feat: action plan code added

.gitignore

@@ -12,3 +12,4 @@
 cerc_hub.egg-info
 /out_files
 /input_files/output_buildings.geojson
+*.pyc

energy_system_modelling_package/energy_system_modelling_factories/archetypes/montreal/archetype_cluster_1.py

@@ -28,12 +28,12 @@ class ArchetypeCluster1:
 
   def heating_system_simulation(self):
     building_heating_hourly_consumption = []
-    boiler = self.building.energy_systems[1].generation_systems[0]
-    hp = self.building.energy_systems[1].generation_systems[1]
-    tes = self.building.energy_systems[1].generation_systems[0].energy_storage_systems[0]
+    boiler = self.building.energy_systems[0].generation_systems[0]
+    hp = self.building.energy_systems[0].generation_systems[1]
+    tes = self.building.energy_systems[0].generation_systems[0].energy_storage_systems[0]
     heating_demand_joules = self.building.heating_demand[cte.HOUR]
     heating_peak_load_watts = self.building.heating_peak_load[cte.YEAR][0]
-    upper_limit_tes_heating = 55
+    upper_limit_tes_heating = 45
     outdoor_temperature = self.building.external_temperature[cte.HOUR]
     results = HeatPumpBoilerTesHeating(hp=hp,
                                        boiler=boiler,
@@ -55,10 +55,10 @@ class ArchetypeCluster1:
     return results, building_heating_hourly_consumption
 
   def cooling_system_simulation(self):
-    hp = self.building.energy_systems[1].generation_systems[1]
+    hp = self.building.energy_systems[1].generation_systems[0]
     cooling_demand_joules = self.building.cooling_demand[cte.HOUR]
     cooling_peak_load = self.building.cooling_peak_load[cte.YEAR][0]
-    cutoff_temperature = 13
+    cutoff_temperature = 11
     outdoor_temperature = self.building.external_temperature[cte.HOUR]
     results = HeatPumpCooling(hp=hp,
                               hourly_cooling_demand_joules=cooling_demand_joules,
@@ -71,8 +71,8 @@ class ArchetypeCluster1:
 
   def dhw_system_simulation(self):
     building_dhw_hourly_consumption = []
-    hp = self.building.energy_systems[2].generation_systems[0]
-    tes = self.building.energy_systems[2].generation_systems[0].energy_storage_systems[0]
+    hp = self.building.energy_systems[-1].generation_systems[0]
+    tes = self.building.energy_systems[-1].generation_systems[0].energy_storage_systems[0]
     dhw_demand_joules = self.building.domestic_hot_water_heat_demand[cte.HOUR]
     upper_limit_tes = 65
     outdoor_temperature = self.building.external_temperature[cte.HOUR]

energy_system_modelling_package/energy_system_modelling_factories/energy_system_sizing_factory.py

@@ -4,11 +4,10 @@ SPDX - License - Identifier: LGPL - 3.0 - or -later
 Copyright © 2024 Concordia CERC group
 Project Coder Saeed Ranjbar saeed.ranjbar@mail.concordia.ca
 """
-
+from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.optimal_sizing import \
+  OptimalSizing
 from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.peak_load_sizing import \
   PeakLoadSizing
-from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.heuristic_sizing import \
-  HeuristicSizing
 from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_sizing import PVSizing
 
 
@@ -30,11 +29,11 @@ class EnergySystemsSizingFactory:
     for building in self._city.buildings:
       building.level_of_detail.energy_systems = 1
 
-  def _heurisitc_sizing(self):
+  def _optimal_sizing(self):
     """
     Size Energy Systems using a Single or Multi Objective GA
     """
-    HeuristicSizing(self._city).enrich_buildings()
+    OptimalSizing(self._city, optimization_scenario='cost_energy_consumption').enrich_buildings()
     self._city.level_of_detail.energy_systems = 1
     for building in self._city.buildings:
       building.level_of_detail.energy_systems = 1

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/domestic_hot_water_heat_pump_with_tes.py

@@ -6,7 +6,7 @@ from hub.helpers.monthly_values import MonthlyValues
 
 class DomesticHotWaterHeatPumpTes:
   def __init__(self, hp, tes, hourly_dhw_demand_joules, upper_limit_tes,
-               outdoor_temperature, dt=900):
+               outdoor_temperature, dt=None):
     self.hp = hp
     self.tes = tes
     self.dhw_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_dhw_demand_joules]
@@ -30,8 +30,8 @@ class DomesticHotWaterHeatPumpTes:
     hp_delta_t = 8
     number_of_ts = int(cte.HOUR_TO_SECONDS / self.dt)
     source_temperature_hourly = self.hp_characteristics.hp_source_temperature()
-    source_temperature = [x for x in source_temperature_hourly for _ in range(number_of_ts)]
-    demand = [x for x in self.dhw_demand for _ in range(number_of_ts)]
+    source_temperature = [0] + [x for x in source_temperature_hourly for _ in range(number_of_ts)]
+    demand = [0] + [x for x in self.dhw_demand for _ in range(number_of_ts)]
     variable_names = ["t_sup_hp", "t_tank", "m_ch", "m_dis", "q_hp", "q_coil", "hp_cop",
                       "hp_electricity", "available hot water (m3)", "refill flow rate (kg/s)", "total_consumption"]
     num_hours = len(demand)
@@ -39,7 +39,7 @@ class DomesticHotWaterHeatPumpTes:
     (t_sup_hp, t_tank, m_ch, m_dis, m_refill, q_hp, q_coil, hp_cop, hp_electricity, v_dhw, total_consumption) = \
         [variables[name] for name in variable_names]
     freshwater_temperature = 18
-    t_tank[0] = 70
+    t_tank[0] = 65
     for i in range(len(demand) - 1):
       delta_t_demand = demand[i] * (self.dt / (cte.WATER_DENSITY * cte.WATER_HEAT_CAPACITY *
                                                         storage_tank.volume))
@@ -73,16 +73,16 @@ class DomesticHotWaterHeatPumpTes:
       t_tank[i + 1] = t_tank[i] + (delta_t_hp - delta_t_freshwater - delta_t_demand + delta_t_coil)
       total_consumption[i] = hp_electricity[i] + q_coil[i]
     tes.temperature = []
-    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
-    heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil]
+    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity[1:]]
+    heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil[1:]]
     hp_hourly = []
     coil_hourly = []
     coil_sum = 0
     hp_sum = 0
-    for i in range(1, len(demand)):
+    for i in range(len(demand) - 1):
       hp_sum += hp_electricity_j[i]
       coil_sum += heating_coil_j[i]
-      if (i - 1) % number_of_ts == 0:
+      if i % number_of_ts == 0 or i == len(demand) - 1:
         tes.temperature.append(t_tank[i])
         hp_hourly.append(hp_sum)
         coil_hourly.append(coil_sum)
@@ -90,29 +90,31 @@ class DomesticHotWaterHeatPumpTes:
         coil_sum = 0
     hp.energy_consumption[cte.DOMESTIC_HOT_WATER] = {}
     hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR] = hp_hourly
-    hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
-      hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
-    hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
-      sum(hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
+    if len(self.dhw_demand) == 8760:
+      hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
+        hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
+      hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
+        sum(hp.energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
     if self.tes.heating_coil_capacity is not None:
       tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER] = {}
       tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR] = coil_hourly
-      tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
-        tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
-      tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
-        sum(tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
+      if len(self.dhw_demand) == 8760:
+        tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH] = MonthlyValues.get_total_month(
+          tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.HOUR])
+        tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.YEAR] = [
+          sum(tes.heating_coil_energy_consumption[cte.DOMESTIC_HOT_WATER][cte.MONTH])]
 
-    self.results['DHW Demand (W)'] = demand
-    self.results['DHW HP Heat Output (W)'] = q_hp
-    self.results['DHW HP Electricity Consumption (W)'] = hp_electricity
-    self.results['DHW HP Source Temperature'] = source_temperature
-    self.results['DHW HP Supply Temperature'] = t_sup_hp
-    self.results['DHW HP COP'] = hp_cop
-    self.results['DHW TES Heating Coil Heat Output (W)'] = q_coil
-    self.results['DHW TES Temperature'] = t_tank
-    self.results['DHW TES Charging Flow Rate (kg/s)'] = m_ch
-    self.results['DHW Flow Rate (kg/s)'] = m_dis
-    self.results['DHW TES Refill Flow Rate (kg/s)'] = m_refill
-    self.results['Available Water in Tank (m3)'] = v_dhw
-    self.results['Total DHW Power Consumption (W)'] = total_consumption
+    self.results['DHW Demand (W)'] = demand[1:]
+    self.results['DHW HP Heat Output (W)'] = q_hp[1:]
+    self.results['DHW HP Electricity Consumption (W)'] = hp_electricity[1:]
+    self.results['DHW HP Source Temperature'] = source_temperature[1:]
+    self.results['DHW HP Supply Temperature'] = t_sup_hp[1:]
+    self.results['DHW HP COP'] = hp_cop[1:]
+    self.results['DHW TES Heating Coil Heat Output (W)'] = q_coil[1:]
+    self.results['DHW TES Temperature'] = t_tank[1:]
+    self.results['DHW TES Charging Flow Rate (kg/s)'] = m_ch[1:]
+    self.results['DHW Flow Rate (kg/s)'] = m_dis[1:]
+    self.results['DHW TES Refill Flow Rate (kg/s)'] = m_refill[1:]
+    self.results['Available Water in Tank (m3)'] = v_dhw[1:]
+    self.results['Total DHW Power Consumption (W)'] = total_consumption[1:]
     return self.results

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_boiler_tes_heating.py

@@ -7,13 +7,16 @@ from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_
 
 
 class HeatPumpBoilerTesHeating:
-  def __init__(self, hp, boiler, tes, hourly_heating_demand_joules, heating_peak_load_watts, upper_limit_tes,
-               outdoor_temperature, dt=900):
+  def __init__(self, hp=None, boiler=None, tes=None, hourly_heating_demand_joules=None, heating_peak_load_watts=None,
+               upper_limit_tes=None, outdoor_temperature=None, dt=None):
     self.hp = hp
     self.boiler = boiler
     self.tes = tes
     self.heating_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_heating_demand_joules]
-    self.heating_peak_load = heating_peak_load_watts
+    if heating_peak_load_watts is not None:
+      self.heating_peak_load = heating_peak_load_watts
+    else:
+      self.heating_peak_load = max(hourly_heating_demand_joules) / cte.HOUR_TO_SECONDS
     self.upper_limit_tes = upper_limit_tes
     self.hp_characteristics = HeatPump(self.hp, outdoor_temperature)
     self.t_out = outdoor_temperature
@@ -22,6 +25,10 @@ class HeatPumpBoilerTesHeating:
 
