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Commit 1fa8c16b authored by László Hartung's avatar László Hartung
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Restructuring Runme.py 

"Process each Prosumer" Method copied in Main.py
deleted excluded code from runme.py
time variables (t_start, t_end etc.) in runme.py
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......@@ -22,31 +22,47 @@ Project host: RWTH Aachen University, Aachen, Germany
Project Website: https://www.fenaachen.net/projekte/fen-ineed-dc
"""
import time
import pandas as pd
import argparse
from tqdm import tqdm
import Model_Library.Prosumer.main as main
from multiprocessing import Pool
import os
from functools import partial
from Model_Library.Prosumer.scripts.results_evaluation.results_evaluation import \
Plot_savings
# from Tooling.results_evaluation.results_evaluation import Plot_savings
# # CONFIG -------------------------------------------------------------------------------------------------------
def process_each_prosumer(prosumer_name, prosumer_dict, data_source, commentary,
no_process_bar_rh):
"""
The method XYZ adds ...
:param
XYZ:
:returns
XYZ
"""
try:
# PLEASE CHANGE HERE
# Path to local data - this is only used when selecting local mode
# 'topology_path': path to matrices that define the prosumer topology
# 'config_path': path to global configurations like prices, injection prices, emission costs, etc.
# data_path
# rolling horizon
prosumer_dict = {
'SCN2_CAT1_PV14_HP_3000_6000': {'elec_demand': 3000, 'therm_demand': 6000,
'hot_water_demand': 1500,
'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP',
'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/config.csv',
'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/data_path.csv',
'rolling_horizon': False}
}
# Select data source
# Options: '1': import from database, '2': import from local folder
data_source = 2
# Select Reference Results for szenario comparisons
reference_results = {}
#select commantary options
commentary = True
#select process bar options
no_process_bar_rh = True
#Todo description parallel processing
parallel_processing = False
# Set the simulation time frame and optional rolling horizon configurations:
# 't_start': start date of simulations, Unit: JJJJ-MM-DD hh:mm:ss
# 't_end': end date of simulations, Unit: JJJJ-MM-DD hh:mm:ss
......@@ -56,37 +72,18 @@ def process_each_prosumer(prosumer_name, prosumer_dict, data_source, commentary,
# 't_rh_shift': rolling horizon shift between intervals, Unit: hours
# 't_current_value_length': number of values at beginning of rolling horizon interval that are replaced by real values, Unit: hours
# 't_history': number of days before actual simulation interval for the demand generator to be able to make required predictions
if prosumer_dict[prosumer_name]['rolling_horizon']:
t_start = pd.Timestamp("2019-09-01 00:00:00")
t_end = pd.Timestamp("2019-09-01 5:00:00")
t_step = 1
t_rh_horizon = 3
t_rh_shift = 1
t_current_value_length = 2
t_history = 14 # days
# PLEASE CHANGE HERE
# Prediction settings
predictions = {'demand_electric': 'SameHourYesterday',
'demand_heat': 'SameHourYesterday',
'day_ahead_price': 'SameHourYesterday',
'intraday_price': 'SameHourYesterday',
'solar_radiation': 'Perfect',
# currently the method generate_g_t_series takes the same t_start as the prediction -> no historical
# data for the prediction available: easy fix would be to set a minus time delta in the t_start
# argument of generate_g_t_series
'temperature': 'SameHourYesterday'}
else:
t_start = pd.Timestamp("2019-07-01 00:00:00")
t_end = pd.Timestamp("2019-7-10 23:00:00") + pd.Timedelta(hours=1)
t_step = 1
t_rh_horizon = (t_end - t_start) / pd.Timedelta(hours=1)
t_rh_shift = t_rh_horizon - 1
t_current_value_length = t_rh_horizon
t_history = 0 # days
t_history = 0
# PLEASE CHANGE HERE
# Prediction settings
predictions = {'demand_electric': 'Perfect',
