diff --git a/Model_Library b/Model_Library
index a23357dda91a859b2aca04bb833d11e935106a60..c9a8706272165916d927210c8f58d9e282388fc2 160000
--- a/Model_Library
+++ b/Model_Library
@@ -1 +1 @@
-Subproject commit a23357dda91a859b2aca04bb833d11e935106a60
+Subproject commit c9a8706272165916d927210c8f58d9e282388fc2
diff --git a/input_files/models/prosumer_models/building_types_aachen_2022/running_179_2022.csv b/input_files/models/prosumer_models/building_types_aachen_2022/running_179_2022.csv
new file mode 100644
index 0000000000000000000000000000000000000000..7174cb266894e0433cb00ceac866ce72bdc58d84
--- /dev/null
+++ b/input_files/models/prosumer_models/building_types_aachen_2022/running_179_2022.csv
@@ -0,0 +1,197 @@
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diff --git a/input_files/models/prosumer_models/building_types_aachen_2022/running_215_2022.csv b/input_files/models/prosumer_models/building_types_aachen_2022/running_215_2022.csv
new file mode 100644
index 0000000000000000000000000000000000000000..f396d8bcc9066ba5174c6de8383ce7923f99426f
--- /dev/null
+++ b/input_files/models/prosumer_models/building_types_aachen_2022/running_215_2022.csv
@@ -0,0 +1,195 @@
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diff --git a/input_files/models/prosumer_models/building_types_aachen_2022/running_77_2022.csv b/input_files/models/prosumer_models/building_types_aachen_2022/running_77_2022.csv
new file mode 100644
index 0000000000000000000000000000000000000000..b4305cda643428f232adba9d306f5f2d1585266a
--- /dev/null
+++ b/input_files/models/prosumer_models/building_types_aachen_2022/running_77_2022.csv
@@ -0,0 +1,200 @@
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+125,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_125
+126,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_126
+127,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_127
+128,A_0_PV9.0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_PV9.0_Q_77_128
+129,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_129
+130,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_130
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+132,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_132
+133,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_133
+134,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_134
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+136,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_136
+137,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_137
+138,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_138
+139,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_139
+140,A_14_STR,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_STR_Q_77_140
+141,A_21_PV28.0_HP_STR,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_PV28.0_HP_STR_Q_77_141
+142,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_142
+143,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_143
+144,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_144
+145,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_145
+146,A_13_PV9.0,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_PV9.0_Q_77_146
+147,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_147
+148,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_148
+149,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_149
+150,A_22_PV9.0,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_PV9.0_Q_77_150
+151,L_0,4160.278216,11840.43691,468.9450257,l2,GGB,0,L_0_Q_77_151
+152,AC_0_PV8.0,13259.27221,53373.41447,3842.154699,h0+g1,GMF,0,AC_0_PV8.0_Q_77_152
+153,A_5,10739.11627,43228.86614,3111.886186,h0,MFH,1,A_5_Q_77_153
+154,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_154
+155,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_155
+156,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_156
+157,L_0,4160.278216,11840.43691,468.9450257,l2,GGB,0,L_0_Q_77_157
+158,A_4_PV27.0,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_PV27.0_Q_77_158
+159,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_159
+160,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_160
+161,C_0,5895.243735,45146.8427,177.0449093,g1,GBD,0,C_0_Q_77_161
