Unified runme and runme_community

Model_Library @ 63dd8424

-Subproject commit 6dadedf53098caad20ccadebdd8b67aa312cbf62
+Subproject commit 63dd8424cf069f35f33428e94f50dee3fe2aab39

runme_community.py

-import time
-import pandas as pd
-import Tooling.input_profile_processor.input_profile_processor
-import Model_Library.Prosumer.main as main
-import Model_Library.District.main_district as main_district
-from functools import partial
-from multiprocessing import Pool
-from tqdm import tqdm
-import os
-
-def process_each_prosumer(prosumer_name, prosumer_specification, input_profiles, t_horizon, t_step, prosumer_strategy):
-    start_time = time.time()
-    prosumer = main.Main(prosumer_name, prosumer_specification, input_profiles, t_horizon, t_step)
-    after_setup_time = time.time()
-    print("process_each_prosumer:\tProsumer Construction [s]: \t" + str(after_setup_time - start_time))
-
-    prosumer.run_optimization(prosumer_strategy)
-    after_optimization_time = time.time()
-    print("process_each_prosumer:\tProsumer Optimization [s]: \t" + str(after_optimization_time - after_setup_time))
-
-    prosumer.show_results()
-
-    return prosumer.prosumer
-
-start_time = time.time()
-t_start = pd.Timestamp("2019-05-10 00:00:00") # start time of simulation
-t_horizon = 240 # number of time steps to be simulated
-t_step = 1 # length of a time step in hours
-
-input_profile_dict = {'irradiance_1': ['irradiance', 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'],
-                      'temperature_1': ['air_temperature', 'input_files/data/temperature/temperature.csv'],
-                      'demand_electric_1': ['elec_demand', 'generate', 3000],
-                      'demand_heat_1': ['therm_demand', 'generate', 6000, 'temperature_1'],
-                      'demand_hot_water_1': ['hot_water_demand', 'generate', 1500, 'temperature_1'],
-                      'irradiance_2': ['irradiance', 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'],
-                      'temperature_2': ['air_temperature', 'input_files/data/temperature/temperature.csv'],
-                      'demand_electric_2': ['elec_demand', 'generate', 3000],
-                      'demand_heat_2': ['therm_demand', 'generate', 6000, 'temperature_2'],
-                      'demand_hot_water_2': ['hot_water_demand', 'generate', 1500, 'temperature_2'],
-                      'irradiance_3': ['irradiance', 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'],
-                      'temperature_3': ['air_temperature', 'input_files/data/temperature/temperature.csv'],
-                      'demand_electric_3': ['elec_demand', 'generate', 0],
-                      'demand_heat_3': ['therm_demand', 'generate', 0, 'temperature_3'],
-                      'demand_hot_water_3': ['hot_water_demand', 'generate', 0, 'temperature_3'],
-                      'elec_price_1': ['elec_price', 'input_files/data/prices/day-ahead/hourly_price.csv']}
-
-input_profiles = Tooling.input_profile_processor.input_profile_processor.process_input_profiles(input_profile_dict, t_start, t_horizon, t_step)
-after_input_processing_time = time.time()
-print("runme:\t\t\tProfile Processing [s]: \t" + str(after_input_processing_time - start_time))
-
-# 'topology_path': path to directory that contains the matrices that define the prosumer topology
-# 'config_path': path to global configurations like prices, injection prices, emission costs, etc.
-prosumer_dict = {'SCN2_CAT1_PV11_3000_6000':{'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11',
-                                             'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11/config.csv',
-                                             'profiles': {'irradiance': 'irradiance_1',
-                                                          'air_temperature': 'temperature_1',
-                                                          'elec_demand': 'demand_electric_1',
-                                                          'therm_demand': 'demand_heat_1',
-                                                          'hot_water_demand': 'demand_hot_water_1'}},
-                 'SCN0_CAT1_3000_6000': {'topology_path': 'input_files/models/prosumer_models/SCN0_CAT1',
-                                         'config_path': 'input_files/models/prosumer_models/SCN0_CAT1/config.csv',
-                                         'profiles': {'irradiance': 'irradiance_2',
-                                                      'air_temperature': 'temperature_2',
-                                                      'elec_demand': 'demand_electric_2',
-                                                      'therm_demand': 'demand_heat_2',
-                                                      'hot_water_demand': 'demand_hot_water_2'}}}
-
-prosumer_strategy = ['annuity']
-parallel_processing = False
-before_optimization_time = time.time()
-print("runme:\t\t\tProsumer Setup [s]: \t\t" + str(before_optimization_time - after_input_processing_time))
-
-# Run multiple independent prosumers in parallel on multiple cores
-prosumers = dict.fromkeys(prosumer_dict.keys())
-if parallel_processing:
-    count_processes = len(prosumer_dict.keys())
-    pool = Pool(os.cpu_count())
-    parallel_func = partial(process_each_prosumer, input_profiles = input_profiles, t_horizon = t_horizon, t_step = t_step, prosumer_strategy = prosumer_strategy)
-    mapped_values = list(tqdm(pool.map(parallel_func, list(prosumer_dict.keys()), list(prosumer_dict.values())), total = count_processes))
-# Normal processing, one core only
-else:
-    for prosumer_name in list(prosumer_dict.keys()):
-        prosumers[prosumer_name] = process_each_prosumer(prosumer_name, prosumer_dict[prosumer_name], input_profiles, t_horizon, t_step, prosumer_strategy)
-after_optimization_time = time.time()
-print("runme:\t\t\tProsumer Optimization [s]: \t" + str(after_optimization_time - before_optimization_time))
-
-start_community_time = time.time()
-community_assets_dict = {'ca_bat': {'topology_path': 'input_files/models/district_models/example_CA',
-                                    'config_path': 'input_files/models/district_models/example_CA/config.csv',
-                                    'profiles': {'irradiance': 'irradiance_3',
-                                                 'air_temperature': 'temperature_3',
-                                                 'elec_demand': 'demand_electric_3',
-                                                 'therm_demand': 'demand_heat_3',
-                                                 'hot_water_demand': 'demand_hot_water_3'}}}
-
-community_assets_strategy = 'sizing_max_operational_profit'
-before_community_assets_time = time.time()
-print("runme:\t\t\tCommunity Assets Setup [s]: \t" + str(before_community_assets_time - start_community_time))
-
-community_assets = main.Main_CA(community_assets_dict, input_profiles, t_horizon, t_step).community_assets
-after_community_assets_time = time.time()
-print("runme:\t\t\tCommunity Assets Constr. [s]: \t" + str(after_community_assets_time - before_community_assets_time))
-
-community_dict = {'community': {'config_path': 'input_files/models/district_models/example_community/config.csv',
-                                'profiles': {'elec_price': 'elec_price_1'}}}
-
-community_strategy = ['max_operational_profit']
-before_community_optimization_time = time.time()
-print("runme:\t\t\tComm. Optimization Setup [s]: \t" + str(before_community_optimization_time - after_community_assets_time))
-
-community_main = main_district.MainDistrict(community_dict, prosumers, community_assets, input_profiles, t_horizon, t_step, community_assets_strategy, community_strategy)
-after_community_optimization_time = time.time()
-print("runme:\t\t\tCommunity Optimization [s]: \t" + str(after_community_optimization_time - before_community_optimization_time))