def create_folders_rename_folders(config):
"""Create scenario name and get paths
Arguments
---------
config : dict
Configuration dictionary containing all the file paths
"""
path_new_scenario = config['PATHS']['path_new_scenario']
# ------------------------------
# Delete previous model results and create result folders
# ------------------------------
result_paths = config['PATHS']['path_result_data']
basic_functions.del_previous_setup(result_paths)
basic_functions.create_folder(path_new_scenario)
folders_to_create = [
os.path.join(result_paths, 'model_run_pop'),
os.path.join(result_paths, 'validation')
]
for folder in folders_to_create:
basic_functions.create_folder(folder)
path_strategy_vars = os.path.join(config['DATA_PATHS']['path_strategy_vars'], name_config_path)
name_region_set = os.path.join(config['PATHS']['path_local_data'], 'region_definitions', "lad_2016_uk_simplified.shp")
name_population_dataset = os.path.join(os.path.join(config['PATHS']['path_local_data'], ".."), 'scenarios', 'MISTRAL_pop_gva', 'data', '{}/population__lad.csv'.format(local_scenario))
name_gva_dataset = os.path.join(os.path.join(config['PATHS']['path_local_data'], ".."),'scenarios', 'MISTRAL_pop_gva', 'data', '{}/gva_per_head__lad_sector.csv'.format(local_scenario))
name_gva_dataset_per_head = os.path.join(os.path.join(config['PATHS']['path_local_data'], ".."), 'scenarios', 'MISTRAL_pop_gva', 'data', '{}/gva_per_head__lad.csv'.format(local_scenario))
# --------------------
# Create scenario path
# --------------------
name_scenario_run = "{}_result_local_{}".format(scenario_name, str(time.ctime()).replace(":", "_").replace(" ", "_"))
path_new_scenario = os.path.join(config['PATHS']['path_result_data'], name_scenario_run)
# -----------------------------------------------------------------------
# Create new folders
# -----------------------------------------------------------------------
basic_functions.del_previous_setup(path_new_scenario)
basic_functions.create_folder(path_new_scenario)
# --------------------
# Load all other paths
# --------------------
data['paths'] = config['CONFIG_DATA']
data['local_paths'] = config['DATA_PATHS']
data['result_paths'] = basic_functions.get_result_paths(path_new_scenario)
for folder, folder_path in data['result_paths'].items():
basic_functions.create_folder(folder_path)
# ----------------------------------------------------------------------
# Load data
# ----------------------------------------------------------------------
def main(path_data_energy_demand, path_shapefile_input):
"""Read in all results and plot PDFs
Arguments
----------
path_data_energy_demand : str
Path to results
path_shapefile_input : str
Path to shapefile
"""
print("Start processing")
# ---------
# Criterias
# ---------
write_shapefiles = False # Write shapefiles
spatial_results = True # Spatial geopanda maps
# Set up logger
logger_setup.set_up_logger(
os.path.join(path_data_energy_demand, "plotting.log"))
# ------------------
# Load necessary inputs for read in
# ------------------
data = {}
data['local_paths'] = data_loader.load_local_paths(path_data_energy_demand)
data['result_paths'] = data_loader.load_result_paths(
os.path.join(path_data_energy_demand, '_result_data'))
data['lookups'] = lookup_tables.basic_lookups()
# ---------------
# Folder cleaning
# ---------------
basic_functions.del_previous_setup(
data['result_paths']['data_results_PDF'])
basic_functions.del_previous_setup(
data['result_paths']['data_results_shapefiles'])
basic_functions.create_folder(data['result_paths']['data_results_PDF'])
basic_functions.create_folder(
data['result_paths']['data_results_shapefiles'])
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data['reg_nrs'], data[
'regions'] = data_loader.load_ini_param(
os.path.join(path_data_energy_demand, '_result_data'))
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions'][
'yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_model_yeardays_daytype(
year_to_model=2015)
# Read scenario data
data['scenario_data'] = {}
data['scenario_data'][
'population'] = read_data.read_scenaric_population_data(
data['result_paths']['model_run_pop'])
# --------------------------------------------
# Reading in results from different model runs
# Read in and plot in same step if memory is a problem
# --------------------------------------------
results_container = read_data.read_in_results(
data['result_paths']['data_results_model_runs'],
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
# ----------------
# Write results to CSV files and merge with shapefile
# ----------------
if write_shapefiles:
write_data.create_shp_results(data, results_container,
data['local_paths'], data['lookups'],
data['regions'])
# ------------------------------
# Plotting other results
# ------------------------------
plotting_results.run_all_plot_functions(results_container, data['reg_nrs'],
data['regions'], data['lookups'],
data['result_paths'],
data['assumptions'],
data['enduses'])
# ------------------------------
# Plotting spatial results
# ------------------------------
print("... plotting spatial results")
if spatial_results:
logging.info("Create spatial geopanda files")
result_mapping.create_geopanda_files(
data, results_container,
data['result_paths']['data_results_shapefiles'], data['regions'],
data['lookups']['fueltypes_nr'], data['lookups']['fueltypes'],
path_shapefile_input)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
def post_install_setup(args):
"""Run this function after installing the energy_demand
model with smif and putting the data folder with all necessary
data into a local drive. This scripts only needs to be
executed once after the energy_demand model has been installed
Arguments
----------
args : object
Arguments defined in ``./cli/__init__.py``