+162,A_19,8523.514643,34310.26021,2469.868732,h0,MFH,1,A_19_Q_77_162
+163,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_163
+164,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_164
+165,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_165
+166,A_5,10739.11627,43228.86614,3111.886186,h0,MFH,1,A_5_Q_77_166
+167,A_4,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_Q_77_167
+168,L_0,4160.278216,11840.43691,468.9450257,l2,GGB,0,L_0_Q_77_168
+169,A_24_PV9.0,29861.09516,120201.8167,8652.884205,h0,MFH,1,A_24_PV9.0_Q_77_169
+170,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_170
+171,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_171
+172,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_172
+173,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_173
+174,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_174
+175,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_175
+176,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_176
+177,E_0,9979.480577,27156.03132,1075.905206,g4,GHA,0,E_0_Q_77_177
+178,A_19,8523.514643,34310.26021,2469.868732,h0,MFH,1,A_19_Q_77_178
+179,AC_0,13259.27221,53373.41447,3842.154699,h0+g1,GMF,0,AC_0_Q_77_179
+180,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_180
+181,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_181
+182,A_4_STR,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_STR_Q_77_182
+183,A_11_STR,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_STR_Q_77_183
+184,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_184
+185,A_13,6666.867267,26836.57626,1931.865866,h0,MFH,1,A_13_Q_77_185
+186,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_186
+187,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_187
+188,A_21,5091.9992,20497.15697,1475.514519,h0,MFH,1,A_21_Q_77_188
+189,A_22,13329.62261,53656.60053,3862.540216,h0,MFH,1,A_22_Q_77_189
+190,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_190
+191,A_11,36178.51608,145631.7438,10483.4906,h0,MFH,1,A_11_Q_77_191
+192,A_27,2365.64327,9522.578367,685.4951961,h0,MFH,1,A_27_Q_77_192
+193,A_0,3642.673994,14663.09355,1055.541872,h0,MFH,1,A_0_Q_77_193
+194,A_4,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_Q_77_194
+195,A_5,10739.11627,43228.86614,3111.886186,h0,MFH,1,A_5_Q_77_195
+196,A_14,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_Q_77_196
+197,A_4,54611.4143,219830.893,15824.81291,h0,MFH,1,A_4_Q_77_197
+198,A_14_PV84.0_STR,16630.23974,66942.79025,4818.963874,h0,MFH,1,A_14_PV84.0_STR_Q_77_198
diff --git a/runme.py b/runme.py
index c56dfbf0975886929da536f3a3c262da030676c6..e24b918216b4ed65a784160c6d116d4a29dcbb2c 100644
--- a/runme.py
+++ b/runme.py
@@ -7,6 +7,8 @@ import Model_Library.Prosumer.main as main_prosumer
import Model_Library.District.main as main_district
from enum import Enum
import os
+import random
+import pickle
class SimulationScope(Enum):
PROSUMER = 1
@@ -14,29 +16,55 @@ class SimulationScope(Enum):
simulation_scope = SimulationScope.DISTRICT
t_start = pd.Timestamp("2019-01-01 00:00:00") # start time of simulation
-t_horizon = 100 # number of time steps to be simulated
+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'
+#inputpath_dataframe = 'input_files/models/district_models/quarter_extraction_df_Q_0.csv'
+building_types = pd.read_csv(inputpath_dataframe)
+
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'],
'temperature_1': ['air_temperature', 'input_files/data/temperature/temperature_TRY2010.csv'],
- 'elec_demand_htw': ['elec_demand', r"input_files\data\demand\electricity\PL_SUM_15.csv"],
+ 'elec_demand_htw': ['elec_demand', 'input_files/data/demand/electricity/PL_SUM_15.csv'],
'room_heating_sfh_passive': ['room_heating_demand', 'input_files/data/demand/room_heating/synPRO_passive_single_family_house_rh_2021_to_2019_kW.csv'],
'dhw_shf_passive': ['hot_water_demand', 'input_files/data/demand/domestic_hot_water/synPRO_passive_single_family_house_dhw_2021_to_2019_kW.csv'],
+ 'room_heating_mfh_passive': ['room_heating_demand', 'input_files/data/demand/room_heating/synPRO_passive_multi_party_house_rh_2021_to_2019_kW.csv'],
+ '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[44:55]:
+ 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],
