def test_autocomplete_switches():
"""testing"""
service_switches = [read_data.ServiceSwitch(
enduse='heating',
technology_install='techA',
switch_yr=2050,
service_share_ey=0.6)]
defined_temporal_narrative_points = {'heating': [2050]}
enduses = {'submodelA': ['heating']}
sectors = {'submodelA': [None]}
out = fuel_service_switch.autocomplete_switches(
service_switches=service_switches,
specified_tech_enduse_by={'heating': {None: ['techA', 'techB', 'techC']}},
s_tech_by_p={None: {'heating': {'techA': 0.2, 'techB': 0.4, 'techC': 0.4}}},
crit_all_the_same=True,
enduses=enduses,
sectors=sectors,
regions=['regA'],
f_diffusion=False,
techs_affected_spatial_f=False,
service_switches_from_capacity={'regA':[]})
assert out[None]['heating']['regA'][2050]['techA'] == 0.6
assert out[None]['heating']['regA'][2050]['techB'] == 0.2
assert out[None]['heating']['regA'][2050]['techC'] == 0.2
# ---
service_switches = [read_data.ServiceSwitch(
enduse='heating',
technology_install='techA',
switch_yr=2050,
service_share_ey=0.6)]
out = fuel_service_switch.autocomplete_switches(
service_switches=service_switches,
specified_tech_enduse_by={'heating': {None: ['techA', 'techB', 'techC']}},
s_tech_by_p= {None: {'heating': {'techA': 0.2, 'techB': 0.4, 'techC': 0.4}}},
crit_all_the_same=True,
enduses=enduses,
sectors=sectors,
regions=['regA'],
f_diffusion={'heating':{'regA':1.0, 'regB': 1.0}},
techs_affected_spatial_f=['techA'],
service_switches_from_capacity={'regA': [], 'regB': []})
assert out[None]['heating']['regA'][2050]['techA'] == 0.6
assert out[None]['heating']['regA'][2050]['techB'] == 0.2
assert out[None]['heating']['regA'][2050]['techC'] == 0.2
def autocomplete_switches():
"""Testing
"""
service_switches = [read_data.ServiceSwitch(
'heating',
'techA',
0.5,
2020)]
specified_tech_enduse_by = {'heating': ['techA', 'techB']}
s_tech_by_p = {'heating': {'techA': 0.8, 'techB': 0.2}}
service_switches = fuel_service_switch.autocomplete_switches(
service_switches=service_switches,
specified_tech_enduse_by=specified_tech_enduse_by,
s_tech_by_p=s_tech_by_p)
for switch in service_switches:
if switch.technology_install == 'techA':
assert switch.service_share_ey == 0.5
if switch.technology_install == 'techB':
assert switch.service_share_ey == 0.5
#---
service_switches = [read_data.ServiceSwitch(
'heating',
'techA',
0.3,
2020)]
specified_tech_enduse_by = {'heating': ['techA', 'techB', 'techC']}
s_tech_by_p = {'heating': {'techA': 0.6, 'techB': 0.2,'techC': 0.1}}
service_switches = fuel_service_switch.autocomplete_switches(
service_switches=service_switches,
specified_tech_enduse_by=specified_tech_enduse_by,
s_tech_by_p=s_tech_by_p)
for switch in service_switches:
if switch.technology_install == 'techA':
assert switch.service_share_ey == 0.3
if switch.technology_install == 'techB':
assert switch.service_share_ey == 0.7 * (2.0 / 3.0)
if switch.technology_install == 'techC':
assert switch.service_share_ey == 0.7 * (1.0 / 3.0)
def test_autocomplete_switches():
"""testing"""
service_switches = [
read_data.ServiceSwitch(enduse='heating',
technology_install='techA',
switch_yr=2050,
service_share_ey=0.6)
]
out_1, out_2 = fuel_service_switch.autocomplete_switches(
service_switches=service_switches,
specified_tech_enduse_by={'heating': ['techA', 'techB', 'techC']},
s_tech_by_p={'heating': {
'techA': 0.2,
'techB': 0.4,
'techC': 0.4
}},
sector=False,
spatial_exliclit_diffusion=False,