"""
print("... start running initialisation scripts")
# Paths
path_main = resource_filename(Requirement.parse("energy_demand"), "config_data")
path_results = resource_filename(Requirement.parse("energy_demand"), "results")
local_data_path = args.local_data
# Initialise logger
logger_setup.set_up_logger(
os.path.join(local_data_path, "logging_post_install_setup.log"))
logging.info("... start local energy demand calculations")
# Load data
base_yr = 2015
data = {}
data['paths'] = data_loader.load_paths(path_main)
data['local_paths'] = data_loader.load_local_paths(local_data_path)
data['result_paths'] = data_loader.load_result_paths(path_results)
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'] = data_loader.load_fuels(
data['paths'], data['lookups'])
# Assumptions
data['assumptions'] = non_param_assumptions.Assumptions(
base_yr=base_yr,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'],
fueltypes=data['lookups']['fueltypes'],
fueltypes_nr=data['lookups']['fueltypes_nr'])
# Delete all previous data from previous model runs
basic_functions.del_previous_setup(data['local_paths']['data_processed'])
basic_functions.del_previous_setup(data['result_paths']['data_results'])
# Create folders and subfolder for data_processed
basic_functions.create_folder(data['local_paths']['data_processed'])
basic_functions.create_folder(data['local_paths']['path_post_installation_data'])
basic_functions.create_folder(data['local_paths']['dir_raw_weather_data'])
basic_functions.create_folder(data['local_paths']['dir_changed_weather_station_data'])
basic_functions.create_folder(data['local_paths']['load_profiles'])
basic_functions.create_folder(data['local_paths']['rs_load_profile_txt'])
basic_functions.create_folder(data['local_paths']['ss_load_profile_txt'])
basic_functions.create_folder(data['local_paths']['dir_disaggregated'])
print("... Read in temperature data from raw files")
s_raw_weather_data.run(
data['local_paths'])
print("... Read in service submodel load profiles")
s_ss_raw_shapes.run(
data['paths'],
data['local_paths'],
data['lookups'])
print("... Read in residential submodel load profiles")
s_rs_raw_shapes.run(
data['paths'],
data['local_paths'],
base_yr)
print("... successfully finished setup")
return
def post_install_setup(args):
"""Run this function after installing the energy_demand
model with smif and putting the data folder with all necessary
data into a local drive. This scripts only needs to be
executed once after the energy_demand model has been installed
Arguments
----------
args : object
Arguments defined in ``./cli/__init__.py``
"""
print("... start running initialisation scripts", flush=True)
path_config_file = args.local_data
config = data_loader.read_config_file(path_config_file)
local_data_path = config['PATHS']['path_local_data']
path_results = resource_filename(Requirement.parse("energy_demand"),
"results")
local_data_path = args.local_data
path_config = config['PATHS']['path_energy_demand_config']
base_yr = config['CONFIG']['base_yr']
data = {}
data['paths'] = config['CONFIG_DATA']
data['local_paths'] = config['DATA_PATHS']
data['result_paths'] = config['RESULT_DATA']
data['lookups'] = lookup_tables.basic_lookups()
data['enduses'], data['sectors'], data['fuels'], lookup_enduses, \
lookup_sector_enduses = data_loader.load_fuels(data['paths'])
data['assumptions'] = general_assumptions.Assumptions(
lookup_enduses=lookup_enduses,
lookup_sector_enduses=lookup_sector_enduses,
base_yr=base_yr,
paths=data['paths'],
enduses=data['enduses'],
sectors=data['sectors'])
# Delete all previous data from previous model runs
basic_functions.del_previous_setup(data['local_paths']['data_processed'])
basic_functions.del_previous_setup(data['result_paths']['data_results'])
basic_functions.del_previous_setup(
data['local_paths']['path_post_installation_data'])
# Create folders and subfolder for data_processed
folders_to_create = [
data['local_paths']['data_processed'],
data['local_paths']['path_post_installation_data'],
data['local_paths']['load_profiles'],
data['local_paths']['rs_load_profile_txt'],
data['local_paths']['ss_load_profile_txt']
]
for folder in folders_to_create:
basic_functions.create_folder(folder)
print("... Read in residential submodel load profiles", flush=True)
s_rs_raw_shapes.run(data['paths'], data['local_paths'], base_yr)
print("... Read in service submodel load profiles", flush=True)
s_ss_raw_shapes.run(data['paths'], data['local_paths'], data['lookups'])
# Input data preparation
print("Generate additional data", flush=True)
# Extract NISMOD population data
path_to_zip_file = os.path.join(
local_data_path, "population-economic-smif-csv-from-nismod-db.zip")
path_extraction = os.path.join(local_data_path, 'scenarios',
"MISTRAL_pop_gva")
zip_ref = zipfile.ZipFile(path_to_zip_file, 'r')
zip_ref.extractall(path_extraction)
zip_ref.close()
# Complete gva and pop data for every sector
data_pop = os.path.join(local_data_path, "scenarios", "MISTRAL_pop_gva",
"data")
path_geography = os.path.join(
local_data_path, "scenarios",
"uk_pop_principal_2015_2050_MSOA_england.csv")
geography_name = "region" # "lad_uk_2016"
script_data_preparation_MISTRAL_pop_gva.run(
path_to_folder=data_pop,
path_MSOA_baseline=path_geography,
MSOA_calculations=False,
geography_name="region") # "lad_uk_2016"
print("... successfully finished setup")
return
def main(scenarios_path, path_shapefile_input, base_yr,
simulation_yrs_to_plot):
"""Read in all results and plot PDFs
Arguments
----------
scenarios_path : str
Path to results
path_shapefile_input : str
Path to shapefile
plot_crit_dict : dict
Criteria to select plots to plot
base_yr : int
Base year
comparison_year : int
Year to generate comparison plots
"""
print("Start creating plots")
