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scenario_random.py
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scenario_random.py
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__author__ = "Stian Backe"
__license__ = "MIT"
__maintainer__ = "Stian Backe"
__email__ = "stian.backe@ntnu.no"
import pandas as pd
import numpy as np
import os
def season_month(season):
if season=="winter":
return [12, 1, 2]
elif season=="spring":
return [3, 4, 5]
elif season=="summer":
return [6, 7, 8]
elif season=="fall":
return [9, 10, 11]
def year_month_filter(data, sample_year, sample_month):
data = data.loc[data.year.isin([sample_year]), :]
data = data.loc[data.month.isin([sample_month]), :]
return data
def remove_time_index(data):
data = data.reset_index(drop=True)
data = data.drop(['time', 'year', 'month', 'dayofweek', 'hour'], axis=1)
return data
def make_datetime(data, time_format):
data["time"] = pd.to_datetime(data["time"],
format=time_format,
exact=False)
data['year'] = data['time'].dt.year
data['month'] = data['time'].dt.month
data['hour'] = data['time'].dt.hour
data['dayofweek'] = data['time'].dt.dayofweek
return data
def gather_regular_sample(data, season, seasons, regularSeasonHours,
sample_hour):
data = data.reset_index(drop=True)
sample_data = data.iloc[sample_hour:sample_hour + regularSeasonHours,:]
# Sort sample_data to start on midnight monday (INACTIVE)
# sample_data = sample_data.sort_values(by=['dayofweek','hour'])
# Drop non-country columns
sample_data = remove_time_index(sample_data)
hours = list(range(1 + regularSeasonHours * seasons.index(season),
regularSeasonHours * (seasons.index(season) + 1) + 1))
return [sample_data, hours]
def sample_generator(data, regularSeasonHours, scenario, season, seasons,
period, generator, sample_hour):
[sample_data, hours] = gather_regular_sample(data, season, seasons,
regularSeasonHours,
sample_hour)
generator_data = pd.DataFrame()
if generator=='Windoffshore':
startNOnode = 2
else:
startNOnode = 1
for c in sample_data.columns:
if c == "NO":
for i in range(startNOnode, 6):
c_no = c + str(i)
df = pd.DataFrame(
data={'Node': c_no, "IntermitentGenerators": generator,
"Operationalhour": hours,
"Scenario": "scenario" + str(scenario),
"Period": period,
"GeneratorStochasticAvailabilityRaw":
sample_data[c].values})
generator_data = generator_data.append(df, ignore_index=True)
else:
df = pd.DataFrame(
data={'Node': c, "IntermitentGenerators": generator,
"Operationalhour": hours,
"Scenario": "scenario" + str(scenario),
"Period": period,
"GeneratorStochasticAvailabilityRaw":
sample_data[c].values})
generator_data = generator_data.append(df, ignore_index=True)
return generator_data
def sample_hydro(data, regularSeasonHours, scenario, season,
seasons, period, sample_hour):
[sample_data, hours] = gather_regular_sample(data, season, seasons,
regularSeasonHours,
sample_hour)
hydro_data = pd.DataFrame()
for c in sample_data.columns:
if c != 'time':
df = pd.DataFrame(
data={'Node': c, "Period": period, "Season": season,
"Operationalhour": hours,
"Scenario": "scenario" + str(scenario),
"HydroGeneratorMaxSeasonalProduction":
sample_data[c].values})
hydro_data = hydro_data.append(df, ignore_index=True)
return hydro_data
def sample_load(data, regularSeasonHours, scenario, season, seasons,
period, sample_hour):
[sample_data, hours] = gather_regular_sample(data, season, seasons,
regularSeasonHours,
sample_hour)
load = pd.DataFrame()
for c in sample_data.columns:
if c != 'time':
df = pd.DataFrame(
data={'Node': c, "Period": period, "Operationalhour": hours,
"Scenario": "scenario" + str(scenario),
"ElectricLoadRaw_in_MW": sample_data[c].values})
load = load.append(df, ignore_index=True)
return load
def gather_peak_sample(data, seasons, regularSeasonHours, peakSeasonHours,
country_sample, overall_sample):
data = data.reset_index(drop=True)
country_peak = data.iloc[
int(country_sample - (peakSeasonHours/2)):int(
country_sample + (peakSeasonHours/2)),
:]
overall_peak = data.iloc[
int(overall_sample - (peakSeasonHours/2)):int(
overall_sample + (peakSeasonHours/2)),
:]
