diff --git a/runme.py b/runme.py
index 493848312945111bb14ea7f15d105b29f9efdb48..c1c3fb6228563888acdb7d85f3c5d633fc83d4aa 100644
--- a/runme.py
+++ b/runme.py
@@ -22,72 +22,69 @@ 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
-
-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
- # 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
- # 't_step': granularity of optimization model, Unit: hours
- # Rolling horizon (RH) can be set by:
- # 't_rh_horizon': width of rolling horizon intervals, Unit: hours, MINIMUM VALUE: 2 !!!
- # '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
-
- # PLEASE CHANGE HERE
- # Prediction settings
- predictions = {'demand_electric': 'Perfect',
+
+
+# # CONFIG -------------------------------------------------------------------------------------------------------
+
+ # 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
+# 't_step': granularity of optimization model, Unit: hours
+# Rolling horizon (RH) can be set by:
+# 't_rh_horizon': width of rolling horizon intervals, Unit: hours, MINIMUM VALUE: 2 !!!
+# '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
+
+
+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
+
+
+
+predictions = {'demand_electric': 'Perfect',
'demand_heat': 'Perfect',
'day_ahead_price': 'Perfect',
'intraday_price': 'Perfect',
@@ -97,138 +94,48 @@ 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 -------------------------------------------------------------------------------------------------------
+
+# Mit 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'}
+
+
+
+
+
+# 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__":
- """
- 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]}"""
-
- 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}}
-
- }
- # 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
-
# Timer output
tic = time.time()
# Start program
@@ -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,