Skip to content

Commit

Permalink
Merge pull request #905 from openego/features/#903-desaggregate-heat-…
Browse files Browse the repository at this point in the history 
…pumps

Desaggregate heat pumps
  • Loading branch information
@nailend
nailend committed Feb 2, 2023
2 parents 3140f2f + 8dfaa24 commit d021716
Show file tree
Hide file tree
Showing 7 changed files with 1,977 additions and 81 deletions.
2 changes: 2 additions & 0 deletions CHANGELOG.rst
View file
Original file line number Diff line number Diff line change
Expand Up @@ -242,6 +242,8 @@ Added
`#1014 <https://github.com/openego/eGon-data/issues/1014>`_
* Add new MaStR dataset
`#1051 <https://github.com/openego/eGon-data/issues/1051>`_
* Heat pump desaggregation to buildings
`PR #903 <https://github.com/openego/eGon-data/pull/903>`_

.. _PR #159: https://github.com/openego/eGon-data/pull/159
.. _PR #703: https://github.com/openego/eGon-data/pull/703
Expand Down
61 changes: 58 additions & 3 deletions src/egon/data/airflow/dags/pipeline.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -49,6 +49,11 @@
from egon.data.datasets.heat_etrago import HeatEtrago
from egon.data.datasets.heat_etrago.hts_etrago import HtsEtragoTable
from egon.data.datasets.heat_supply import HeatSupply
from egon.data.datasets.heat_supply.individual_heating import (
HeatPumps2035,
HeatPumps2050,
HeatPumpsPypsaEurSec,
)
from egon.data.datasets.hydrogen_etrago import (
HydrogenBusEtrago,
HydrogenGridEtrago,
Expand Down Expand Up @@ -329,6 +334,24 @@
]
)

cts_demand_buildings = CtsDemandBuildings(
dependencies=[
osm_buildings_streets,
cts_electricity_demand_annual,
hh_demand_buildings_setup,
tasks["heat_demand_timeseries.export-etrago-cts-heat-profiles"],
]
)

# Minimum heat pump capacity for pypsa-eur-sec
heat_pumps_pypsa_eur_sec = HeatPumpsPypsaEurSec(
dependencies=[
cts_demand_buildings,
DistrictHeatingAreas,
heat_time_series,
]
)

# run pypsa-eur-sec
run_pypsaeursec = PypsaEurSec(
dependencies=[
Expand All @@ -339,6 +362,7 @@
data_bundle,
electrical_load_etrago,
heat_time_series,
heat_pumps_pypsa_eur_sec,
]
)

Expand Down Expand Up @@ -533,19 +557,50 @@
# CHP to eTraGo
chp_etrago = ChpEtrago(dependencies=[chp, heat_etrago])

# Storages to eTraGo
storage_etrago = StorageEtrago(
dependencies=[pumped_hydro, scenario_parameters, setup_etrago]
)

mit_charging_infrastructure = MITChargingInfrastructure(
dependencies=[mv_grid_districts, hh_demand_buildings_setup]
)

# eMobility: heavy duty transport
heavy_duty_transport = HeavyDutyTransport(
dependencies=[vg250, setup_etrago, create_gas_polygons_egon2035]
)

# Heat pump disaggregation for eGon2035
heat_pumps_2035 = HeatPumps2035(
dependencies=[
cts_demand_buildings,
DistrictHeatingAreas,
heat_supply,
heat_time_series,
heat_pumps_pypsa_eur_sec,
tasks["power_plants.pv_rooftop_buildings.pv-rooftop-to-buildings"],
]
)

# HTS to eTraGo table
hts_etrago_table = HtsEtragoTable(
dependencies=[
district_heating_areas,
heat_etrago,
heat_time_series,
mv_grid_districts,
heat_pumps_2035,
]
)

# Storages to eTraGo
storage_etrago = StorageEtrago(
dependencies=[pumped_hydro, scenario_parameters, setup_etrago]
# Heat pump disaggregation for eGon100RE
heat_pumps_2050 = HeatPumps2050(
dependencies=[
run_pypsaeursec,
heat_pumps_pypsa_eur_sec,
heat_supply,
]
)

