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This commit is contained in:
Fabian Neumann 2024-01-22 09:29:32 +01:00
parent d3cf329456
commit 9865a97089
8 changed files with 105 additions and 82 deletions
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11
rules/build_sector.smk
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@ -710,8 +710,6 @@ rule build_transport_demand:
"../scripts/build_transport_demand.py"
rule build_district_heat_share:
params:
sector=config["sector"],
@ -719,7 +717,8 @@ rule build_district_heat_share:
district_heat_share=RESOURCES + "district_heat_share.csv",
clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
output:
district_heat_share=RESOURCES + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
district_heat_share=RESOURCES
+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
threads: 1
resources:
mem_mb=1000,
@ -782,8 +781,10 @@ rule prepare_sector_network:
simplified_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}.csv",
industrial_demand=RESOURCES
+ "industrial_energy_demand_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
hourly_heat_demand_total=RESOURCES + "hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc",
district_heat_share=RESOURCES + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
hourly_heat_demand_total=RESOURCES
+ "hourly_heat_demand_total_elec_s{simpl}_{clusters}.nc",
district_heat_share=RESOURCES
+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
temp_soil_total=RESOURCES + "temp_soil_total_elec_s{simpl}_{clusters}.nc",
temp_soil_rural=RESOURCES + "temp_soil_rural_elec_s{simpl}_{clusters}.nc",
temp_soil_urban=RESOURCES + "temp_soil_urban_elec_s{simpl}_{clusters}.nc",

6
rules/solve_myopic.smk
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@ -11,8 +11,10 @@ rule build_existing_heating_distribution:
input:
existing_heating="data/existing_infrastructure/existing_heating_raw.csv",
clustered_pop_layout=RESOURCES + "pop_layout_elec_s{simpl}_{clusters}.csv",
clustered_pop_energy_layout=RESOURCES + "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
district_heat_share=RESOURCES + "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
clustered_pop_energy_layout=RESOURCES
+ "pop_weighted_energy_totals_s{simpl}_{clusters}.csv",
district_heat_share=RESOURCES
+ "district_heat_share_elec_s{simpl}_{clusters}_{planning_horizons}.csv",
output:
existing_heating_distribution=RESOURCES
+ "existing_heating_distribution_elec_s{simpl}_{clusters}_{planning_horizons}.csv",

15
scripts/add_existing_baseyear.py
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@ -409,15 +409,13 @@ def add_heating_capacities_installed_before_baseyear(
# file: "WP2_DataAnnex_1_BuildingTechs_ForPublication_201603.xls" -> "existing_heating_raw.csv".
# TODO start from original file
existing_heating = pd.read_csv(snakemake.input.existing_heating_distribution,
header=[0,1],
index_col=0)
existing_heating = pd.read_csv(
snakemake.input.existing_heating_distribution, header=[0, 1], index_col=0
)
techs = existing_heating.columns.get_level_values(1).unique()
for name in existing_heating.columns.get_level_values(0).unique():
name_type = "central" if name == "urban central" else "decentral"
nodes = pd.Index(n.buses.location[n.buses.index.str.contains(f"{name} heat")])
@ -451,7 +449,9 @@ def add_heating_capacities_installed_before_baseyear(
efficiency=efficiency,
capital_cost=costs.at[costs_name, "efficiency"]
* costs.at[costs_name, "fixed"],
p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")] * ratio / costs.at[costs_name, "efficiency"],
p_nom=existing_heating.loc[nodes, (name, f"{heat_pump_type} heat pump")]
* ratio
/ costs.at[costs_name, "efficiency"],
build_year=int(grouping_year),
lifetime=costs.at[costs_name, "lifetime"],
)
@ -516,7 +516,8 @@ def add_heating_capacities_installed_before_baseyear(
p_nom=(
existing_heating.loc[nodes, (name, "oil boiler")]
* ratio
/ costs.at["decentral oil boiler", "efficiency"]),
/ costs.at["decentral oil boiler", "efficiency"]
),
build_year=int(grouping_year),
lifetime=costs.at[f"{name_type} gas boiler", "lifetime"],
)