# -------------------
# Create result folder
# -------------------
result_path = os.path.join(scenarios_path, '_results_weather_plots')
basic_functions.del_previous_setup(result_path)
basic_functions.create_folder(result_path)
for simulation_yr_to_plot in simulation_yrs_to_plot:
print("-----------")
print("...simulation_yr_to_plot: " + str(simulation_yr_to_plot))
print("-----------")
data = {}
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
to_ignores = [
'model_run_pop', 'PDF_validation', '_results_weather_plots'
]
endings_to_ignore = ['.pdf', '.txt', '.ini']
all_scenarios_incl_ignored = os.listdir(scenarios_path)
all_scenarios = []
for scenario in all_scenarios_incl_ignored:
if scenario not in to_ignores:
all_scenarios.append(scenario)
scenario_result_container = []
for scenario_nr, scenario_name in enumerate(all_scenarios):
print(" ")
print("Scenario: {}".format(scenario_name))
print(" ")
scenario_path = os.path.join(scenarios_path, scenario_name)
all_result_folders = os.listdir(scenario_path)
paths_folders_result = []
for result_folder in all_result_folders:
if result_folder not in to_ignores and result_folder[
-4:] not in endings_to_ignore:
paths_folders_result.append(
os.path.join(scenario_path, result_folder))
fueltype_str_to_create_maps = ['electricity']
fueltype_str = 'electricity'
fueltype_int = tech_related.get_fueltype_int(fueltype_str)
####################################################################
# Collect regional simulation data for every realisation
####################################################################
total_regional_demand_electricity = pd.DataFrame()
peak_hour_demand = pd.DataFrame()
national_peak = pd.DataFrame()
regional_share_national_peak = pd.DataFrame()
national_electricity = pd.DataFrame()
national_gas = pd.DataFrame()
national_hydrogen = pd.DataFrame()
for path_result_folder in paths_folders_result:
data = {}
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(
os.path.join(path_result_folder))
pop_data = read_data.read_scenaric_population_data(
os.path.join(path_result_folder, 'model_run_pop'))
path_result_folder = os.path.join(path_result_folder,
'simulation_results')
path_result_folder_model_runs = os.path.join(
path_result_folder, 'model_run_results_txt')
data['lookups'] = lookup_tables.basic_lookups()
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(
year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data[
'assumptions']['yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_yeardays_daytype(
year_to_model=2015)
# --------------------------------------------
# Reading in results from different model runs
# --------------------------------------------
results_container = read_weather_results.read_in_weather_results(
path_result_folder_model_runs,
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'],
fueltype_str='electricity')
# --Total demand (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame([
results_container['ed_reg_tot_y'][simulation_yr_to_plot]
[fueltype_int]
],
columns=data['regions'])
total_regional_demand_electricity = total_regional_demand_electricity.append(
realisation_data)
# National per fueltype electricity
fueltype_elec_int = tech_related.get_fueltype_int(
'electricity')
simulation_yrs_result = [
results_container['national_all_fueltypes'][year]
[fueltype_elec_int] for year in
results_container['national_all_fueltypes'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_electricity = national_electricity.append(
realisation_data)
# National per fueltype gas
fueltype_elec_int = tech_related.get_fueltype_int('gas')
simulation_yrs_result = [
results_container['national_all_fueltypes'][year]
[fueltype_elec_int] for year in
results_container['national_all_fueltypes'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_gas = national_gas.append(realisation_data)
# National per fueltype hydrogen
fueltype_elec_int = tech_related.get_fueltype_int('hydrogen')
simulation_yrs_result = [
results_container['national_all_fueltypes'][year]
[fueltype_elec_int] for year in
results_container['national_all_fueltypes'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_hydrogen = national_hydrogen.append(realisation_data)
# --Peak day demand (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame([
results_container['ed_reg_peakday_peak_hour']
[simulation_yr_to_plot][fueltype_int]
],
columns=data['regions'])
peak_hour_demand = peak_hour_demand.append(realisation_data)
# --National peak
simulation_yrs_result = [
results_container['national_peak'][year][fueltype_int]
for year in results_container['national_peak'].keys()
]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_peak = national_peak.append(realisation_data)
# --Regional percentage of national peak demand
realisation_data = pd.DataFrame([
results_container['regional_share_national_peak']
[simulation_yr_to_plot]
],
columns=data['regions'])
regional_share_national_peak = regional_share_national_peak.append(
realisation_data)
# Add to scenario container
scenario_result_container.append({
'scenario_name':
scenario_name,
'peak_hour_demand':
peak_hour_demand,
'national_peak':
national_peak,
'regional_share_national_peak':
regional_share_national_peak,
'total_regional_demand_electricity':
total_regional_demand_electricity,
'national_electricity':
national_electricity,
'national_gas':
national_gas,
'national_hydrogen':
national_hydrogen,
})
# ------------------------------
# Plot national sum over time per fueltype and scenario
# ------------------------------
print("... plotting national sum of fueltype over time ")
fig_3_plot_over_time.fueltypes_over_time(
scenario_result_container=scenario_result_container,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="fueltypes_over_time__{}__{}.pdf".format(
simulation_yr_to_plot, fueltype_str),
fueltypes=['electricity', 'gas', 'hydrogen'],