# eMobility: motorized individual travel
Expand Down
3 changes: 3 additions & 0 deletions src/egon/data/datasets.yml
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1130,6 +1130,9 @@ emobility_mit:
export_results_to_csv: True
parallel_tasks: 10

demand_timeseries_mvgd:
parallel_tasks: 10

charging_infrastructure:
original_data:
sources:
Expand Down
159 changes: 85 additions & 74 deletions src/egon/data/datasets/electricity_demand_timeseries/cts_buildings.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -26,9 +26,9 @@
Table including the mv substation heat profile share of all selected
cts buildings for scenario eGon2035 and eGon100RE. This table is created
within :func:`cts_heat()`
* `demand.egon_building_peak_loads`:
Mapping of demand time series and buildings including cell_id, building
area and peak load. This table is already created within
* `demand.egon_building_electricity_peak_loads`:
Mapping of electricity demand time series and buildings including cell_id,
building area and peak load. This table is already created within
:func:`hh_buildings.get_building_peak_loads()`
* `boundaries.egon_map_zensus_mvgd_buildings`:
A final mapping table including all buildings used for residential and
Expand Down Expand Up @@ -262,7 +262,7 @@ def __init__(self, dependencies):
tasks=(
cts_buildings,
{cts_electricity, cts_heat},
{get_cts_electricity_peak_load, get_cts_heat_peak_load},
get_cts_electricity_peak_load,
map_all_used_buildings,
assign_voltage_level_to_buildings,
),
Expand Down Expand Up @@ -918,33 +918,6 @@ def calc_building_amenity_share(df_cts_buildings):
"building_amenity_share"
].multiply(df_demand_share["cell_share"])

# # Fix #989
# # Mismatched bus_id
# # May result in failing sanity checks
# from saio.boundaries import egon_map_zensus_buildings_filtered_all
#
# with db.session_scope() as session:
# query = session.query(
# egon_map_zensus_buildings_filtered_all.id,
# MapZensusGridDistricts.bus_id,
# ).filter(
# egon_map_zensus_buildings_filtered_all.id.in_(
# df_cts_buildings.id.values
# ),
# MapZensusGridDistricts.zensus_population_id
# == egon_map_zensus_buildings_filtered_all.zensus_population_id,
# )
#
# df_map_bus_id = pd.read_sql(
# query.statement, session.connection(), index_col=None
# )
#
# df_demand_share = pd.merge(
# left=df_demand_share.drop(columns="bus_id"),
# right=df_map_bus_id,
# on="id",
# )

# only pass selected columns
df_demand_share = df_demand_share[
["id", "bus_id", "scenario", "profile_share"]
Expand All @@ -963,8 +936,51 @@ def calc_building_amenity_share(df_cts_buildings):
return df_demand_share


def get_peta_demand(mvgd, scenario):
"""
Retrieve annual peta heat demand for CTS for either
eGon2035 or eGon100RE scenario.
Parameters
----------
mvgd : int
ID of substation for which to get CTS demand.
scenario : str
Possible options are eGon2035 or eGon100RE
Returns
-------
df_peta_demand : pd.DataFrame
Annual residential heat demand per building and scenario. Columns of
the dataframe are zensus_population_id and demand.
"""

with db.session_scope() as session:
query = (
session.query(
MapZensusGridDistricts.zensus_population_id,
EgonPetaHeat.demand,
)
.filter(MapZensusGridDistricts.bus_id == int(mvgd))
.filter(
MapZensusGridDistricts.zensus_population_id
== EgonPetaHeat.zensus_population_id
)
.filter(
EgonPetaHeat.sector == "service",
EgonPetaHeat.scenario == scenario,
)
)

df_peta_demand = pd.read_sql(
query.statement, query.session.bind, index_col=None
)

return df_peta_demand


def calc_cts_building_profiles(
egon_building_ids,
bus_ids,
scenario,
sector,
Expand All @@ -975,8 +991,6 @@ def calc_cts_building_profiles(
Parameters
----------
egon_building_ids: list of int
Ids of the building for which the profile is calculated.
bus_ids: list of int
Ids of the substation for which selected building profiles are
calculated.
Expand All @@ -988,7 +1002,9 @@ def calc_cts_building_profiles(
Returns
-------
df_building_profiles: pd.DataFrame
Table of demand profile per building
Table of demand profile per building. Column names are building IDs
and index is hour of the year as int (0-8759).
"""
if sector == "electricity":
# Get cts building electricity demand share of selected buildings
Expand All @@ -1001,9 +1017,7 @@ def calc_cts_building_profiles(
EgonCtsElectricityDemandBuildingShare.scenario == scenario
)
.filter(
EgonCtsElectricityDemandBuildingShare.building_id.in_(
egon_building_ids
)
EgonCtsElectricityDemandBuildingShare.bus_id.in_(bus_ids)
)
)