regions=False,
f_diffusion=False,
techs_affected_spatial_f=False,
service_switches_from_capacity=[])
for switch in out_2:
if switch.technology_install == 'techA':
assert switch.service_share_ey == 0.6
if switch.technology_install == 'techB':
assert switch.service_share_ey == 0.2
if switch.technology_install == 'techC':
assert switch.service_share_ey == 0.2
# ---
service_switches = [
read_data.ServiceSwitch(enduse='heating',
technology_install='techA',
switch_yr=2050,
service_share_ey=0.6)
]
out_1, out_2 = fuel_service_switch.autocomplete_switches(
service_switches=service_switches,
specified_tech_enduse_by={'heating': ['techA', 'techB', 'techC']},
s_tech_by_p={'heating': {
'techA': 0.2,
'techB': 0.4,
'techC': 0.4
}},
sector=False,
spatial_exliclit_diffusion=True,
regions=['regA', 'regB'],
f_diffusion={'heating': {
'regA': 1.0,
'regB': 1.0
}},
techs_affected_spatial_f=['techA'],
service_switches_from_capacity={
'regA': [],
'regB': []
})
for reg in out_2:
for switch in out_2[reg]:
if switch.technology_install == 'techA':
assert switch.service_share_ey == 0.6
if switch.technology_install == 'techB':
assert switch.service_share_ey == 0.2
if switch.technology_install == 'techC':
assert switch.service_share_ey == 0.2
def switch_calculations(sim_yrs, data, f_reg, f_reg_norm, f_reg_norm_abs,
crit_all_the_same, service_switches, fuel_switches,
capacity_switches):
"""This function creates sigmoid diffusion values based
on defined switches
Arguments
----------
sim_yrs : list
Simulated years
data : dict
Data container
f_reg, f_reg_norm, f_reg_norm_abs : str
Different spatial diffusion values
crit_all_the_same : dict
Criteria whether the diffusion is spatial explicit or not
Returns
-------
annual_tech_diff_params : dict
All values for every simluation year for every technology
"""
# ---------------------------------------
# Convert base year fuel input assumptions to energy service for every submodule
# ---------------------------------------
s_tech_by_p = {}
s_fueltype_by_p = {}
for submodel in data['assumptions'].submodels_names:
for sector in data['sectors'][submodel]:
s_tech_by_p[sector], s_fueltype_by_p[
sector] = s_fuel_to_service.get_s_fueltype_tech(
data['enduses'][submodel],
data['assumptions'].fuel_tech_p_by,
data['fuels'][submodel], data['assumptions'].technologies,
sector)
# Get all defined narrative timesteps of all switch types
narrative_timesteps = {}
narrative_timesteps.update(get_all_narrative_timesteps(service_switches))
narrative_timesteps.update(get_all_narrative_timesteps(fuel_switches))
narrative_timesteps.update(get_all_narrative_timesteps(capacity_switches))
# ========================================================================================
# Capacity switches
#
# Calculate service shares considering potential capacity installations
# ========================================================================================
# Convert globally defined switches to regional switches
f_diffusion = f_reg_norm_abs # Select diffusion value
reg_capacity_switches = global_to_reg_capacity_switch(
data['regions'], capacity_switches, f_diffusion=f_diffusion)
service_switches_incl_cap = fuel_service_switch.capacity_switch(
narrative_timesteps, data['regions'], reg_capacity_switches,
data['assumptions'].technologies, data['fuels']['aggr_sector_fuels'],
data['assumptions'].fuel_tech_p_by, data['assumptions'].base_yr)
