Merge pull request 'pv_sizing_modelling' (#21) from pv_sizing_modelling into main

Reviewed-on: #21

building_modelling/geojson_creator.py

@@ -4,16 +4,16 @@ from shapely import Point
 from pathlib import Path
 
 
-def process_geojson(x, y, diff, expansion=False):
+def process_geojson(x, y, diff, path, expansion=False):
   selection_box = Polygon([[x + diff, y - diff],
                            [x - diff, y - diff],
                            [x - diff, y + diff],
                            [x + diff, y + diff]])
-  geojson_file = Path('./data/collinear_clean 2.geojson').resolve()
+  geojson_file = Path(path / 'data/collinear_clean 2.geojson').resolve()
   if not expansion:
-    output_file = Path('./input_files/output_buildings.geojson').resolve()
+    output_file = Path(path / 'input_files/output_buildings.geojson').resolve()
   else:
-    output_file = Path('./input_files/output_buildings_expanded.geojson').resolve()
+    output_file = Path(path / 'input_files/output_buildings_expanded.geojson').resolve()
   buildings_in_region = []
 
   with open(geojson_file, 'r') as file:

energy_system_modelling_package/energy_system_modelling_factories/archetypes/montreal/archetype_cluster_1.py

@@ -6,8 +6,6 @@ from energy_system_modelling_package.energy_system_modelling_factories.hvac_dhw_
   HeatPumpCooling
 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.pv_assessment.pv_model import PVModel
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
 import hub.helpers.constants as cte
 from hub.helpers.monthly_values import MonthlyValues
 
@@ -21,10 +19,6 @@ class ArchetypeCluster1:
     self.heating_results, self.building_heating_hourly_consumption = self.heating_system_simulation()
     self.cooling_results, self.total_cooling_consumption_hourly = self.cooling_system_simulation()
     self.dhw_results, self.total_dhw_consumption_hourly = self.dhw_system_simulation()
-    if 'PV' in self.building.energy_systems_archetype_name:
-      self.pv_results = self.pv_system_simulation()
-    else:
-      self.pv_results = None
 
   def heating_system_simulation(self):
     building_heating_hourly_consumption = []
@@ -55,7 +49,7 @@ 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[2].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
@@ -71,8 +65,8 @@ 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]
@@ -93,18 +87,6 @@ class ArchetypeCluster1:
         dhw_consumption = 0
     return results, building_dhw_hourly_consumption
 
-  def pv_system_simulation(self):
-    results = None
-    pv = self.building.energy_systems[0].generation_systems[0]
-    hourly_electricity_demand = HourlyElectricityDemand(self.building).calculate()
-    model_type = 'fixed_efficiency'
-    if model_type == 'fixed_efficiency':
-      results = PVModel(pv=pv,
-                        hourly_electricity_demand_joules=hourly_electricity_demand,
-                        solar_radiation=self.building.roofs[0].global_irradiance_tilted[cte.HOUR],
-                        installed_pv_area=self.building.roofs[0].installed_solar_collector_area,
-                        model_type='fixed_efficiency').fixed_efficiency()
-    return results
 
   def enrich_building(self):
     results = self.heating_results | self.cooling_results | self.dhw_results
@@ -121,19 +103,6 @@ class ArchetypeCluster1:
       MonthlyValues.get_total_month(self.building.domestic_hot_water_consumption[cte.HOUR]))
     self.building.domestic_hot_water_consumption[cte.YEAR] = [
       sum(self.building.domestic_hot_water_consumption[cte.MONTH])]
-    if self.pv_results is not None:
-      self.building.onsite_electrical_production[cte.HOUR] = [x * cte.WATTS_HOUR_TO_JULES for x in
-                                                              self.pv_results['PV Output (W)']]
-      self.building.onsite_electrical_production[cte.MONTH] = MonthlyValues.get_total_month(self.building.onsite_electrical_production[cte.HOUR])
-      self.building.onsite_electrical_production[cte.YEAR] = [sum(self.building.onsite_electrical_production[cte.MONTH])]
-      if self.csv_output:
-        file_name = f'pv_system_simulation_results_{self.building.name}.csv'
-        with open(self.output_path / file_name, 'w', newline='') as csvfile:
-          output_file = csv.writer(csvfile)
-          # Write header
-          output_file.writerow(self.pv_results.keys())
-          # Write data
-          output_file.writerows(zip(*self.pv_results.values()))
     if self.csv_output:
       file_name = f'energy_system_simulation_results_{self.building.name}.csv'
       with open(self.output_path / file_name, 'w', newline='') as csvfile:

energy_system_modelling_package/energy_system_modelling_factories/energy_system_sizing_factory.py

@@ -9,7 +9,6 @@ from energy_system_modelling_package.energy_system_modelling_factories.system_si
   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
 
 
 class EnergySystemsSizingFactory:
@@ -39,33 +38,6 @@ class EnergySystemsSizingFactory:
     for building in self._city.buildings:
       building.level_of_detail.energy_systems = 1
 
