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+377,A_16_PV84.0_STR,67895.81632,273305.4641,19674.2495,h0,MFH,1.0 +378,AC_0_PV7.0_STR,13259.27221,53373.414469999996,3842.154699,h0+g1,GMF,0.0 +379,A_26_PV16.0_HP,44777.02232,180243.8726,12975.08973,h0,MFH,1.0 +380,AC_0_PV73.0,13259.27221,53373.414469999996,3842.154699,h0+g1,GMF,0.0 +381,A_8_PV27.0_STR,82147.63936,330674.2582,23804.01681,h0,MFH,1.0 +382,A_26_PV16.0,44777.02232,180243.8726,12975.08973,h0,MFH,1.0 +383,A_18_PV9.0_STR,93997.95422,378376.1047,27237.8963,h0,MFH,1.0 +384,A_8_PV27.0,82147.63936,330674.2582,23804.01681,h0,MFH,1.0 +385,G_1_PV25.0,6694.2224719999995,19832.34201,2013.477135,g2,GGA,0.0 +386,A_18_PV390.0_HP_STR,93997.95422,378376.1047,27237.8963,h0,MFH,1.0 +387,A_11_PV10.0,36178.51608,145631.7438,10483.4906,h0,MFH,1.0 +388,AC_0_PV178.0,13259.27221,53373.414469999996,3842.154699,h0+g1,GMF,0.0 +389,D_2_PV8.0,40124.77842,114203.2368,729.4101188999999,l0,GMK,0.0 +390,D_1,116834.373,332534.2617,2123.878991,l0,GMK,0.0 +391,V_2_PV30.0,20032.90823,43132.10587,43132.10587,g4,GBD,0.0 +392,C_0_PV103.0_STR,5895.243735,45146.8427,177.0449093,g1,GBD,0.0 +393,AC_0_PV25.0,13259.27221,53373.414469999996,3842.154699,h0+g1,GMF,0.0 +394,A_25_PV28.0_STR,194011.9042,780968.79,56219.054679999994,h0,MFH,1.0 +395,AE_0_PV7.0,8873.897794,29415.64755,1691.719368,h0+g4,GMF,0.0 +396,D_1_PV103.0,116834.373,332534.2617,2123.878991,l0,GMK,0.0 +397,A_26_PV80.0_STR,44777.02232,180243.8726,12975.08973,h0,MFH,1.0 +398,A_9_PV152.0_HP_STR,24734.64265,99565.97261,7167.386111,h0,MFH,1.0 +399,A_12_PV80.0_STR,158691.1773,638789.961,45984.126639999995,h0,MFH,1.0 +400,A_12_PV28.0_HP_STR,158691.1773,638789.961,45984.126639999995,h0,MFH,1.0 +401,AC_0_PV8.0_HP,7063.27682,44458.153289999995,900.9139905999999,h0+g1,GMF,0.0 +402,A_26_PV4.0_STR,44777.02232,180243.8726,12975.08973,h0,MFH,1.0 +403,A_11_PV80.0_HP_STR,36178.51608,145631.7438,10483.4906,h0,MFH,1.0 +404,G_1_PV7.0_STR,6694.2224719999995,19832.34201,2013.477135,g2,GGA,0.0 +405,A_23_PV28.0_STR,112411.8281,452498.6737,32573.7059,h0,MFH,1.0 +406,A_26_PV152.0_STR,44777.02232,180243.8726,12975.08973,h0,MFH,1.0 +407,A_26_PV152.0_HP_STR,44777.02232,180243.8726,12975.08973,h0,MFH,1.0 +408,A_11_PV50.0_STR,36178.51608,145631.7438,10483.4906,h0,MFH,1.0 +409,Q_0_STR,4343.104444,30488.79776,1389.32441,g1,GKO,0.0 +410,N_0_PV390.0_STR,16602.35612,29318.565169999998,1312.394883,g1,GBD,0.0 +411,L_0_PV79.0,4160.278216,11840.436909999999,468.94502570000003,l2,GGB,0.0 +412,Q_0_PV30.0,4343.104444,30488.79776,1389.32441,g1,GKO,0.0 +413,E_0_PV53.0,9979.480577,27156.03132,1075.9052060000001,g4,GHA,0.0 +414,A_9_PV16.0_STR,24734.64265,99565.97261,7167.386111,h0,MFH,1.0 +415,A_1_PV84.0_HP_STR,134909.9709,543061.9178,39093.01888,h0,MFH,1.0 +416,AD_1,41576.5126,167360.6518,12047.67432,h0+l1,GMF,0.0 +417,A_26_PV288.0_HP_STR,44777.02232,180243.8726,12975.08973,h0,MFH,1.0 +418,A_1_PV80.0_STR,134909.9709,543061.9178,39093.01888,h0,MFH,1.0 diff --git a/runme.py b/runme.py index 18a464ddc6c52d8ec082fe26ac2a631f7e704ad2..910617a75be287865360d035b6778787b4a1614a 100644 --- a/runme.py +++ b/runme.py @@ -14,14 +14,57 @@ class SimulationScope(Enum): PROSUMER = 1 DISTRICT = 2 + +# Define a function to split strings and extract base names +def extract_base_name(s): + splits = s.split("_") + return splits[0] + "_" + splits[1] + + +# Define a function to split strings and extract base names +def extract_str_name(s): + splits = s.split("_") + if len(splits) == 3 and 'STR' in splits[2]: # only storage in prosumer -> return original + return s + elif len(splits) == 3 and 'PV' in splits[2]: # only pv in prosumer --> add storage + return s + '_STR' + else: # pv and storage in prosumer --> return original + return s + + +# Define a function to split strings and extract base names +def extract_pv_name(s): + splits = s.split("_") + if len(splits) > 2 and 'PV' in splits[2]: # only give back pv system + return splits[0] + "_" + splits[1] + "_" + splits[2] + else: # there is a storage in the prosumer --> exclude it + return splits[0] + "_" + splits[1] + simulation_scope = SimulationScope.DISTRICT t_start = pd.Timestamp("2019-01-01 00:00:00") # start time of simulation t_horizon = 8760 # number of time steps to be simulated t_step = 1 # length of a time step in hours -inputpath_dataframe = 'input_files/models/prosumer_models/building_types_aachen_2022/running_77_2022.csv' +folder_building_configs = 'building_types_aachen_2022' +inputpath_dataframe_district = 'input_files/models/prosumer_models/building_types_aachen_2022/running_77_2022.csv' +inputpath_dataframe_all = 'input_files/models/prosumer_models/building_types_aachen_2022/running_info_2022.csv' #inputpath_dataframe = 'input_files/models/district_models/quarter_extraction_df_Q_0.csv' -building_types = pd.read_csv(inputpath_dataframe) +building_types_district = pd.read_csv(inputpath_dataframe_district) +building_types = pd.read_csv(inputpath_dataframe_all) + +# Initialize empty lists to store the results +only_pv = [] # contains the community without storage +original_names = [] # contains the original community names +full_storage = [] # community with added storage to all prosumers + +# Loop over the column of interest and store the indices and strings that match the conditions +indices = [] +for i, s in enumerate(building_types_district["ID_Building"]): + if "STR" in s or "PV" in s: + indices.append(i) + original_names.append(s) + full_storage.append(extract_str_name(s)) + only_pv.append(extract_pv_name(s)) input_profile_dict = {'pv_factors_aachen_1': ['pv_factors', 'input_files/data/irradiance/aachen_pv_factors_ninja_2019_1h_to_2019_15min.csv'], 'pv_factors_germany_1': ['pv_factors', 'input_files/data/irradiance/irr_ren_ninja_avg_Germany.csv'], @@ -33,22 +76,24 @@ input_profile_dict = {'pv_factors_aachen_1': ['pv_factors', 'input_files/data/ir 'dhw_mhf_passive': ['hot_water_demand', 'input_files/data/demand/domestic_hot_water/synPRO_passive_multi_party_house_dhw_2021_to_2019_kW.csv'], 'elec_price_1': ['prices', 'input_files/data/prices/day-ahead/hourly_price.csv']} -for i in building_types.index[53:56]: - if 'A_' in building_types.iloc[i]['ID_Building']: +for i in building_types.index[:]: + if building_types.iloc[i]['ID_building'] not in (only_pv + original_names + full_storage): + continue + if 'A_' in building_types.iloc[i]['ID_building']: pass else: - if len(building_types['profile_el'][i]) > 2: - profile_list = building_types['profile_el'][i].split('+') - input_profile_dict['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = ['elec_demand','generate', - building_types['reference_el_demand'][i], + if len(building_types['Profile_el'][i]) > 2: + profile_list = building_types['Profile_el'][i].split('+') + input_profile_dict['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])] = ['elec_demand','generate', + building_types['Electricity_demand'][i], profile_list[0]] - input_profile_dict['Add_elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = ['elec_demand','generate', - building_types['reference_el_demand'][i], + input_profile_dict['Add_elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])] = ['elec_demand','generate', + building_types['Electricity_demand'][i], profile_list[1]] else: - input_profile_dict['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = ['elec_demand', 