# ======================================================================================
# Service switches
#
# Get service shares of technologies for future year by considering
# service switches. Potential capacity switches are used as inputs.
#
# Autocomplement defined service switches with technologies not
# explicitly specified in switch on a global scale and distribute spatially
# in oder that all technologies match up to 100% service share
# ========================================================================================
# Select spatial diffusion
f_diffusion = f_reg_norm
share_s_tech_ey_p = fuel_service_switch.autocomplete_switches(
service_switches,
data['assumptions'].specified_tech_enduse_by,
s_tech_by_p,
data['enduses'],
data['sectors'],
crit_all_the_same=crit_all_the_same,
regions=data['regions'],
f_diffusion=f_diffusion,
techs_affected_spatial_f=data['assumptions'].techs_affected_spatial_f,
service_switches_from_capacity=service_switches_incl_cap)
# ========================================================================================
# Fuel switches
#
# Calculate sigmoid diffusion considering fuel switches and service switches.
# As inputs, service (and thus also capacity switches) are used
# ========================================================================================
diffusion_param_tech = defaultdict(dict)
for submodel_name in data['assumptions'].submodels_names:
for enduse in data['enduses'][submodel_name]:
for sector in data['sectors'][submodel_name]:
diffusion_param_tech[enduse][
sector] = sig_param_calc_incl_fuel_switch(
narrative_timesteps,
data['assumptions'].base_yr,
data['assumptions'].crit_switch_happening,
data['assumptions'].technologies,
enduse=enduse,
sector=sector,
fuel_switches=fuel_switches,
s_tech_by_p=s_tech_by_p[sector][enduse],
s_fueltype_by_p=s_fueltype_by_p[sector][enduse],
share_s_tech_ey_p=share_s_tech_ey_p,
fuel_tech_p_by=data['assumptions'].
fuel_tech_p_by[enduse][sector],
regions=data['regions'],
crit_all_the_same=crit_all_the_same)
# ------------------
# Calculate annual values based on calculated parameters for every simulation year
# ------------------
annual_tech_diff_params = s_scenario_param.calc_annual_switch_params(
sim_yrs,
data['regions'],
dict(diffusion_param_tech),
base_yr=data['assumptions'].base_yr,
s_tech_by_p=s_tech_by_p)
# ------------------------------------------------------
# Test whether the calculated service shares sum up to 1
# and if not, then distribute not assigned demand according to
# base year distribution
# Can happen because multiple sigmoid for different technologies
# ------------------------------------------------------
for region in annual_tech_diff_params:
for enduse in annual_tech_diff_params[region]:
for sector in annual_tech_diff_params[region][enduse]:
for year in sim_yrs:
if annual_tech_diff_params[region][enduse][sector] != []:
assigned_service_p = 0
for tech in annual_tech_diff_params[region][enduse][
sector]:
assigned_service_p += annual_tech_diff_params[
region][enduse][sector][tech][year]
# Distribute if not sum up to one according to base year distribution
if round(assigned_service_p, 2) != 1:
total_not_assined = 1 - assigned_service_p
for tech in annual_tech_diff_params[region][
enduse][sector]:
# Fraction in base year
p_by = annual_tech_diff_params[region][enduse][
sector][tech][data['assumptions'].base_yr]
tech_to_add_not_assigned = total_not_assined * p_by
annual_tech_diff_params[region][enduse][
sector][tech][
year] += tech_to_add_not_assigned
assigned_service_p = 0
for tech in annual_tech_diff_params[region][enduse][
sector]:
assigned_service_p += annual_tech_diff_params[
region][enduse][sector][tech][year]
assert round(assigned_service_p, 2) == 1
return annual_tech_diff_params
def scenario_initalisation(path_data_ed, data=False):
"""Scripts which need to be run for every different scenario.
Only needs to be executed once for each scenario (not for every
simulation year).