result_path=result_path,
unit='TWh',
plot_points=True,
crit_smooth_line=True,
seperate_legend=False)
# ------------------------------
# Plot national peak change over time for each scenario including weather variability
# ------------------------------
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="scenarios_peak_over_time__{}__{}.pdf".format(
simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=True,
seperate_legend=False)
# ------------------------------
# Plotting spatial results for electricity
# ------------------------------
for i in scenario_result_container:
scenario_name = i['scenario_name']
total_regional_demand_electricity = i[
'total_regional_demand_electricity']
peak_hour_demand = i['peak_hour_demand']
regional_share_national_peak = i['regional_share_national_peak']
print("... plot spatial map of total annual demand")
field_to_plot = 'std_dev'
fig_3_weather_map.total_annual_demand(
total_regional_demand_electricity,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__tot_demand__{}_{}_{}.pdf".format(
scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='GW',
seperate_legend=False)
print("... plot spatial map of peak hour demand")
field_to_plot = 'std_dev'
fig_3_weather_map.total_annual_demand(
peak_hour_demand,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__peak_h_demand_{}_{}_{}.pdf".format(
scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='GW',
seperate_legend=False)
print(
"... plot spatial map of percentage of regional peak hour demand"
)
field_to_plot = 'mean'
fig_3_weather_map.total_annual_demand(
regional_share_national_peak,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__regional_share_national_peak_{}_{}_{}.pdf".
format(scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='percentage',
seperate_legend=False,
bins=[0.000001, 0.25, 0.5, 0.75, 1, 1.25, 1.5])
field_to_plot = 'std_dev'
fig_3_weather_map.total_annual_demand(
regional_share_national_peak,
path_shapefile_input,
data['regions'],
pop_data=pop_data,
simulation_yr_to_plot=simulation_yr_to_plot,
result_path=result_path,
fig_name="{}__regional_share_national_peak_{}_{}_{}.pdf".
format(scenario_name, field_to_plot, fueltype_str,
simulation_yr_to_plot),
field_to_plot=field_to_plot,
unit='percentage',
seperate_legend=False)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
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
def main(path_data_ed, path_shapefile_input, plot_crit_dict, base_yr,
comparison_year):
"""Read in all results and plot PDFs
Arguments
----------
path_data_ed : str
Path to results
path_shapefile_input : str
Path to shapefile
plot_crit_dict : dict
Criteria to select plots to plot
base_yr : int
Base year
comparison_year : int
Year to generate comparison plots
"""
print("...Start creating plots")
data = {}
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
to_ignores = ['model_run_pop', 'PDF_validation']
endings_to_ignore = ['.pdf', '.txt', '.ini']
all_result_folders = os.listdir(path_data_ed)
paths_folders_result = []
for result_folder in all_result_folders:
if result_folder not in to_ignores and result_folder[
-4:] not in endings_to_ignore:
paths_folders_result.append(
os.path.join(path_data_ed, result_folder))
####################################################################
# Calculate results for every weather year
####################################################################
for path_result_folder in paths_folders_result:
print("-----------------------")
print("path_result_folder: " + str(path_result_folder))
print("-----------------------")
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(os.path.join(path_data_ed))
# ------------------
# Load necessary inputs for read in
# ------------------
data = {}
data['local_paths'] = data_loader.get_local_paths(path_result_folder)
data['result_paths'] = basic_functions.get_result_paths(
os.path.join(path_result_folder))
data['lookups'] = lookup_tables.basic_lookups()
# ---------------
# Folder cleaning
# ---------------
basic_functions.del_previous_setup(
data['result_paths']['data_results_PDF'])
basic_functions.del_previous_setup(
data['result_paths']['data_results_shapefiles'])
basic_functions.create_folder(data['result_paths']['data_results_PDF'])
basic_functions.create_folder(
data['result_paths']['data_results_shapefiles'])
basic_functions.create_folder(
data['result_paths']['individual_enduse_lp'])
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data[
'regions'] = data_loader.load_ini_param(os.path.join(path_data_ed))
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(
year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions'][
'yeardays_month'], data['assumptions'][
'yeardays_month_days'] = date_prop.get_yeardays_daytype(
year_to_model=2015)
data['scenario_data'] = {}
data['scenario_data'][
'population'] = read_data.read_scenaric_population_data(
os.path.join(path_data_ed, 'model_run_pop'))
# --------------------------------------------
# Reading in results from different model runs
# --------------------------------------------
results_container = read_data.read_in_results(
data['result_paths']['data_results_model_run_results_txt'],
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'])
# ------------------------------
# Plotting other results
# ------------------------------
plotting_results.run_all_plot_functions(
results_container,
data['assumptions']['reg_nrs'],
data['regions'],
data['lookups'],
data['result_paths'],
data['assumptions'],
data['enduses'],
plot_crit=plot_crit_dict,
base_yr=base_yr,
comparison_year=comparison_year)
# ------------------------------
# Plotting spatial results
# ------------------------------
if plot_crit_dict['spatial_results']:
result_mapping.spatial_maps(
data,
results_container,