   def simulation(self):
     hp, boiler, tes = self.hp, self.boiler, self.tes
+    if boiler is not None:
+      if hp.nominal_heat_output < 0 or boiler.nominal_heat_output < 0:
+        raise ValueError("Heat output values must be non-negative. Check the nominal_heat_output for hp and boiler.")
+
     heating_coil_nominal_output = 0
     if tes.heating_coil_capacity is not None:
       heating_coil_nominal_output = float(tes.heating_coil_capacity)
@@ -55,41 +62,54 @@ class HeatPumpBoilerTesHeating:
                                                                        ambient_temperature=t_out[i],
                                                                        dt=self.dt)
       # hp operation
-      if t_tank[i + 1] < 40:
-        q_hp[i + 1] = hp.nominal_heat_output
-        m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
-        t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
-      elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] == 0:
-        q_hp[i + 1] = 0
-        m_ch[i + 1] = 0
-        t_sup_hp[i + 1] = t_tank[i + 1]
-      elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] > 0:
-        q_hp[i + 1] = hp.nominal_heat_output
-        m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
-        t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
-      else:
-        q_hp[i + 1], m_ch[i + 1], t_sup_hp[i + 1] = 0, 0, t_tank[i + 1]
-      if q_hp[i + 1] > 0:
-        if hp.source_medium == cte.AIR and self.hp.supply_medium == cte.WATER:
-          hp_cop[i + 1] = self.hp_characteristics.air_to_water_cop(source_temperature[i + 1], t_tank[i + 1], mode=cte.HEATING)
-          hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
-      else:
-        hp_cop[i + 1] = 0
-        hp_electricity[i + 1] = 0
-      # boiler operation
-      if q_hp[i + 1] > 0:
-        if t_sup_hp[i + 1] < 45:
-          q_boiler[i + 1] = boiler.nominal_heat_output
-        elif demand[i + 1] > 0.5 * self.heating_peak_load / self.dt:
-          q_boiler[i + 1] = 0.5 * boiler.nominal_heat_output
-        boiler_energy_consumption[i + 1] = q_boiler[i + 1] / float(boiler.heat_efficiency)
-        if boiler.fuel_type == cte.ELECTRICITY:
-          boiler_fuel_consumption[i + 1] = boiler_energy_consumption[i + 1]
+      if t_out[i + 1] > -20:
+        if t_tank[i + 1] < 40:
+          q_hp[i + 1] = hp.nominal_heat_output
+          m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
+          t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
+        elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] == 0:
+          q_hp[i + 1] = 0
+          m_ch[i + 1] = 0
+          t_sup_hp[i + 1] = t_tank[i + 1]
+        elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_hp[i] > 0:
+          q_hp[i + 1] = hp.nominal_heat_output
+          m_ch[i + 1] = q_hp[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
+          t_sup_hp[i + 1] = (q_hp[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY)) + t_tank[i + 1]
         else:
-            # TODO: Other fuels should be considered
-          boiler_fuel_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (
-              float(boiler.heat_efficiency) * cte.NATURAL_GAS_LHV)
+          q_hp[i + 1], m_ch[i + 1], t_sup_hp[i + 1] = 0, 0, t_tank[i + 1]
+        if q_hp[i + 1] > 0:
+          if hp.source_medium == cte.AIR and self.hp.supply_medium == cte.WATER:
+            hp_cop[i + 1] = self.hp_characteristics.air_to_water_cop(source_temperature[i + 1], t_tank[i + 1], mode=cte.HEATING)
+            hp_electricity[i + 1] = q_hp[i + 1] / hp_cop[i + 1]
+        else:
+          hp_cop[i + 1] = 0
+          hp_electricity[i + 1] = 0
+      else:
+        q_hp[i + 1] = 0
+        t_sup_hp[i + 1] = t_tank[i + 1]
+        if t_tank[i + 1] < 40:
+          q_boiler[i + 1] = boiler.nominal_heat_output
+          m_ch[i + 1] = q_boiler[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
+        elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_boiler[i] == 0:
+          q_boiler[i + 1] = 0
+          m_ch[i + 1] = 0
+        elif 40 <= t_tank[i + 1] < self.upper_limit_tes and q_boiler[i] > 0:
+          q_boiler[i + 1] = boiler.nominal_heat_output
+          m_ch[i + 1] = q_boiler[i + 1] / (cte.WATER_HEAT_CAPACITY * hp_delta_t)
+        else:
+          q_boiler[i + 1], m_ch[i + 1] = 0, 0
+
+      boiler_energy_consumption[i + 1] = q_boiler[i + 1] / float(boiler.heat_efficiency)
+      if boiler.fuel_type == cte.ELECTRICITY:
+        boiler_fuel_consumption[i + 1] = boiler_energy_consumption[i + 1]
+      else:
+          # TODO: Other fuels should be considered
+        boiler_fuel_consumption[i + 1] = (q_boiler[i + 1] * self.dt) / (
+            float(boiler.heat_efficiency) * cte.NATURAL_GAS_LHV)
+      if m_ch[i + 1] > 0:
         t_sup_boiler[i + 1] = t_sup_hp[i + 1] + (q_boiler[i + 1] / (m_ch[i + 1] * cte.WATER_HEAT_CAPACITY))
+      else:
+        t_sup_boiler[i + 1] = t_sup_hp[i + 1]
       # heating coil operation
       if t_tank[i + 1] < 35:
         q_coil[i + 1] = heating_coil_nominal_output
@@ -107,20 +127,20 @@ class HeatPumpBoilerTesHeating:
       # total consumption
       total_consumption[i + 1] = hp_electricity[i + 1] + boiler_energy_consumption[i + 1] + q_coil[i + 1]
     tes.temperature = []
-    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
-    boiler_consumption_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in boiler_energy_consumption]
-    heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil]
+    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity[1:]]
+    boiler_consumption_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in boiler_energy_consumption[1:]]
+    heating_coil_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in q_coil[1:]]
     hp_hourly = []
     boiler_hourly = []
     coil_hourly = []
     boiler_sum = 0
     hp_sum = 0
     coil_sum = 0
-    for i in range(1, len(demand)):
+    for i in range(len(demand) - 1):
       hp_sum += hp_electricity_j[i]
       boiler_sum += boiler_consumption_j[i]
       coil_sum += heating_coil_j[i]
-      if (i - 1) % number_of_ts == 0:
+      if i % number_of_ts == 0 or i == len(demand) - 1:
         tes.temperature.append(t_tank[i])
         hp_hourly.append(hp_sum)
         boiler_hourly.append(boiler_sum)
@@ -130,37 +150,41 @@ class HeatPumpBoilerTesHeating:
         coil_sum = 0
     hp.energy_consumption[cte.HEATING] = {}
     hp.energy_consumption[cte.HEATING][cte.HOUR] = hp_hourly
-    hp.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
-      hp.energy_consumption[cte.HEATING][cte.HOUR])
-    hp.energy_consumption[cte.HEATING][cte.YEAR] = [
-      sum(hp.energy_consumption[cte.HEATING][cte.MONTH])]
-    boiler.energy_consumption[cte.HEATING] = {}
-    boiler.energy_consumption[cte.HEATING][cte.HOUR] = boiler_hourly
-    boiler.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
-      boiler.energy_consumption[cte.HEATING][cte.HOUR])
-    boiler.energy_consumption[cte.HEATING][cte.YEAR] = [
-      sum(boiler.energy_consumption[cte.HEATING][cte.MONTH])]
+    if boiler is not None:
+      boiler.energy_consumption[cte.HEATING] = {}
+      boiler.energy_consumption[cte.HEATING][cte.HOUR] = boiler_hourly
+    if len(self.heating_demand) == 8760:
+      hp.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
+        hp.energy_consumption[cte.HEATING][cte.HOUR])
+      hp.energy_consumption[cte.HEATING][cte.YEAR] = [
+        sum(hp.energy_consumption[cte.HEATING][cte.MONTH])]
+      if boiler is not None:
+        boiler.energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
+          boiler.energy_consumption[cte.HEATING][cte.HOUR])
+        boiler.energy_consumption[cte.HEATING][cte.YEAR] = [
+          sum(boiler.energy_consumption[cte.HEATING][cte.MONTH])]
     if tes.heating_coil_capacity is not None:
       tes.heating_coil_energy_consumption[cte.HEATING] = {}
-      tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR] = coil_hourly
-      tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
-        tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR])
-      tes.heating_coil_energy_consumption[cte.HEATING][cte.YEAR] = [
-        sum(tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH])]
-    self.results['Heating Demand (W)'] = demand
-    self.results['HP Heat Output (W)'] = q_hp
-    self.results['HP Source Temperature'] = source_temperature
-    self.results['HP Supply Temperature'] = t_sup_hp
-    self.results['HP COP'] = hp_cop
-    self.results['HP Electricity Consumption (W)'] = hp_electricity
-    self.results['Boiler Heat Output (W)'] = q_boiler
-    self.results['Boiler Power Consumption (W)'] = boiler_energy_consumption
-    self.results['Boiler Supply Temperature'] = t_sup_boiler
-    self.results['Boiler Fuel Consumption'] = boiler_fuel_consumption
-    self.results['TES Temperature'] = t_tank
-    self.results['Heating Coil heat input'] = q_coil
-    self.results['TES Charging Flow Rate (kg/s)'] = m_ch
-    self.results['TES Discharge Flow Rate (kg/s)'] = m_dis
-    self.results['Heating Loop Return Temperature'] = t_ret
-    self.results['Total Heating Power Consumption (W)'] = total_consumption
+      if len(self.heating_demand) == 8760:
+        tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR] = coil_hourly
+        tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH] = MonthlyValues.get_total_month(
+          tes.heating_coil_energy_consumption[cte.HEATING][cte.HOUR])
+        tes.heating_coil_energy_consumption[cte.HEATING][cte.YEAR] = [
+          sum(tes.heating_coil_energy_consumption[cte.HEATING][cte.MONTH])]
+    self.results['Heating Demand (W)'] = demand[1:]
+    self.results['HP Heat Output (W)'] = q_hp[1:]
+    self.results['HP Source Temperature'] = source_temperature[1:]
+    self.results['HP Supply Temperature'] = t_sup_hp[1:]
+    self.results['HP COP'] = hp_cop[1:]
+    self.results['HP Electricity Consumption (W)'] = hp_electricity[1:]
+    self.results['Boiler Heat Output (W)'] = q_boiler[1:]
+    self.results['Boiler Power Consumption (W)'] = boiler_energy_consumption[1:]
+    self.results['Boiler Supply Temperature'] = t_sup_boiler[1:]
+    self.results['Boiler Fuel Consumption'] = boiler_fuel_consumption[1:]
+    self.results['TES Temperature'] = t_tank[1:]
+    self.results['Heating Coil heat input'] = q_coil[1:]
+    self.results['TES Charging Flow Rate (kg/s)'] = m_ch[1:]
+    self.results['TES Discharge Flow Rate (kg/s)'] = m_dis[1:]
+    self.results['Heating Loop Return Temperature'] = t_ret[1:]
+    self.results['Total Heating Power Consumption (W)'] = total_consumption[1:]
     return self.results

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_characteristics.py

@@ -38,7 +38,7 @@ class HeatPump:
                                 cop_curve_coefficients[2] * t_inlet_water_fahrenheit ** 2 +
                                 cop_curve_coefficients[3] * t_source_fahrenheit +
                                 cop_curve_coefficients[4] * t_source_fahrenheit ** 2 +
-                                cop_curve_coefficients[5] * t_inlet_water_fahrenheit * t_source_fahrenheit))
+                                cop_curve_coefficients[5] * t_inlet_water_fahrenheit * t_source_fahrenheit)) / 3.41214
       hp_efficiency = float(self.hp.cooling_efficiency)
     else:
       if self.hp.heat_efficiency_curve is not None:

energy_system_modelling_package/energy_system_modelling_factories/hvac_dhw_systems_simulation_models/heat_pump_cooling.py

@@ -53,32 +53,35 @@ class HeatPumpCooling:
         if self.hp.source_medium == cte.AIR and self.hp.supply_medium == cte.WATER:
           hp_cop[i] = self.heat_pump_characteristics.air_to_water_cop(source_temperature[i], t_ret[i],
                                                                       mode=cte.COOLING)
-          hp_electricity[i] = q_hp[i] / cooling_efficiency
+          hp_electricity[i] = q_hp[i] / hp_cop[i]
       else:
         hp_cop[i] = 0
         hp_electricity[i] = 0
-    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity]
+    hp_electricity_j = [(x * cte.WATTS_HOUR_TO_JULES) / number_of_ts for x in hp_electricity[1:]]
     hp_hourly = []
+    hp_supply_temperature_hourly = []
     hp_sum = 0
-    for i in range(1, len(demand)):
+    for i in range(len(demand) - 1):
       hp_sum += hp_electricity_j[i]
       if (i - 1) % number_of_ts == 0:
         hp_hourly.append(hp_sum)
+        hp_supply_temperature_hourly.append(t_sup_hp[i])
         hp_sum = 0
+    self.hp.cooling_supply_temperature = hp_supply_temperature_hourly
     self.hp.energy_consumption[cte.COOLING] = {}
     self.hp.energy_consumption[cte.COOLING][cte.HOUR] = hp_hourly
     self.hp.energy_consumption[cte.COOLING][cte.MONTH] = MonthlyValues.get_total_month(
       self.hp.energy_consumption[cte.COOLING][cte.HOUR])
     self.hp.energy_consumption[cte.COOLING][cte.YEAR] = [
       sum(self.hp.energy_consumption[cte.COOLING][cte.MONTH])]
-    self.results['Cooling Demand (W)'] = demand
-    self.results['HP Cooling Output (W)'] = q_hp
-    self.results['HP Source Temperature'] = source_temperature
-    self.results['HP Cooling Supply Temperature'] = t_sup_hp
-    self.results['HP Cooling COP'] = hp_cop
-    self.results['HP Electricity Consumption'] = hp_electricity
-    self.results['Cooling Loop Flow Rate (kg/s)'] = m
-    self.results['Cooling Loop Return Temperature'] = t_ret
+    self.results['Cooling Demand (W)'] = demand[1:]
+    self.results['HP Cooling Output (W)'] = q_hp[1:]
+    self.results['HP Cooling Source Temperature'] = source_temperature[1:]
+    self.results['HP Cooling Supply Temperature'] = t_sup_hp[1:]
+    self.results['HP Cooling COP'] = hp_cop[1:]
+    self.results['HP Electricity Consumption'] = hp_electricity[1:]
+    self.results['Cooling Loop Flow Rate (kg/s)'] = m[1:]
+    self.results['Cooling Loop Return Temperature'] = t_ret[1:]
     return self.results
 
 

energy_system_modelling_package/energy_system_modelling_factories/montreal_energy_system_archetype_modelling_factory.py

@@ -13,17 +13,18 @@ class MontrealEnergySystemArchetypesSimulationFactory:
   EnergySystemsFactory class
   """
 
-  def __init__(self, handler, building, output_path):
+  def __init__(self, handler, building, output_path, csv_output=True):
     self._output_path = output_path
     self._handler = '_' + handler.lower()
     self._building = building
+    self._csv_output = csv_output
 
   def _archetype_cluster_1(self):
     """
     Enrich the city by using the sizing and simulation model developed for archetype13 of montreal_future_systems
     """
     dt = 900
-    ArchetypeCluster1(self._building, dt, self._output_path, csv_output=True).enrich_building()
+    ArchetypeCluster1(self._building, dt, self._output_path, self._csv_output).enrich_building()
     self._building.level_of_detail.energy_systems = 2
     self._building.level_of_detail.energy_systems = 2
 