27
scripts/build_district_heat_share.py
View File

@ -6,12 +6,10 @@
Build district heat shares at each node, depending on investment year.
"""
import pandas as pd
from prepare_sector_network import get
import logging
import pandas as pd
from prepare_sector_network import get
logger = logging.getLogger(__name__)
@ -29,11 +27,11 @@ if __name__ == "__main__":
investment_year = int(snakemake.wildcards.planning_horizons[-4:])
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
index_col=0)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
district_heat_share = pd.read_csv(snakemake.input.district_heat_share,
index_col=0).squeeze()
district_heat_share = pd.read_csv(
snakemake.input.district_heat_share, index_col=0
).squeeze()
# make ct-based share nodal
district_heat_share = district_heat_share.loc[pop_layout.ct]
@ -62,17 +60,22 @@ if __name__ == "__main__":
# difference of max potential and today's share of district heating
diff = (urban_fraction * central_fraction) - dist_fraction_node
progress = get(snakemake.config["sector"]["district_heating"]["progress"], investment_year)
progress = get(
snakemake.config["sector"]["district_heating"]["progress"], investment_year
)
dist_fraction_node += diff * progress
logger.info(
f"Increase district heating share by a progress factor of {progress:.2%} "
f"resulting in new average share of {dist_fraction_node.mean():.2%}"
)
df = pd.DataFrame({
df = pd.DataFrame(
{
"original district heat share": district_heat_share,
"district fraction of node": dist_fraction_node,
"urban fraction": urban_fraction
}, dtype=float)
"urban fraction": urban_fraction,
},
dtype=float,
)
df.to_csv(snakemake.output.district_heat_share)

19
scripts/build_energy_totals.py
View File

@ -569,21 +569,24 @@ def build_energy_totals(countries, eurostat, swiss, idees):
def build_district_heat_share(countries, idees):
# district heating share
district_heat = idees[
["derived heat residential", "derived heat services"]
].sum(axis=1)
total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(axis=1)
district_heat = idees[["derived heat residential", "derived heat services"]].sum(
axis=1
)
total_heat = idees[["thermal uses residential", "thermal uses services"]].sum(
axis=1
)
district_heat_share = district_heat / total_heat
district_heat_share = district_heat_share.reindex(countries)
# Missing district heating share
dh_share = pd.read_csv(
snakemake.input.district_heat_share, index_col=0, usecols=[0, 1]
).div(100).squeeze()
dh_share = (
pd.read_csv(snakemake.input.district_heat_share, index_col=0, usecols=[0, 1])
.div(100)
.squeeze()
)
# make conservative assumption and take minimum from both data sets
district_heat_share = pd.concat(
[district_heat_share, dh_share.reindex_like(district_heat_share)], axis=1

