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Integrate bundled landuse and load data instead of relying on vresutils

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This commit is contained in:
Jonas Hörsch 2018-08-03 11:53:14 +02:00
parent 8e4abc3fce
commit 6866ce6b06
5 changed files with 45 additions and 11 deletions
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9
Snakefile
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@ -78,6 +78,9 @@ rule build_bus_regions:
script: "scripts/build_bus_regions.py"
rule build_renewable_potentials:
input:
corine="data/bundle/corine/g250_clc06_V18_5.tif",
natura="data/bundle/natura/Natura2000_end2015.shp"
output: "resources/potentials_{technology}.nc"
resources: mem_mb=10000
benchmark: "benchmarks/build_renewable_potentials_{technology}"
@ -97,6 +100,9 @@ rule build_renewable_profiles:
script: "scripts/build_renewable_profiles.py"
rule build_hydro_profile:
input:
country_shapes='resources/country_shapes.geojson',
eia_hydro_generation='data/bundle/EIA_hydro_generation_2000_2014.csv'
output: 'resources/profile_hydro.nc'
resources: mem_mb=5000
script: 'scripts/build_hydro_profile.py'
@ -107,6 +113,9 @@ rule add_electricity:
tech_costs='data/costs.csv',
regions="resources/regions_onshore.geojson",
powerplants='resources/powerplants.csv',
hydro_capacities='data/bundle/hydro_capacities.csv',
opsd_load='data/bundle/time_series_60min_singleindex_filtered.csv',
nuts3_shapes='resources/nuts3_shapes.geojson',
**{'profile_' + t: "resources/profile_" + t + ".nc"
for t in config['renewable']}
output: "networks/elec.nc"

35
scripts/add_electricity.py
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@ -13,8 +13,7 @@ import xarray as xr
import geopandas as gpd
from vresutils.costdata import annuity
from vresutils.load import timeseries_shapes as timeseries_load
from vresutils import hydro as vhydro
from vresutils.load import timeseries_opsd
import pypsa
import powerplantmatching as ppm
@ -108,9 +107,31 @@ def load_powerplants(n, ppl_fn=None):
def attach_load(n):
substation_lv_i = n.buses.index[n.buses['substation_lv']]
regions = gpd.read_file(snakemake.input.regions).set_index('name').reindex(substation_lv_i)
n.madd("Load", substation_lv_i,
bus=substation_lv_i,
p_set=timeseries_load(regions.geometry, regions.country))
opsd_load = timeseries_opsd(snakemake.input.opsd_load)
nuts3 = gpd.read_file(snakemake.input.nuts3_shapes).set_index('id')
def normed(x): return x.divide(x.sum())
def upsample(cntry, group):
l = opsd_load[cntry]
if len(group) == 1:
return pd.DataFrame({group.index[0]: l})
else:
nuts3_cntry = nuts3.loc[nuts3.country == cntry]
transfer = vtransfer.Shapes2Shapes(group, nuts3_cntry.geometry, normed=False).T.tocsr()
gdp_n = pd.Series(transfer.dot(nuts3.gdp.fillna(1.).values), index=group.index)
pop_n = pd.Series(transfer.dot(nuts3.pop.fillna(1.).values), index=group.index)
# relative factors 0.6 and 0.4 have been determined from a linear
# regression on the country to continent load data (refer to vresutils.load._upsampling_weights)
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
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)
n.madd("Load", substation_lv_i, bus=substation_lv_i, p_set=load)
### Set line costs
@ -234,7 +255,7 @@ def attach_hydro(n, costs, ppl):
hydro_max_hours = c.get('hydro_max_hours')
if hydro_max_hours is None:
hydro_e_country = vhydro.get_hydro_capas()['E_store[TWh]'].clip(lower=0.2)*1e6
hydro_e_country = pd.read_csv(snakemake.input.hydro_capacities, index_col=0).clip(lower=0.2)*1e6
hydro_max_hours_country = hydro_e_country / hydro.p_nom.groupby(country).sum()
hydro_max_hours = country.loc[hydro.index].map(hydro_max_hours_country)
@ -368,6 +389,8 @@ if __name__ == "__main__":
tech_costs='data/costs/costs.csv',
regions="resources/regions_onshore.geojson",
powerplants="resources/powerplants.csv",
hydro_capacities='data/hydro_capacities.csv',
opsd_load='data/time_series_60min_singleindex_filtered.csv',
**{'profile_' + t: "resources/profile_" + t + ".nc"
for t in snakemake.config['renewable']})
)

7
scripts/build_hydro_profile.py
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@ -2,7 +2,8 @@
import atlite
import pandas as pd
from vresutils import shapes as vshapes, hydro as vhydro
import geopandas as gpd
from vresutils import hydro as vhydro
import logging
logger = logging.getLogger(__name__)
logger.setLevel(level=snakemake.config['logging_level'])
@ -10,10 +11,10 @@ logger.setLevel(level=snakemake.config['logging_level'])
cutout = atlite.Cutout(snakemake.config['renewable']['hydro']['cutout'])
countries = snakemake.config['countries']
country_shapes = pd.Series(vshapes.countries(countries)).reindex(countries)
country_shapes = gpd.read_file(snakemake.input.country_shapes).set_index('id')['geometry'].reindex(countries)
country_shapes.index.name = 'countries'
eia_stats = vhydro.get_eia_annual_hydro_generation().reindex(columns=countries)
eia_stats = vhydro.get_eia_annual_hydro_generation(snakemake.input.eia_hydro_generation).reindex(columns=countries)
inflow = cutout.runoff(shapes=country_shapes,
smooth=True,
lower_threshold_quantile=True,

4
scripts/build_renewable_potentials.py
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@ -9,7 +9,9 @@ config = snakemake.config['renewable'][snakemake.wildcards.technology]
cutout = atlite.Cutout(config['cutout'])
total_capacity = config['capacity_per_sqm'] * vlanduse._cutout_cell_areas(cutout)
potentials = xr.DataArray(total_capacity * vlanduse.corine_for_cutout(cutout, **config['corine']),
potentials = xr.DataArray(total_capacity *
vlanduse.corine_for_cutout(cutout, fn=snakemake.input.corine,
natura_fn=snakemake.input.natura, **config['corine']),
[cutout.meta.indexes['y'], cutout.meta.indexes['x']])
if 'height_cutoff' in config:

1
scripts/build_renewable_profiles.py
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@ -6,7 +6,6 @@ import xarray as xr
import pandas as pd
import geopandas as gpd
from vresutils import landuse as vlanduse
import logging
logger = logging.getLogger(__name__)
logging.basicConfig(level=snakemake.config['logging_level'])