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/individual.py

@@ -0,0 +1,506 @@
+import math
+import random
+from hub.helpers.dictionaries import Dictionaries
+from hub.catalog_factories.costs_catalog_factory import CostsCatalogFactory
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.domestic_hot_water_heat_pump_with_tes import \
+  DomesticHotWaterHeatPumpTes
+from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_systems_simulation_models.heat_pump_boiler_tes_heating import \
+  HeatPumpBoilerTesHeating
+import numpy_financial as npf
+
+
+class Individual:
+  def __init__(self, building, energy_system, design_period_energy_demands, optimization_scenario,
+               heating_design_load=None, cooling_design_load=None, dt=900, fuel_price_index=0.05,
+               electricity_tariff_type='fixed', consumer_price_index=0.04, interest_rate=0.04,
+               discount_rate=0.03, percentage_credit=0, credit_years=15):
+    """
+    :param building: building object
+    :param energy_system: energy system to be optimized
+    :param design_period_energy_demands: A dictionary of design period heating, cooling and dhw demands. Design period
+    is the day with the highest total demand and the two days before and after it
+    :param optimization_scenario: a string indicating the objective function from minimization of cost,
+    energy consumption, and both together
+    :param heating_design_load: heating design load in W
+    :param cooling_design_load: cooling design load in W
+    :param dt the time step size used for simulations
+    :param fuel_price_index the price increase index of all fuels. A single value is used for all fuels.
+    :param electricity_tariff_type the electricity tariff type between 'fixed' and 'variable' for economic optimization
+    :param consumer_price_index
+    """
+    self.building = building
+    self.energy_system = energy_system
+    self.design_period_energy_demands = design_period_energy_demands
+    self.demand_types = energy_system.demand_types
+    self.optimization_scenario = optimization_scenario
+    self.heating_design_load = heating_design_load
+    self.cooling_design_load = cooling_design_load
+    self.available_space = building.volume / building.storeys_above_ground
+    self.dt = dt
+    self.fuel_price_index = fuel_price_index
+    self.electricity_tariff_type = electricity_tariff_type
+    self.consumer_price_index = consumer_price_index
+    self.interest_rate = interest_rate
+    self.discount_rate = discount_rate
+    self.credit_years = credit_years
+    self.percentage_credit = percentage_credit
+    self.costs = self.costs_archetype()
+    self.feasibility = True
+    self.fitness_score = 0
+    self.rank = 0
+    self.crowding_distance = 0
+    self.individual = {}
+
+  def system_components(self):
+    """
+    Extracts system components (generation and storage) for a given energy system.
+    :return: Dictionary of system components.
+    """
+    self.individual['Generation Components'] = []
+    self.individual['Energy Storage Components'] = []
+    self.individual['End of Life Cost'] = self.costs.end_of_life_cost
+    for generation_component in self.energy_system.generation_systems:
+      investment_cost, reposition_cost, lifetime = self.unit_investment_cost('Generation',
+                                                                             generation_component.system_type)
+      maintenance_cost = self.unit_maintenance_cost(generation_component)
+      if generation_component.system_type == cte.PHOTOVOLTAIC:
+        heating_capacity = None
+        cooling_capacity = None
+        heat_efficiency = None
+        cooling_efficiency = None
+        unit_fuel_cost = 0
+      else:
+        heating_capacity = 0
+        cooling_capacity = 0
+        heat_efficiency = generation_component.heat_efficiency
+        cooling_efficiency = generation_component.cooling_efficiency
+        unit_fuel_cost = self.fuel_cost_per_kwh(generation_component.fuel_type, 'fixed')
+      self.individual['Generation Components'].append({
+        'type': generation_component.system_type,
+        'heating_capacity': heating_capacity,
+        'cooling_capacity': cooling_capacity,
+        'electricity_capacity': 0,
+        'nominal_heating_efficiency': heat_efficiency,
+        'nominal_cooling_efficiency': cooling_efficiency,
+        'nominal_electricity_efficiency': generation_component.electricity_efficiency,
+        'fuel_type': generation_component.fuel_type,
+        'unit_investment_cost': investment_cost,
+        'unit_reposition_cost': reposition_cost,
+        'unit_fuel_cost(CAD/kWh)': unit_fuel_cost,
+        'unit_maintenance_cost': maintenance_cost,
+        'lifetime': lifetime
+      })
+      if generation_component.energy_storage_systems is not None:
+        for energy_storage_system in generation_component.energy_storage_systems:
+          investment_cost, reposition_cost, lifetime = (
+            self.unit_investment_cost('Storage',
+                                      f'{energy_storage_system.type_energy_stored}_storage'))
+          if energy_storage_system.type_energy_stored == cte.THERMAL:
+            heating_coil_capacity = energy_storage_system.heating_coil_capacity
+            heating_coil_fuel_cost = self.fuel_cost_per_kwh(f'{cte.ELECTRICITY}', 'fixed')
+            volume = 0
+            capacity = None
+          else:
+            heating_coil_capacity = None
+            heating_coil_fuel_cost = None
+            volume = None
+            capacity = 0
+          self.individual['Energy Storage Components'].append(
+            {'type': f'{energy_storage_system.type_energy_stored}_storage',
+             'capacity': capacity,
+             'volume': volume,
+             'heating_coil_capacity': heating_coil_capacity,
+             'unit_investment_cost': investment_cost,
+             'unit_reposition_cost': reposition_cost,
+             'heating_coil_fuel_cost': heating_coil_fuel_cost,
+             'unit_maintenance_cost': 0,
+             'lifetime': lifetime
+             })
+
+  def initialization(self):
+    """
+    Assigns initial sizes to generation and storage components based on heating and cooling design loads and
+    available space in the building.
+    :return:
+    """
+    self.system_components()
+    generation_components = self.individual['Generation Components']
+    storage_components = self.individual['Energy Storage Components']
+    for generation_component in generation_components:
+      if generation_component[
+        'nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
+        if self.heating_design_load is not None:
+          generation_component['heating_capacity'] = random.uniform(0, self.heating_design_load)
+        else:
+          if cte.HEATING in self.demand_types:
+            generation_component['heating_capacity'] = random.uniform(0,
+                                                                      self.building.heating_peak_load[cte.YEAR][0])
+          else:
+            generation_component['heating_capacity'] = random.uniform(0,
+                                                                      self.building.domestic_hot_water_peak_load[cte.YEAR][0])
+      else:
+        generation_component['heating_capacity'] = None
+      if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in self.demand_types:
+        if self.cooling_design_load is not None:
+          generation_component['cooling_capacity'] = random.uniform(0, self.cooling_design_load)
+        else:
+          generation_component['cooling_capacity'] = random.uniform(0,
+                                                                    self.building.cooling_peak_load[cte.YEAR][0])
+      else:
+        generation_component['cooling_capacity'] = None
+      if generation_component['nominal_electricity_efficiency'] is None:
+        generation_component['electricity_capacity'] = None
+    for storage_component in storage_components:
+      if storage_component['type'] == f'{cte.THERMAL}_storage':
+        storage_operation_range = 10 if cte.DOMESTIC_HOT_WATER in self.demand_types else 20
+        storage_energy_capacity = random.uniform(0, 6)
+        volume = (storage_energy_capacity * self.building.heating_peak_load[cte.YEAR][0] * 3600) / (cte.WATER_HEAT_CAPACITY * 1000 * storage_operation_range)
+        max_available_space = min(0.01 * self.available_space, volume)
+        storage_component['volume'] = random.uniform(0, max_available_space)
+        if storage_component['heating_coil_capacity'] is not None:
+          if self.heating_design_load is not None:
+            storage_component['heating_coil_capacity'] = random.uniform(0, self.heating_design_load)
+          else:
+            if cte.HEATING in self.demand_types:
+              storage_component['heating_coil_capacity'] = random.uniform(0,
+                                                                          self.building.heating_peak_load[cte.YEAR][0])
+            else:
+              storage_component['heating_coil_capacity'] = random.uniform(0,
+                                                                          self.building.domestic_hot_water_peak_load[cte.YEAR][0])
+
+  def score_evaluation(self):
+    self.system_simulation()
+    self.individual['feasible'] = self.feasibility
+    lcc = 0
+    total_energy_consumption = 0
+    if self.feasibility:
+      if 'cost' in self.optimization_scenario:
+        investment_cost = 0
+        operation_cost_year_0 = 0
+        maintenance_cost_year_0 = 0
+        for generation_system in self.individual['Generation Components']:
+          heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
+            'heating_capacity']
+          cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
+            'cooling_capacity']
+          capacity = max(heating_capacity, cooling_capacity)
+          investment_cost += capacity * generation_system['unit_investment_cost'] / 1000
+          maintenance_cost_year_0 += capacity * generation_system['unit_maintenance_cost'] / 1000
+          operation_cost_year_0 += (generation_system['total_energy_consumption(kWh)'] *
+                                    generation_system['unit_fuel_cost(CAD/kWh)'])
+        for storage_system in self.individual['Energy Storage Components']:
+          if cte.THERMAL in storage_system['type']:
+            investment_cost += storage_system['volume'] * storage_system['unit_investment_cost']
+            if storage_system['heating_coil_capacity'] is not None:
+              operation_cost_year_0 += (storage_system['total_energy_consumption(kWh)'] *
+                                        storage_system['heating_coil_fuel_cost'])
+        lcc = self.life_cycle_cost_calculation(investment_cost=investment_cost,
+                                               operation_cost_year_0=operation_cost_year_0,
+                                               maintenance_cost_year_0=maintenance_cost_year_0)
+        self.individual['lcc'] = lcc
+      if 'energy-consumption' in self.optimization_scenario:
+        total_energy_consumption = 0
+        for generation_system in self.individual['Generation Components']:
+          total_energy_consumption += generation_system['total_energy_consumption(kWh)']
+        for storage_system in self.individual['Energy Storage Components']:
+          if cte.THERMAL in storage_system['type'] and storage_system['heating_coil_capacity'] is not None:
+            total_energy_consumption += storage_system['total_energy_consumption(kWh)']
+        self.individual['total_energy_consumption'] = total_energy_consumption
+    # Fitness score based on the optimization scenario
+      if self.optimization_scenario == 'cost':
+        self.fitness_score = lcc
+        self.individual['fitness_score'] = lcc
+      elif self.optimization_scenario == 'energy-consumption':
+        self.fitness_score = total_energy_consumption
+        self.individual['fitness_score'] = total_energy_consumption
+      elif self.optimization_scenario == 'cost_energy-consumption':
+        self.fitness_score = (lcc, total_energy_consumption)
+      elif self.optimization_scenario == 'energy-consumption_cost':
+        self.fitness_score = (total_energy_consumption, lcc)
+        self.individual['fitness_score'] = (lcc, total_energy_consumption)
+    else:
+      lcc = float('inf')
+      total_energy_consumption = float('inf')
+      self.individual['lcc'] = lcc
+      self.individual['total_energy_consumption'] = total_energy_consumption
+      if self.optimization_scenario == 'cost_energy-consumption' or self.optimization_scenario == 'energy-consumption_cost':
+        self.individual['fitness_score'] = (float('inf'), float('inf'))
+        self.fitness_score = (float('inf'), float('inf'))
+      else:
+        self.individual['fitness_score'] = float('inf')
+        self.fitness_score = float('inf')
+
+  def system_simulation(self):
+    """
+    The method to run the energy system model using the existing models in the energy_system_modelling_package.
+    Based on cluster id and demands, model is imported and run.
+    :return: dictionary of results
+    """
+    if self.building.energy_systems_archetype_cluster_id == '1':
+      if cte.HEATING in self.demand_types:
+        boiler = self.energy_system.generation_systems[0]
+        boiler.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
+        hp = self.energy_system.generation_systems[1]
+        hp.nominal_heat_output = self.individual['Generation Components'][1]['heating_capacity']
+        tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
+        tes.volume = self.individual['Energy Storage Components'][0]['volume']
+        tes.height = self.building.average_storey_height - 1
+        tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
+          if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
+        heating_demand_joules = self.design_period_energy_demands[cte.HEATING]['demands']
+        heating_peak_load_watts = max(self.design_period_energy_demands[cte.HEATING]) if \
+          (self.heating_design_load is not None) else self.building.heating_peak_load[cte.YEAR][0]
+        upper_limit_tes_heating = 55
+        design_period_start_index = self.design_period_energy_demands[cte.HEATING]['start_index']
+        design_period_end_index = self.design_period_energy_demands[cte.HEATING]['end_index']
+        outdoor_temperature = self.building.external_temperature[cte.HOUR][
+                              design_period_start_index:design_period_end_index]
+        results = HeatPumpBoilerTesHeating(hp=hp,
+                                           boiler=boiler,
+                                           tes=tes,
+                                           hourly_heating_demand_joules=heating_demand_joules,
+                                           heating_peak_load_watts=heating_peak_load_watts,