'demand_heat': 'Perfect',
'day_ahead_price': 'Perfect',
......@@ -97,137 +94,47 @@ def process_each_prosumer(prosumer_name, prosumer_dict, data_source, commentary,
# argument of generate_g_t_series
'temperature': 'Perfect'}
# Fixed variables - DO NOT CHANGE
storage_states = {}
interim_results = {}
final_iteration = False
# Set aggregation options
parser = argparse.ArgumentParser(
description='Start optimization from DB or local data')
parser.add_argument('-a', '--aggregate', action="store_true",
dest="aggregate",
help="activating aggregation of input time series",
default=False)
options = parser.parse_args()
# Calculate number of rolling horizon intervals and loop through them
for t in tqdm(pd.date_range(t_start,
t_end - pd.Timedelta(hours=t_rh_shift + 1),
freq=str(t_rh_shift) + 'H'),
disable=no_process_bar_rh):
# ToDo: replace first value with perfect value (can be done in runme)
# set end date for current loop
t_end_loop = t + pd.Timedelta(hours=t_rh_horizon)
# exceptions that occur at global end of simulation horizon
if t_end_loop > t_end:
t_end_loop = t_end
if t_current_value_length > (t_end_loop - t) / pd.Timedelta(
hours=1):
t_current_value_length = (t_end_loop - t) / pd.Timedelta(
hours=1)
# Set flag for final iteration
if t == t_end - pd.Timedelta(hours=t_rh_shift + 1):
final_iteration = True
# Start main programme
prosumer = main.Main(data_source,
{prosumer_name: prosumer_dict[prosumer_name]},
t, t_end_loop, t_step,
predictions, t_current_value_length, t_end,
t_history, commentary, storage_states,
t_rh_shift, aggregation=options.aggregate)
# Run optimization
prosumer.run_optimization(prosumer.prosumer_name_list)
# Show results - Results are only plotted after last iteration of rolling horizon
prosumer.show_results(prosumer.prosumer_name_list, interim_results,
final_iteration)
# Get storage states from this iteration
storage_states = prosumer.charge_status
# Get interim results of current rolling horizon interval
interim_results = prosumer.interim_results
except ValueError:
print(prosumer_name + " could not be optimized!")
# # MAIN PROGRAM -------------------------------------------------------------------------------------------------------
if __name__ == "__main__":
"""
The method XYZ adds ...
:param
XYZ:
:returns
XYZ
"""
# Initialization scenario path and global variables for the prosumer optimization
# Start timer
start = time.time()
# PLEASE CHANGE HERE
# Path to local data - this is only used when selecting local mode
# 'topology_path': path to matrices that define the prosumer topology
# 'config_path': path to global configurations like prices, injection prices, emission costs, etc.
# topology_path = 'input_files/scenarios/Study_Base'
# config_path = topology_path + '/config.csv'
# data_path = topology_path + '/data_path.csv'
# prosumer_name = 'office'
# prosumer_dict = {prosumer_name: {'topology_path': topology_path, 'config_path': config_path, 'data_path': data_path}}
# topology_path = ['input_files/models/SCN0_CAT1']
# prosumer_name = ['SCN0_CAT1']
# rolling_horizon = [False]
# elec_demand = [1500, 10000]
# therm_demand = [5000, 20000]
# hot_water_demand = 1500 # [1500, 1500]
# step_elec_demand = 500
# step_therm_demand = 500
# step_hot_water_demand = 0
# prosumer_dict = {}
""" for i in range(len(prosumer_name)):
for j in range(elec_demand[0], elec_demand[1], step_elec_demand):
for k in range(therm_demand[0], therm_demand[1], step_therm_demand):
#for l in range(hot_water_demand[0], hot_water_demand[1], step_hot_water_demand):
prosumer_dict[prosumer_name[i]+'_'+str(j)+'_'+str(k)] = {'elec_demand': j,
'therm_demand': k,
'hot_water_demand': hot_water_demand,
'topology_path': topology_path[i],
'config_path': topology_path[i] + '/config.csv',
'data_path': topology_path[i] + '/data_path.csv',
'rolling_horizon': rolling_horizon[i]}"""
# Mit Rolling horizon
prosumer_dict = {
