def test_read_results_dicts():
# Using temp dir to ensure that each time a new directory is used
inputs, results = ds.get_sim_results(path=SIM_DIR,
return_xarray=False,
return_status=False)
assert isinstance(inputs, dict)
assert isinstance(results, dict)
# Load the configuration file
config = ds.load_config_excel('../ConfigFiles/ConfigEU.xlsx')
# Limit the simulation period (for testing purposes, comment the line to run the whole year)
config['StartDate'] = (2016, 1, 1, 0, 0, 0)
config['StopDate'] = (2016, 1, 7, 0, 0, 0)
# Build the simulation environment:
SimData = ds.build_simulation(config)
# Solve using GAMS:
_ = ds.solve_GAMS(config['SimulationDirectory'], config['GAMS_folder'])
# Load the simulation results:
inputs, results = ds.get_sim_results(config['SimulationDirectory'],
cache=False)
# Generate country-specific plots
ds.plot_country(inputs, results)
# Bar plot with the installed capacities in all countries:
cap = ds.plot_country_capacities(inputs)
# Bar plot with the energy balances in all countries:
ds.plot_energy_country_fuel(inputs, results,
ds.get_indicators_powerplant(inputs, results))
# Analyse the results for each country and provide quantitative indicators:
r = ds.get_result_analysis(inputs, results)
# Plot the reservoir levels
import datetime as dt
from dispaset.postprocessing import postprocessing as post
import importlib
importlib.reload(post)
import dispaset as ds
import matplotlib.pyplot as plt
from matplotlib.pyplot import *
import seaborn as sns
import matplotlib.dates as mdates
import pickle
import time as tm
import logging
import shutil
import json
shutil.copy('input_data/ConfigGCC2.xlsx', 'Simulations/ConfigGCC2.xlsx')
# Load the configuration file
config = ds.load_config_excel('Simulations/ConfigGCC2.xlsx')
# Build the simulation environment:
SimData, FuelPrices, FuelPrices2 = ds.build_simulation(
config, LocalSubsidyMultiplier=1, ExportCostMultiplier=1)
r = ds.solve_GAMS(config['SimulationDirectory'], config['GAMS_folder'])
path = 'Simulations/simulation_GCC'
inputs, results = ds.get_sim_results(path=path, cache=True)
with open('output_data/results.json', 'w') as fp:
json.dump(str(results), fp)
#%% Import results
import sys,os
sys.path.append(os.path.abspath(r'C:\Users\Andrea\GitHub\Dispa-SET-2.3'))
# Import Dispa-SET
import dispaset as ds
inputs, results = ds.get_sim_results(path=r"C:\Users\Andrea\GitHub\Dispa-SET-2.3\Simulations\Article PROres1 coupling\TIMES_ProRes_2050_EVFLEX_new",cache=False)
inputs0, results_aug = ds.get_sim_results(path=r"C:\Users\Andrea\GitHub\Dispa-SET-2.3\Simulations\Article PROres1 coupling\TIMES_ProRes_2050_EVFLEX_new_aug",cache=False)
#inputs1, results_apr = ds.get_sim_results(path=r"C:\Users\Andrea\GitHub\Dispa-SET-2.3\Simulations\Article PROres1 coupling\TIMES_ProRes_2050_ALLFLEX_new_apr",cache=False)
#inputs2, results_aug = ds.get_sim_results(path=r"C:\Users\Andrea\GitHub\Dispa-SET-2.3\Simulations\Article PROres1 coupling\TIMES_ProRes_2050_ALLFLEX_new_aug",cache=False)
#inputs3, results_oct = ds.get_sim_results(path=r"C:\Users\Andrea\GitHub\Dispa-SET-2.3\Simulations\Article PROres1 coupling\TIMES_ProRes_2050_ALLFLEX_new_oct",cache=False)
#inputs4, results_nov = ds.get_sim_results(path=r"C:\Users\Andrea\GitHub\Dispa-SET-2.3\Simulations\Article PROres1 coupling\TIMES_ProRes_2050_ALLFLEX_new_nov",cache=False)
#
import pandas as pd
start_remove1 = pd.to_datetime('2016-8-28')
end_remove1 = pd.to_datetime('2016-9-5 23')
#start_remove2 = pd.to_datetime('2016-4-3')