+                                           upper_limit_tes=upper_limit_tes_heating,
+                                           outdoor_temperature=outdoor_temperature,
+                                           dt=self.dt).simulation()
+        if min(results['TES Temperature']) < 35:
+          self.feasibility = False
+      elif cte.DOMESTIC_HOT_WATER in self.demand_types:
+        hp = self.energy_system.generation_systems[0]
+        hp.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
+        tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
+        tes.volume = self.individual['Energy Storage Components'][0]['volume']
+        tes.height = self.building.average_storey_height - 1
+        tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
+          if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
+        dhw_demand_joules = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']
+        upper_limit_tes = 65
+        design_period_start_index = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['start_index']
+        design_period_end_index = self.design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['end_index']
+        outdoor_temperature = self.building.external_temperature[cte.HOUR][
+                              design_period_start_index:design_period_end_index]
+        results = DomesticHotWaterHeatPumpTes(hp=hp,
+                                              tes=tes,
+                                              hourly_dhw_demand_joules=dhw_demand_joules,
+                                              upper_limit_tes=upper_limit_tes,
+                                              outdoor_temperature=outdoor_temperature,
+                                              dt=self.dt).simulation()
+        if min(results['DHW TES Temperature']) < 55:
+          self.feasibility = False
+    elif self.building.energy_systems_archetype_cluster_id == '3':
+      if cte.HEATING in self.demand_types:
+        hp = self.energy_system.generation_systems[0]
+        hp.nominal_heat_output = self.individual['Generation Components'][0]['heating_capacity']
+        tes = self.energy_system.generation_systems[0].energy_storage_systems[0]
+        tes.volume = self.individual['Energy Storage Components'][0]['volume']
+        tes.height = self.building.average_storey_height - 1
+        tes.heating_coil_capacity = self.individual['Energy Storage Components'][0]['heating_coil_capacity'] \
+          if self.individual['Energy Storage Components'][0]['heating_coil_capacity'] is not None else None
+        heating_demand_joules = self.design_period_energy_demands[cte.HEATING]['demands']
+        heating_peak_load_watts = max(self.design_period_energy_demands[cte.HEATING]) if \
+          (self.heating_design_load is not None) else self.building.heating_peak_load[cte.YEAR][0]
+        upper_limit_tes_heating = 55
+        design_period_start_index = self.design_period_energy_demands[cte.HEATING]['start_index']
+        design_period_end_index = self.design_period_energy_demands[cte.HEATING]['end_index']
+        outdoor_temperature = self.building.external_temperature[cte.HOUR][
+                              design_period_start_index:design_period_end_index]
+        results = HeatPumpBoilerTesHeating(hp=hp,
+                                           boiler=None,
+                                           tes=tes,
+                                           hourly_heating_demand_joules=heating_demand_joules,
+                                           heating_peak_load_watts=heating_peak_load_watts,
+                                           upper_limit_tes=upper_limit_tes_heating,
+                                           outdoor_temperature=outdoor_temperature,
+                                           dt=self.dt).simulation()
+        if min(results['TES Temperature']) < 35:
+          self.feasibility = False
+
+    if self.feasibility:
+      generation_system_types = [generation_system.system_type for generation_system in
+                                 self.energy_system.generation_systems]
+      for generation_component in self.individual['Generation Components']:
+        if generation_component['type'] in generation_system_types:
+          index = generation_system_types.index(generation_component['type'])
+          for demand_type in self.demand_types:
+            if demand_type in self.energy_system.generation_systems[index].energy_consumption:
+              generation_component['total_energy_consumption(kWh)'] = (sum(
+                self.energy_system.generation_systems[index].energy_consumption[demand_type][cte.HOUR]) / 3.6e6)
+      for storage_component in self.individual['Energy Storage Components']:
+        if storage_component['type'] == f'{cte.THERMAL}_storage' and storage_component[
+          'heating_coil_capacity'] is not None:
+          for generation_system in self.energy_system.generation_systems:
+            if generation_system.energy_storage_systems is not None:
+              for storage_system in generation_system.energy_storage_systems:
+                if storage_system.type_energy_stored == cte.THERMAL:
+                  for demand_type in self.demand_types:
+                    if demand_type in storage_system.heating_coil_energy_consumption:
+                      storage_component['total_energy_consumption(kWh)'] = (sum(
+                        storage_system.heating_coil_energy_consumption[demand_type][cte.HOUR]) / 3.6e6)
+
+  def life_cycle_cost_calculation(self, investment_cost, operation_cost_year_0, maintenance_cost_year_0,
+                                  life_cycle_duration=41):
+    """
+    Calculating the life cycle cost of the energy system. The original cost workflow in hub is not used to reduce
+    computation time.Here are the steps:
+        1- Costs catalog and different components are imported.
+        2- Capital costs (investment and reposition) are calculated and appended to a list to have the capital cash
+           flow.
+        3-
+    :param maintenance_cost_year_0:
+    :param operation_cost_year_0:
+    :param investment_cost:
+    :param life_cycle_duration:
+    :return:
+    """
+    capital_costs_cash_flow = [investment_cost]
+    operational_costs_cash_flow = [0]
+    maintenance_costs_cash_flow = [0]
+    end_of_life_costs = [0] * (life_cycle_duration + 1)
+    for i in range(1, life_cycle_duration + 1):
+      yearly_operational_cost = math.pow(1 + self.fuel_price_index, i) * operation_cost_year_0
+      yearly_maintenance_cost = math.pow(1 + self.consumer_price_index, i) * maintenance_cost_year_0
+      yearly_capital_cost = 0
+      for generation_system in self.individual['Generation Components']:
+        if (i % generation_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
+          cost_increase = math.pow(1 + self.consumer_price_index, i)
+          heating_capacity = 0 if generation_system['heating_capacity'] is None else generation_system[
+            'heating_capacity']
+          cooling_capacity = 0 if generation_system['cooling_capacity'] is None else generation_system[
+            'cooling_capacity']
+          capacity = max(heating_capacity, cooling_capacity)
+          yearly_capital_cost += generation_system['unit_reposition_cost'] * capacity * cost_increase / 1000
+          yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
+                                          investment_cost * self.percentage_credit)
+      for storage_system in self.individual['Energy Storage Components']:
+        if (i % storage_system['lifetime']) == 0 and i != (life_cycle_duration - 1):
+          cost_increase = math.pow(1 + self.consumer_price_index, i)
+          capacity = storage_system['volume'] if cte.THERMAL in storage_system['type'] else storage_system['capacity']
+          yearly_capital_cost += storage_system['unit_reposition_cost'] * capacity * cost_increase
+          yearly_capital_cost += -npf.pmt(self.interest_rate, self.credit_years,
+                                          investment_cost * self.percentage_credit)
+      capital_costs_cash_flow.append(yearly_capital_cost)
+      operational_costs_cash_flow.append(yearly_operational_cost)
+      maintenance_costs_cash_flow.append(yearly_maintenance_cost)
+    for year in range(1, life_cycle_duration + 1):
+      price_increase = math.pow(1 + self.consumer_price_index, year)
+      if year == life_cycle_duration:
+        end_of_life_costs[year] = (
+            self.building.thermal_zones_from_internal_zones[0].total_floor_area *
+            self.individual['End of Life Cost'] * price_increase
+        )
+
+    life_cycle_capital_cost = npf.npv(self.discount_rate, capital_costs_cash_flow)
+    life_cycle_operational_cost = npf.npv(self.discount_rate, operational_costs_cash_flow)
+    life_cycle_maintenance_cost = npf.npv(self.discount_rate, maintenance_costs_cash_flow)
+    life_cycle_end_of_life_cost = npf.npv(self.discount_rate, end_of_life_costs)
+    total_life_cycle_cost = life_cycle_capital_cost + life_cycle_operational_cost + life_cycle_maintenance_cost + life_cycle_end_of_life_cost
+    return total_life_cycle_cost
+
+  def costs_archetype(self):
+    costs_catalogue = CostsCatalogFactory('montreal_new').catalog.entries().archetypes
+    dictionary = Dictionaries().hub_function_to_montreal_custom_costs_function
+    costs_archetype = None
+    for archetype in costs_catalogue:
+      if dictionary[str(self.building.function)] == str(archetype.function):
+        costs_archetype = archetype
+    return costs_archetype
+
+  def unit_investment_cost(self, component_category, component_type):
+    """
+    Reading the investment and reposition costs of any component from costs catalogue
+    :param component_category: Due to the categorizations in the cost catalogue, we need this parameter to realize if
+    the component is a generation or storage component
+    :param component_type: Type of the component
+    :return:
+    """
+    investment_cost = 0
+    reposition_cost = 0
+    lifetime = 0
+    name = ''
+    capital_costs_chapter = self.costs.capital_cost.chapter('D_services')
+    if component_category == 'Generation':
+      generation_systems = self.energy_system.generation_systems
+      for generation_system in generation_systems:
+        if component_type == generation_system.system_type:
+          if generation_system.system_type == cte.HEAT_PUMP:
+            name += (
+                generation_system.source_medium.lower() + '_to_' + generation_system.supply_medium.lower() + '_' +
+                generation_system.system_type.lower().replace(' ', '_'))
+          elif generation_system.system_type == cte.BOILER:
+            if generation_system.fuel_type == cte.ELECTRICITY:
+              name += cte.ELECTRICAL.lower() + f'_{generation_system.system_type}'.lower()
+            else:
+              name += generation_system.fuel_type.lower() + f'_{generation_system.system_type}'.lower()
+          elif generation_system.system_type == cte.PHOTOVOLTAIC:
+            name += 'D2010_photovoltaic_system'
+          else:
+            if cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
+              name += 'template_heat'
+            else:
+              name += 'template_cooling'
+      for item in capital_costs_chapter.items:
+        if name in item.type:
+          investment_cost += float(capital_costs_chapter.item(item.type).initial_investment[0])
+          reposition_cost += float(capital_costs_chapter.item(item.type).reposition[0])
+          lifetime += float(capital_costs_chapter.item(item.type).lifetime)
+    elif component_category == 'Storage':
+      for generation_system in self.energy_system.generation_systems:
+        if generation_system.energy_storage_systems is not None:
+          for energy_storage_system in generation_system.energy_storage_systems:
+            if energy_storage_system.type_energy_stored == cte.THERMAL:
+              if energy_storage_system.heating_coil_capacity is not None:
+                investment_cost += float(capital_costs_chapter.item('D306010_storage_tank').initial_investment[0])
+                reposition_cost += float(capital_costs_chapter.item('D306010_storage_tank').reposition[0])
+                lifetime += float(capital_costs_chapter.item('D306010_storage_tank').lifetime)
+              else:
+                investment_cost += float(
+                  capital_costs_chapter.item('D306020_storage_tank_with_coil').initial_investment[0])
+                reposition_cost += float(capital_costs_chapter.item('D306020_storage_tank_with_coil').reposition[0])
+                lifetime += float(capital_costs_chapter.item('D306020_storage_tank_with_coil').lifetime)
+
+    return investment_cost, reposition_cost, lifetime
+
+  def unit_maintenance_cost(self, generation_system):
+    hvac_maintenance = self.costs.operational_cost.maintenance_hvac
+    pv_maintenance = self.costs.operational_cost.maintenance_pv
+    maintenance_cost = 0
+    component = None
+    if generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.AIR:
+      component = 'air_source_heat_pump'
+    elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.GROUND:
+      component = 'ground_source_heat_pump'
+    elif generation_system.system_type == cte.HEAT_PUMP and generation_system.source_medium == cte.WATER:
+      component = 'water_source_heat_pump'
+    elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.GAS:
+      component = 'gas_boiler'
+    elif generation_system.system_type == cte.BOILER and generation_system.fuel_type == cte.ELECTRICITY:
+      component = 'electric_boiler'
+    elif generation_system.system_type == cte.PHOTOVOLTAIC:
+      maintenance_cost += pv_maintenance
+    else:
+      if cte.HEATING or cte.DOMESTIC_HOT_WATER in self.demand_types:
+        component = 'general_heating_equipment'
+      else:
+        component = 'general_cooling_equipment'
+    for item in hvac_maintenance:
+      if item.type == component:
+        maintenance_cost += item.maintenance[0]
+    return maintenance_cost
+
+  def fuel_cost_per_kwh(self, fuel_type, fuel_cost_tariff_type):
+    fuel_cost = 0
+    catalog_fuels = self.costs.operational_cost.fuels
+    for fuel in catalog_fuels:
+      if fuel_type == fuel.type and fuel_cost_tariff_type == fuel.variable.rate_type:
+        if fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'fixed':
+          fuel_cost = fuel.variable.values[0]
+        elif fuel.type == cte.ELECTRICITY and fuel_cost_tariff_type == 'variable':
+          fuel_cost = fuel.variable.values[0]
+        else:
+          if fuel.type == cte.BIOMASS:
+            conversion_factor = 1
+          else:
+            conversion_factor = fuel.density[0]
+          fuel_cost = fuel.variable.values[0] / (conversion_factor * fuel.lower_heating_value[0] * 0.277)
+    return fuel_cost

