def before_simulation(data, config, sim_yrs, pop_density, service_switches,
fuel_switches, capacity_switches):
"""
"""
# ------------------------------------------------
# Disaggregate national energy demand to regional demands
# ------------------------------------------------
fuel_disagg = s_disaggregation.disaggr_demand(
data,
config['CRITERIA']['crit_temp_min_max'],
spatial_calibration=config['CRITERIA']['spatial_calibration'])
# ------------------------------------------------
# Calculate spatial diffusion factors
#
# Here the real values used for the spatial disaggregation (speec_con_max)
# need to be defined. If not population density is used,
# this needs to be replaced by any other values which are loaded from
# a csv file in the form of: {{region_name: value}}
# ------------------------------------------------
f_reg, f_reg_norm, f_reg_norm_abs, crit_all_the_same = init_scripts.create_spatial_diffusion_factors(
narrative_spatial_explicit_diffusion=data['assumptions'].
strategy_vars['spatial_explicit_diffusion'],
fuel_disagg=fuel_disagg,
regions=data['regions'],
real_values=pop_density,
narrative_speed_con_max=data['assumptions'].
strategy_vars['speed_con_max'])
# ------------------------------------------------
# Calculate parameter values for every region
# ------------------------------------------------
regional_vars = init_scripts.spatial_explicit_modelling_strategy_vars(
data['assumptions'].strategy_vars,
data['assumptions'].spatially_modelled_vars, data['regions'],
fuel_disagg, f_reg, f_reg_norm, f_reg_norm_abs)
data['assumptions'].update('strategy_vars', regional_vars)
# ------------------------------------------------
# Calculate parameter values for every simulated year
# based on narratives. Also calculate annual parameters for
# technologies diffused by switches.
# ------------------------------------------------
regional_vars, non_regional_vars = s_scenario_param.generate_annual_param_vals(
data['regions'], data['assumptions'].strategy_vars, sim_yrs)
# ------------------------------------------------
# Switches calculations
# ------------------------------------------------
# Update assumptions
crit_switch_happening = testing_functions.switch_testing(
fuel_switches=fuel_switches,
service_switches=service_switches,
capacity_switches=capacity_switches)
setattr(data['assumptions'], 'crit_switch_happening',
crit_switch_happening)
annual_tech_diff_params = init_scripts.switch_calculations(
sim_yrs,
data,
f_reg,
f_reg_norm,
f_reg_norm_abs,
crit_all_the_same,
service_switches=service_switches,
fuel_switches=fuel_switches,
capacity_switches=capacity_switches)
for region in data['regions']:
regional_vars[region][
'annual_tech_diff_params'] = annual_tech_diff_params[region]
return regional_vars, non_regional_vars, fuel_disagg, crit_switch_happening
load_np=False,
load_parquet=False,
load_csv=True)
# Plot map with weather station
'''if config['CRITERIA']['cluster_calc'] != True:
data_loader.create_weather_station_map(
data['weather_stations'],
os.path.join(path_new_scenario, 'weatherst_distr_weathyr.pdf'),
path_shapefile=data['local_paths']['lad_shapefile'])'''
# ------------------------------------------------------------
# Disaggregate national energy demand to regional demands
# ------------------------------------------------------------
data['fuel_disagg'] = s_disaggregation.disaggr_demand(
data,
crit_temp_min_max=config['CRITERIA']['crit_temp_min_max'],
spatial_calibration=config['CRITERIA']['spatial_calibration'])
# ------------------------------------------------------------
# Calculate spatial diffusion factors
# ------------------------------------------------------------
real_values = data['pop_density']
f_reg, f_reg_norm, f_reg_norm_abs, crit_all_the_same = init_scripts.create_spatial_diffusion_factors(
narrative_spatial_explicit_diffusion=data['assumptions'].strategy_vars['spatial_explicit_diffusion'],
fuel_disagg=data['fuel_disagg'],
regions=data['regions'],
real_values=real_values,
narrative_speed_con_max=data['assumptions'].strategy_vars['speed_con_max'])
print("Criteria all regions the same: " + str(crit_all_the_same))