# Sort data to start on midnight (INACTIVE)
# country_peak = country_peak.sort_values(by=['hour'])
# overall_peak = overall_peak.sort_values(by=['hour'])
# Drop non-country columns
country_peak = remove_time_index(country_peak)
overall_peak = remove_time_index(overall_peak)
country_hours = list(
range(1 + regularSeasonHours * len(seasons),
regularSeasonHours * len(seasons) + peakSeasonHours + 1)
)
overall_hours = list(
range(1 + regularSeasonHours * len(seasons) + peakSeasonHours,
regularSeasonHours * len(seasons) + 2 * peakSeasonHours + 1)
)
return [country_peak, overall_peak, country_hours, overall_hours]
def sample_hydro_peak(data, seasons, scenario, period, regularSeasonHours,
peakSeasonHours, overall_sample, country_sample):
peak_data = pd.DataFrame()
[country_peak, overall_peak,
country_hours, overall_hours] = gather_peak_sample(data, seasons,
regularSeasonHours,
peakSeasonHours,
country_sample,
overall_sample)
for c in country_peak.columns:
df = pd.DataFrame(
data={'Node': c, "Period": period, "Season": "peak1",
"Operationalhour": country_hours,
"Scenario": "scenario" + str(scenario),
"HydroGeneratorMaxSeasonalProduction":
country_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
df = pd.DataFrame(
data={'Node': c, "Period": period, "Season": "peak2",
"Operationalhour": overall_hours,
"Scenario": "scenario" + str(scenario),
"HydroGeneratorMaxSeasonalProduction":
overall_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
return peak_data
def sample_load_peak(data, seasons, scenario, period, regularSeasonHours,
peakSeasonHours, overall_sample, country_sample):
peak_data = pd.DataFrame()
[country_peak, overall_peak,
country_hours, overall_hours] = gather_peak_sample(data, seasons,
regularSeasonHours,
peakSeasonHours,
country_sample,
overall_sample)
for c in country_peak.columns:
df = pd.DataFrame(
data={'Node': c, "Period": period,
"Operationalhour": country_hours,
"Scenario": "scenario" + str(scenario),
"ElectricLoadRaw_in_MW": country_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
df = pd.DataFrame(
data={'Node': c, "Period": period,
"Operationalhour": overall_hours,
"Scenario": "scenario" + str(scenario),
"ElectricLoadRaw_in_MW": overall_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
return peak_data
def sample_generator_peak(data, seasons, g, scenario,
period, regularSeasonHours, peakSeasonHours,
overall_sample, country_sample):
peak_data = pd.DataFrame()
[country_peak, overall_peak,
country_hours, overall_hours] = gather_peak_sample(data, seasons,
regularSeasonHours,
peakSeasonHours,
country_sample,
overall_sample)
if g=='Windoffshore':
startNOnode = 2
else:
startNOnode = 1
for c in country_peak.columns:
if c == "NO":
for i in range(startNOnode, 6):
c_no = c + str(i)
df = pd.DataFrame(
data={'Node': c_no, "IntermitentGenerators": g,
"Operationalhour": country_hours,
"Scenario": "scenario" + str(scenario),
"Period": period,
"GeneratorStochasticAvailabilityRaw":
country_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
df = pd.DataFrame(
data={'Node': c_no, "IntermitentGenerators": g,
"Operationalhour": overall_hours,
"Scenario": "scenario" + str(scenario),
"Period": period,
"GeneratorStochasticAvailabilityRaw":
overall_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
else:
df = pd.DataFrame(
data={'Node': c, "IntermitentGenerators": g,
"Operationalhour": country_hours,
"Scenario": "scenario" + str(scenario),
"Period": period,
"GeneratorStochasticAvailabilityRaw":
country_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
df = pd.DataFrame(
data={'Node': c, "IntermitentGenerators": g,
"Operationalhour": overall_hours,
"Scenario": "scenario" + str(scenario),
"Period": period,
"GeneratorStochasticAvailabilityRaw":
overall_peak[c].values})
peak_data = peak_data.append(df, ignore_index=True)
return peak_data
def generate_random_scenario(filepath, tab_file_path, scenarios, seasons,
Periods, regularSeasonHours, peakSeasonHours,
dict_countries, time_format, fix_sample):
if fix_sample:
print("Generating scenarios according to key...")
else:
print("Generating random scenarios...")