#end_remove2 = pd.to_datetime('2016-4-11 23')
#start_remove3 = pd.to_datetime('2016-8-10')
#end_remove3 = pd.to_datetime('2016-8-18 23')
#start_remove4 = pd.to_datetime('2016-10-24')
#end_remove4 = pd.to_datetime('2016-11-1 23')
#start_remove5 = pd.to_datetime('2016-11-14')
#end_remove5 = pd.to_datetime('2016-11-22 23')
#%% Create merged results
# -*- coding: utf-8 -*-
"""
Minimalist example file showing how to read the results of a Dispa-SET run
@author: Sylvain Quoilin
"""
# Add the root folder of Dispa-SET to the path so that the library can be loaded:
import sys, os
sys.path.append(os.path.abspath('../../Dispa-SET'))
# Import Dispa-SET
import dispaset as ds
# Load the inputs and the results of the simulation
inputs, results = ds.get_sim_results(path='../Simulations/bolivia_base_2025',
cache=False)
# if needed, define the plotting range for the dispatch plot:
import pandas as pd
rng = pd.date_range(start='2025-01-01', end='2025-12-31', freq='h')
# Generate country-specific plots
ds.plot_zone(inputs, results, rng=rng)
# Bar plot with the installed capacities in all countries:
cap = ds.plot_zone_capacities(inputs)
# Bar plot with the energy balances in all countries:
ds.plot_energy_zone_fuel(inputs, results,
ds.get_indicators_powerplant(inputs, results))
# -*- coding: utf-8 -*-
"""
Minimalist example file showing how to read the results of a Dispa-SET run
@author: Sylvain Quoilin
"""
# Add the root folder of Dispa-SET to the path so that the library can be loaded:
import sys,os
sys.path.append(os.path.abspath('..'))
# Import Dispa-SET
import dispaset as ds
# Load the inputs and the results of the simulation
inputs,results = ds.get_sim_results(path='../Simulations/simulation_test',cache=False)
# if needed, define the plotting range for the dispatch plot:
import pandas as pd
rng = pd.date_range(start='2016-01-01',end='2016-12-31',freq='h')
# Generate country-specific plots
ds.plot_country(inputs,results,rng=rng)
# Bar plot with the installed capacities in all countries:
cap = ds.plot_country_capacities(inputs)
# Bar plot with the energy balances in all countries:
ds.plot_energy_country_fuel(inputs,results,ds.get_indicators_powerplant(inputs,results))
# Analyse the results for each country and provide quantitative indicators:
all_combinations = list(
product(heat_demand_scen, power_scen, cost_heat_slack_scen, res_mod_scen))
logging.info(str(len(all_combinations)) +
' runs will be made') # 36 for this case
i = 0
for iheat, ipower, icost_heat, iresmod in all_combinations:
i += 1
scenario_name = "run_1_" + str(i)
# Load run specific inputs
config['HeatDemand'] = iheat
config['PowerPlantData'] = ipower
config['default']['CostHeatSlack'] = icost_heat
config['modifiers']['Solar'] = config['modifiers']['wind'] = iresmod
# Build simulation
SimData = ds.build_simulation(config)
# Solve using GAMS:
r = ds.solve_GAMS(config['SimulationDirectory'], config['GAMS_folder'])
# Load inputs and results to memory
inputs, results = ds.get_sim_results(path=config['SimulationDirectory'],
cache=True)
# Save inputs and results to a file
filename = os.path.join(path_to_save, scenario_name + ".p")
with open(filename, "wb") as f:
pickle.dump((inputs, results), f, protocol=pickle.HIGHEST_PROTOCOL)
logging.info('Saved {} to pickle file'.format(scenario_name))
r = ds.solve_GAMS(folder, config['GAMS_folder'])
#%%
# Read all the simulation folders one by one and store key results in dataframe:
paths = os.listdir(sim_folder)
