|
||||
)
|
||||
|
||||
heating_efficiencies = heating_efficiencies.reindex(index=new_index)
|
||||
|
||||
for col in cols:
|
||||
unstacked = heating_efficiencies[col].unstack()
|
||||
|
||||
fillvalue = unstacked.mean()
|
||||
|
||||
for ct in unstacked.index:
|
||||
mask = unstacked.loc[ct].isna()
|
||||
unstacked.loc[ct, mask] = fillvalue[mask]
|
||||
heating_efficiencies[col] = unstacked.stack()
|
||||
|
||||
return heating_efficiencies
|
||||
|
||||
|
||||
# %%
|
||||
if __name__ == "__main__":
|
||||
if "snakemake" not in globals():
|
||||
@ -1556,3 +1649,6 @@ if __name__ == "__main__":
|
||||
|
||||
transport = build_transport_data(countries, population, idees)
|
||||
transport.to_csv(snakemake.output.transport_name)
|
||||
|
||||
heating_efficiencies = build_heating_efficiencies(countries, idees)
|
||||
heating_efficiencies.to_csv(snakemake.output.heating_efficiencies)
|
||||
|
||||
@ -115,7 +115,9 @@ def plot_h2_map(n, regions):
|
||||
|
||||
retro_wo_new_i = h2_retro.index.difference(h2_new.index)
|
||||
h2_retro_wo_new = h2_retro.loc[retro_wo_new_i]
|
||||
h2_retro_wo_new.index = h2_retro_wo_new.index_orig
|
||||
h2_retro_wo_new.index = h2_retro_wo_new.index_orig.apply(
|
||||
lambda x: x.split("-2")[0]
|
||||
)
|
||||
|
||||
to_concat = [h2_new, h2_retro_w_new, h2_retro_wo_new]
|
||||
h2_total = pd.concat(to_concat).p_nom_opt.groupby(level=0).sum()
|
||||
@ -126,7 +128,12 @@ def plot_h2_map(n, regions):
|
||||
link_widths_total = h2_total / linewidth_factor
|
||||
|
||||
n.links.rename(index=lambda x: x.split("-2")[0], inplace=True)
|
||||
n.links = n.links.groupby(level=0).first()
|
||||
# group links by summing up p_nom values and taking the first value of the rest of the columns
|
||||
other_cols = dict.fromkeys(n.links.columns.drop(["p_nom_opt", "p_nom"]), "first")
|
||||
n.links = n.links.groupby(level=0).agg(
|
||||
{"p_nom_opt": "sum", "p_nom": "sum", **other_cols}
|
||||
)
|
||||
|
||||
link_widths_total = link_widths_total.reindex(n.links.index).fillna(0.0)
|
||||
link_widths_total[n.links.p_nom_opt < line_lower_threshold] = 0.0
|
||||
|
||||
|
||||
@ -2143,9 +2143,14 @@ def build_heat_demand(n):
|
||||
for sector, use in product(sectors, uses):
|
||||
name = f"{sector} {use}"
|
||||
|
||||
# efficiency for final energy to thermal energy service
|
||||
eff = pop_weighted_energy_totals.index.str[:2].map(
|
||||
heating_efficiencies[f"total {sector} {use} efficiency"]
|
||||
)
|
||||
|
||||
heat_demand[name] = (
|
||||
heat_demand_shape[name] / heat_demand_shape[name].sum()
|
||||
).multiply(pop_weighted_energy_totals[f"total {sector} {use}"]) * 1e6
|
||||
).multiply(pop_weighted_energy_totals[f"total {sector} {use}"] * eff) * 1e6
|
||||
electric_heat_supply[name] = (
|
||||
heat_demand_shape[name] / heat_demand_shape[name].sum()
|
||||
).multiply(pop_weighted_energy_totals[f"electricity {sector} {use}"]) * 1e6
|
||||
@ -4709,6 +4714,10 @@ if __name__ == "__main__":
|
||||
)
|
||||
pop_weighted_energy_totals.update(pop_weighted_heat_totals)
|
||||
|
||||
fn = snakemake.input.heating_efficiencies
|
||||
year = int(snakemake.params["energy_totals_year"])
|
||||
heating_efficiencies = pd.read_csv(fn, index_col=[1, 0]).loc[year]
|
||||
|
||||
patch_electricity_network(n)
|
||||
|
||||
spatial = define_spatial(pop_layout.index, options)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user