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/multi_objective_genetic_algorithm.py

@@ -0,0 +1,827 @@
+import copy
+import math
+import random
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
+  Individual
+import matplotlib.pyplot as plt
+
+
+class MultiObjectiveGeneticAlgorithm:
+  """
+  A class to perform NSGA-II optimization. This class is specifically designed to optimize
+  multiple conflicting objectives, facilitating evolutionary-based search to determine the optimal sizes of all
+  components in an energy system.
+
+  Attributes:
+  ----------
+  population_size : int
+      The total number of individuals in the population.
+  generations : int
+      The number of generations to evolve the population.
+  crossover_rate : float
+      Probability of crossover between pairs of individuals.
+  mutation_rate : float
+      Probability of mutation in individual genes.
+  number_of_selected_solutions : int, optional
+      Number of optimal solutions to select from the final population.
+  optimization_scenario : str
+      The specific optimization scenario defining the objectives to be optimized.
+  pareto_front : list, optional
+      Stores the Pareto front solutions after optimization, representing non-dominated solutions.
+
+  Methods:
+  --------
+  __init__(self, population_size=40, generations=50, crossover_rate=0.9, mutation_rate=0.33,
+           number_of_selected_solutions=None, optimization_scenario=None)
+      Initializes the MultiObjectiveGeneticAlgorithm with population size, generation count, and evolutionary
+      operators such as crossover and mutation rates.
+  """
+
+  def __init__(self, population_size=100, generations=50, initial_crossover_rate=0.9, final_crossover_rate=0.5,
+               mutation_rate=0.1, number_of_selected_solutions=None, optimization_scenario=None):
+    self.population_size = population_size
+    self.population = []
+    self.generations = generations
+    self.initial_crossover_rate = initial_crossover_rate
+    self.final_crossover_rate = final_crossover_rate
+    self.crossover_rate = initial_crossover_rate  # Initial value
+    self.mutation_rate = mutation_rate
+    self.optimization_scenario = optimization_scenario
+    self.number_of_selected_solutions = number_of_selected_solutions
+    self.selected_solutions = None
+    self.pareto_front = None
+
+  # Initialize Population
+  def initialize_population(self, building, energy_system):
+    """
+    Initializes the population for the genetic algorithm with feasible individuals based on building constraints
+    and energy system characteristics.
+
+    Parameters:
+    ----------
+    building : Building
+        The building object containing data on building demands and properties.
+    energy_system : EnergySystem
+        The energy system object containing demand types and components to be optimized.
+
+    Procedure:
+    ----------
+    1. Identifies a design period based on building energy demands.
+    2. Attempts to create and initialize individuals for the population until the specified population size
+       is reached or the maximum number of attempts is exceeded.
+    3. Each individual is evaluated for feasibility based on defined constraints.
+    4. Only feasible individuals are added to the population.
+
+    Raises:
+    -------
+    RuntimeError
+        If a feasible population of the specified size cannot be generated within the set maximum attempts.
+
+    Notes:
+    ------
+    - This method stops when we achieve feasible population size or raises an error if unsuccessful.
+    - The feasibility of each individual is checked to ensure that it satisfies the constraints of the
+      building and energy system demands.
+    - The `design_period_energy_demands` is used to evaluate individual feasibility based on specific demand
+      periods within the building’s operation.
+    """
+    design_period_energy_demands = self.design_period_identification(building)
+    attempts = 0
+    max_attempts = self.population_size * 20
+    while len(self.population) < self.population_size and attempts < max_attempts:
+      individual = Individual(building=building,
+                              energy_system=energy_system,
+                              design_period_energy_demands=design_period_energy_demands,
+                              optimization_scenario=self.optimization_scenario)
+      individual.initialization()
+      individual.score_evaluation()
+      attempts += 1
+      self.population.append(individual)
+      # if individual.feasibility:
+      #   self.population.append(individual)
+    if len(self.population) < self.population_size:
+      raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
+                         f"Only {len(self.population)} feasible individuals were generated.")
+
+  def sbx_crossover(self, parent1, parent2):
+    """
+    Simulated Binary Crossover (SBX) operator to produce offspring from two parent individuals by recombining their
+    attributes. This method is applied to both generation and storage components, using a distribution index
+    to control the diversity of the generated offspring.
+
+    Parameters:
+    ----------
+    parent1 : Individual
+        The first parent individual, containing attributes for generation and storage components.
+    parent2 : Individual
+        The second parent individual with a similar structure to parent1.
+
+    Returns:
+    -------
+    tuple
+        Two new offspring individuals (child1, child2) generated via the SBX crossover process. If crossover is
+        not performed (based on crossover probability), it returns deep copies of the parent individuals.
+
+    Process:
+    -------
+    1. Determines whether crossover should occur based on a randomly generated probability and the crossover rate.
+    2. For each component in the generation and storage attributes of both parents:
+       - Calculates crossover coefficients for each attribute based on a randomly generated value `u`.
+       - Uses a distribution index (`eta_c`) to compute the value of `beta`, which controls the spread of offspring.
+       - Generates two offspring values (`alpha1`, `alpha2`) based on beta and the parent values (x1 and x2).
+       - Ensures that all offspring values are positive; if `alpha1` or `alpha2` is non-positive, assigns the
+         average of `x1` and `x2` instead.
+    3. Returns the two newly created offspring if crossover is successful; otherwise, returns copies of the parents.
+
+    Details:
+    --------
+    - `eta_c` (8): Distribution index influencing the SBX process; higher values result in offspring closer
+      to the parents, while lower values create more diversity.
+    - Each attribute in `Generation Components` (e.g., `heating_capacity`, `cooling_capacity`) and
+      `Energy Storage Components` (e.g., `capacity`, `volume`, `heating_coil_capacity`) has crossover applied.
+    - Ensures robustness by preventing the assignment of non-positive values to offspring attributes.
+    """
+    eta_c = 8  # Distribution index for SBX
+    if random.random() < self.crossover_rate:
+      child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
+      # Crossover for Generation Components
+      for i in range(len(parent1.individual['Generation Components'])):
+        for cap_type in ['heating_capacity', 'cooling_capacity']:
+          if parent1.individual['Generation Components'][i][cap_type] is not None:
+            x1 = parent1.individual['Generation Components'][i][cap_type]
+            x2 = parent2.individual['Generation Components'][i][cap_type]
+            u = random.random()
+            if u <= 0.5:
+              beta = (2 * u) ** (1 / (eta_c + 1))
+            else:
+              beta = (1 / (2 * (1 - u))) ** (1 / (eta_c + 1))
+            alpha1 = 0.5 * ((1 + beta) * x1 + (1 - beta) * x2)
+            alpha2 = 0.5 * ((1 - beta) * x1 + (1 + beta) * x2)
+            if alpha1 > 0:
+              child1.individual['Generation Components'][i][cap_type] = 0.5 * ((1 + beta) * x1 + (1 - beta) * x2)
+            else:
+              child1.individual['Generation Components'][i][cap_type] = 0.5 * (x1 + x2)
+            if alpha2 > 0:
+              child2.individual['Generation Components'][i][cap_type] = 0.5 * ((1 - beta) * x1 + (1 + beta) * x2)
+            else:
+              child2.individual['Generation Components'][i][cap_type] = 0.5 * (x1 + x2)
+        # Crossover for Energy Storage Components
+      for i in range(len(parent1.individual['Energy Storage Components'])):
+        for item in ['capacity', 'volume', 'heating_coil_capacity']:
+          if parent1.individual['Energy Storage Components'][i][item] is not None:
+            x1 = parent1.individual['Energy Storage Components'][i][item]
+            x2 = parent2.individual['Energy Storage Components'][i][item]
+            u = random.random()
+            if u <= 0.5:
+              beta = (2 * u) ** (1 / (eta_c + 1))
+            else:
+              beta = (1 / (2 * (1 - u))) ** (1 / (eta_c + 1))
+            alpha1 = 0.5 * ((1 + beta) * x1 + (1 - beta) * x2)
+            alpha2 = 0.5 * ((1 - beta) * x1 + (1 + beta) * x2)
+            if alpha1 > 0:
+              child1.individual['Energy Storage Components'][i][item] = 0.5 * ((1 + beta) * x1 + (1 - beta) * x2)
+            else:
+              child1.individual['Energy Storage Components'][i][item] = 0.5 * (x1 + x2)
+            if alpha2 > 0:
+              child2.individual['Energy Storage Components'][i][item] = 0.5 * ((1 - beta) * x1 + (1 + beta) * x2)
+            else:
+              child2.individual['Energy Storage Components'][i][item] = 0.5 * (x1 + x2)
+      return child1, child2
+    else:
+      return copy.deepcopy(parent1), copy.deepcopy(parent2)
+
+  def polynomial_mutation(self, individual, building, energy_system):
+    """
+    Applies a Polynomial Mutation operator to modify the attributes of an individual's generation and storage components.
+    This operator introduces controlled variation in the population, guided by the mutation distribution index, to enhance
+    exploration within the search space.
+
+    Parameters:
+    ----------
+    individual : Individual
+        The individual to mutate, containing attributes for generation and storage components.
+    building : Building
+        The building data, used to determine constraints such as peak heating load and available space.
+    energy_system : EnergySystem
+        The energy system configuration with specified demand types (e.g., heating, cooling, domestic hot water).
+
+    Returns:
+    -------
+    Individual
+        The mutated individual, with potentially adjusted values for generation and storage components.
+
+    Process:
+    -------
+    1. Defines the `polynomial_mutation_operator`, which applies the mutation based on the distribution index `eta_m`
+       and ensures the mutated value remains within defined bounds.
+    2. For each generation component of the individual:
+       - If the mutation rate threshold is met, performs mutation on `heating_capacity` and `cooling_capacity`,
+         bounded by the design period demands for the specified energy types.
+    3. For each energy storage component of the individual:
+       - If the mutation rate threshold is met, performs mutation on `volume` and `heating_coil_capacity` based on
+         available storage space and design period demands.
+    4. Returns the mutated individual.
+
+    Details:
+    --------
+    - `eta_m` (20): Mutation distribution index that controls the spread of the mutated values; higher values concentrate
+      mutated values near the original, while lower values increase variability.
+    - Uses design period demands to set mutation bounds, ensuring capacities stay within feasible ranges for heating,
+      cooling, and hot water demands.
+    - Each mutation is bounded to avoid negative or unreasonably large values.
+    """
+    eta_m = 20  # Mutation distribution index
+    design_period_energy_demands = self.design_period_identification(building)
+
+    def polynomial_mutation_operator(value, lower_bound, upper_bound):
+      u = random.random()
+      if u < 0.5:
+        delta_q = (2 * u) ** (1 / (eta_m + 1)) - 1
+      else:
+        delta_q = 1 - (2 * (1 - u)) ** (1 / (eta_m + 1))
+      mutated_value = value + delta_q * (upper_bound - lower_bound)
+      return mutated_value
+
+    # Mutate Generation Components
+    for generation_component in individual['Generation Components']:
+      if random.random() < self.mutation_rate:
+        if (generation_component['nominal_heating_efficiency'] is not None and
+            (cte.HEATING or cte.DOMESTIC_HOT_WATER in energy_system.demand_types)):
+          if cte.HEATING in energy_system.demand_types:
+            max_demand = max(design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES
+          else:
+            max_demand = max(design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES
+          generation_component['heating_capacity'] = abs(polynomial_mutation_operator(
+            generation_component['heating_capacity'], 0, max_demand))
+        if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in energy_system.demand_types:
+          max_cooling_demand = max(design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES
+          generation_component['cooling_capacity'] = polynomial_mutation_operator(
+            generation_component['cooling_capacity'], 0, max_cooling_demand)
+    # Mutate Storage Components
+    for storage_component in individual['Energy Storage Components']:
+      if random.random() < self.mutation_rate:
+        if storage_component['type'] == f'{cte.THERMAL}_storage':
+          storage_operation_range = 10 if cte.DOMESTIC_HOT_WATER in energy_system.demand_types else 20
+          storage_energy_capacity = random.uniform(0, 6)
+          volume = (storage_energy_capacity * max(design_period_energy_demands[cte.HEATING]['demands'])) / (
+                cte.WATER_HEAT_CAPACITY * 1000 * storage_operation_range)
+          max_available_space = min(0.01 * building.volume / building.storeys_above_ground, volume)
+          storage_component['volume'] = abs(polynomial_mutation_operator(storage_component['volume'], 0,
+                                                                     max_available_space))
+          if storage_component['heating_coil_capacity'] is not None:
+            if cte.HEATING in energy_system.demand_types:
+              max_heating_demand = max(design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES
+            else:
+              max_heating_demand = max(
+                design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES
+
+            storage_component['heating_coil_capacity'] = abs(polynomial_mutation_operator(
+              storage_component['heating_coil_capacity'], 0, max_heating_demand))
+    return individual
+
+  def fast_non_dominated_sorting(self):
+    """
+        Performs fast non-dominated sorting on the current population to categorize individuals
+        into different Pareto fronts based on dominance. This method is part of the NSGA-II algorithm
+        and is used to identify non-dominated solutions at each front level.
+
+        Returns:
+            fronts (list of lists): A list of Pareto fronts, where each front is a list of indices
+                                    corresponding to individuals in the population. The first front
+                                    (fronts[0]) contains non-dominated individuals, the second front
+                                    contains solutions dominated by the first front, and so on.
+
+        Workflow:
+            1. Initializes an empty list `s` for each individual, representing individuals it dominates.
+               Also initializes `n`, a count of how many individuals dominate each individual, and
+               `rank` to store the Pareto front rank of each individual.
+
+            2. For each individual `p`, compares it with every other individual `q` to determine
+               dominance. If `p` dominates `q`, `q` is added to `s[p]`. If `q` dominates `p`,
+               `n[p]` is incremented.
+
+            3. Individuals with `n[p] == 0` are non-dominated and belong to the first front, so they
+               are assigned a rank of 0 and added to `fronts[0]`.
+
+            4. Iteratively builds additional fronts:
+               - For each front, checks each individual in the front, reducing the `n` count of
+                 individuals it dominates (`s[p]`).
+               - If `n[q]` becomes zero, `q` is non-dominated relative to the current front and is
+                 added to the next front.
+               - This continues until no more individuals can be added to new fronts.
+
+            5. After identifying all fronts, assigns a rank to each individual in each front.
+        """
+    population = self.population
+    s = [[] for _ in range(len(population))]
+    n = [0] * len(population)
+    rank = [0] * len(population)
+    fronts = [[]]
+    for p in range(len(population)):
+      for q in range(len(population)):
+        if self.domination_status(population[p], population[q]):
+          s[p].append(q)
+        elif self.domination_status(population[q], population[p]):
+          n[p] += 1
+      if n[p] == 0:
+        rank[p] = 0
+        fronts[0].append(p)
+    i = 0
+    while fronts[i]:
+      next_front = set()
+      for p in fronts[i]:
+        for q in s[p]:
+          n[q] -= 1
+          if n[q] == 0:
+            rank[q] = i + 1
+            next_front.add(q)
+      i += 1
+      fronts.append(list(next_front))
+    del fronts[-1]
+    for (j, front) in enumerate(fronts):
+      for i in front:
+        self.population[i].rank = j + 1
+    return fronts
+
+  def calculate_crowding_distance(self, front, crowding_distance):
+    """
+    Calculates the crowding distance for individuals within a given front in the population.
+    The crowding distance is a measure used in multi-objective optimization to maintain diversity
+    among non-dominated solutions by evaluating the density of solutions surrounding each individual.
+
+    Parameters:
+        front (list): A list of indices representing the individuals in the current front.
+        crowding_distance (list): A list of crowding distances for each individual in the population, updated in place.
+
+    Returns:
+        list: The updated crowding distances for each individual in the population.
+
+    Methodology:
+        1. For each objective (indexed by `j`), sorts the individuals in `front` based on their objective values.
+        2. Assigns a very large crowding distance (1e12) to boundary individuals in each objective (first and last individuals after sorting).
+        3. Retrieves the minimum and maximum values for the current objective within the sorted front.
+        4. For non-boundary individuals, calculates the crowding distance by:
+           - Finding the difference in objective values between the neighboring individuals in the sorted list.
+           - Normalizing this difference by dividing by the range of the objective values (objective_max - objective_min).
+           - Adding this value to the individual's crowding distance.
+        5. Updates each individual in `front` with their calculated crowding distance for later use in selection.
+
+    Example:
+        crowding_distances = [0] * len(self.population)
+        self.calculate_crowding_distance(front, crowding_distances)
+
+    Note:
+        The crowding distance helps prioritize individuals that are more isolated within the objective space,
+        thus promoting diversity within the Pareto front.
+    """
+    for j in range(len(self.population[0].fitness_score)):
+      sorted_front = sorted(front, key=lambda x: self.population[x].individual['fitness_score'][j])
+      crowding_distance[sorted_front[0]] = float(1e12)
+      crowding_distance[sorted_front[-1]] = float(1e12)
+      objective_min = self.population[sorted_front[0]].individual['fitness_score'][j]
+      objective_max = self.population[sorted_front[-1]].individual['fitness_score'][j]
+      if objective_max != objective_min:
+        for i in range(1, len(sorted_front) - 1):
+          crowding_distance[sorted_front[i]] += (
+              (self.population[sorted_front[i + 1]].individual['fitness_score'][j] -
+               self.population[sorted_front[i - 1]].individual['fitness_score'][j]) /
+              (objective_max - objective_min))
+      for i in front:
+        self.population[i].crowding_distance = crowding_distance[i]
+    return crowding_distance
+
+  def nsga2_selection(self, fronts):
+    """
+    Selects the next generation population using the NSGA-II selection strategy.
+    This method filters the individuals in `fronts` based on non-dominated sorting and crowding distance,
+    ensuring a diverse and high-quality set of solutions for the next generation.
+
+    Parameters:
+        fronts (list of lists): A list of fronts, where each front is a list of indices
+                                representing non-dominated solutions in the population.
+
+    Returns:
+        list: The selected population of individuals for the next generation, limited to `self.population_size`.
+
+    Methodology:
+        1. Iterates through each front in `fronts` (sorted by rank) to add individuals to the new population.
+        2. For each front:
+            - Filters individuals with finite fitness scores to prevent propagation of invalid solutions.