# Only take into account the ones for which a valid dispa-set result file is present
paths_ok = [
x for x in paths if os.path.isfile(sim_folder + x + '/Results.gdx')
]
N = len(paths_ok)
data = pd.DataFrame(index=range(N))
for i, path in enumerate(paths_ok):
inputs, results = ds.get_sim_results(path=sim_folder + path, cache=True)
FuelPower = ds.aggregate_by_fuel(results['OutputPower'],
inputs,
SpecifyFuels=None)
# get installed capacities by fuel:
cap = ds.plot_country_capacities(inputs, plot=False)
# Capacity factors:
CF = {}
for f in ['GAS', 'NUC', 'WAT', 'WIN', 'SUN']:
CF[f] = FuelPower[f].sum() / (cap['PowerCapacity'].loc['BE', f] * 8760)
r = ds.get_result_analysis(inputs, results)
# Compute total lost load
def get_OSE_MC(model,scenario,coverage):
if scenario == 'Baseline battery costs':
scenario_folder = '_Baseline battery costs'
elif scenario == '50percent battery costs':
scenario_folder = '_50percent battery costs'
elif scenario == '25percent battery costs':
scenario_folder = '_25percent battery costs'
if coverage == 'Full geographic coverage':
coverage_folder = '_Full_geographic_coverage'
elif coverage == 'Germany only':
coverage_folder = '_Germany_only'
path = input_folder + 'simulation_OSE_' + model + scenario_folder + coverage_folder
if os.path.isdir(path) == False:
print('Path: ' + path + ' does not exist')
else:
inputs,results = ds.get_sim_results(path=path,cache=False)
ppt = ds.get_indicators_powerplant(inputs,results)
ppt.reset_index(inplace=True)
## Get caoacities
def capacity_df(df):
df.loc[(df['Fuel'] == 'HRD') & (df['Technology'] == 'STUR'),'Variable'] = 'Capacity|Electricity|Hard coal'
df.loc[(df['Fuel'] == 'NUC') & (df['Technology'] == 'STUR'),'Variable'] = 'Capacity|Electricity|Nuclear'
df.loc[(df['Fuel'] == 'LIG') & (df['Technology'] == 'STUR'),'Variable'] = 'Capacity|Electricity|Lignite'
df.loc[(df['Fuel'] == 'OIL') & (df['Technology'] == 'STUR'),'Variable'] = 'Capacity|Electricity|Oil'
df.loc[(df['Fuel'] == 'BIO') & (df['Technology'] == 'STUR'),'Variable'] = 'Capacity|Electricity|Bioenergy'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'STUR'),'Variable'] = 'Capacity|Electricity|Other nonrenewable'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'GTUR'),'Variable'] = 'Capacity|Electricity|Gas OCGT'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'COMC'),'Variable'] = 'Capacity|Electricity|Gas CCGT'
df.loc[(df['Fuel'] == 'WST') & (df['Technology'] == 'STUR'),'Variable'] = 'Capacity|Electricity|Other renewable'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTON'),'Variable'] = 'Capacity|Electricity|Wind Onshore'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTOF'),'Variable'] = 'Capacity|Electricity|Wind Offshore'
df.loc[(df['Fuel'] == 'SUN') & (df['Technology'] == 'PHOT'),'Variable'] = 'Capacity|Electricity|Solar PV'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HROR'),'Variable'] = 'Capacity|Electricity|Hydro ROR'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HPHS'),'Variable'] = 'Capacity|Electricity|Storage|Pumped hydro|Power'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HDAM'),'Variable'] = 'Capacity|Electricity|Hydro Reservoir'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'BATS'),'Variable'] = 'Capacity|Electricity|Storage|Liion|Power'
df['Value'] = df['Nunits'] * df['PowerCapacity'] / 1e3
# Total capacity
df_total = df.groupby(['Variable'])['Value'].sum().reset_index()
df_total.reset_index(inplace=True)