# 'SCN0_CAT1_2000_6000': {'elec_demand': 2000, 'therm_demand': 6000, 'hot_water_demand': 1500, 'topology_path': 'input_files/models/SCN0_CAT1', 'config_path': 'input_files/models/SCN0_CAT1/config.csv', 'data_path': 'input_files/models/SCN0_CAT1/data_path.csv', 'rolling_horizon': False},
# 'SCN3_CAT1_3000_6000': {'elec_demand': 3000, 'therm_demand': 6000, 'hot_water_demand': 1500, 'topology_path': 'input_files/models/SCN3_CAT1', 'config_path': 'input_files/models/SCN3_CAT1/config.csv', 'data_path': 'input_files/models/SCN3_CAT1/data_path.csv', 'rolling_horizon': False},
# 'SCN1_CAT1_3000_6000': {'elec_demand': 3000, 'therm_demand': 6000, 'hot_water_demand': 1500, 'topology_path': 'input_files/models/SCN1_CAT1', 'config_path': 'input_files/models/SCN1_CAT1/config.csv', 'data_path': 'input_files/models/SCN1_CAT1/data_path.csv', 'rolling_horizon': False},
# 'SCN2_CAT1_PV14_HP_3000_6000': {'elec_demand': 3000, 'therm_demand': 6000,
# 'hot_water_demand': 1500,
# 'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP',
# 'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/config.csv',
# 'data_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV14_HP/data_path.csv',
# 'rolling_horizon': False}
'office_pv_heatpump': {'elec_demand': 32905,
'therm_demand': 115154,
'hot_water_demand': 11882,
'topology_path': 'input_files/models/prosumer_models/office_pv_heatpump',
'config_path': 'input_files/models/prosumer_models/office_pv_heatpump/config.csv',
'data_path': 'input_files/models/prosumer_models/office_pv_heatpump/data_path.csv',
'rolling_horizon': False}
# 'SCN2_CAT1_PV12_BA_3000_6000': {'elec_demand': 9000, 'therm_demand': 20000, 'hot_water_demand': 1500, 'topology_path': 'input_files/models/SCN2_CAT1_PV12_BA', 'config_path': 'input_files/models/SCN2_CAT1_PV12_BA/config.csv', 'data_path': 'input_files/models/SCN3_CAT1_PV12_BA/data_path.csv', 'rolling_horizon': True}}
#t_start = pd.Timestamp("2019-09-01 00:00:00")
#t_end = pd.Timestamp("2019-09-01 5:00:00")
#t_step = 1
#t_rh_horizon = 3
#t_rh_shift = 1
#t_current_value_length = 2
#t_history = 14 # days
}
# PLEASE CHANGE HERE
# Select data source
# Options: '1': import from database, '2': import from local folder
data_source = 2
reference_results = {}
commentary = True
no_process_bar_rh = True
parallel_processing = False
# Prediction settings
#predictions = {'demand_electric': 'SameHourYesterday',
#'demand_heat': 'SameHourYesterday',
#'day_ahead_price': 'SameHourYesterday',
#'intraday_price': 'SameHourYesterday',
#'solar_radiation': 'Perfect',
# currently the method generate_g_t_series takes the same t_start as the prediction -> no historical
# data for the prediction available: easy fix would be to set a minus time delta in the t_start
# argument of generate_g_t_series
#'temperature': 'SameHourYesterday'}
# MAIN PROGRAMM---------------------------------------------------------------------------------------------------------
import time
from tqdm import tqdm
import Model_Library.Prosumer.main as main
from multiprocessing import Pool
import os
from functools import partial
if __name__ == "__main__":
# Start timer
start = time.time()
# Timer output
tic = time.time()
......@@ -236,7 +143,7 @@ if __name__ == "__main__":
if parallel_processing:
count_processes = len(prosumer_dict.keys())
pool = Pool(os.cpu_count())
parallel_func = partial(process_each_prosumer,
parallel_func = partial(main.process_each_prosumer,
prosumer_dict=prosumer_dict,
data_source=data_source,
commentary=commentary,
......@@ -247,7 +154,7 @@ if __name__ == "__main__":
# Normal processing, one core only
else:
for prosumer_name in list(prosumer_dict.keys()):
process_each_prosumer(prosumer_name=prosumer_name,
main.process_each_prosumer(prosumer_name=prosumer_name,
prosumer_dict=prosumer_dict,
data_source=data_source,
commentary=commentary,
......
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