Expand All @@ -1019,12 +1033,12 @@ def calc_cts_building_profiles(
)
).filter(EgonEtragoElectricityCts.bus_id.in_(bus_ids))

df_cts_profiles = pd.read_sql(
df_cts_substation_profiles = pd.read_sql(
cells_query.statement,
cells_query.session.bind,
)
df_cts_profiles = pd.DataFrame.from_dict(
df_cts_profiles.set_index("bus_id")["p_set"].to_dict(),
df_cts_substation_profiles = pd.DataFrame.from_dict(
df_cts_substation_profiles.set_index("bus_id")["p_set"].to_dict(),
orient="index",
)
# df_cts_profiles = calc_load_curves_cts(scenario)
Expand All @@ -1037,33 +1051,45 @@ def calc_cts_building_profiles(
EgonCtsHeatDemandBuildingShare,
)
.filter(EgonCtsHeatDemandBuildingShare.scenario == scenario)
.filter(
EgonCtsHeatDemandBuildingShare.building_id.in_(
egon_building_ids
)
)
.filter(EgonCtsHeatDemandBuildingShare.bus_id.in_(bus_ids))
)

df_demand_share = pd.read_sql(
cells_query.statement, cells_query.session.bind, index_col=None
)

# Get substation cts heat load profiles of selected bus_ids
# (this profile only contains zensus cells with individual heating;
# in order to obtain a profile for the whole MV grid it is afterwards
# scaled by the grids total CTS demand from peta)
with db.session_scope() as session:
cells_query = (
session.query(EgonEtragoHeatCts).filter(
EgonEtragoHeatCts.scn_name == scenario
)
).filter(EgonEtragoHeatCts.bus_id.in_(bus_ids))

df_cts_profiles = pd.read_sql(
df_cts_substation_profiles = pd.read_sql(
cells_query.statement,
cells_query.session.bind,
)
df_cts_profiles = pd.DataFrame.from_dict(
df_cts_profiles.set_index("bus_id")["p_set"].to_dict(),
df_cts_substation_profiles = pd.DataFrame.from_dict(
df_cts_substation_profiles.set_index("bus_id")["p_set"].to_dict(),
orient="index",
)
for bus_id in bus_ids:
if bus_id in df_cts_substation_profiles.index:
# get peta demand to scale load profile to
peta_cts_demand = get_peta_demand(bus_id, scenario)
scaling_factor = (
peta_cts_demand.demand.sum()
/ df_cts_substation_profiles.loc[bus_id, :].sum()
)
# scale load profile
df_cts_substation_profiles.loc[bus_id, :] *= scaling_factor

else:
raise KeyError("Sector needs to be either 'electricity' or 'heat'")

# TODO remove after #722
df_demand_share.rename(columns={"id": "building_id"}, inplace=True)
Expand All @@ -1072,7 +1098,16 @@ def calc_cts_building_profiles(
df_building_profiles = pd.DataFrame()
for bus_id, df in df_demand_share.groupby("bus_id"):
shares = df.set_index("building_id", drop=True)["profile_share"]
profile_ts = df_cts_profiles.loc[bus_id]
try:
profile_ts = df_cts_substation_profiles.loc[bus_id]
except KeyError:
# This should only happen within the SH cutout
log.info(
f"No CTS profile found for substation with bus_id:"
f" {bus_id}"
)
continue

building_profiles = np.outer(profile_ts, shares)
building_profiles = pd.DataFrame(
building_profiles, index=profile_ts.index, columns=shares.index
Expand Down Expand Up @@ -1340,30 +1375,6 @@ def adapt_numpy_int64(numpy_int64):
columns={"index": "serial"}
)

# # Fix #989
# # Mismatched zensus population id
# from saio.boundaries import egon_map_zensus_buildings_filtered_all
#
# with db.session_scope() as session:
# query = session.query(
# egon_map_zensus_buildings_filtered_all.id,
# egon_map_zensus_buildings_filtered_all.zensus_population_id,
# ).filter(
# egon_map_zensus_buildings_filtered_all.id.in_(
# df_cts_buildings.id.values
# )
# )
#
# df_map_zensus_population_id = pd.read_sql(
# query.statement, session.connection(), index_col=None
# )
#
# df_cts_buildings = pd.merge(
# left=df_cts_buildings.drop(columns="zensus_population_id"),
# right=df_map_zensus_population_id,
# on="id",
# )

# Write table to db for debugging and postprocessing
write_table_to_postgis(
df_cts_buildings,
Expand Down

0 comments on commit d021716

Please sign in to comment.