The following calculations are performed:
I. Disaggregation of fuel for every region
II. Switches calculations
III. Spatial explicit diffusion modelling
Arguments
----------
path_data_ed : str
Path to the energy demand data folder
data : dict
Data container
Info
-----
# Non spatiall differentiated modelling of
# technology diffusion (same diffusion pattern for
# the whole UK) or spatially differentiated (every region)
"""
logging.info("... Start initialisation scripts")
init_cont = defaultdict(dict)
fuel_disagg = {}
logger_setup.set_up_logger(os.path.join(path_data_ed, "scenario_init.log"))
# --------------------------------------------
# Delete results from previous model runs and initialise folders
# --------------------------------------------
basic_functions.del_previous_results(
data['local_paths']['data_processed'],
data['local_paths']['path_post_installation_data'])
basic_functions.del_previous_setup(data['result_paths']['data_results'])
folders_to_create = [
data['local_paths']['dir_services'],
data['local_paths']['path_sigmoid_data'],
data['result_paths']['data_results'],
data['result_paths']['data_results_PDF'],
data['result_paths']['data_results_model_run_pop'],
data['result_paths']['data_results_validation'],
data['result_paths']['data_results_model_runs']
]
for folder in folders_to_create:
basic_functions.create_folder(folder)
# ===========================================
# I. Disaggregation
# ===========================================
# Load data for disaggregateion
data['scenario_data'][
'employment_stats'] = data_loader.read_employment_stats(
data['paths']['path_employment_statistics'])
# Disaggregate fuel for all regions
fuel_disagg['rs_fuel_disagg'], fuel_disagg['ss_fuel_disagg'], fuel_disagg[
'is_fuel_disagg'] = s_disaggregation.disaggregate_base_demand(
data['regions'], data['assumptions'].base_yr,
data['assumptions'].curr_yr, data['fuels'], data['scenario_data'],
data['assumptions'], data['reg_coord'], data['weather_stations'],
data['temp_data'], data['sectors'], data['sectors']['all_sectors'],
data['enduses'])
# Sum demand across all sectors for every region
fuel_disagg[
'ss_fuel_disagg_sum_all_sectors'] = sum_across_sectors_all_regs(
fuel_disagg['ss_fuel_disagg'])
fuel_disagg['is_aggr_fuel_sum_all_sectors'] = sum_across_sectors_all_regs(
fuel_disagg['is_fuel_disagg'])
# ---------------------------------------
# Convert base year fuel input assumptions to energy service
# ---------------------------------------
# Residential
rs_s_tech_by_p, _, rs_s_fueltype_by_p = s_fuel_to_service.get_s_fueltype_tech(
data['enduses']['rs_enduses'], data['assumptions'].tech_list,
data['lookups']['fueltypes'], data['assumptions'].rs_fuel_tech_p_by,
data['fuels']['rs_fuel_raw'], data['technologies'])
# Service
ss_s_tech_by_p = {}
ss_s_fueltype_by_p = {}
for sector in data['sectors']['ss_sectors']:
ss_s_tech_by_p[sector], _, ss_s_fueltype_by_p[
sector] = s_fuel_to_service.get_s_fueltype_tech(
data['enduses']['ss_enduses'], data['assumptions'].tech_list,
data['lookups']['fueltypes'],
data['assumptions'].ss_fuel_tech_p_by,
data['fuels']['ss_fuel_raw'], data['technologies'], sector)
# Industry
is_s_tech_by_p = {}
is_s_fueltype_by_p = {}
for sector in data['sectors']['is_sectors']:
is_s_tech_by_p[sector], _, is_s_fueltype_by_p[
sector] = s_fuel_to_service.get_s_fueltype_tech(
data['enduses']['is_enduses'], data['assumptions'].tech_list,
data['lookups']['fueltypes'],
data['assumptions'].is_fuel_tech_p_by,
data['fuels']['is_fuel_raw'], data['technologies'], sector)
# ===========================================
# SPATIAL CALCULATIONS factors
#
# Calculate spatial diffusion factors
# ===========================================
if data['criterias']['spatial_exliclit_diffusion']:
f_reg, f_reg_norm, f_reg_norm_abs = spatial_diffusion.calc_spatially_diffusion_factors(
regions=data['regions'],
fuel_disagg=fuel_disagg,
real_values=data['pop_density'], # Real value to select