df_total.rename(columns={'index': 'Zone'}, inplace=True)
df_total['Zone'] = 'Whole Region'
df = df.append(df_total, ignore_index=True, sort=True)
df['Unit'] = 'GW'
df = df[['Zone','Variable','Unit','Value']]
return df
df_capacity = capacity_df(ppt)
## Get generation, shedding,emmisions and curtailment
def generation_df(df,results,inputs):
df.set_index('index', inplace=True)
df['CO2'] = df['Generation'] / inputs['units']['Efficiency'] * inputs['units']['EmissionRate']
co2_zone = df.groupby(['Zone'])['CO2'].sum().reset_index()
co2_zone.reset_index(inplace=True)
co2_zone['Variable'] = 'Carbon emissions'
co2_zone['Unit'] = 't_CO2'
co2_zone.rename(columns={'CO2': 'Value'}, inplace=True)
# df.groupby(['Fruit', 'Name'])['Number'].sum().reset_index()
# CO2 Emissions
co2 = {'Value': [df['CO2'].sum()]}
co2 = pd.DataFrame(co2)
co2.reset_index(inplace=True)
co2['Variable'] = 'Carbon emissions'
co2['Unit'] = 't_CO2'
co2['Zone'] = 'Whole Region'
df.loc[(df['Fuel'] == 'HRD') & (df['Technology'] == 'STUR'),'Variable'] = 'Energy|Electricity|Hard coal'
df.loc[(df['Fuel'] == 'NUC') & (df['Technology'] == 'STUR'),'Variable'] = 'Energy|Electricity|Nuclear'
df.loc[(df['Fuel'] == 'LIG') & (df['Technology'] == 'STUR'),'Variable'] = 'Energy|Electricity|Lignite'
df.loc[(df['Fuel'] == 'OIL') & (df['Technology'] == 'STUR'),'Variable'] = 'Energy|Electricity|Oil'
df.loc[(df['Fuel'] == 'BIO') & (df['Technology'] == 'STUR'),'Variable'] = 'Energy|Electricity|Bioenergy'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'STUR'),'Variable'] = 'Energy|Electricity|Other nonrenewable'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'GTUR'),'Variable'] = 'Energy|Electricity|Gas OCGT'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'COMC'),'Variable'] = 'Energy|Electricity|Gas CCGT'
df.loc[(df['Fuel'] == 'WST') & (df['Technology'] == 'STUR'),'Variable'] = 'Energy|Electricity|Other renewable'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTON'),'Variable'] = 'Energy|Electricity|Wind Onshore'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTOF'),'Variable'] = 'Energy|Electricity|Wind Offshore'
df.loc[(df['Fuel'] == 'SUN') & (df['Technology'] == 'PHOT'),'Variable'] = 'Energy|Electricity|Solar PV'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HROR'),'Variable'] = 'Energy|Electricity|Hydro ROR'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HPHS'),'Variable'] = 'Energy|Electricity|Storage|Pumped hydro|Power'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HDAM'),'Variable'] = 'Energy|Electricity|Hydro Reservoir'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'BATS'),'Variable'] = 'Energy|Electricity|Storage|Liion|Power'
df['Value'] = df['Generation'] / 1e6
# Curtailment
tmp_df = pd.DataFrame(results['OutputCurtailedPower'].sum() / 1e6)
tmp_df.reset_index(inplace=True)
tmp_df.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
tmp_df['Variable'] = 'Energy|Electricity|Renewable curtailment|Absolute'
df = df.append(tmp_df, ignore_index=True, sort = True)
# Total generation
df_total = df.groupby(['Variable'])['Value'].sum().reset_index()
df_total.reset_index(inplace=True)
df_total.rename(columns={'index': 'Zone'}, inplace=True)
df_total['Zone'] = 'Whole Region'
df = df.append(df_total, ignore_index=True, sort = True)
# Shedding
tmp_df = pd.DataFrame(results['OutputShedLoad'].sum() / 1e6)
tmp_df.reset_index(inplace=True)
tmp_df.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
tmp_df['Variable'] = 'Energy|Electricity|Load Shedding'
