data['assumptions'].seasons,
data['assumptions'].model_yeardays_daytype,
config['CRITERIA']['plot_crit'])
# -------------------------------------
# # Generate YAML file with keynames for `sector_model`
# -------------------------------------
if config['CRITERIA']['mode_constrained']:
supply_results = demand_supply_interaction.constrained_results(
sim_obj.results_constrained,
sim_obj.results_unconstrained,
data['assumptions'].submodels_names,
data['assumptions'].technologies)
else:
supply_results = demand_supply_interaction.unconstrained_results(
sim_obj.results_unconstrained,
data['assumptions'].submodels_names)
# --------------------------
# Write out all calculations
# --------------------------
if config['CRITERIA']['write_txt_additional_results']:
if config['CRITERIA']['crit_plot_enduse_lp']:
# Maybe move to result folder in a later step
path_folder_lp = os.path.join(data['weather_yr_result_paths']['data_results'], 'individual_enduse_lp')
basic_functions.delete_folder(path_folder_lp)
basic_functions.create_folder(path_folder_lp)
winter_week, _, _, _ = date_prop.get_seasonal_weeks()
# Plot electricity
def aggregate_across_all_regs(aggr_results, fueltypes_nr, reg_nrs, enduses,
assumptions, criterias, technologies):
"""Aggregate the regional results into different
forms used for outputs
Arguments
---------
aggr_results : dict
Aggregation container
fueltypes_nr : int
Number of fueltypes
reg_nrs : int
Number of regions
enduses : dict
Submodels and their sectors
assumptions : dict
Assumptions
criterias : dict
Criteria
technologies : dict
Technologies per enduse
"""
fueltypes = lookup_tables.basic_lookups()['fueltypes']
# ----------------------------------------------------
# Aggregate: [fueltype, region, fuel_yh_8760]
# ----------------------------------------------------
array_init = np.zeros((fueltypes_nr, reg_nrs, 8760))
aggr_results['ed_fueltype_regs_yh'] = aggregate_from_full_results(
assumptions.lookup_enduses,
array_init,
aggr_results['ed_enduse_fueltype_regs_yh'],
time_resolution='8760_h',
per_region=True,
per_sector=False,
per_enduse=False)
# ----------------------------------------------------
# Aggregate: [fueltype, fuel_yh_8760]
# ----------------------------------------------------
array_init = np.zeros((fueltypes_nr, 365, 24))
aggr_results['ed_fueltype_national_yh'] = aggregate_from_full_results(
assumptions.lookup_enduses,
array_init,
aggr_results['ed_enduse_fueltype_regs_yh'],
time_resolution='365_24',
per_region=False,
per_sector=False,
per_enduse=False)
# ----------------------------------------------------
# Aggregate: [fueltype, enduse, fuel_yh, 8760]
# ----------------------------------------------------
array_init = {}
for submodel in enduses:
for enduse in enduses[submodel]:
array_init[enduse] = np.zeros((fueltypes_nr, 8760))
aggr_results[
'tot_fuel_y_enduse_specific_yh'] = aggregate_from_full_results(
assumptions.lookup_enduses,
array_init,
aggr_results['ed_enduse_fueltype_regs_yh'],
time_resolution='8760_h',
per_region=False,
per_sector=False,
per_enduse=True)
# ----------------------------------------------------
# Calculate averaged hour profile per season
# ----------------------------------------------------
aggr_results['averaged_h'] = averaged_season_hourly(
aggr_results['ed_fueltype_regs_yh'], fueltypes.values(), fueltypes_nr,
assumptions.seasons, reg_nrs)
# ----------------------------------------------------
# Regional load factor calculations
# ----------------------------------------------------
aggr_results['reg_load_factor_y'], aggr_results[
'reg_load_factor_yd'] = aggregate_load_factors(
aggr_results['ed_fueltype_regs_yh'], fueltypes_nr, reg_nrs)
# ----------------------------------------------------
# Generate dict for supply model
# ----------------------------------------------------
print("Mode: " + str(criterias['mode_constrained']))
if criterias['mode_constrained']:
aggr_results[
'supply_results'] = demand_supply_interaction.constrained_results(
aggr_results['results_constrained'],
aggr_results['results_unconstrained_no_heating'],
assumptions.submodels_names, technologies)
else:
aggr_results[
'supply_results'] = demand_supply_interaction.unconstrained_results(
aggr_results['results_unconstrained_no_heating'],
assumptions.submodels_names)
return aggr_results