+ 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],
+ 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_profiles = Tooling.input_profile_processor.input_profile_processor.process_input_profiles(input_profile_dict, t_start, t_horizon, t_step)
for i in input_profiles['elec_demand_htw']:
input_profiles['ref_elec_demand_id_' + str(i)] = input_profiles['elec_demand_htw'][i]
+for i in input_profiles.keys():
+ if 'Add_' in i:
+ strings = i.split('Add_')
+ profile2 = input_profiles[i]
+ input_profiles[strings[1]] += profile2
+
# get the annual electric, heating and hw demand s of the example buildings
ref_yearly_elec_demand = input_profiles['elec_demand_htw'].sum()*t_step # in kWh
-ref_yearly_rh_demand = input_profiles['room_heating_sfh_passive'].sum()*t_step # in kWh
-ref_yearly_dhw_demand = input_profiles['dhw_shf_passive'].sum()*t_step # in kWh
-
-inputpath_dataframe = r'input_files\models\prosumer_models\building_types_aachen\running_77_2022.csv'
-#inputpath_dataframe = 'input_files/models/district_models/quarter_extraction_df_Q_0.csv'
-building_types = pd.read_csv(inputpath_dataframe)
+ref_yearly_rh_demand = input_profiles['room_heating_mfh_passive'].sum()*t_step # in kWh
+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(
@@ -47,47 +75,78 @@ building_types['closest_index'] = building_types.apply(
# 'topology_path': path to directory that contains the matrices that define the prosumer topology
prosumer_dict = {}
-for i in building_types.index[0:10]:
+# drop the heatpump
+building_types.drop(index=141, inplace=True)
+building_types.reset_index(inplace=True)
- # 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
+# building_types.index[53:61]
+community_sample = random.sample(range(197),30)
+
+# how many storages
+ids = building_types.loc[:]['ID_Building']
+string_str = 'STR'
+string_pv = 'PV'
+string_ac = 'AC'
+
+count_str = 0
+count_pv = 0
+count_ac = 0
+for string in ids:
+ count_str += string.count(string_str)
+ count_pv += string.count(string_pv)
+ count_ac += string.count(string_ac)
+
+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[44:55]:
+
+ 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
# ROOM HEATING
- scaled_heat_demand = building_types['reference_heat_demand'][i] / ref_yearly_rh_demand * input_profiles['room_heating_sfh_passive']
+ 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
# HOT WATER
- scaled_hot_water_demand = building_types['reference_hot_water'][i] / ref_yearly_rh_demand * input_profiles['dhw_shf_passive']
+ 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
+
if 'PV' in building_types.loc[i,'ID_Building'] and '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/'+ str(building_types.loc[i,'ID_Building']) + '/config.csv',
- 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(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']),
'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/'+ str(building_types.loc[i, 'ID_Building']) + '/config.csv',
- 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(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']),
'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/'+ str(building_types.loc[i, 'ID_Building']) + '/config.csv',
- 'topology_path': 'input_files/models/prosumer_models/building_types_aachen/'+ str(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'])}}
+
+
num=0
"""
@@ -104,7 +163,7 @@ prosumer_dict = {'SCN2_CAT1_PV11_3000_6000':{'config_path': 'input_files/models/
'dhw_dmd': 'demand_hot_water_2'}}}
"""
-prosumer_main = main_prosumer.ProsumerMain(prosumer_dict, input_profiles, t_horizon, t_step)
+prosumer_main = main_prosumer.ProsumerMain(prosumer_dict, input_profiles, t_horizon, t_step, new_prosumers=True)
prosumer_sizing_strategy = 'annuity'
prosumer_main.optimize_sizing(prosumer_sizing_strategy)
@@ -119,6 +178,7 @@ if simulation_scope == SimulationScope.PROSUMER:
district_assets_dict = {'da_bat': {'config_path': 'input_files/models/district_models/example_CA/config.csv',
'topology_path': 'input_files/models/district_models/example_CA',
'profiles': {}}}
+district_assets_dict = {}
district_assets = main_prosumer.DistrictAssetMain(district_assets_dict, input_profiles, t_horizon, t_step).district_assets