df = df.append(tmp_df, ignore_index=True, sort = True)
df['Unit'] = 'TWh'
df = df[['Zone','Variable','Unit','Value']]
# Reserves
# LL_2D
lost_load_2D = pd.DataFrame(results['LostLoad_2D'].sum() / 1e6)
lost_load_2D.reset_index(inplace=True)
lost_load_2D.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load_2D['Variable'] = 'Energy|Electricity|Lost Load 2D'
lost_load_2D['Unit'] = 'TWh'
df = df.append(lost_load_2D, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# LL_2U
lost_load_2U = pd.DataFrame(results['LostLoad_2U'].sum() / 1e6)
lost_load_2U.reset_index(inplace=True)
lost_load_2U.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load_2U['Variable'] = 'Energy|Electricity|Lost Load 2U'
lost_load_2U['Unit'] = 'TWh'
df = df.append(lost_load_2U, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# LL_3U
lost_load_3U = pd.DataFrame(results['LostLoad_3U'].sum() / 1e6)
lost_load_3U.reset_index(inplace=True)
lost_load_3U.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load_3U['Variable'] = 'Energy|Electricity|Lost Load 3U'
lost_load_3U['Unit'] = 'TWh'
df = df.append(lost_load_3U, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# LL_Max_Power
lost_load_max_power = pd.DataFrame(results['LostLoad_MaxPower'].sum() / 1e6)
lost_load_max_power.reset_index(inplace=True)
lost_load_max_power.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load_max_power['Variable'] = 'Energy|Electricity|Lost Load Max Power'
lost_load_max_power['Unit'] = 'TWh'
df = df.append(lost_load_max_power, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# LL_Min_Power
lost_load_min_power = pd.DataFrame(results['LostLoad_MinPower'].sum() / 1e6)
lost_load_min_power.reset_index(inplace=True)
lost_load_min_power.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load_min_power['Variable'] = 'Energy|Electricity|Lost Load Min Power'
lost_load_min_power['Unit'] = 'TWh'
df = df.append(lost_load_min_power, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# LL_Ramp_Down
lost_load_ramp_down = pd.DataFrame(results['LostLoad_RampDown'].sum() / 1e6)
lost_load_ramp_down.reset_index(inplace=True)
lost_load_ramp_down.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load_ramp_down['Variable'] = 'Energy|Electricity|Lost Load Ramp Down'
lost_load_ramp_down['Unit'] = 'TWh'
df = df.append(lost_load_ramp_down, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# LL_Ramp_Up
lost_load_ramp_up = pd.DataFrame(results['LostLoad_RampUp'].sum() / 1e6)
lost_load_ramp_up.reset_index(inplace=True)
lost_load_ramp_up.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load_ramp_up['Variable'] = 'Energy|Electricity|Lost Load Ramp Up'
lost_load_ramp_up['Unit'] = 'TWh'
df = df.append(lost_load_ramp_up, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# LL_Total
lost_load = pd.DataFrame([results['LostLoad_2D'].sum(),results['LostLoad_2U'].sum(),
results['LostLoad_3U'].sum(),results['LostLoad_MaxPower'].sum(),
results['LostLoad_MinPower'].sum(), results['LostLoad_RampDown'].sum(),
results['LostLoad_RampUp'].sum()])
lost_load = lost_load / 1e6
lost_load = lost_load.sum(skipna=True)
lost_load.reset_index(inplace=True)
lost_load.rename(columns={'index':'Zone', 0:'Value'}, inplace=True)
lost_load['Variable'] = 'Energy|Electricity|Lost Load Total'
lost_load['Unit'] = 'TWh'
df = df.append(lost_load, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
# CO2
df = df.append(co2_zone, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