# Generate dataframes to print as stochastic-files
genAvail = pd.DataFrame()
elecLoad = pd.DataFrame()
hydroSeasonal = pd.DataFrame()
# Load all the raw scenario data
solar_data = pd.read_csv(filepath + "/solar.csv")
windonshore_data = pd.read_csv(filepath + "/windonshore.csv")
windoffshore_data = pd.read_csv(filepath + "/windoffshore.csv")
hydroror_data = pd.read_csv(filepath + "/hydroror.csv")
hydroseasonal_data = pd.read_csv(filepath + "/hydroseasonal.csv")
electricload_data = pd.read_csv(filepath + "/electricload.csv")
# Make datetime columns
solar_data = make_datetime(solar_data, time_format)
windonshore_data = make_datetime(windonshore_data, time_format)
windoffshore_data = make_datetime(windoffshore_data, time_format)
hydroror_data = make_datetime(hydroror_data, time_format)
hydroseasonal_data = make_datetime(hydroseasonal_data, time_format)
electricload_data = make_datetime(electricload_data, time_format)
if fix_sample:
sampling_key = pd.read_csv(filepath + "/sampling_key.csv")
sampling_key = sampling_key.set_index(['Period','Scenario','Season'])
else:
sampling_key = pd.DataFrame(columns=['Period','Scenario','Season','Year','Month','Hour'])
for i in range(1,Periods+1):
for scenario in range(1,scenarios+1):
for s in seasons:
###################
##REGULAR SEASONS##
###################
# Get sample year (2015-2019) and month for each season/scenario
sample_year = np.random.choice(list(range(2015,2020)))
sample_month = np.random.choice(season_month(s))
# Set sample year and month according to key
if fix_sample:
sample_year = sampling_key.loc[(i,scenario,s),'Year']
sample_month = sampling_key.loc[(i,scenario,s),'Month']
# Filter out the hours within the sample year
solar_month = year_month_filter(solar_data,
sample_year,
sample_month)
windonshore_month = year_month_filter(windonshore_data,
sample_year,
sample_month)
windoffshore_month = year_month_filter(windoffshore_data,
sample_year,
sample_month)
hydroror_month = year_month_filter(hydroror_data,
sample_year,
sample_month)
hydroseasonal_month = year_month_filter(hydroseasonal_data,
sample_year,
sample_month)
electricload_month = year_month_filter(electricload_data,
sample_year,
sample_month)
sample_hour = np.random.randint(
0, solar_month.shape[0] - regularSeasonHours - 1)
# Choose sample_hour from key or save sampling key
if fix_sample:
sample_hour = sampling_key.loc[(i,scenario,s),'Hour']
else:
sampling_key = sampling_key.append({'Period': i,
'Scenario': scenario,
'Season': s,
'Year': sample_year,
'Month': sample_month,
'Hour': sample_hour},
ignore_index=True)
# Sample generator availability for regular seasons
genAvail = genAvail.append(
sample_generator(data=solar_month,
regularSeasonHours=regularSeasonHours,
scenario=scenario, season=s,
seasons=seasons, period=i,
generator="Solar",
sample_hour=sample_hour))
genAvail = genAvail.append(
sample_generator(data=windonshore_month,
regularSeasonHours=regularSeasonHours,
scenario=scenario, season=s,
seasons=seasons, period=i,
generator="Windonshore",
sample_hour=sample_hour))
genAvail = genAvail.append(
sample_generator(data=windoffshore_month,
regularSeasonHours=regularSeasonHours,
scenario=scenario, season=s,
seasons=seasons, period=i,
generator="Windoffshore",
sample_hour=sample_hour))
genAvail = genAvail.append(
sample_generator(data=hydroror_month,
regularSeasonHours=regularSeasonHours,
scenario=scenario, season=s,
seasons=seasons, period=i,
generator="Hydrorun-of-the-river",
sample_hour=sample_hour))
# Sample electric load for regular seasons
elecLoad = elecLoad.append(
sample_load(data=electricload_month,
regularSeasonHours=regularSeasonHours,
scenario=scenario, season=s,
seasons=seasons, period=i,
sample_hour=sample_hour))
# Sample seasonal hydro limit for regular seasons
hydroSeasonal = hydroSeasonal.append(
sample_hydro(data=hydroseasonal_month,
regularSeasonHours=regularSeasonHours,
scenario=scenario, season=s,
seasons=seasons, period=i,
sample_hour=sample_hour))
################
##PEAK SEASONS##
################
# Get peak sample year (2015-2019)
sample_year = np.random.choice(list(range(2015,2020)))
if fix_sample:
sample_year = sampling_key.loc[(i,scenario,'peak'),'Year']
else:
sampling_key = sampling_key.append({'Period': i,
'Scenario': scenario,
'Season': 'peak',
'Year': sample_year,
'Month': 0,
'Hour': 0},
ignore_index=True)