speed_con_max=1.0) # diffusion speed differences
# ---------------------
# Plot figure for paper
# ---------------------
plot_fig_paper = True #FALSE
plot_fig_paper = False #FALSE
if plot_fig_paper:
# Global value to distribute
global_value = 50
# Select spatial diffusion factor
#diffusion_vals = f_reg # not weighted
diffusion_vals = f_reg_norm[
'rs_space_heating'] # Weighted with enduse
#diffusion_vals = f_reg_norm_abs['rs_space_heating'] # Absolute distribution (only for capacity installements)
path_shapefile_input = os.path.abspath(
'C:/Users/cenv0553/ED/data/_raw_data/C_LAD_geography/same_as_pop_scenario/lad_2016_uk_simplified.shp'
)
result_mapping.plot_spatial_mapping_example(
diffusion_vals=diffusion_vals,
global_value=global_value,
paths=data['result_paths'],
regions=data['regions'],
path_shapefile_input=path_shapefile_input)
else:
f_reg = False
f_reg_norm = False
f_reg_norm_abs = False
init_cont['regional_strategy_variables'] = None
# ===========================================
# II. Switches
# ===========================================
# ========================================================================================
# Capacity switches
#
# Calculate service shares considering potential capacity installations
# ========================================================================================
# Service
ss_aggr_sector_fuels = s_fuel_to_service.sum_fuel_enduse_sectors(
data['fuels']['ss_fuel_raw'], data['enduses']['ss_enduses'])
# Industry
is_aggr_sector_fuels = s_fuel_to_service.sum_fuel_enduse_sectors(
data['fuels']['is_fuel_raw'], data['enduses']['is_enduses'])
if data['criterias']['spatial_exliclit_diffusion']:
# Select diffusion value
f_diffusion = f_reg_norm_abs
# Convert globally defined switches to regional switches
reg_capacity_switches_rs = global_to_reg_capacity_switch(
data['regions'], data['assumptions'].rs_capacity_switches,
f_diffusion)
reg_capacity_switches_ss = global_to_reg_capacity_switch(
data['regions'], data['assumptions'].ss_capacity_switches,
f_diffusion)
reg_capacity_switches_is = global_to_reg_capacity_switch(
data['regions'], data['assumptions'].is_capacity_switches,
f_diffusion)
rs_service_switches_incl_cap = {}
ss_service_switches_inlc_cap = {}
is_service_switches_incl_cap = {}
for region in data['regions']:
# Residential
rs_service_switches_incl_cap[
region] = fuel_service_switch.capacity_switch(
reg_capacity_switches_rs[region], data['technologies'],
data['assumptions'].
enduse_overall_change['other_enduse_mode_info'],
data['fuels']['rs_fuel_raw'],
data['assumptions'].rs_fuel_tech_p_by,
data['assumptions'].base_yr)
ss_service_switches_inlc_cap[
region] = fuel_service_switch.capacity_switch(
reg_capacity_switches_ss[region], data['technologies'],
data['assumptions'].
enduse_overall_change['other_enduse_mode_info'],
ss_aggr_sector_fuels,
data['assumptions'].ss_fuel_tech_p_by,
data['assumptions'].base_yr)
is_service_switches_incl_cap[
region] = fuel_service_switch.capacity_switch(
reg_capacity_switches_is[region], data['technologies'],
data['assumptions'].
enduse_overall_change['other_enduse_mode_info'],
is_aggr_sector_fuels,
data['assumptions'].is_fuel_tech_p_by,
data['assumptions'].base_yr)
else: #Not spatial explicit
rs_service_switches_incl_cap = fuel_service_switch.capacity_switch(
data['assumptions'].rs_capacity_switches, data['technologies'],
data['assumptions'].
enduse_overall_change['other_enduse_mode_info'],
data['fuels']['rs_fuel_raw'],
data['assumptions'].rs_fuel_tech_p_by, data['assumptions'].base_yr)
ss_service_switches_inlc_cap = fuel_service_switch.capacity_switch(
data['assumptions'].ss_capacity_switches, data['technologies'],
data['assumptions'].
enduse_overall_change['other_enduse_mode_info'],
ss_aggr_sector_fuels, data['assumptions'].ss_fuel_tech_p_by,
data['assumptions'].base_yr)
is_service_switches_incl_cap = fuel_service_switch.capacity_switch(
data['assumptions'].is_capacity_switches, data['technologies'],
data['assumptions'].