df = df.append(co2, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
return df
df_generation = generation_df(ppt,results,inputs)
tmp_df = df_capacity.append(df_generation, ignore_index=True)
# Get startups & costs
def startups_df(df,results):
df.loc[(df['Fuel'] == 'HRD') & (df['Technology'] == 'STUR'),'Variable'] = 'Startups|Electricity|Hard coal'
df.loc[(df['Fuel'] == 'NUC') & (df['Technology'] == 'STUR'),'Variable'] = 'Startups|Electricity|Nuclear'
df.loc[(df['Fuel'] == 'LIG') & (df['Technology'] == 'STUR'),'Variable'] = 'Startups|Electricity|Lignite'
df.loc[(df['Fuel'] == 'OIL') & (df['Technology'] == 'STUR'),'Variable'] = 'Startups|Electricity|Oil'
df.loc[(df['Fuel'] == 'BIO') & (df['Technology'] == 'STUR'),'Variable'] = 'Startups|Electricity|Bioenergy'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'STUR'),'Variable'] = 'Startups|Electricity|Other nonrenewable'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'GTUR'),'Variable'] = 'Startups|Electricity|Gas OCGT'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'COMC'),'Variable'] = 'Startups|Electricity|Gas CCGT'
df.loc[(df['Fuel'] == 'WST') & (df['Technology'] == 'STUR'),'Variable'] = 'Startups|Electricity|Other renewable'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTON'),'Variable'] = 'Startups|Electricity|Wind Onshore'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTOF'),'Variable'] = 'Startups|Electricity|Wind Offshore'
df.loc[(df['Fuel'] == 'SUN') & (df['Technology'] == 'PHOT'),'Variable'] = 'Startups|Electricity|Solar PV'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HROR'),'Variable'] = 'Startups|Electricity|Hydro ROR'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HPHS'),'Variable'] = 'Startups|Electricity|Storage|Pumped hydro|Power'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HDAM'),'Variable'] = 'Startups|Electricity|Hydro Reservoir'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'BATS'),'Variable'] = 'Startups|Electricity|Storage|Liion|Power'
df['Value'] = df['startups']
df['Unit'] = '##'
tmp_df = results['OutputSystemCost'].sum()
tmp_df = {'Value': [results['OutputSystemCost'].sum()]}
tmp_df = pd.DataFrame(tmp_df)
tmp_df.reset_index(inplace=True)
tmp_df['Variable'] = 'Cost|Total system'
tmp_df['Zone'] = 'Whole Region'
tmp_df['Unit'] = 'EUR'
df = df.append(tmp_df, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
return df
df_startups = startups_df(ppt,results)
tmp_df = tmp_df.append(df_startups, ignore_index=True)
# Get capacity factors and storage cycles
def cap_factor_df(df,results):
df.loc[(df['Fuel'] == 'HRD') & (df['Technology'] == 'STUR'),'Variable'] = 'Full load hours|Electricity|Hard coal'
df.loc[(df['Fuel'] == 'NUC') & (df['Technology'] == 'STUR'),'Variable'] = 'Full load hours|Electricity|Nuclear'
df.loc[(df['Fuel'] == 'LIG') & (df['Technology'] == 'STUR'),'Variable'] = 'Full load hours|Electricity|Lignite'
df.loc[(df['Fuel'] == 'OIL') & (df['Technology'] == 'STUR'),'Variable'] = 'Full load hours|Electricity|Oil'
df.loc[(df['Fuel'] == 'BIO') & (df['Technology'] == 'STUR'),'Variable'] = 'Full load hours|Electricity|Bioenergy'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'STUR'),'Variable'] = 'Full load hours|Electricity|Other nonrenewable'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'GTUR'),'Variable'] = 'Full load hours|Electricity|Gas OCGT'
df.loc[(df['Fuel'] == 'GAS') & (df['Technology'] == 'COMC'),'Variable'] = 'Full load hours|Electricity|Gas CCGT'