data['result_paths']['data_results_shapefiles'],
data['regions'],
data['lookups']['fueltypes_nr'],
data['lookups']['fueltypes'],
path_shapefile_input,
plot_crit_dict,
base_yr=base_yr)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
}
}
print("Start Energy Demand Model with python version: " + str(sys.version))
print("Info model run")
print("Nr of Regions " + str(data['reg_nrs']))
# In order to load these data, the initialisation scripts need to be run
print("... Load data from script calculations")
data = read_data.load_script_data(data) #SCENARIO INITIALISATION
#-------------------
# Folder cleaning
#--------------------
print("... delete previous model run results")
basic_functions.del_previous_setup(data['result_paths']['data_results'])
basic_functions.create_folder(data['result_paths']['data_results'])
basic_functions.create_folder(data['result_paths']['data_results_PDF'])
basic_functions.create_folder(
data['result_paths']['data_results_model_run_pop'])
# Create .ini file with simulation information
write_data.write_simulation_inifile(data['result_paths']['data_results'],
data['enduses'], data['assumptions'],
data['reg_nrs'], data['regions'])
for sim_yr in data['assumptions'].simulated_yrs:
setattr(data['assumptions'], 'curr_yr', sim_yr)
print("Simulation for year --------------: " + str(sim_yr))
fuel_in, fuel_in_biomass, fuel_in_elec, fuel_in_gas, fuel_in_heat, fuel_in_hydro, fuel_in_solid_fuel, fuel_in_oil, tot_heating = testing.test_function_fuel_sum(
def paper_II_plots(
#/soge-home/staff/cenv0553/ED
#scenario_path_all_stations_single_weather_yr="C:/Users/cenv0553/ed/results/_multiple_TEST_two_scenarios",
#scenario_path_all_stations_single_weather_yr="C:/Users/cenv0553/ed/results/_Fig2_multiple_all_yrs_all_stations",
ed_path='C:/Users/cenv0553/ED',
scenario_path_single_stations='C:/Users/cenv0553/ED/results/_Fig2_multiple_2015_weather_stations',
scenario_path_all_stations_single_weather_yr="C:/Users/cenv0553/ED/results/_Fig2_multiple_all_yrs_all_stations",
path_shapefile_input="C:/Users/cenv0553/ED/data/region_definitions/lad_2016_uk_simplified.shp"
):
"""Iterate the folders with scenario
runs and generate PDF results of individual
simulation runs
"""
# Chose which plots should be generated
plot_crit_dict = {
# Scenario plots
'scenario_plots': False, #True,
#Plot weather map
'weather_map': False, #True,
# Spatio temporal validation
'spatio_temporal_validation': False, #True,
# Spatial map of distribution of peak and contribution in terms of overall variability
"plot_spatial_distribution_of_peak": True,
# Needs to have weatehryr_stationid files in folder
"plot_national_regional_hdd_disaggregation": False,
# Plot weather variability for every weather station
# of demand generated with regional HDD calculation
"plot_weather_day_year": False,
# Plot spatial distribution of differences in
# variability of demand by weather variability
# normed by population
"plot_spatial_weather_var_peak": False,
# Plot scenario years sorted with weather variability
"plot_scenarios_sorted": True,
}
####################################################################
# Plot scenarios
####################################################################
if plot_crit_dict['scenario_plots']:
sim_yrs = [2015, 2020, 2030, 2040, 2050]
sim_yrs = [2015, 2050]
scenario_result_paths = [
"C:/Users/cenv0553/ed/results/_scenario_low_elec",
"C:/Users/cenv0553/ed/results/_scenario_high_elec"
]
scenario_result_paths = [
"C:/Users/cenv0553/ed/results/_Fig2_multiple_all_yrs_all_stations"
]
# Create result folder
path_out_plots = os.path.join(
"C:/Users/cenv0553/ed/results/_results_nationalFIGS")
basic_functions.del_previous_setup(path_out_plots)
basic_functions.create_folder(path_out_plots)
fig_p2_total_demand_national_scenarios.total_demand_national_scenarios(
scenario_result_paths,
sim_yrs=sim_yrs,
fueltype_str='electricity',
path_out_plots=os.path.join(path_out_plots,
"PDF_national_scenarios.pdf"))
####################################################################
# Plot weather station availability map
####################################################################
if plot_crit_dict['weather_map']:
# Wheather station distribution map
path_to_weather_data = os.path.join(
ed_path,
"data/_raw_data/A-temperature_data/cleaned_weather_stations_data")
folder_path_weater_stations = os.path.join(
ed_path,
"data/_raw_data/A-temperature_data/cleaned_weather_stations.csv")
fig_path = os.path.join(ed_path, scenario_path_single_stations,
"weather_station_maps.pdf")
fig_p2_spatial_weather_map.run(path_to_weather_data,
folder_path_weater_stations,
path_shapefile=path_shapefile_input,
fig_path=fig_path)
print("... plotted weather station distribution map")
####################################################################
# Plot regional vs national spatio-temporal validation
####################################################################
if plot_crit_dict['spatio_temporal_validation']:
path_regional_calculations = os.path.join(
scenario_path_single_stations, "_regional_calculations")
path_non_regional_elec_2015 = os.path.abspath(
os.path.join(path_regional_calculations, '..',
"_all_stations_wy_2015"))
path_rolling_elec_demand = os.path.join(ed_path, "energy_demand",
"energy_demand", "config_data",
'01-validation_datasets',
'01_national_elec_2015',
'elec_demand_2015.csv')
path_temporal_elec_validation = os.path.join(
ed_path, "energy_demand", "energy_demand", "config_data",
'01-validation_datasets', '02_subnational_elec',
'data_2015_elec_domestic.csv')
path_temporal_gas_validation = os.path.join(
ed_path, "energy_demand", "energy_demand", "config_data",
'01-validation_datasets', '03_subnational_gas',
'data_2015_gas_domestic.csv')
path_results = os.path.abspath(
os.path.join(path_regional_calculations, '..'))