+            - Adds these individuals to the new population until the population size limit is met.
+        3. If the new population size limit is not yet reached:
+            - Sorts the current front based on crowding distance in descending order.
+            - Adds individuals based on crowding distance until reaching the population limit.
+        4. Returns the selected population, ensuring that only the top solutions (by rank and crowding distance) are kept.
+
+    Notes:
+        - Non-dominated sorting prioritizes individuals in lower-ranked fronts.
+        - Crowding distance promotes diversity within the population by favoring isolated solutions.
+
+    Example:
+        selected_population = self.nsga2_selection(fronts)
+
+    Important:
+        This method assumes that each individual's fitness score contains at least two objectives,
+        and any infinite fitness values are excluded from selection.
+    """
+    new_population = []
+    i = 0
+    while len(new_population) + len(fronts[i]) <= self.population_size:
+      for index in fronts[i]:
+        # Skip individuals with infinite fitness values to avoid propagation
+        if not math.isinf(self.population[index].individual['fitness_score'][0]) and \
+            not math.isinf(self.population[index].individual['fitness_score'][1]):
+          new_population.append(self.population[index])
+      i += 1
+      if i >= len(fronts):
+        break
+    if len(new_population) < self.population_size and i < len(fronts):
+      sorted_front = sorted(fronts[i], key=lambda x: self.population[x].crowding_distance, reverse=True)
+      for index in sorted_front:
+        if len(new_population) < self.population_size:
+          if not math.isinf(self.population[index].individual['fitness_score'][0]) and \
+              not math.isinf(self.population[index].individual['fitness_score'][1]):
+            new_population.append(self.population[index])
+        else:
+          break
+    return new_population
+
+  def solve_ga(self, building, energy_system):
+    self.initialize_population(building, energy_system)
+    solutions = {}
+    for n in range(self.generations + 1):
+      # self.crossover_rate = self.initial_crossover_rate + \
+      #                       (self.final_crossover_rate - self.initial_crossover_rate) * (n / self.generations)
+      solutions[f'generation_{n}'] = [individual.fitness_score for individual in self.population]
+      print(n)
+      progeny_population = []
+      while len(progeny_population) < self.population_size:
+        parent1, parent2 = random.choice(self.population), random.choice(self.population)
+        child1, child2 = self.sbx_crossover(parent1, parent2)
+        self.polynomial_mutation(child1.individual, building, energy_system)
+        self.polynomial_mutation(child2.individual, building, energy_system)
+        child1.score_evaluation()
+        child2.score_evaluation()
+        progeny_population.extend([child1, child2])
+      self.population.extend(progeny_population)
+      fronts = self.fast_non_dominated_sorting()
+      print(fronts)
+      crowding_distances = [0] * len(self.population)
+      for front in fronts:
+        self.calculate_crowding_distance(front=front, crowding_distance=crowding_distances)
+      new_population = self.nsga2_selection(fronts=fronts)
+      self.population = new_population
+      if n == self.generations:
+        fronts = self.fast_non_dominated_sorting()
+        self.pareto_front = [self.population[i] for i in fronts[0]]
+    pareto_fitness_scores = [individual.fitness_score for individual in self.pareto_front]
+    normalized_objectives = self.euclidean_normalization(pareto_fitness_scores)
+    performance_scores, selected_solution_indexes = self.topsis_decision_making(normalized_objectives)
+    self.selected_solutions = self.postprocess(self.pareto_front, selected_solution_indexes)
+    print(self.selected_solutions)
+    self.plot_pareto_front(self.pareto_front)
+
+  def plot_pareto_front(self, pareto_front):
+    """
+    Plots the Pareto front for a given set of individuals in the population, using normalized fitness scores.
+    This method supports any number of objective functions, with dynamically labeled axes.
+
+    Parameters:
+        pareto_front (list): List of individuals that are part of the Pareto front.
+
+    Returns:
+        None: Displays a scatter plot with lines connecting the Pareto-optimal points.
+
+    Process:
+        - Normalizes the fitness scores using Euclidean normalization.
+        - Generates scatter plots for each pair of objectives.
+        - Connects the sorted Pareto-optimal points with a smooth, thick red line.
+
+    Attributes:
+        - Objectives on each axis are dynamically labeled as "Objective Function <index>_<name>", where the name is
+          derived from the optimization scenario.
+        - Adjusted marker and line thicknesses ensure enhanced visibility of the plot.
+
+    Steps:
+        1. Normalize the fitness scores of the Pareto front.
+        2. Create scatter plots for each pair of objectives.
+        3. Connect the sorted points for each objective pair with a red line.
+        4. Label each axis based on objective function index and scenario name.
+
+    Example:
+        self.plot_pareto_front(pareto_front)
+
+    Note:
+        - This method assumes that each individual in the Pareto front has a fitness score with at least two objectives.
+    """
+    # Normalize Pareto scores
+    pareto_scores = [individual.fitness_score for individual in pareto_front]
+    normalized_pareto_scores = self.euclidean_normalization(pareto_scores)
+    normalized_pareto_scores = list(zip(*normalized_pareto_scores))  # Transpose for easy access by objective index
+
+    # Extract the number of objectives and the objective names
+    num_objectives = len(normalized_pareto_scores)
+    objectives = self.optimization_scenario.split('_')
+
+    # Set up subplots for each pair of objectives
+    fig, axs = plt.subplots(num_objectives - 1, num_objectives - 1, figsize=(15, 10))
+    for i in range(num_objectives - 1):
+      for j in range(i + 1, num_objectives):
+        # Sort points for smoother line connections
+        sorted_pairs = sorted(zip(normalized_pareto_scores[i], normalized_pareto_scores[j]))
+        sorted_x, sorted_y = zip(*sorted_pairs)
+
+        # Plot each objective pair
+        ax = axs[i, j - 1] if num_objectives > 2 else axs
+        ax.scatter(
+          normalized_pareto_scores[i],
+          normalized_pareto_scores[j],
+          color='blue',
+          alpha=0.7,
+          edgecolors='black',
+          s=100  # Larger point size for visibility
+        )
+        # Plot smoother, thicker line connecting Pareto points
+        ax.plot(
+          sorted_x,
+          sorted_y,
+          color='red',
+          linewidth=3  # Thicker line for better visibility
+        )
+
+        # Dynamic axis labels based on objective function names
+        ax.set_xlabel(f"Objective Function {i + 1}_{objectives[i]}")
+        ax.set_ylabel(f"Objective Function {j + 1}_{objectives[j]}")
+        ax.grid(True)
+
+    # Set title and layout adjustments
+    fig.suptitle("Pareto Front (Normalized Fitness Scores)")
+    plt.tight_layout()
+    plt.show()
+
+  @staticmethod
+  def design_period_identification(building):
+    """
+    Identifies the design period for heating, cooling, and domestic hot water (DHW) demands based on the building's
+    daily energy requirements. This method focuses on selecting time windows with the highest daily demands for
+    accurate energy system sizing.
+
+    Parameters:
+    ----------
+    building : Building
+        The building object containing hourly energy demand data for heating, cooling, and domestic hot water.
+
+    Returns:
+    -------
+    dict
+        A dictionary with keys for each demand type ('heating', 'cooling', 'domestic_hot_water'), where each entry
+        contains:
+        - 'demands': A list of hourly demand values within the identified design period.
+        - 'start_index': The start hour index for the identified period.
+        - 'end_index': The end hour index for the identified period.
+
+    Procedure:
+    ----------
+    1. Calculates the total demand for each day for heating, cooling, and DHW.
+    2. Identifies the day with the highest demand for each type.
+    3. Selects a time window around each peak-demand day as the design period:
+       - For middle days, it selects the preceding and following days.
+       - For first and last days, it adjusts the window to ensure it stays within the range of available data.
+
+    Notes:
+    ------
+    - This design period serves as a critical input for system sizing, ensuring the energy system meets the
+      maximum demand requirements efficiently.
+    - Each demand type has a separate design period tailored to its specific daily peak requirements.
+    {'heating': {'demands': [...], 'start_index': ..., 'end_index': ...},
+     'cooling': {'demands': [...], 'start_index': ..., 'end_index': ...},
+     'domestic_hot_water': {'demands': [...], 'start_index': ..., 'end_index': ...}
+
+    """
+
+    def get_start_end_indices(max_day_index, total_days):
+      if 0 < max_day_index < total_days - 1:
+        start_index = (max_day_index - 5) * 24
+        end_index = (max_day_index + 5) * 24
+      elif max_day_index == 0:
+        start_index = 0
+        end_index = (max_day_index + 10) * 24
+      else:
+        start_index = (max_day_index - 10) * 24
+        end_index = total_days * 24
+      return start_index, end_index
+
+    # Calculate daily demands
+    heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
+                             range(0, len(building.heating_demand[cte.HOUR]), 24)]
+    cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
+                             range(0, len(building.cooling_demand[cte.HOUR]), 24)]
+    dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
+                         range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
+    # Get the day with maximum demand for each type
+    heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
+    cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
+    dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
+    # Get the start and end indices for each demand type
+    heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
+    cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
+    dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
+    # Return the design period energy demands
+    return {
+      f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
+                         'start_index': heating_start, 'end_index': heating_end},
+      f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
+                         'start_index': cooling_start, 'end_index': cooling_end},
+      f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
+                                    'start_index': dhw_start, 'end_index': dhw_end}
+    }
+
+  @staticmethod
+  def domination_status(individual1, individual2):
+    """
+    Determines if `individual1` dominates `individual2` based on their fitness scores (objective values).
+    Domination in this context means that `individual1` is no worse than `individual2` in all objectives
+    and strictly better in at least one.
+
+    Parameters:
+        individual1: The first individual (object) being compared.
+        individual2: The second individual (object) being compared.
+
+    Returns:
+        bool: True if `individual1` dominates `individual2`, False otherwise.
+
+    Methodology:
+        1. Retrieves the list of objective scores (fitness scores) for both individuals.
+        2. Ensures both individuals have the same number of objectives; otherwise, raises an AssertionError.
+        3. Initializes two flags:
+           - `better_in_all`: Checks if `individual1` is equal to or better than `individual2` in all objectives.
+           - `strictly_better_in_at_least_one`: Checks if `individual1` is strictly better than `individual2` in at
+           least one objective.
+        4. Iterates over each objective of both individuals:
+           - If `individual1` has a worse value in any objective, sets `better_in_all` to False.
+           - If `individual1` has a better value in any objective, sets `strictly_better_in_at_least_one` to True.
+        5. Concludes that `individual1` dominates `individual2` if `better_in_all` is True and
+           `strictly_better_in_at_least_one` is True.
+    """
+    # Extract the list of objectives for both individuals
+    objectives1 = individual1.individual['fitness_score']
+    objectives2 = individual2.individual['fitness_score']
+    # Ensure both individuals have the same number of objectives
+    assert len(objectives1) == len(objectives2), "Both individuals must have the same number of objectives"
+    # Flags to check if one dominates the other
+    better_in_all = True  # Whether individual1 is better in all objectives
+    strictly_better_in_at_least_one = False  # Whether individual1 is strictly better in at least one objective
+    for obj1, obj2 in zip(objectives1, objectives2):
+      if obj1 > obj2:
+        better_in_all = False  # individual1 is worse in at least one objective
+      if obj1 < obj2:
+        strictly_better_in_at_least_one = True  # individual1 is better in at least one objective
+    result = True if better_in_all and strictly_better_in_at_least_one else False
+    return result
+
+  @staticmethod
+  def euclidean_normalization(fitness_scores):
+    """
+    Normalizes the fitness scores for a set of individuals using Euclidean normalization.
+
+    Parameters:
+        fitness_scores (list of lists/tuples): A list where each element represents an individual's fitness scores
+                                               across multiple objectives, e.g., [(obj1, obj2, ...), ...].
+
+    Returns:
+        list of tuples: A list of tuples representing the normalized fitness scores for each individual. Each tuple
+                        contains the normalized values for each objective, scaled between 0 and 1 relative to the
+                        Euclidean length of each objective across the population.
+
+    Process:
+        - For each objective, calculates the sum of squares across all individuals.
+        - Normalizes each individual's objective score by dividing it by the square root of the sum of squares
+          for that objective.
+        - If the sum of squares for any objective is zero (to avoid division by zero), assigns a normalized score
+          of zero for that objective.
+
+    Example:
+        Given a fitness score input of [[3, 4], [1, 2]], this method will normalize each objective and return:
+        [(0.9487, 0.8944), (0.3162, 0.4472)].
+
+    Notes:
+        - This normalization is helpful for multi-objective optimization, as it scales each objective score
+          independently, ensuring comparability even with different objective ranges.
+
+    Steps:
+        1. Calculate the sum of squares for each objective across all individuals.
+        2. Divide each objective value by the square root of the corresponding sum of squares.
+        3. Return the normalized values as a list of tuples, preserving the individual-objective structure.
+    """
+    num_objectives = len(fitness_scores[0])
+    # Calculate sum of squares for each objective
+    sum_squared_objectives = [
+      sum(fitness_score[i] ** 2 for fitness_score in fitness_scores)
+      for i in range(num_objectives)
+    ]
+    # Normalize the objective values for each individual
+    normalized_objective_functions = [
+      tuple(
+        fitness_score[j] / math.sqrt(sum_squared_objectives[j]) if sum_squared_objectives[j] > 0 else 0
+        for j in range(num_objectives)
+      )
+      for fitness_score in fitness_scores
+    ]
+    return normalized_objective_functions
+
+  @staticmethod
+  def topsis_decision_making(normalized_objective_functions):
+    """
+    Applies the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for decision-making
+    in multi-objective optimization. This method ranks solutions based on their proximity to the ideal best and
+    worst solutions.
+
+    Parameters:
+        normalized_objective_functions (list of tuples): A list where each element represents a set of normalized
+                                                         objective values for an individual, e.g.,
+                                                         [(obj1, obj2, ...), ...].
+
+    Returns:
+        list of float: A list of performance scores for each individual, where higher scores indicate closer
+                       proximity to the ideal best solution.
+
+    Process:
+        - Identifies the ideal best and ideal worst values for each objective based on normalized objective scores.
+        - Computes the Euclidean distance of each individual from both the ideal best and ideal worst.
+        - Calculates a performance score for each individual by dividing the distance to the ideal worst by the
+          sum of the distances to the ideal best and worst.
+
+    Example:
+        Given normalized objectives [[0.8, 0.6], [0.3, 0.4]], the method will return performance scores
+        based on their proximity to ideal solutions.
+
+    Notes:
+        - TOPSIS helps to balance trade-offs in multi-objective optimization by quantifying how close each solution
+          is to the ideal, enabling effective ranking of solutions.
+        - This method is commonly used when both minimization and maximization objectives are present.
+
+    Steps:
+        1. Determine the ideal best (minimum) and ideal worst (maximum) for each objective.
+        2. Compute Euclidean distances from each solution to the ideal best and ideal worst.
+        3. Calculate the performance score for each solution based on these distances.
+    """
+    # Find ideal best and ideal worst
+    selected_solutions = {}
+    ideal_best = []
+    ideal_worst = []
+    num_objectives = len(normalized_objective_functions[0])
+    for j in range(num_objectives):
+      objective_values = [ind[j] for ind in normalized_objective_functions]
+      ideal_best.append(min(objective_values))
+      ideal_worst.append(max(objective_values))
+      selected_solutions[f'best_objective_{j + 1}_index'] = objective_values.index(min(objective_values))
+    # Calculate Euclidean distances to ideal best and ideal worst
+    distances_to_best = []
+    distances_to_worst = []
+    for normalized_values in normalized_objective_functions:
+      # Distance to ideal best
+      distance_best = math.sqrt(
+        sum((normalized_values[j] - ideal_best[j]) ** 2 for j in range(num_objectives))
+      )
+      distances_to_best.append(distance_best)
+      # Distance to ideal worst
+      distance_worst = math.sqrt(
+        sum((normalized_values[j] - ideal_worst[j]) ** 2 for j in range(num_objectives))
+      )
+      distances_to_worst.append(distance_worst)
+    performance_scores = [distances_to_worst[i] / (distances_to_best[i] + distances_to_worst[i])
+                          for i in range(len(normalized_objective_functions))]
+    selected_solutions['best_solution_index'] = performance_scores.index(max(performance_scores))
+    return performance_scores, selected_solutions
+
+  def postprocess(self, pareto_front, selected_solution_indexes):
+    selected_solutions = {}
+    for solution_type in selected_solution_indexes:
+      selected_solutions[solution_type] = {}
+      index = selected_solution_indexes[solution_type]
+      individual = pareto_front[index].individual
+      selected_solutions[solution_type]['Generation Components'] = []
+      selected_solutions[solution_type]['Storage Components'] = []
+      for generation_component in individual['Generation Components']:
+        generation_type = generation_component['type']
+        heating_capacity = generation_component['heating_capacity']
+        cooling_capacity = generation_component['cooling_capacity']
+        selected_solutions[solution_type]['Generation Components'].append({'component type': generation_type,
+                                                                           'heating capacity (W)': heating_capacity,
+                                                                           'cooling capacity (W)': cooling_capacity})
+      for storage_component in individual['Energy Storage Components']:
+        storage_type = storage_component['type']
+        capacity = storage_component['capacity']
+        volume = storage_component['volume']
+        heating_coil = storage_component['heating_coil_capacity']
+        selected_solutions[solution_type]['Storage Components'].append({'storage type': storage_type,
+                                                                        'capacity (W)': capacity,
+                                                                        'volume (m3)': volume,
+                                                                        'heating coil capacity (W)': heating_coil})
+      if 'energy-consumption' in self.optimization_scenario:
+        selected_solutions[solution_type]['total energy consumption kWh'] = individual['total_energy_consumption']
+      if 'cost' in self.optimization_scenario:
+        selected_solutions[solution_type]['life cycle cost'] = individual['lcc']
+
+    return selected_solutions

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/genetic_algorithm/single_objective_genetic_algorithm.py

@@ -0,0 +1,342 @@
+import copy
+import math
+import random
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.individual import \
+  Individual
+
+
+class SingleObjectiveGeneticAlgorithm:
+  def __init__(self, population_size=100, generations=20, crossover_rate=0.8, mutation_rate=0.1,
+               optimization_scenario=None, output_path=None):
+    self.population_size = population_size
+    self.population = []
+    self.generations = generations
+    self.crossover_rate = crossover_rate
+    self.mutation_rate = mutation_rate
+    self.optimization_scenario = optimization_scenario
+    self.list_of_solutions = []
+    self.best_solution = None
+    self.best_solution_generation = None
+    self.output_path = output_path
+
+  # Initialize Population
+  def initialize_population(self, building, energy_system):
+    """
+    Initialize a population of individuals with feasible configurations for optimizing the sizes of
+    generation and storage components of an energy system.
+
+    :param building: Building object with associated data
+    :param energy_system: Energy system to optimize
+    """
+    design_period_energy_demands = self.design_period_identification(building)
+    attempts = 0  # Track attempts to avoid an infinite loop in rare cases
+    max_attempts = self.population_size * 5
+
+    while len(self.population) < self.population_size and attempts < max_attempts:
+      individual = Individual(building=building,
+                              energy_system=energy_system,
+                              design_period_energy_demands=design_period_energy_demands,
+                              optimization_scenario=self.optimization_scenario)
+
+      individual.initialization()
+      attempts += 1
+
+    # Enhanced feasibility check
+      if self.initial_population_feasibility_check(individual, energy_system.demand_types, design_period_energy_demands):
+        self.population.append(individual)
+
+    # Raise an error or print a warning if the population size goal is not met after max_attempts
+    if len(self.population) < self.population_size:
+      raise RuntimeError(f"Could not generate a feasible population of size {self.population_size}. "
+                         f"Only {len(self.population)} feasible individuals were generated.")
+
+  @staticmethod
+  def initial_population_feasibility_check(individual, demand_types, design_period_demands):
+    """
+    Check if the individual meets basic feasibility requirements for heating, cooling, and DHW capacities
+    and storage volume.
+
+    :param individual: Individual to check
+    :param demand_types: List of demand types (e.g., heating, cooling, DHW)
+    :param design_period_demands: Design period demand values for heating, cooling, and DHW
+    :return: True if feasible, False otherwise
+    """
+    # Calculate total heating and cooling capacities
+    total_heating_capacity = sum(
+      component['heating_capacity'] for component in individual.individual['Generation Components']
+      if component['heating_capacity'] is not None
+    )
+    total_cooling_capacity = sum(
+      component['cooling_capacity'] for component in individual.individual['Generation Components']
+      if component['cooling_capacity'] is not None
+    )
+
+    # Maximum demands for each demand type (converted to kW)
+    max_heating_demand = max(design_period_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES
+    max_cooling_demand = max(design_period_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES
+    max_dhw_demand = max(design_period_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES
+
+    # Check heating capacity feasibility
+    if cte.HEATING in demand_types and total_heating_capacity < 0.5 * max_heating_demand:
+      return False
+
+    # Check DHW capacity feasibility
+    if cte.DOMESTIC_HOT_WATER in demand_types and total_heating_capacity < 0.5 * max_dhw_demand:
+      return False
+
+    # Check cooling capacity feasibility
+    if cte.COOLING in demand_types and total_cooling_capacity < 0.5 * max_cooling_demand:
+      return False
+
+    # Check storage volume feasibility
+    total_volume = sum(
+      component['volume'] for component in individual.individual['Energy Storage Components']
+      if component['volume'] is not None
+    )
+    # Limit storage to 10% of building's available space
+    max_storage_volume = individual.available_space * 0.1
+    if total_volume > max_storage_volume:
+      return False
+
+    return True  # Feasible if all checks are passed
+
+  def order_population(self):
+    """
+    ordering the population based on the fitness score in ascending order
+    :return:
+    """
+    self.population = sorted(self.population, key=lambda x: x.fitness_score)
+
+  def tournament_selection(self):
+    selected = []
+    for _ in range(len(self.population)):
+      i, j = random.sample(range(self.population_size), 2)
+      if self.population[i].individual['fitness_score'] < self.population[j].individual['fitness_score']:
+        selected.append(copy.deepcopy(self.population[i]))
+      else:
+        selected.append(copy.deepcopy(self.population[j]))
+    return selected
+
+  def crossover(self, parent1, parent2):
+    """
+    Crossover between two parents to produce two children.
+
+    swaps generation components and storage components between the two parents with a 50% chance.
+
+    :param parent1: First parent individual.
+    :param parent2: second parent individual.
+    :return: Two child individuals (child1 and child2).
+    """
+    if random.random() < self.crossover_rate:
+        # Deep copy of the parents to create children
+      child1, child2 = copy.deepcopy(parent1), copy.deepcopy(parent2)
+    # Crossover for Generation Components
+      for i in range(len(parent1.individual['Generation Components'])):
+        if random.random() < 0.5:
+            # swap the entire generation component
+          child1.individual['Generation Components'][i], child2.individual['Generation Components'][i] = (
+            child2.individual['Generation Components'][i],
+            child1.individual['Generation Components'][i]
+          )
+
+    # Crossover for Energy storage Components
+      for i in range(len(parent1.individual['Energy Storage Components'])):
+        if random.random() < 0.5:
+            # swap the entire storage component
+          child1.individual['Energy Storage Components'][i], child2.individual['Energy Storage Components'][i] = (
+            child2.individual['Energy Storage Components'][i],
+            child1.individual['Energy Storage Components'][i]
+          )
+
+      return child1, child2
+    else:
+        # If crossover does not happen, return copies of the original parents
+      return copy.deepcopy(parent1), copy.deepcopy(parent2)
+
+  def mutate(self, individual, building, energy_system):
+    """
+    Mutates the individual's generation and storage components.
+
+    - `individual`: The individual to mutate (contains generation and storage components).
+    - `building`: Building data that contains constraints such as peak heating load and available space.
+
+    Returns the mutated individual.
+    """
+    design_period_energy_demands = self.design_period_identification(building)
+    # Mutate Generation Components
+    for generation_component in individual['Generation Components']:
+      if random.random() < self.mutation_rate:
+        if (generation_component['nominal_heating_efficiency'] is not None and cte.HEATING or cte.DOMESTIC_HOT_WATER in
+                energy_system.demand_types):
+            # Mutate heating capacity
+          if cte.HEATING in energy_system.demand_types:
+            generation_component['heating_capacity'] = random.uniform(
+              0, max(design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
+          else:
+            generation_component['heating_capacity'] = random.uniform(
+              0, max(design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
+        if generation_component['nominal_cooling_efficiency'] is not None and cte.COOLING in energy_system.demand_types:
+            # Mutate cooling capacity
+          generation_component['cooling_capacity'] = random.uniform(
+            0, max(design_period_energy_demands[cte.COOLING]['demands']) / cte.WATTS_HOUR_TO_JULES)
+    # Mutate storage Components
+    for storage_component in individual['Energy Storage Components']:
+      if random.random() < self.mutation_rate:
+        if storage_component['type'] == f'{cte.THERMAL}_storage':
+            # Mutate the volume of thermal storage
+          max_available_space = 0.01 * building.volume / building.storeys_above_ground
+          storage_component['volume'] = random.uniform(0, max_available_space)
+          if storage_component['heating_coil_capacity'] is not None:
+            if cte.HEATING in energy_system.demand_types:
+              storage_component['heating_coil_capacity'] = random.uniform(0, max(
+                design_period_energy_demands[cte.HEATING]['demands']) / cte.WATTS_HOUR_TO_JULES)
+            else:
+              storage_component['heating_coil_capacity'] = random.uniform(0, max(
+                design_period_energy_demands[cte.DOMESTIC_HOT_WATER]['demands']) / cte.WATTS_HOUR_TO_JULES)
+    return individual
+
+  def solve_ga(self, building, energy_system):
+    """
+    solving GA for a single energy system. Here are the steps:
+    1. Initialize population using the "initialize_population" method in this class.
+    2. Evaluate the initial population using the "score_evaluation" method in the Individual class.
+    3. sort population based on fitness score.
+    4. Repeat selection, crossover, and mutation for a fixed number of generations.
+    5. Track the best solution found during the optimization process.
+
+    :param building: Building object for the energy system.
+    :param energy_system: Energy system to optimize.
+    :return: Best solution after running the GA.
+    """
+    # step 1: Initialize the population
+    self.initialize_population(building, energy_system)
+    # step 2: Evaluate the initial population
+    for individual in self.population:
+      individual.score_evaluation()
+    # step 3: Order population based on fitness scores
+    self.order_population()
+    print([individual.fitness_score for individual in self.population])
+    # Track the best solution
+    self.best_solution = self.population[0]
+    self.best_solution_generation = 0
+    self.list_of_solutions.append(copy.deepcopy(self.best_solution.individual))
+    # step 4: Run GA for a fixed number of generations
+    for generation in range(1, self.generations):
+      print(f"Generation {generation}")
+    # selection (using tournament selection)
+      selected_population = self.tournament_selection()
+    # Create the next generation through crossover and mutation
+      next_population = []
+      for i in range(0, self.population_size, 2):
+        parent1 = selected_population[i]
+        parent2 = selected_population[i + 1] if (i + 1) < len(selected_population) else selected_population[0]
+    # step 5: Apply crossover
+        child1, child2 = self.crossover(parent1, parent2)
+    # step 6: Apply mutation
+        self.mutate(child1.individual, building, energy_system)
+        self.mutate(child2.individual, building, energy_system)
+    # step 7: Evaluate the children
+        child1.score_evaluation()
+        child2.score_evaluation()
+        next_population.extend([child1, child2])
+    # Replace old population with the new one
+      self.population = next_population
+    # step 8: sort the new population based on fitness
+      self.order_population()
+      print([individual.fitness_score for individual in self.population])
+    # Track the best solution found in this generation
+      if self.population[0].individual['fitness_score'] < self.best_solution.individual['fitness_score']:
+        self.best_solution = self.population[0]
+        self.best_solution_generation = generation
+    # store the best solution in the list of solutions
+      self.list_of_solutions.append(copy.deepcopy(self.population[0].individual))
+    print(f"Best solution found in generation {self.best_solution_generation}")
+    print(f"Best solution: {self.best_solution.individual}")
+    return self.best_solution
+
+  @staticmethod
+  def topsis_decision_making(pareto_front):
+    """
+    Perform TOPSIS decision-making to choose the best solution from the Pareto front.
+
+    :param pareto_front: List of individuals in the Pareto front
+    :return: The best individual based on TOPSIS ranking
+    """
+    # Step 1: Normalize the objective functions (cost and energy consumption)
+    min_lcc = min([ind.individual['lcc'] for ind in pareto_front])
+    max_lcc = max([ind.individual['lcc'] for ind in pareto_front])
+    min_lce = min([ind.individual['total_energy_consumption'] for ind in pareto_front])
+    max_lce = max([ind.individual['total_energy_consumption'] for ind in pareto_front])
+
+    normalized_pareto_front = []
+    for ind in pareto_front:
+      normalized_lcc = (ind.individual['lcc'] - min_lcc) / (max_lcc - min_lcc) if max_lcc > min_lcc else 0
+      normalized_lce = (ind.individual['total_energy_consumption'] - min_lce) / (
+            max_lce - min_lce) if max_lce > min_lce else 0
+      normalized_pareto_front.append((ind, normalized_lcc, normalized_lce))
+
+    # Step 2: Calculate the ideal and worst solutions
+    ideal_solution = (0, 0)  # Ideal is minimum LCC and minimum LCE (0, 0 after normalization)
+    worst_solution = (1, 1)  # Worst is maximum LCC and maximum LCE (1, 1 after normalization)
+
+    # Step 3: Calculate the distance to the ideal and worst solutions
+    best_distances = []
+    worst_distances = []
+
+    for ind, normalized_lcc, normalized_lce in normalized_pareto_front:
+      distance_to_ideal = math.sqrt(
+        (normalized_lcc - ideal_solution[0]) ** 2 + (normalized_lce - ideal_solution[1]) ** 2)
+      distance_to_worst = math.sqrt(
+        (normalized_lcc - worst_solution[0]) ** 2 + (normalized_lce - worst_solution[1]) ** 2)
+      best_distances.append(distance_to_ideal)
+      worst_distances.append(distance_to_worst)
+
+    # Step 4: Calculate relative closeness to the ideal solution
+    similarity = [worst / (best + worst) for best, worst in zip(best_distances, worst_distances)]
+
+    # Step 5: Select the individual with the highest similarity score
+    best_index = similarity.index(max(similarity))
+    best_solution = pareto_front[best_index]
+
+    return best_solution
+
+  @staticmethod
+  def design_period_identification(building):
+    def get_start_end_indices(max_day_index, total_days):
+      if max_day_index > 0 and max_day_index < total_days - 1:
+        start_index = (max_day_index - 1) * 24
+        end_index = (max_day_index + 2) * 24
+      elif max_day_index == 0:
+        start_index = 0
+        end_index = (max_day_index + 2) * 24
+      else:
+        start_index = (max_day_index - 1) * 24
+        end_index = total_days * 24
+      return start_index, end_index
+
+    # Calculate daily demands
+    heating_daily_demands = [sum(building.heating_demand[cte.HOUR][i:i + 24]) for i in
+                             range(0, len(building.heating_demand[cte.HOUR]), 24)]
+    cooling_daily_demands = [sum(building.cooling_demand[cte.HOUR][i:i + 24]) for i in
+                             range(0, len(building.cooling_demand[cte.HOUR]), 24)]
+    dhw_daily_demands = [sum(building.domestic_hot_water_heat_demand[cte.HOUR][i:i + 24]) for i in
+                         range(0, len(building.domestic_hot_water_heat_demand[cte.HOUR]), 24)]
+    # Get the day with maximum demand for each type
+    heating_max_day = heating_daily_demands.index(max(heating_daily_demands))
+    cooling_max_day = cooling_daily_demands.index(max(cooling_daily_demands))
+    dhw_max_day = dhw_daily_demands.index(max(dhw_daily_demands))
+    # Get the start and end indices for each demand type
+    heating_start, heating_end = get_start_end_indices(heating_max_day, len(heating_daily_demands))
+    cooling_start, cooling_end = get_start_end_indices(cooling_max_day, len(cooling_daily_demands))
+    dhw_start, dhw_end = get_start_end_indices(dhw_max_day, len(dhw_daily_demands))
+    # Return the design period energy demands
+    return {
+      f'{cte.HEATING}': {'demands': building.heating_demand[cte.HOUR][heating_start:heating_end],
+                         'start_index': heating_start, 'end_index': heating_end},
+      f'{cte.COOLING}': {'demands': building.cooling_demand[cte.HOUR][cooling_start:cooling_end],
+                         'start_index': cooling_start, 'end_index': cooling_end},
+      f'{cte.DOMESTIC_HOT_WATER}': {'demands': building.domestic_hot_water_heat_demand[cte.HOUR][dhw_start:dhw_end],
+                                    'start_index': dhw_start, 'end_index': dhw_end}
+    }
+