df.loc[(df['Fuel'] == 'WST') & (df['Technology'] == 'STUR'),'Variable'] = 'Full load hours|Electricity|Other renewable'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTON'),'Variable'] = 'Full load hours|Electricity|Wind Onshore'
df.loc[(df['Fuel'] == 'WIN') & (df['Technology'] == 'WTOF'),'Variable'] = 'Full load hours|Electricity|Wind Offshore'
df.loc[(df['Fuel'] == 'SUN') & (df['Technology'] == 'PHOT'),'Variable'] = 'Full load hours|Electricity|Solar PV'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HROR'),'Variable'] = 'Full load hours|Electricity|Hydro ROR'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HPHS'),'Variable'] = 'Full load hours|Electricity|Storage|Pumped hydro|Power'
df.loc[(df['Fuel'] == 'WAT') & (df['Technology'] == 'HDAM'),'Variable'] = 'Full load hours|Electricity|Hydro Reservoir'
df.loc[(df['Fuel'] == 'OTH') & (df['Technology'] == 'BATS'),'Variable'] = 'Full load hours|Electricity|Storage|Liion|Power'
df['Value'] = df['Generation']/df['Nunits']/df['PowerCapacity']
df['Unit'] = 'hours'
tmp_stor_input = pd.DataFrame(results['OutputStorageInput'].sum())
isstorage = pd.Series(index=inputs['units'].index)
for u in isstorage.index:
isstorage[u] = inputs['units'].Technology[u] in commons['tech_storage']
sto_units = inputs['units'][isstorage]
sto_units['Value'] = pd.DataFrame(tmp_stor_input[0]/sto_units['StorageCapacity'])
sto_units.reset_index(inplace=True)
sto_units.loc[(sto_units['Fuel'] == 'OTH') & (sto_units['Technology'] == 'BATS'),'Variable'] = \
'Cycles|Electricity|Storage|Liion'
sto_units.loc[(sto_units['Fuel'] == 'WAT') & (sto_units['Technology'] == 'HPHS'),'Variable'] = \
'Cycles|Electricity|Storage|Pumped hydro'
sto_units = sto_units.drop(sto_units.loc[(sto_units['Fuel'] == 'WAT') & (sto_units['Technology'] == 'HDAM')].index)
sto_units['Unit'] = '##'
df = df.append(sto_units, ignore_index=True, sort = True)
sto_capacity = inputs['units']
sto_capacity.rename(columns={'StorageCapacity':'Value'}, inplace=True)
sto_capacity.loc[(sto_capacity['Fuel'] == 'OTH') & (sto_capacity['Technology'] == 'BATS'),'Variable'] = \
'Capacity|Electricity|Storage|Liion|Energy'
sto_capacity.loc[(sto_capacity['Fuel'] == 'WAT') & (sto_capacity['Technology'] == 'HPHS'),'Variable'] = \
'Capacity|Electricity|Storage|Pumped hydro|Energy'
sto_capacity['Value'] = sto_capacity['Value'] / 1e3
sto_capacity['Unit'] = 'GWh'
df1 = sto_capacity.loc[(sto_capacity['Fuel'] == 'OTH') & (sto_capacity['Technology'] == 'BATS')]
df2 = sto_capacity.loc[(sto_capacity['Fuel'] == 'WAT') & (sto_capacity['Technology'] == 'HPHS')]
sto_capacity = df1.append(df2, ignore_index=True, sort = True)
df = df.append(sto_capacity, ignore_index=True, sort = True)
# Reserves
lost_load_2D = {'Value': [results['LostLoad_2D'].sum().sum() / 1e6]}
lost_load_2D = pd.DataFrame(lost_load_2D)
lost_load_2D.reset_index(inplace=True)
lost_load_2D['Variable'] = 'Energy|Electricity|Lost Load 2D'
lost_load_2D['Unit'] = 'TWh'
lost_load_2D['Zone'] = 'Whole Region'
df = df.append(lost_load_2D, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
lost_load_2U = {'Value': [results['LostLoad_2U'].sum().sum() / 1e6]}
lost_load_2U = pd.DataFrame(lost_load_2U)
lost_load_2U.reset_index(inplace=True)
lost_load_2U['Variable'] = 'Energy|Electricity|Lost Load 2U'
lost_load_2U['Unit'] = 'TWh'
lost_load_2U['Zone'] = 'Whole Region'
df = df.append(lost_load_2U, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
lost_load_3U = {'Value': [results['LostLoad_3U'].sum().sum() / 1e6]}