'generate', - building_types['reference_el_demand'][i], - building_types['profile_el'][i]] + input_profile_dict['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])] = ['elec_demand', 'generate', + building_types['Electricity_demand'][i], + building_types['Profile_el'][i]] input_profiles = Tooling.input_profile_processor.input_profile_processor.process_input_profiles(input_profile_dict, t_start, t_horizon, t_step) @@ -68,7 +113,7 @@ ref_yearly_dhw_demand = input_profiles['dhw_mhf_passive'].sum()*t_step # in kWh # Create a new column in building_types to store the index of the closest value building_types['closest_index'] = building_types.apply( - lambda row: np.abs(row['reference_el_demand'] - ref_yearly_elec_demand).idxmin(), + lambda row: np.abs(row['Electricity_demand'] - ref_yearly_elec_demand).idxmin(), axis=1) # 'config_path': path to global configurations like prices, injection prices, emission costs, etc. @@ -76,15 +121,15 @@ building_types['closest_index'] = building_types.apply( prosumer_dict = {} # drop the heatpump -building_types.drop(index=141, inplace=True) -building_types.reset_index(inplace=True) +building_types_district.drop(index=141, inplace=True) +building_types_district.reset_index(inplace=True) # building_types.index[53:61] community_sample = random.sample(range(197),30) # how many storages -ids = building_types.loc[:]['ID_Building'] +ids = building_types_district.loc[:]['ID_Building'] string_str = 'STR' string_pv = 'PV' string_ac = 'AC' @@ -101,64 +146,62 @@ print('Number of storages: ' + str(count_str)) print('Number of PV: ' + str(count_pv)) print('Number of AC: ' + str(count_ac)) -consumer_count = 0 -for i in building_types.index[54:56]: +ps_count = 1 +for i in building_types.index[:]: - if 'A_' in building_types.iloc[i]['ID_Building']: + if len(prosumer_dict) > 2: + continue + + if building_types.iloc[i]['ID_building'] not in original_names or 'HP' in building_types.iloc[i]['ID_building']: + continue + else: + prosumer_name = building_types.iloc[i]['ID_building'] + '_Q_' + str(ps_count) + ps_count += 1 + + if building_types.iloc[i]['ID_building'] != 'A_21_PV28.0_STR': + pass + + if 'A_' in building_types.iloc[i]['ID_building']: # get the index of the reference profile for this building type index_ref_elec_demand = building_types['closest_index'][i] # scale profiles based on the reference data # ELECTRICITY - scaled_heat_demand = building_types['reference_el_demand'][i] / ref_yearly_elec_demand[index_ref_elec_demand] * input_profiles['ref_elec_demand_id_' + str(index_ref_elec_demand)] - input_profiles['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = scaled_heat_demand + scaled_heat_demand = building_types['Electricity_demand'][i] / ref_yearly_elec_demand[index_ref_elec_demand] * input_profiles['ref_elec_demand_id_' + str(index_ref_elec_demand)] + input_profiles['elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])] = scaled_heat_demand # ROOM HEATING - scaled_heat_demand = building_types['reference_heat_demand'][i] / ref_yearly_rh_demand * input_profiles['room_heating_mfh_passive'] - input_profiles['room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_Building'])] = scaled_heat_demand + scaled_heat_demand = building_types['Thermal_demand'][i] / ref_yearly_rh_demand * input_profiles['room_heating_mfh_passive'] + input_profiles['room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_building'])] = scaled_heat_demand # HOT WATER - scaled_hot_water_demand = building_types['reference_hot_water'][i] / ref_yearly_rh_demand * input_profiles['dhw_mhf_passive'] - input_profiles['hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])] = scaled_hot_water_demand + scaled_hot_water_demand = building_types['HotWater_demand'][i] / ref_yearly_rh_demand * input_profiles['dhw_mhf_passive'] + input_profiles['hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])] = scaled_hot_water_demand - if 'PV' in building_types.loc[i,'ID_Building'] and 'HP' in building_types.loc[i, 'ID_Building']: + if 'HP' in building_types.loc[i, 'ID_building']: - prosumer_dict[building_types.loc[i,'name_building']]= {'config_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i,'ID_Building']) + '/config.csv', - 'topology_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i,'ID_Building']), - 'profiles':{'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']), - 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_Building']), - 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']), + prosumer_dict[prosumer_name]= {'config_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i,'ID_building']) + '/config.csv', + 'topology_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i,'ID_building']), + 'profiles':{'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building']), + 'therm_cns': 'room_heat_demand_prosumer_' + str(building_types.loc[i,'ID_building']), + 'dhw_dmd': 'hot_water_demand_prosumer_' + str(building_types.loc[i, 'ID_building']), 'pv_roof': ['pv_factors_aachen_1'], 'heat_pump': 'temperature_1'}} - elif 'PV' in building_types.loc[i,'ID_Building']: - prosumer_dict[building_types.loc[i, 'name_building']] = {'config_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i, 'ID_Building']) + '/config.csv', - 'topology_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i, 'ID_Building']), - 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building']), + elif 'PV' in building_types.loc[i,'ID_building']: + prosumer_dict[prosumer_name] = {'config_path': 'input_files/models/prosumer_models/' + folder_building_configs + '/' + str(building_types.loc[i, 'ID_building']) + '/config.csv', + 'topology_path': 'input_files/models/prosumer_models/' + folder_building_configs + '/' + str(building_types.loc[i, 'ID_building']), + 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building']), 'pv_roof': ['pv_factors_aachen_1']}} else: - prosumer_dict[building_types.loc[i, 'name_building']] = {'config_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i, 'ID_Building']) + '/config.csv', - 'topology_path': 'input_files/models/prosumer_models/building_types_aachen_2022/'+ str(building_types.loc[i, 'ID_Building']), - 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_Building'])}} + prosumer_dict[prosumer_name] = {'config_path': 'input_files/models/prosumer_models/' + folder_building_configs + '/' + str(building_types.loc[i, 'ID_building']) + '/config.csv', + 'topology_path': 'input_files/models/prosumer_models/' + folder_building_configs + '/' + str(building_types.loc[i, 'ID_building']), + 'profiles': {'elec_cns': 'elec_demand_prosumer_' + str(building_types.loc[i, 'ID_building'])}} num=0 -""" -prosumer_dict = {'SCN2_CAT1_PV11_3000_6000':{'config_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11/config.csv', - 'topology_path': 'input_files/models/prosumer_models/SCN2_CAT1_PV11', - 'profiles': {'pv_roof': ['irradiance_1', 'temperature_1'], - 'elec_cns': 'demand_electric_1', - 'therm_cns': 'demand_heat_1', - 'dhw_dmd': 'demand_hot_water_1'}}, - 'SCN0_CAT1_3000_6000': {'config_path': 'input_files/models/prosumer_models/SCN0_CAT1/config.csv', - 'topology_path': 'input_files/models/prosumer_models/SCN0_CAT1', - 'profiles': {'elec_cns': 'demand_electric_2', - 'therm_cns': 'demand_heat_2', - 'dhw_dmd': 'demand_hot_water_2'}}} -""" - prosumer_main = main_prosumer.ProsumerMain(prosumer_dict, input_profiles, t_horizon, t_step) prosumer_sizing_strategy = 'annuity'