path_out_plots = os.path.join(path_results, '_results_PDF_figs')
basic_functions.del_previous_setup(path_out_plots)
basic_functions.create_folder(path_out_plots)
# Plot figure national an regional validation comparison
figs_p2.plot_fig_spatio_temporal_validation(
path_regional_calculations=path_regional_calculations,
path_rolling_elec_demand=path_rolling_elec_demand,
path_temporal_elec_validation=path_temporal_elec_validation,
path_temporal_gas_validation=path_temporal_gas_validation,
path_non_regional_elec_2015=path_non_regional_elec_2015,
path_out_plots=path_out_plots)
print("... plotted spatio-temporal validation")
####################################################################
# Plot spatial distribution of variability depending on weather year
# Plot the variability of the contribution of regional peak demand
# to national peak demand
####################################################################
if plot_crit_dict['plot_spatial_distribution_of_peak']:
# Create result folder
path_out_plots = os.path.join(
scenario_path_all_stations_single_weather_yr, '_results_PDF_figs')
basic_functions.del_previous_setup(path_out_plots)
basic_functions.create_folder(path_out_plots)
# Get all folders with scenario run results (name of folder is scenario)
scenarios = os.listdir(scenario_path_all_stations_single_weather_yr)
scenario_names_ignored = [
'__results_multiple_scenarios', '_FigII_non_regional_2015',
'_results_PDF_figs'
]
only_scenarios = []
for scenario in scenarios:
if scenario in scenario_names_ignored:
pass
else:
only_scenarios.append(scenario)
# Select simulation years
sim_yrs = [2015, 2050]
unit = 'kW' #'GW'
# Plot standard deviation of demand per person in peak hour
fig_spatial_distribution_of_peak.run_fig_spatial_distribution_of_peak(
only_scenarios,
scenario_path_all_stations_single_weather_yr,
path_shapefile_input,
sim_yrs=sim_yrs,
field_to_plot="std_deviation_df_abs_demand_in_peak_h_pp",
unit=unit,
fig_path=path_out_plots)
# Plot standard deviation of percentage of national peak
fig_spatial_distribution_of_peak.run_fig_spatial_distribution_of_peak(
only_scenarios,
scenario_path_all_stations_single_weather_yr,
path_shapefile_input,
sim_yrs=sim_yrs,
field_to_plot='std_deviation_p_demand_peak_h',
unit='percent',
fig_path=path_out_plots)
# Plot standard deviation of absolute demand of national peak
fig_spatial_distribution_of_peak.run_fig_spatial_distribution_of_peak(
only_scenarios,
scenario_path_all_stations_single_weather_yr,
path_shapefile_input,
sim_yrs=sim_yrs,
field_to_plot='std_deviation_abs_demand_peak_h',
unit=unit,
fig_path=path_out_plots)
# Plot maximum peak hour - mean peak hour demand
fig_spatial_distribution_of_peak.run_fig_spatial_distribution_of_peak(
only_scenarios,
scenario_path_all_stations_single_weather_yr,
path_shapefile_input,
sim_yrs=sim_yrs,
field_to_plot='diff_peak_h_minus_mean',
unit='GW',
fig_path=path_out_plots)
print("... plotted variability maps")
print("Finihsed")
raise Exception("Finished")
####################################################################
# left overs
####################################################################
for scenario in only_scenarios:
# -----------
# Other plots
# -----------
figs_p2.main(
os.path.join(scenario_path_all_stations_single_weather_yr,
scenario), path_shapefile_input, path_out_plots,
plot_crit_dict)
# ####################################################################
# Plot demand over time and peak over time (for modassar paper)
# ####################################################################
'''if plot_crit_dict['plot_weather_day_year']:
# --------------------------------------------
# Plot peak demand and total demand per fueltype
# --------------------------------------------
for fueltype_str in data['lookups']['fueltypes'].keys():
fig_total_demand_peak.run(
data_input=weather_yr_container['tot_fueltype_yh'],
fueltype_str=fueltype_str,
fig_name=os.path.join(
path_out_plots, "tot_{}_h.pdf".format(fueltype_str)))
# plot over period of time across all weather scenario
fig_weather_variability_priod.run(
data_input=weather_yr_container['tot_fueltype_yh'],
fueltype_str='electricity',
simulation_yr_to_plot=2015, # Simulation year to plot
period_h=list(range(200,500)), #period to plot
fig_name=os.path.join(
path_out_plots, "weather_var_period.pdf"))'''
return
def main(
scenarios_path,
path_shapefile_input,
base_yr,
simulation_yrs_to_plot
):
"""Read in all results and plot PDFs
Arguments
----------
scenarios_path : str
Path to results
path_shapefile_input : str
Path to shapefile
plot_crit_dict : dict
Criteria to select plots to plot
base_yr : int
Base year
comparison_year : int
Year to generate comparison plots
"""
print("Start creating plots")
# -------------------
# Create temperatuere figur plot
# -------------------
plot_weather_chart = False
if plot_weather_chart:
from energy_demand.plotting import fig3_weather_at_home_plot
path_weather_data = "//linux-filestore.ouce.ox.ac.uk/mistral/nismod/data/energy_demand/J-MARIUS_data/_weather_realisation"
fig3_weather_at_home_plot.plotting_weather_data(path_weather_data)
# -------------------
# Create result folder
# -------------------
result_path = os.path.join(scenarios_path, '_results_weather_plots')
basic_functions.del_previous_setup(result_path)
basic_functions.create_folder(result_path)
x_chart_yrs_storage = {}
for simulation_yr_to_plot in simulation_yrs_to_plot:
print("=================")
