Skip to content
Snippets Groups Projects
Commit 1fa8c16b authored by László Hartung's avatar László Hartung
Browse files

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
parent 9f04aa95
No related branches found
No related tags found
No related merge requests found
Show whitespace changes
Inline Side-by-side
Showing
with 102 additions and 195 deletions
runme.py
+ 102
− 195
View file @ 1fa8c16b
......@@ -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,
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment