First implementation

Model_Library @ 6e57b6db

-Subproject commit ba6678d2f4b50bc525d62221dd99440d03993efe
+Subproject commit 6e57b6dbd8ccd5adfc5346ab689e005568c793cb

Tooling @ 3e220d97

-Subproject commit f82ba6f7d9f1316a3cd36b8af27c99953803d17d
+Subproject commit 3e220d972c875cff3025a6c19f0ea29a5c9b080c

runme.py

@@ -24,6 +24,7 @@ THE SOFTWARE.
 
 import pandas as pd
 import Tooling.input_profile_processor.input_profile_processor
+from Tooling.dynamics.Dynamic import TrivialDynamic
 import Model_Library.Prosumer.main as main_prosumer
 import Model_Library.District.main as main_district
 from enum import Enum
@@ -35,8 +36,7 @@ class SimulationScope(Enum):
 
 simulation_scope = SimulationScope.DISTRICT
 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
+dynamic = TrivialDynamic([1 for i in range(240)])
 
 input_profile_dict = {'irradiance_1': {'type': 'irradiance', 'file': 'input_files/data/irradiance/Lindenberg2006BSRN_Irradiance_60sec.csv'},
                       'temperature_1': {'type': 'air_temperature', 'file': 'input_files/data/temperature/temperature.csv'},
@@ -55,7 +55,7 @@ input_profile_dict = {'irradiance_1': {'type': 'irradiance', 'file': 'input_file
                       'demand_hot_water_3': {'type': 'hot_water_demand', 'generate': {'yearly_demand': 0, 'temperature': 'temperature_3'}},
                       'elec_price_1': {'type': 'elec_price', 'file': '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)
+input_profiles = Tooling.input_profile_processor.input_profile_processor.process_input_profiles(input_profile_dict, t_start, dynamic)
 
 prosumer_paths = {'SCN2_CAT1_PV11_3000_6000': 'input_files/models/prosumer_models/SCN2_CAT1_PV11/prosumer.json',
                   'SCN0_CAT1_3000_6000': 'input_files/models/prosumer_models/SCN0_CAT1/prosumer.json'}
@@ -75,7 +75,7 @@ for prosumer_name, component_profiles in prosumer_profiles.items():
     for component_name, profiles in component_profiles.items():
         prosumer_dict[prosumer_name]['components'][component_name].update(profiles)
 
-prosumer_main = main_prosumer.ProsumerMain(prosumer_dict, input_profiles, t_horizon, t_step)
+prosumer_main = main_prosumer.ProsumerMain(prosumer_dict, input_profiles, dynamic)
 
 prosumer_sizing_strategy = 'annuity'
 prosumer_main.optimize_sizing(prosumer_sizing_strategy)
@@ -98,7 +98,7 @@ for district_asset_name, component_profiles in district_asset_profiles.items():
     for component_name, profiles in component_profiles.items():
         district_asset_dict[district_asset_name]['components'][component_name].update(profiles)
 
-district_assets = main_prosumer.DistrictAssetMain(district_asset_dict, input_profiles, t_horizon, t_step).district_assets
+district_assets = main_prosumer.DistrictAssetMain(district_asset_dict, input_profiles, dynamic).district_assets
 
 district_paths = {'community': 'input_files/models/district_models/example_community/district.json'}
 district_profiles = {'community': {'wholesale_price': 'elec_price_1', 'injection_price': 'elec_price_1'}}
@@ -110,7 +110,7 @@ for district_name, district_path in district_paths.items():
 for district_name, profiles in district_profiles.items():
     district_dict[district_name].update(profiles)
 
-district_main = main_district.DistrictMain(district_dict, prosumers, district_assets, input_profiles, t_horizon, t_step)
+district_main = main_district.DistrictMain(district_dict, prosumers, district_assets, input_profiles, dynamic)
 
 district_sizing_strategy = 'max_operational_profit'
 district_main.optimize_sizing(district_sizing_strategy)