print("...simulation_yr_to_plot: " + str(simulation_yr_to_plot))
print("=================")
data = {}
x_chart_yrs_storage[simulation_yr_to_plot] = {}
# ---------------------------------------------------------
# Iterate folders and read out all weather years and stations
# ---------------------------------------------------------
to_ignores = [
'model_run_pop',
'PDF_validation',
'_results_weather_plots']
endings_to_ignore = [
'.pdf',
'.txt',
'.ini']
all_scenarios_incl_ignored = os.listdir(scenarios_path)
all_scenarios = []
for scenario in all_scenarios_incl_ignored:
if scenario not in to_ignores:
all_scenarios.append(scenario)
scenario_result_container = []
for scenario_nr, scenario_name in enumerate(all_scenarios):
print(" ")
print("Scenario: {}".format(scenario_name))
print(" ")
scenario_path = os.path.join(scenarios_path, scenario_name)
all_result_folders = os.listdir(scenario_path)
paths_folders_result = []
for result_folder in all_result_folders:
if result_folder not in to_ignores and result_folder[-4:] not in endings_to_ignore:
paths_folders_result.append(
os.path.join(scenario_path, result_folder))
fueltype_str_to_create_maps = ['electricity']
fueltype_str ='electricity'
fueltype_elec_int = tech_related.get_fueltype_int('electricity')
####################################################################
# Collect regional simulation data for every realisation
####################################################################
total_regional_demand_electricity = pd.DataFrame()
peak_hour_demand = pd.DataFrame()
peak_hour_demand_per_person = pd.DataFrame()
national_peak = pd.DataFrame()
regional_share_national_peak = pd.DataFrame()
regional_share_national_peak_pp = pd.DataFrame()
national_electricity = pd.DataFrame()
national_gas = pd.DataFrame()
national_hydrogen = pd.DataFrame()
national_heating_peak = pd.DataFrame()
daily_mean_peak_day = pd.DataFrame()
for path_result_folder in paths_folders_result:
print("... path_result_folder: {}".format(path_result_folder))
data = {}
ed_national_heating_peak = {}
try:
# ================================
# Loading in only heating peak demands (seperate calculations)
# ================================
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data['regions'] = data_loader.load_ini_param(os.path.join(path_result_folder))
pop_data = read_data.read_scenaric_population_data(os.path.join(path_result_folder, 'model_run_pop'))
path_result_folder_heating = os.path.join(path_result_folder, 'simulation_results')
path_result_folder_model_runs = os.path.join(path_result_folder_heating, 'model_run_results_txt')
data['lookups'] = lookup_tables.basic_lookups()
# Read in heating deamnds
path_heating_demands = os.path.join(path_result_folder_model_runs, 'enduse_specific_results')
all_files = os.listdir(path_heating_demands)
for file_name in all_files:
ending = file_name[-4:]
if ending == ".npy":
year = int(file_name.split("__")[2][:-4])
file_path = os.path.join(path_heating_demands, file_name)
heating_demand = np.load(file_path)
maximum_hour_of_peak_day = heating_demand[fueltype_elec_int].argmax() #get maxim hour of peak day
ed_national_heating_peak[year] = heating_demand[fueltype_elec_int][maximum_hour_of_peak_day]
simulation_yrs_result = [ed_national_heating_peak[year] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_heating_peak = national_heating_peak.append(realisation_data)
except:
raise Exception("... no heating peak data available " + str(path_result_folder))
try:
# Simulation information is read in from .ini file for results
data['enduses'], data['assumptions'], data['regions'] = data_loader.load_ini_param(os.path.join(path_result_folder))
pop_data = read_data.read_scenaric_population_data(os.path.join(path_result_folder, 'model_run_pop'))
path_result_folder = os.path.join(path_result_folder, 'simulation_results')
path_result_folder_model_runs = os.path.join(path_result_folder, 'model_run_results_txt')
data['lookups'] = lookup_tables.basic_lookups()
# Other information is read in
data['assumptions']['seasons'] = date_prop.get_season(year_to_model=2015)
data['assumptions']['model_yeardays_daytype'], data['assumptions']['yeardays_month'], data['assumptions']['yeardays_month_days'] = date_prop.get_yeardays_daytype(year_to_model=2015)
# --------------------------------------------
# Reading in results from different model runs
# --------------------------------------------
results_container = read_weather_results.read_in_weather_results(
path_result_folder_model_runs,
data['assumptions']['seasons'],
data['assumptions']['model_yeardays_daytype'],
pop_data,
fueltype_str='electricity')
# --Total demand (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame(
[results_container['ed_reg_tot_y'][simulation_yr_to_plot][fueltype_elec_int]],
columns=data['regions'])
total_regional_demand_electricity = total_regional_demand_electricity.append(realisation_data)
# National per fueltype electricity
simulation_yrs_result = [results_container['national_all_fueltypes'][year][fueltype_elec_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_electricity = national_electricity.append(realisation_data)
# National per fueltype gas
fueltype_gas_int = tech_related.get_fueltype_int('gas')
simulation_yrs_result = [results_container['national_all_fueltypes'][year][fueltype_gas_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_gas = national_gas.append(realisation_data)