enduse_overall_change['other_enduse_mode_info'],
is_aggr_sector_fuels, data['assumptions'].is_fuel_tech_p_by,
data['assumptions'].base_yr)
# ========================================================================================
# Service switches
#
# Get service shares of technologies for future year by considering
# service switches. Potential capacity switches are used as inputs.
#
# Autocomplement defined service switches with technologies not
# explicitly specified in switch on a global scale and distribute
# spatially.
# Autocomplete and regional diffusion levels calculations
# ========================================================================================
# Select spatial diffusion
f_diffusion = f_reg_norm
# Residential
rs_share_s_tech_ey_p, rs_switches_autocompleted = fuel_service_switch.autocomplete_switches(
data['assumptions'].rs_service_switches,
data['assumptions'].rs_specified_tech_enduse_by,
rs_s_tech_by_p,
spatial_exliclit_diffusion=data['criterias']
['spatial_exliclit_diffusion'],
regions=data['regions'],
f_diffusion=f_diffusion,
techs_affected_spatial_f=data['assumptions'].techs_affected_spatial_f,
service_switches_from_capacity=rs_service_switches_incl_cap)
# Service
ss_switches_autocompleted = {}
ss_share_s_tech_ey_p = {}
for sector in data['sectors']['ss_sectors']:
# Get all switches of a sector
sector_switches = get_sector_switches(
sector, data['assumptions'].ss_service_switches)
ss_share_s_tech_ey_p[sector], ss_switches_autocompleted[
sector] = fuel_service_switch.autocomplete_switches(
sector_switches,
data['assumptions'].ss_specified_tech_enduse_by,
ss_s_tech_by_p[sector],
sector=sector,
spatial_exliclit_diffusion=data['criterias']
['spatial_exliclit_diffusion'],
regions=data['regions'],
f_diffusion=f_diffusion,
techs_affected_spatial_f=data['assumptions'].
techs_affected_spatial_f,
service_switches_from_capacity=ss_service_switches_inlc_cap)
# Industry
is_switches_autocompleted = {}
is_share_s_tech_ey_p = {}
for sector in data['sectors']['is_sectors']:
# Get all switches of a sector
sector_switches = get_sector_switches(
sector, data['assumptions'].is_service_switches)
is_share_s_tech_ey_p[sector], is_switches_autocompleted[
sector] = fuel_service_switch.autocomplete_switches(
sector_switches,
data['assumptions'].is_specified_tech_enduse_by,
is_s_tech_by_p[sector],
sector=sector,
spatial_exliclit_diffusion=data['criterias']
['spatial_exliclit_diffusion'],
regions=data['regions'],
f_diffusion=f_diffusion,
techs_affected_spatial_f=data['assumptions'].
techs_affected_spatial_f,
service_switches_from_capacity=is_service_switches_incl_cap)
# ========================================================================================
# Fuel switches
#
# Calculate sigmoid diffusion considering fuel switches
# and service switches. As inputs, service (and thus also capacity switches) are used
# ========================================================================================
# Residential
for enduse in data['enduses']['rs_enduses']:
init_cont['rs_sig_param_tech'][
enduse] = sig_param_calc_incl_fuel_switch(
data['assumptions'].base_yr,
data['assumptions'].crit_switch_happening,
data['technologies'],
enduse=enduse,
fuel_switches=data['assumptions'].rs_fuel_switches,
service_switches=rs_switches_autocompleted,
s_tech_by_p=rs_s_tech_by_p[enduse],
s_fueltype_by_p=rs_s_fueltype_by_p[enduse],
share_s_tech_ey_p=rs_share_s_tech_ey_p[enduse],
fuel_tech_p_by=data['assumptions'].rs_fuel_tech_p_by[enduse],
regions=data['regions'],
regional_specific=data['criterias']
['spatial_exliclit_diffusion'])
# Service
for enduse in data['enduses']['ss_enduses']:
init_cont['ss_sig_param_tech'][enduse] = {}
for sector in data['sectors']['ss_sectors']:
init_cont['ss_sig_param_tech'][enduse][
sector] = sig_param_calc_incl_fuel_switch(
data['assumptions'].base_yr,
data['assumptions'].crit_switch_happening,
data['technologies'],
enduse=enduse,
fuel_switches=data['assumptions'].ss_fuel_switches,
service_switches=ss_switches_autocompleted[sector],
s_tech_by_p=ss_s_tech_by_p[sector][enduse],
s_fueltype_by_p=ss_s_fueltype_by_p[sector][enduse],
share_s_tech_ey_p=ss_share_s_tech_ey_p[sector][enduse],
fuel_tech_p_by=data['assumptions'].
