def get_nutslist_for_regions(regions):
"""
Parameters
----------
regions = Geodataframe
Geodataframe containing the geometry where NUTS-regions should be mapped to
Returns: DataFrame
List of nuts3 regions for all zones in the overall geometry
-------
"""
# Fetch NUTS3-geometries from disaggregator database
nuts3_disaggregator = data.database_shapes()
# Transform CRS System to match reegis geometries
nuts_centroid = nuts3_disaggregator.centroid.to_crs(4326)
nuts_geo = nuts3_disaggregator.to_crs(crs=4326)
# Match NUTS3-regions with federal states
nuts_geo = geo.spatial_join_with_buffer(nuts_centroid , regions, 'fs', limit=0)
# Create dictionary with lists of all NUTS3-regions for each state
mapped_nuts = pd.DataFrame(index=regions.index, columns=["nuts"])
for zone in regions.index:
mapped_nuts.loc[zone, "nuts"] = list(nuts_geo.loc[nuts_geo['fs'] == zone].index)
return mapped_nuts
def save_nuts3_to_geojson(path):
# Apparently this doesn't work with geopandas 0.8.0 but with geopandas 0.4.1
nuts3_gdf = data.database_shapes()
if not os.path.isdir(path):
os.mkdir(path)
for i, r in nuts3_gdf.iterrows():
gs = gpd.GeoSeries()
gs[i] = r["geometry"]
gs.crs = "epsg:25832"
#gs.to_file(os.path.join(path, str(i) + '.geojson'), driver='GeoJSON')
gs.to_file(path + '/' + str(i) + '.geojson', driver='GeoJSON')
def get_demandregio_electricity_consumption_by_nuts3(year, region_pick=None):
"""
Parameters
----------
year : int
Year of interest, so far only 2015 and 2016 are valid inputs
region_pick : list
Selected regions in NUTS-3 format, if None function will return demand for all regions
Returns: pd.DataFrame
Dataframe containing aggregated yearly load (households, CTS and industry) for selection
-------
"""
if region_pick is None:
region_pick = data.database_shapes().index # Select all NUTS3 Regions
fn_pattern = "elc_consumption_by_nuts3_{year}.csv".format(year=year)
fn = os.path.join(cfg.get("paths", "disaggregator"), fn_pattern)
if not os.path.isfile(fn):
# Works unfortunately just for 2015, 2016 due to limited availability of householdpower
data.cfg["base_year"] = year
ec_hh = spatial.disagg_households_power(by='households', weight_by_income=True).sum(axis=1) * 1000
ec_CTS_detail = spatial.disagg_CTS_industry(sector='CTS', source='power', use_nuts3code=True)
ec_CTS = ec_CTS_detail.sum()
ec_industry_detail = spatial.disagg_CTS_industry(sector='industry', source='power', use_nuts3code=True)
ec_industry = ec_industry_detail.sum()
ec_sum = pd.concat([ec_hh, ec_CTS, ec_industry], axis=1)
ec_sum.columns = ['households', 'CTS', 'industry']
ec_sum.to_csv(fn)
ec_sel = ec_sum.loc[region_pick]
else:
ec_sum = pd.read_csv(fn)
ec_sum.set_index('Unnamed: 0', drop=True, inplace=True)
ec_sel = ec_sum.loc[region_pick]
return ec_sel
def calc_wind_pv_areas(path):
nuts3_gdf = data.database_shapes()
list_filenames = list()
suitable_area = pd.DataFrame(index=nuts3_gdf.index,
columns=["wind_area", "pv_area"])
for nuts3_name in nuts3_gdf.index:
list_filenames.append(path + '/' + nuts3_name + '.geojson')
#list_filenames = list_filenames[0:20]
for n in list_filenames:
idx = n[len(path) + 1:len(path) + 6]
area_wind = calculate_wind_area(n)
suitable_area["wind_area"][idx] = area_wind
for n in list_filenames:
idx = n[len(path) + 1:len(path) + 6]
area_pv = calculate_pv_area(n)
suitable_area["pv_area"][idx] = area_pv
return suitable_area
def get_combined_heatload_for_region(year, region_pick=None):
"""
Parameters
----------
year : int