# National per fueltype hydrogen
fueltype_hydrogen_int = tech_related.get_fueltype_int('hydrogen')
simulation_yrs_result = [results_container['national_all_fueltypes'][year][fueltype_hydrogen_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_hydrogen = national_hydrogen.append(realisation_data)
# --Peak hour demand per region (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame(
[results_container['ed_reg_peakday_peak_hour'][simulation_yr_to_plot][fueltype_elec_int]],
columns=data['regions'])
peak_hour_demand = peak_hour_demand.append(realisation_data)
# --Peak hour demand / pop per region (dataframe with row: realisation, column=region)
realisation_data = pd.DataFrame(
[results_container['ed_reg_peakday_peak_hour_per_pop'][simulation_yr_to_plot][fueltype_elec_int]],
columns=data['regions'])
peak_hour_demand_per_person = peak_hour_demand_per_person.append(realisation_data)
# --National peak
simulation_yrs_result = [results_container['national_peak'][year][fueltype_elec_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
national_peak = national_peak.append(realisation_data)
# --Regional percentage of national peak demand
realisation_data = pd.DataFrame(
[results_container['regional_share_national_peak'][simulation_yr_to_plot]],
columns=data['regions'])
regional_share_national_peak = regional_share_national_peak.append(realisation_data)
# --Regional percentage of national peak demand per person
realisation_data = pd.DataFrame(
[results_container['regional_share_national_peak_pp'][simulation_yr_to_plot]],
columns=data['regions'])
regional_share_national_peak_pp = regional_share_national_peak_pp.append(realisation_data)
# Mean demand of peak day
simulation_yrs_result = [results_container['mean_peak_day_demand'][year][fueltype_elec_int] for year in simulation_yrs_to_plot]
realisation_data = pd.DataFrame(
[simulation_yrs_result],
columns=data['assumptions']['sim_yrs'])
daily_mean_peak_day = daily_mean_peak_day.append(realisation_data)
except:
raise Exception("The run '{}' is corrupted".format(path_result_folder))
# Add to scenario container
result_entry = {
'national_heating_peak': national_heating_peak,
'scenario_name': scenario_name,
'peak_hour_demand': peak_hour_demand,
'peak_hour_demand_per_person': peak_hour_demand_per_person,
'national_peak': national_peak,
'regional_share_national_peak': regional_share_national_peak,
'regional_share_national_peak_pp': regional_share_national_peak_pp,
'total_regional_demand_electricity': total_regional_demand_electricity,
'national_electricity': national_electricity,
'national_gas': national_gas,
'national_hydrogen': national_hydrogen,
'daily_mean_peak_day': daily_mean_peak_day}
scenario_result_container.append(result_entry)
# ---------------------------------------------------------------
# TEST PLOT X-axis: Contribution to peak y-axis: Std: deviation
# ---------------------------------------------------------------
x_chart_yrs_storage[simulation_yr_to_plot][scenario_name] = result_entry
# ------------------------------
# Plot national sum over time per fueltype and scenario
# ------------------------------
crit_smooth_line = False
seperate_legend = True
try:
print("... plotting national sum of fueltype over time ")
fig_3_plot_over_time.fueltypes_over_time(
scenario_result_container=scenario_result_container,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="fueltypes_over_time__{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
fueltypes=['electricity', 'gas', 'hydrogen'],
result_path=result_path,
unit='TWh',
plot_points=True,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILS national sum")
# ------------------------------
# Plot national peak change over time for each scenario including weather variability
# ------------------------------
try:
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
field_name='national_peak',
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="scenarios_peak_over_time__{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILED")
pass
# ------------------------------
# Plot heating peak change over time for each scenario including weather variability
# ------------------------------
try:
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
field_name='national_heating_peak',
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="scenarios_heating_peak_over_time__{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILED")
pass
# ------------------------------
# plot PEAK DAY mean
# ------------------------------
try:
fig_3_plot_over_time.scenario_over_time(
scenario_result_container=scenario_result_container,
field_name='daily_mean_peak_day',
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="mean_demand_of_peak_day{}__{}.pdf".format(simulation_yr_to_plot, fueltype_str),
plot_points=True,
result_path=result_path,
crit_smooth_line=crit_smooth_line,
seperate_legend=seperate_legend)
except:
raise Exception("FAILED")
pass
## ------------------------------
## Plotting x-chart
## ------------------------------
fig_3_plot_over_time.plot_std_dev_vs_contribution(
scenario_result_container=x_chart_yrs_storage,
sim_yrs=data['assumptions']['sim_yrs'],
fig_name="_scenarios_4_chart_absolute.pdf",
fueltypes=['electricity'],
result_path=result_path,
path_shapefile_input=path_shapefile_input,
unit='TWh',
plot_points=True)
print("===================================")
print("... finished reading and plotting results")
print("===================================")