# Filter out the hours within the sample year
solar_data_year = solar_data.loc[solar_data.year.isin([sample_year]), :]
windonshore_data_year = windonshore_data.loc[windonshore_data.year.isin([sample_year]), :]
windoffshore_data_year = windoffshore_data.loc[windoffshore_data.year.isin([sample_year]), :]
hydroror_data_year = hydroror_data.loc[hydroror_data.year.isin([sample_year]), :]
hydroseasonal_data_year = hydroseasonal_data.loc[hydroseasonal_data.year.isin([sample_year]), :]
electricload_data_year = electricload_data.loc[electricload_data.year.isin([sample_year]), :]
#Peak1: The highest load when all loads are summed together
electricload_data_year_notime = remove_time_index(electricload_data_year)
overall_sample = electricload_data_year_notime.sum(axis=1).idxmax()
#Peak2: The highest load of a single country
max_load_country = electricload_data_year_notime.max().idxmax()
country_sample = electricload_data_year_notime[max_load_country].idxmax()
#Sample generator availability for peak seasons
genAvail = genAvail.append(
sample_generator_peak(data=solar_data_year,
seasons=seasons,
g="Solar", scenario=scenario, period=i,
regularSeasonHours=regularSeasonHours,
peakSeasonHours=peakSeasonHours,
overall_sample=overall_sample,
country_sample=country_sample))
genAvail = genAvail.append(
sample_generator_peak(data=windonshore_data_year,
seasons=seasons,
g="Windonshore", scenario=scenario,
period=i,
regularSeasonHours=regularSeasonHours,
peakSeasonHours=peakSeasonHours,
overall_sample=overall_sample,
country_sample=country_sample))
genAvail = genAvail.append(
sample_generator_peak(data=windoffshore_data_year,
seasons=seasons,
g="Windoffshore", scenario=scenario,
period=i,
regularSeasonHours=regularSeasonHours,
peakSeasonHours=peakSeasonHours,
overall_sample=overall_sample,
country_sample=country_sample))
genAvail = genAvail.append(
sample_generator_peak(data=hydroror_data_year,
seasons=seasons,
g="Hydrorun-of-the-river",
scenario=scenario, period=i,
regularSeasonHours=regularSeasonHours,
peakSeasonHours=peakSeasonHours,
overall_sample=overall_sample,
country_sample=country_sample))
#Sample electric load for peak seasons
elecLoad = elecLoad.append(
sample_load_peak(data=electricload_data_year,
seasons=seasons,
scenario=scenario, period=i,
regularSeasonHours=regularSeasonHours,
peakSeasonHours=peakSeasonHours,
overall_sample=overall_sample,
country_sample=country_sample))
#Sample seasonal hydro limit for peak seasons
hydroSeasonal = hydroSeasonal.append(
sample_hydro_peak(data=hydroseasonal_data_year,
seasons=seasons,
scenario=scenario, period=i,
regularSeasonHours=regularSeasonHours,
peakSeasonHours=peakSeasonHours,
overall_sample=overall_sample,
country_sample=country_sample))
#Replace country codes with country names
genAvail = genAvail.replace({"Node": dict_countries})
elecLoad = elecLoad.replace({"Node": dict_countries})
hydroSeasonal = hydroSeasonal.replace({"Node": dict_countries})
#Make header for .tab-file
genAvail = genAvail[["Node", "IntermitentGenerators", "Operationalhour",
"Scenario", "Period",
"GeneratorStochasticAvailabilityRaw"]]
elecLoad = elecLoad[["Node", "Operationalhour", "Scenario","Period",
'ElectricLoadRaw_in_MW']]
hydroSeasonal = hydroSeasonal[["Node", "Period", "Season",
"Operationalhour", "Scenario",
"HydroGeneratorMaxSeasonalProduction"]]
genAvail.loc[genAvail["GeneratorStochasticAvailabilityRaw"] <= 0.001,"GeneratorStochasticAvailabilityRaw"] = 0
elecLoad.loc[elecLoad['ElectricLoadRaw_in_MW'] <= 0.001,'ElectricLoadRaw_in_MW'] = 0
hydroSeasonal.loc[hydroSeasonal["HydroGeneratorMaxSeasonalProduction"] <= 0.001,"HydroGeneratorMaxSeasonalProduction"] = 0
#Make filepath (if it does not exist) and print .tab-files
if not os.path.exists(tab_file_path):
os.makedirs(tab_file_path)
# Save sampling key
if fix_sample:
sampling_key = sampling_key.reset_index(level=['Period','Scenario','Season'])
sampling_key.to_csv(
tab_file_path + "/sampling_key" + '.csv',
header=True, index=None, mode='w')
genAvail.to_csv(
tab_file_path + "/Stochastic_StochasticAvailability" + '.tab',
header=True, index=None, sep='\t', mode='w')
elecLoad.to_csv(
tab_file_path + "/Stochastic_ElectricLoadRaw" + '.tab',
header=True, index=None, sep='\t', mode='w')
hydroSeasonal.to_csv(
tab_file_path + "/Stochastic_HydroGenMaxSeasonalProduction" + '.tab',
header=True, index=None, sep='\t', mode='w')