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GDP for UA and MD (draft) - not working yet!

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This commit is contained in:
martacki 2022-03-04 20:02:27 +01:00
parent d1d42213b1
commit 025dd35878
2 changed files with 24 additions and 3 deletions
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1
Snakefile
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@ -226,6 +226,7 @@ rule add_electricity:
geth_hydro_capacities='data/geth2015_hydro_capacities.csv',
load='resources/load.csv',
nuts3_shapes='resources/nuts3_shapes.geojson',
gdp='data/bundle/GDP_PPP_30arcsec_v3.nc',
**{f"profile_{tech}": f"resources/profile_{tech}.nc"
for tech in config['renewable']}
output: "networks/elec.nc"

26
scripts/add_electricity.py
View File

@ -90,6 +90,7 @@ import pypsa
import pandas as pd
import numpy as np
import xarray as xr
import xagg as xa
import geopandas as gpd
import powerplantmatching as pm
from powerplantmatching.export import map_country_bus
@ -189,7 +190,7 @@ def load_powerplants(ppl_fn):
.replace({'carrier': carrier_dict}))
def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.):
def attach_load(n, regions, load, nuts3_shapes, gdp, countries, scaling=1.):
substation_lv_i = n.buses.index[n.buses['substation_lv']]
regions = (gpd.read_file(regions).set_index('name')
@ -197,6 +198,18 @@ def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.):
opsd_load = (pd.read_csv(load, index_col=0, parse_dates=True)
.filter(items=countries))
#ToDo: adapt time+slices from config etc. (cover all data)
gdp = (xr.open_dataset(gdp)
.sel(time=2015)
.sel(longitude=slice(10,30))
.sel(latitude=slice(50, 30)))
weightmap = xa.pixel_overlaps(gdp, regions.iloc[0:2])
aggregated = xa.aggregate(gdp, weightmap)
print(aggregated.to_dataset().name)
print(aggregated.to_dataset().GDP_per_capita_PPP)
print(martha)
logger.info(f"Load data scaled with scalling factor {scaling}.")
opsd_load *= scaling
@ -204,6 +217,7 @@ def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.):
def upsample(cntry, group):
l = opsd_load[cntry]
if len(group) == 1:
return pd.DataFrame({group.index[0]: l})
else:
@ -218,12 +232,18 @@ def attach_load(n, regions, load, nuts3_shapes, countries, scaling=1.):
# relative factors 0.6 and 0.4 have been determined from a linear
# regression on the country to continent load data
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
if cntry in ['UA', 'MD']:
#generate new factors in this case
print('ToDo: adjust load for UA and MD here')
return pd.DataFrame(factors.values * l.values[:,np.newaxis],
index=l.index, columns=factors.index)
load = pd.concat([upsample(cntry, group) for cntry, group
in regions.geometry.groupby(regions.country)], axis=1)
print(some_error)
n.madd("Load", substation_lv_i, bus=substation_lv_i, p_set=load)
@ -257,7 +277,7 @@ def update_transmission_costs(n, costs, length_factor=1.0, simple_hvdc_costs=Fal
def attach_wind_and_solar(n, costs, input_profiles, technologies, line_length_factor=1):
# TODO: rename tech -> carrier, technologies -> carriers
for tech in technologies:
if tech == 'hydro': continue
@ -551,7 +571,7 @@ if __name__ == "__main__":
ppl = load_powerplants(snakemake.input.powerplants)
attach_load(n, snakemake.input.regions, snakemake.input.load, snakemake.input.nuts3_shapes,
snakemake.config['countries'], snakemake.config['load']['scaling_factor'])
snakemake.input.gdp, snakemake.config['countries'], snakemake.config['load']['scaling_factor'])
update_transmission_costs(n, costs, snakemake.config['lines']['length_factor'])