ss_fuel_tech_p_by[enduse][sector],
regions=data['regions'],
sector=sector,
regional_specific=data['criterias']
['spatial_exliclit_diffusion'])
# Industry
for enduse in data['enduses']['is_enduses']:
init_cont['is_sig_param_tech'][enduse] = {}
for sector in data['sectors']['is_sectors']:
init_cont['is_sig_param_tech'][enduse][
sector] = sig_param_calc_incl_fuel_switch(
data['assumptions'].base_yr,
data['assumptions'].crit_switch_happening,
data['technologies'],
enduse=enduse,
fuel_switches=data['assumptions'].is_fuel_switches,
service_switches=is_switches_autocompleted[sector],
s_tech_by_p=is_s_tech_by_p[sector][enduse],
s_fueltype_by_p=is_s_fueltype_by_p[sector][enduse],
share_s_tech_ey_p=is_share_s_tech_ey_p[sector][enduse],
fuel_tech_p_by=data['assumptions'].
is_fuel_tech_p_by[enduse][sector],
regions=data['regions'],
sector=sector,
regional_specific=data['criterias']
['spatial_exliclit_diffusion'])
# ===========================================
# III. Spatial explicit modelling of scenario variables
#
# From UK factors to regional specific factors
# Convert strategy variables to regional variables
# ===========================================
if data['criterias']['spatial_exliclit_diffusion']:
init_cont['regional_strategy_variables'] = defaultdict(dict)
# Iterate strategy variables and calculate regional variable
for var_name, strategy_var in data[
'assumptions'].strategy_variables.items():
logging.info("Spatially explicit diffusion modelling %s", var_name)
logging.info(data['assumptions'].spatially_modelled_vars)
# Check whether scenario varaible is regionally modelled
if var_name not in data['assumptions'].spatially_modelled_vars:
# Variable is not spatially modelled
for region in data['regions']:
init_cont['regional_strategy_variables'][region][
var_name] = {
'scenario_value':
float(strategy_var['scenario_value']),
'affected_enduse':
data['assumptions'].strategy_variables[var_name]
['affected_enduse']
}
else:
if strategy_var['affected_enduse'] == []:
logging.info(
"For scenario var %s no affected enduse is defined. Thus speed is used for diffusion",
var_name)
else:
pass
# Get enduse specific fuel for each region
fuels_reg = spatial_diffusion.get_enduse_regs(
enduse=strategy_var['affected_enduse'],
fuels_disagg=[
fuel_disagg['rs_fuel_disagg'],
fuel_disagg['ss_fuel_disagg'],
fuel_disagg['is_fuel_disagg']
])
# Calculate regional specific strategy variables values
reg_specific_variables = spatial_diffusion.factor_improvements_single(
factor_uk=strategy_var['scenario_value'],
regions=data['regions'],
f_reg=f_reg,
f_reg_norm=f_reg_norm,
f_reg_norm_abs=f_reg_norm_abs,
fuel_regs_enduse=fuels_reg)
# Add regional specific strategy variables values
for region in data['regions']:
init_cont['regional_strategy_variables'][region][
var_name] = {
'scenario_value':
float(reg_specific_variables[region]),
'affected_enduse': strategy_var['affected_enduse']
}
init_cont['regional_strategy_variables'] = dict(
init_cont['regional_strategy_variables'])
logging.info("... finished scenario initialisation")
return dict(init_cont), fuel_disagg