73
scripts/build_existing_heating_distribution.py
View File

@ -6,9 +6,9 @@
Builds table of existing heat generation capacities for initial planning
horizon.
"""
import pandas as pd
import numpy as np
import country_converter as coco
import numpy as np
import pandas as pd
cc = coco.CountryConverter()
@ -16,9 +16,9 @@ cc = coco.CountryConverter()
def build_existing_heating():
# retrieve existing heating capacities
existing_heating = pd.read_csv(snakemake.input.existing_heating,
index_col=0,
header=0)
existing_heating = pd.read_csv(
snakemake.input.existing_heating, index_col=0, header=0
)
# data for Albania, Montenegro and Macedonia not included in database
existing_heating.loc["Albania"] = np.nan
@ -33,24 +33,25 @@ def build_existing_heating():
existing_heating.index = cc.convert(existing_heating.index, to="iso2")
# coal and oil boilers are assimilated to oil boilers
existing_heating["oil boiler"] = existing_heating["oil boiler"] + existing_heating["coal boiler"]
existing_heating["oil boiler"] = (
existing_heating["oil boiler"] + existing_heating["coal boiler"]
)
existing_heating.drop(["coal boiler"], axis=1, inplace=True)
# distribute technologies to nodes by population
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout,
index_col=0)
pop_layout = pd.read_csv(snakemake.input.clustered_pop_layout, index_col=0)
nodal_heating = existing_heating.loc[pop_layout.ct]
nodal_heating.index = pop_layout.index
nodal_heating = nodal_heating.multiply(pop_layout.fraction, axis=0)
district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
index_col=0)
district_heat_info = pd.read_csv(snakemake.input.district_heat_share, index_col=0)
dist_fraction = district_heat_info["district fraction of node"]
urban_fraction = district_heat_info["urban fraction"]
energy_layout = pd.read_csv(snakemake.input.clustered_pop_energy_layout,
index_col=0)
energy_layout = pd.read_csv(
snakemake.input.clustered_pop_energy_layout, index_col=0
)
uses = ["space", "water"]
sectors = ["residential", "services"]
@ -58,39 +59,51 @@ def build_existing_heating():
nodal_sectoral_totals = pd.DataFrame(dtype=float)
for sector in sectors:
nodal_sectoral_totals[sector] = energy_layout[[f"total {sector} {use}" for use in uses]].sum(axis=1)
nodal_sectoral_fraction = nodal_sectoral_totals.div(nodal_sectoral_totals.sum(axis=1),
axis=0)
nodal_sectoral_totals[sector] = energy_layout[
[f"total {sector} {use}" for use in uses]
].sum(axis=1)
nodal_sectoral_fraction = nodal_sectoral_totals.div(
nodal_sectoral_totals.sum(axis=1), axis=0
)
nodal_heat_name_fraction = pd.DataFrame(dtype=float)
nodal_heat_name_fraction["urban central"] = dist_fraction
for sector in sectors:
nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[
sector
] * (1 - urban_fraction)
nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[
sector
] * (urban_fraction - dist_fraction)
nodal_heat_name_fraction[f"{sector} rural"] = nodal_sectoral_fraction[sector]*(1 - urban_fraction)
nodal_heat_name_fraction[f"{sector} urban decentral"] = nodal_sectoral_fraction[sector]*(urban_fraction - dist_fraction)
nodal_heat_name_tech = pd.concat({name : nodal_heating .multiply(nodal_heat_name_fraction[name],
axis=0) for name in nodal_heat_name_fraction.columns},
nodal_heat_name_tech = pd.concat(
{
name: nodal_heating.multiply(nodal_heat_name_fraction[name], axis=0)
for name in nodal_heat_name_fraction.columns
},
axis=1,
names=["heat name","technology"])
names=["heat name", "technology"],
)
# move all ground HPs to rural, all air to urban
for sector in sectors:
nodal_heat_name_tech[(f"{sector} rural","ground heat pump")] += (nodal_heat_name_tech[("urban central","ground heat pump")]*nodal_sectoral_fraction[sector]
+ nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")])
nodal_heat_name_tech[(f"{sector} urban decentral","ground heat pump")] = 0.
nodal_heat_name_tech[(f"{sector} rural", "ground heat pump")] += (
nodal_heat_name_tech[("urban central", "ground heat pump")]
* nodal_sectoral_fraction[sector]
+ nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")]
)
nodal_heat_name_tech[(f"{sector} urban decentral", "ground heat pump")] = 0.0
nodal_heat_name_tech[(f"{sector} urban decentral","air heat pump")] += nodal_heat_name_tech[(f"{sector} rural","air heat pump")]
nodal_heat_name_tech[(f"{sector} rural","air heat pump")] = 0.
nodal_heat_name_tech[
(f"{sector} urban decentral", "air heat pump")
] += nodal_heat_name_tech[(f"{sector} rural", "air heat pump")]
nodal_heat_name_tech[(f"{sector} rural", "air heat pump")] = 0.0
nodal_heat_name_tech[("urban central","ground heat pump")] = 0.
nodal_heat_name_tech[("urban central", "ground heat pump")] = 0.0
nodal_heat_name_tech.to_csv(snakemake.output.existing_heating_distribution)

8
scripts/build_hourly_heat_demand.py
View File

@ -48,13 +48,13 @@ if __name__ == "__main__":
)
if use == "space":
heat_demand[f"{sector} {use}"] = daily_space_heat_demand * intraday_year_profile
heat_demand[f"{sector} {use}"] = (
daily_space_heat_demand * intraday_year_profile
)
else:
heat_demand[f"{sector} {use}"] = intraday_year_profile
heat_demand = pd.concat(heat_demand,
axis=1,
names = ["sector use", "node"])
heat_demand = pd.concat(heat_demand, axis=1, names=["sector use", "node"])
heat_demand.index.name = "snapshots"

12
scripts/prepare_sector_network.py
View File

@ -1639,8 +1639,11 @@ def add_land_transport(n, costs):
def build_heat_demand(n):
heat_demand_shape = xr.open_dataset(snakemake.input.hourly_heat_demand_total).to_dataframe().unstack(level=1)
heat_demand_shape = (
xr.open_dataset(snakemake.input.hourly_heat_demand_total)
.to_dataframe()
.unstack(level=1)
)
sectors = ["residential", "services"]
uses = ["water", "space"]
@ -1648,7 +1651,6 @@ def build_heat_demand(n):
heat_demand = {}
electric_heat_supply = {}
for sector, use in product(sectors, uses):
name = f"{sector} {use}"
heat_demand[name] = (
@ -1678,8 +1680,7 @@ def add_heat(n, costs):
heat_demand = build_heat_demand(n)
district_heat_info = pd.read_csv(snakemake.input.district_heat_share,
index_col=0)
district_heat_info = pd.read_csv(snakemake.input.district_heat_share, index_col=0)
dist_fraction = district_heat_info["district fraction of node"]
urban_fraction = district_heat_info["urban fraction"]
@ -1718,7 +1719,6 @@ def add_heat(n, costs):
# 1e3 converts from W/m^2 to MW/(1000m^2) = kW/m^2
solar_thermal = options["solar_cf_correction"] * solar_thermal / 1e3
for name in heat_systems:
name_type = "central" if name == "urban central" else "decentral"