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/heuristic_sizing.py

@@ -1,6 +0,0 @@
-class HeuristicSizing:
-  def __init__(self, city):
-    pass
-
-  def enrich_buildings(self):
-    pass

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/optimal_sizing.py

@@ -0,0 +1,42 @@
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.system_sizing_methods.genetic_algorithm.single_objective_genetic_algorithm import \
+  SingleObjectiveGeneticAlgorithm
+
+
+class OptimalSizing:
+  def __init__(self, city, optimization_scenario):
+    self.city = city
+    self.optimization_scenario = optimization_scenario
+
+  def enrich_buildings(self):
+    for building in self.city.buildings:
+      for energy_system in building.energy_systems:
+        if len(energy_system.generation_systems) == 1 and energy_system.generation_systems[0].energy_storage_systems is None:
+          if energy_system.generation_systems[0].system_type == cte.PHOTOVOLTAIC:
+            pass
+          else:
+            if cte.HEATING in energy_system.demand_types:
+              if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+                design_load = max([building.heating_demand[cte.HOUR][i] +
+                                   building.domestic_hot_water_heat_demand[cte.HOUR][i] for i in
+                                   range(len(building.heating_demand))]) / cte.WATTS_HOUR_TO_JULES
+              else:
+                design_load = building.heating_peak_load[cte.YEAR][0]
+              energy_system.generation_systems[0].nominal_heat_output = design_load
+            elif cte.COOLING in energy_system.demand_types:
+              energy_system.generation_systems[0].nominal_cooling_output = building.cooling_peak_load[cte.YEAR][0]
+        else:
+          optimized_system = SingleObjectiveGeneticAlgorithm(optimization_scenario=self.optimization_scenario).solve_ga(building, energy_system)
+          for generation_system in energy_system.generation_systems:
+            system_type = generation_system.system_type
+            for generation_component in optimized_system.individual['Generation Components']:
+              if generation_component['type'] == system_type:
+                generation_system.nominal_heat_output = generation_component['heating_capacity']
+                generation_system.nominal_cooling_output = generation_component['cooling_capacity']
+            if generation_system.energy_storage_systems is not None:
+              for storage_system in generation_system.energy_storage_systems:
+                storage_type = f'{storage_system.type_energy_stored}_storage'
+                for storage_component in optimized_system.individual['Energy Storage Components']:
+                  if storage_component['type'] == storage_type:
+                    storage_system.nominal_capacity = storage_component['capacity']
+                    storage_system.volume = storage_component['volume']

energy_system_modelling_package/energy_system_modelling_factories/system_sizing_methods/peak_load_sizing.py

@@ -49,7 +49,7 @@ class PeakLoadSizing:
     if len(energy_system.generation_systems) == 1:
       # If there's only one generation system, it gets the full design load.
       if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
-        energy_system.generation_systems[0].nominal_heat_output = design_load
+        energy_system.generation_systems[0].nominal_heat_output = 0.7 * design_load
       elif demand_type == cte.COOLING:
         energy_system.generation_systems[0].nominal_cooling_output = design_load
     else:
@@ -78,7 +78,7 @@ class PeakLoadSizing:
             generation_system.nominal_cooling_output = design_load
         else:
           if demand_type == cte.HEATING or demand_type == cte.DOMESTIC_HOT_WATER:
-            generation_system.nominal_heat_output = round(((1 - default_primary_unit_percentage) * design_load /
+            generation_system.nominal_heat_output = round((default_primary_unit_percentage * design_load /
                                                      (len(energy_system.generation_systems) - 1)))
           elif demand_type == cte.COOLING and cooling_equipments_number > 1:
             generation_system.nominal_cooling_output = round(((1 - default_primary_unit_percentage) * design_load /

energy_system_modelling_package/energy_system_retrofit/energy_system_retrofit_report.py

@@ -22,7 +22,7 @@ class EnergySystemRetrofitReport:
     self.retrofit_scenario = retrofit_scenario
     self.report = LatexReport('energy_system_retrofit_report',
                               'Energy System Retrofit Report', self.retrofit_scenario, output_path)
-    self.system_schemas_path = (Path(__file__).parent.parent / 'hub' / 'data' / 'energy_systems' / 'schemas')
+    self.system_schemas_path = (Path(__file__).parent.parent.parent / 'hub' / 'data' / 'energy_systems' / 'schemas')
     self.charts_path = Path(output_path) / 'charts'
     self.charts_path.mkdir(parents=True, exist_ok=True)
     files = glob.glob(f'{self.charts_path}/*')
@@ -233,7 +233,7 @@ class EnergySystemRetrofitReport:
     for archetype, buildings in energy_system_archetypes.items():
       buildings_str = ", ".join(buildings)
       text += f"Figure 4 shows the schematic of the proposed energy system for buildings {buildings_str}.\n"
-      if archetype in ['PV+4Pipe+DHW', 'PV+ASHP+GasBoiler+TES']:
+      if archetype in ['PV+4Pipe+DHW', 'PV+ASHP+GasBoiler+TES', 'Central 4 Pipes Air to Water Heat Pump and Gas Boiler with Independent Water Heating and PV']:
         text += "This energy system archetype is formed of the following systems: \par"
         items = ['Rooftop Photovoltaic System: The rooftop PV system is tied to the grid and in case there is surplus '
                  'energy, it is sold to Hydro-Quebec through their Net-Meterin program.',
@@ -257,8 +257,7 @@ class EnergySystemRetrofitReport:
     for i in range(len(months)):
       tilted_radiation = 0
       for building in self.city.buildings:
-        tilted_radiation += (building.roofs[0].global_irradiance_tilted[cte.MONTH][i] /
-                             (cte.WATTS_HOUR_TO_JULES * 1000))
+        tilted_radiation += (building.roofs[0].global_irradiance_tilted[cte.MONTH][i] / 1000)
       monthly_roof_radiation.append(tilted_radiation)
 
     def plot_bar_chart(ax, months, values, color, ylabel, title):
@@ -565,7 +564,7 @@ class EnergySystemRetrofitReport:
                           placement='H')
     self.report.add_section(f'{self.retrofit_scenario}')
     self.report.add_subsection('Proposed Systems')
-    # self.energy_system_archetype_schematic()
+    self.energy_system_archetype_schematic()
     if 'PV' in self.retrofit_scenario:
       self.report.add_subsection('Rooftop Photovoltaic System Implementation')
       self.pv_system()

energy_system_modelling_package/random_assignation.py

@@ -29,18 +29,20 @@ residential_systems_percentage = {'system 1 gas': 15,
                                   'system 8 electricity': 35}
 
 residential_new_systems_percentage = {
-    'Central 4 Pipes Air to Water Heat Pump and Gas Boiler with Independent Water Heating and PV': 100,
-    'Central 4 Pipes Air to Water Heat Pump and electrical Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and Gas Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and electrical Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Water to Water Heat Pump and Gas Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Water to Water Heat Pump and electrical Boiler with Independent Water Heating and PV': 0,
-    'Central 4 Pipes Air to Water Heat Pump and Gas Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Air to Water Heat Pump and electrical Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and Gas Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Ground to Water Heat Pump and electrical Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Water to Water Heat Pump and Gas Boiler with Independent Water Heating': 0,
-    'Central 4 Pipes Water to Water Heat Pump and electrical Boiler with Independent Water Heating': 0,
+    'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 100,
+    'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
+    'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
+    'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+    'and PV': 0,
+    'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and PV': 0,
+    'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+    'and PV': 0,
+    'Central Hydronic Air and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Air and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Ground and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Ground and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Water and Gas Source Heating System with Unitary Split and Air Source HP DHW': 0,
+    'Central Hydronic Water and Electricity Source Heating System with Unitary Split and Air Source HP DHW': 0,
     'Rooftop PV System': 0
 }
 

energy_system_retrofit.py

@@ -49,7 +49,7 @@ ExportsFactory('sra', city, sra_output_path).export()
 sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
 subprocess.run(['sra', str(sra_path)])
 ResultFactory('sra', city, sra_output_path).enrich()
-pv_feasibility(-73.5681295982132, 45.49218262677643, 0.0001, selected_buildings=city.buildings)
+# pv_feasibility(-73.5681295982132, 45.49218262677643, 0.0001, selected_buildings=city.buildings)
 energy_plus_workflow(city, energy_plus_output_path)
 random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
 EnergySystemsFactory('montreal_custom', city).enrich()

hub/catalog_factories/data_models/energy_systems/energy_storage_system.py

@@ -14,9 +14,10 @@ class EnergyStorageSystem(ABC):
   Energy Storage System Abstract Class
   """
 
-  def __init__(self, storage_id, model_name=None, manufacturer=None,
+  def __init__(self, storage_id=None, name=None, model_name=None, manufacturer=None,
                nominal_capacity=None, losses_ratio=None):
     self._storage_id = storage_id
+    self._name = name
     self._model_name = model_name
     self._manufacturer = manufacturer
     self._nominal_capacity = nominal_capacity
@@ -30,6 +31,14 @@ class EnergyStorageSystem(ABC):
     """
     return self._storage_id
 
+  @property
+  def name(self):
+    """
+    Get storage name
+    :return: string
+    """
+    return self._name
+
   @property
   def type_energy_stored(self):
     """

hub/catalog_factories/data_models/energy_systems/thermal_storage_system.py

@@ -15,11 +15,11 @@ class ThermalStorageSystem(EnergyStorageSystem):
   Energy Storage System Class
   """
 
-  def __init__(self, storage_id, type_energy_stored=None, model_name=None, manufacturer=None, storage_type=None,
+  def __init__(self, storage_id=None, name=None, type_energy_stored=None, model_name=None, manufacturer=None, storage_type=None,
                nominal_capacity=None, losses_ratio=None, volume=None, height=None, layers=None,
                maximum_operating_temperature=None, storage_medium=None, heating_coil_capacity=None):
 
-    super().__init__(storage_id, model_name, manufacturer, nominal_capacity, losses_ratio)
+    super().__init__(storage_id, name, model_name, manufacturer, nominal_capacity, losses_ratio)
     self._type_energy_stored = type_energy_stored
     self._storage_type = storage_type
     self._volume = volume
@@ -109,6 +109,7 @@ class ThermalStorageSystem(EnergyStorageSystem):
       'Storage component':
       {
         'storage id': self.id,
+        'name': self.name,
         'type of energy stored': self.type_energy_stored,
         'model name': self.model_name,
         'manufacturer': self.manufacturer,

hub/catalog_factories/energy_systems/montreal_future_system_catalogue.py

@@ -278,6 +278,7 @@ class MontrealFutureSystemCatalogue(Catalog):
     template_storages = self._archetypes['EnergySystemCatalog']['energy_storage_components']['templateStorages']
     for tes in thermal_storages:
       storage_id = tes['storage_id']
+      storage_name = tes['name']
       type_energy_stored = tes['type_energy_stored']
       model_name = tes['model_name']
       manufacturer = tes['manufacturer']
@@ -302,6 +303,7 @@ class MontrealFutureSystemCatalogue(Catalog):
       losses_ratio = tes['losses_ratio']
       heating_coil_capacity = tes['heating_coil_capacity']
       storage_component = ThermalStorageSystem(storage_id=storage_id,
+                                               name=storage_name,
                                                model_name=model_name,
                                                type_energy_stored=type_energy_stored,
                                                manufacturer=manufacturer,
@@ -318,6 +320,7 @@ class MontrealFutureSystemCatalogue(Catalog):
 
     for template in template_storages:
       storage_id = template['storage_id']
+      storage_name = template['name']
       storage_type = template['storage_type']
       type_energy_stored = template['type_energy_stored']
       maximum_operating_temperature = template['maximum_operating_temperature']
@@ -342,6 +345,7 @@ class MontrealFutureSystemCatalogue(Catalog):
       volume = template['physical_characteristics']['volume']
       heating_coil_capacity = template['heating_coil_capacity']
       storage_component = ThermalStorageSystem(storage_id=storage_id,
+                                               name=storage_name,
                                                model_name=model_name,
                                                type_energy_stored=type_energy_stored,
                                                manufacturer=manufacturer,