lost_load_3U = pd.DataFrame(lost_load_3U)
lost_load_3U.reset_index(inplace=True)
lost_load_3U['Variable'] = 'Energy|Electricity|Lost Load 3U'
lost_load_3U['Unit'] = 'TWh'
lost_load_3U['Zone'] = 'Whole Region'
df = df.append(lost_load_3U, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
lost_load_max_power = {'Value': [results['LostLoad_MaxPower'].sum().sum() / 1e6]}
lost_load_max_power = pd.DataFrame(lost_load_max_power)
lost_load_max_power.reset_index(inplace=True)
lost_load_max_power['Variable'] = 'Energy|Electricity|Lost Load Max Power'
lost_load_max_power['Unit'] = 'TWh'
lost_load_max_power['Zone'] = 'Whole Region'
df = df.append(lost_load_max_power, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
lost_load_min_power = {'Value': [results['LostLoad_MinPower'].sum().sum() / 1e6]}
lost_load_min_power = pd.DataFrame(lost_load_min_power)
lost_load_min_power.reset_index(inplace=True)
lost_load_min_power['Variable'] = 'Energy|Electricity|Lost Load Min Power'
lost_load_min_power['Unit'] = 'TWh'
lost_load_min_power['Zone'] = 'Whole Region'
df = df.append(lost_load_min_power, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
lost_load_ramp_down = {'Value': [results['LostLoad_RampDown'].sum().sum() / 1e6]}
lost_load_ramp_down = pd.DataFrame(lost_load_ramp_down)
lost_load_ramp_down.reset_index(inplace=True)
lost_load_ramp_down['Variable'] = 'Energy|Electricity|Lost Load Ramp Down'
lost_load_ramp_down['Unit'] = 'TWh'
lost_load_ramp_down['Zone'] = 'Whole Region'
df = df.append(lost_load_ramp_down, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
lost_load_ramp_up = {'Value': [results['LostLoad_RampUp'].sum().sum() / 1e6]}
lost_load_ramp_up = pd.DataFrame(lost_load_ramp_up)
lost_load_ramp_up.reset_index(inplace=True)
lost_load_ramp_up['Variable'] = 'Energy|Electricity|Lost Load Ramp Up'
lost_load_ramp_up['Unit'] = 'TWh'
lost_load_ramp_up['Zone'] = 'Whole Region'
df = df.append(lost_load_ramp_up, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
lost_load = {'Value': [(results['LostLoad_2D'].sum().sum() + results['LostLoad_2U'].sum().sum() +
results['LostLoad_3U'].sum().sum() + results['LostLoad_MaxPower'].sum().sum() +
results['LostLoad_MinPower'].sum().sum() + results['LostLoad_RampDown'].sum().sum() +
results['LostLoad_RampUp'].sum().sum()) / 1e6]}
lost_load = pd.DataFrame(lost_load)
lost_load.reset_index(inplace=True)
lost_load['Variable'] = 'Energy|Electricity|Lost Load Total'
lost_load['Unit'] = 'TWh'
lost_load['Zone'] = 'Whole Region'
df = df.append(lost_load, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
shed_load = {'Value': [results['OutputShedLoad'].sum().sum() / 1e6]}
shed_load = pd.DataFrame(shed_load)
shed_load.reset_index(inplace=True)
shed_load['Variable'] = 'Energy|Electricity|Load Shedding'
shed_load['Unit'] = 'TWh'
shed_load['Zone'] = 'Whole Region'
df = df.append(shed_load, ignore_index=True, sort = True)
df = df[['Zone','Variable','Unit','Value']]
return df
df_cap_factor = cap_factor_df(ppt,results)
OSE_report = tmp_df.append(df_cap_factor, ignore_index=True)
# Get additional info for each scenario
OSE_report['Model'] = 'Dispa-SET (' + model + ')'
OSE_report['Scenario'] = scenario + '|' + coverage
OSE_report.rename(columns={'Zone':'Region'}, inplace=True)
OSE_report['Unnamed: 6'] = np.nan
OSE_report['1'] = scenario
OSE_report['2'] = coverage
OSE_report['3'] = np.nan
OSE_report = OSE_report[['Model','Scenario','Region','Variable','Unit','Value','Unnamed: 6','1','2','3']]
return OSE_report