Year of interest, so far only 2015 and 2016 are valid inputs
region_pick : list
Selected regions in NUTS-3 format, if None function will return demand for all regions
Returns: pd.DataFrame
Dataframe containing aggregated yearly low temperature heat demand (households, CTS, industry) as well
as high temperature heat demand (ProcessHeat) for selection
-------
"""
if region_pick is None:
nuts3_index = data.database_shapes().index # Select all NUTS3 Regions
fn_pattern = "heat_consumption_by_nuts3_{year}.csv".format(year=year)
fn = os.path.join(cfg.get("paths", "disaggregator"), fn_pattern)
if not os.path.isfile(fn):
tmp0 = get_household_heatload_by_NUTS3(year, nuts3_index) # Nur bis 2016
tmp1 = get_CTS_heatload(year, nuts3_index) # 2015 - 2035 (projection)
tmp2 = get_industry_heating_hotwater(year, nuts3_index)
tmp3 = get_industry_CTS_process_heat(year, nuts3_index)
df_heating = pd.concat([tmp0, tmp1, tmp2, tmp3], axis=1)
df_heating.columns = ['Households', 'CTS', 'Industry', 'ProcessHeat']
df_heating.to_csv(fn)
else:
df_heating = pd.read_csv(fn)
df_heating.set_index('nuts3', drop=True, inplace=True)
return df_heating
from disaggregator import data, config, spatial
from deflex import geometries as geo_deflex
from reegis import geometries as geo_reegis
import integrate_demandregio, Land_Availability_GLAES
import pandas as pd
import os
#data.cfg['base_year'] = 2015
nuts3_regions = data.database_shapes().index
# Fetch electricity consumption for all NUTS3-Regions
elc_consumption = integrate_demandregio.get_demandregio_electricity_consumption_by_nuts3(2015)
# Fetch heat consumption for all NUTS3-Regions
heat_consumption = integrate_demandregio.get_combined_heatload_for_region(2015)
# Load suitable PV/Wind-areas from csv
filename = os.getcwd() + '/GLAES_Eignungsflaechen_Wind_PV_own_assumptions.csv'
suitable_area = pd.read_csv(filename)
suitable_area.set_index('nuts3', drop=True, inplace=True)
# Alternatively if no precalculation is available:
# path = os.getcwd() + '/nuts3_geojson/'
# suitable_area = Land_Availability_GLAES.get_pv_wind_areas_by_nuts3(path, create_geojson=True)
# Define installable capacity per square meter in MW
p_per_qm_wind = 8 / 1e6 # 8 W/m² Fläche
### Within this script releant energy system data for a mid-term scenario is fetched and processed
from disaggregator import data
from scenario_builder import cop_precalc, snippets, heatload_scenario_calculator
from deflex import geometries as geo_deflex
from reegis import land_availability_glaes, demand_disaggregator, entsoe, demand_heat
from scenario_builder import emobpy_processing
import pandas as pd
import os
# Set parameters and get data needed for all scenarios
nuts3_index = data.database_shapes().index
de21 = geo_deflex.deflex_regions(rmap='de21')
year = 2015
# Excel findet sich auch hier: SeaDrive/Für meine Gruppen/QUARREE 100/02_Modellierung/09_Szenarien Q100
path_to_data = '/home/dbeier/reegis/data/scenario_data/commodity_sources_costs.xls'
# Get ENTSO-E load profile from reegis
profile = entsoe.get_entsoe_load(2014).reset_index(drop=True)["DE_load_"]
norm_profile = profile.div(profile.sum())
heat_profiles_reegis = demand_heat.get_heat_profiles_by_region(de21,
2014,
name='test')
profile_lt = snippets.return_normalized_domestic_profiles(
de21, heat_profiles_reegis)
profile_ht = snippets.return_normalized_industrial_profiles(
de21, heat_profiles_reegis)
# Fetch costs and emission applicable for scenario (sheet1)
costs = snippets.get_cost_emission_scenario_data(path_to_data)