-  def _pv_sizing(self):
-    """
-    Size rooftop, facade or mixture of them for buildings
-    """
-    system_type = 'rooftop'
-    results = {}
-    if system_type == 'rooftop':
-      surface_azimuth = 180
-      maintenance_factor = 0.1
-      mechanical_equipment_factor = 0.3
-      orientation_factor = 0.1
-      tilt_angle = self._city.latitude
-      pv_sizing = PVSizing(self._city,
-                           tilt_angle=tilt_angle,
-                           surface_azimuth=surface_azimuth,
-                           mechanical_equipment_factor=mechanical_equipment_factor,
-                           maintenance_factor=maintenance_factor,
-                           orientation_factor=orientation_factor,
-                           system_type=system_type)
-      results = pv_sizing.rooftop_sizing()
-      pv_sizing.rooftop_tilted_radiation()
-
-    self._city.level_of_detail.energy_systems = 1
-    for building in self._city.buildings:
-      building.level_of_detail.energy_systems = 1
-    return results
-
   def _district_heating_cooling_sizing(self):
     """
     Size District Heating and Cooling Network

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/electricity_demand_calculator.py

@@ -6,8 +6,8 @@ class HourlyElectricityDemand:
   def calculate(self):
     hourly_electricity_consumption = []
     energy_systems = self.building.energy_systems
-    appliance = self.building.appliances_electrical_demand[cte.HOUR]
-    lighting = self.building.lighting_electrical_demand[cte.HOUR]
+    appliance = self.building.appliances_electrical_demand[cte.HOUR] if self.building.appliances_electrical_demand else 0
+    lighting = self.building.lighting_electrical_demand[cte.HOUR] if self.building.lighting_electrical_demand else 0
     elec_heating = 0
     elec_cooling = 0
     elec_dhw = 0
@@ -59,10 +59,12 @@ class HourlyElectricityDemand:
               else:
                 cooling = self.building.cooling_consumption[cte.HOUR]
 
-    for i in range(len(self.building.heating_demand[cte.HOUR])):
+    for i in range(8760):
       hourly = 0
-      hourly += appliance[i]
-      hourly += lighting[i]
+      if isinstance(appliance, list):
+        hourly += appliance[i]
+      if isinstance(lighting, list):
+        hourly += lighting[i]
       if heating is not None:
         hourly += heating[i]
       if cooling is not None:

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_feasibility.py

@@ -1,37 +0,0 @@
-from pathlib import Path
-import subprocess
-from hub.imports.geometry_factory import GeometryFactory
-from building_modelling.geojson_creator import process_geojson
-from hub.helpers.dictionaries import Dictionaries
-from hub.imports.weather_factory import WeatherFactory
-from hub.imports.results_factory import ResultFactory
-from hub.exports.exports_factory import ExportsFactory
-
-
-def pv_feasibility(current_x, current_y, current_diff, selected_buildings):
-  input_files_path = (Path(__file__).parent.parent.parent.parent / 'input_files')
-  output_path = (Path(__file__).parent.parent.parent.parent / 'out_files').resolve()
-  sra_output_path = output_path / 'sra_outputs' / 'extended_city_sra_outputs'
-  sra_output_path.mkdir(parents=True, exist_ok=True)
-  new_diff = current_diff * 5
-  geojson_file = process_geojson(x=current_x, y=current_y, diff=new_diff, expansion=True)
-  file_path = input_files_path / 'output_buildings.geojson'
-  city = GeometryFactory('geojson',
-                         path=file_path,
-                         height_field='height',
-                         year_of_construction_field='year_of_construction',
-                         function_field='function',
-                         function_to_hub=Dictionaries().montreal_function_to_hub_function).city
-  WeatherFactory('epw', city).enrich()
-  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()
-  for selected_building in selected_buildings:
-    for building in city.buildings:
-      if selected_building.name == building.name:
-        selected_building.roofs[0].global_irradiance = building.roofs[0].global_irradiance
-
-
-
-

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_model.py

@@ -1,42 +0,0 @@
-import math
-import hub.helpers.constants as cte
-from hub.helpers.monthly_values import MonthlyValues
-
-
-class PVModel:
-  def __init__(self, pv, hourly_electricity_demand_joules, solar_radiation, installed_pv_area, model_type, ns=None,
-               np=None):
-    self.pv = pv
-    self.hourly_electricity_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in hourly_electricity_demand_joules]
-    self.solar_radiation = solar_radiation
-    self.installed_pv_area = installed_pv_area
-    self._model_type = '_' + model_type.lower()
-    self.ns = ns
-    self.np = np
-    self.results = {}
-
-  def fixed_efficiency(self):
-    module_efficiency = float(self.pv.electricity_efficiency)
-    variable_names = ["pv_output", "import", "export", "self_sufficiency_ratio"]
-    variables = {name: [0] * len(self.hourly_electricity_demand) for name in variable_names}
-    (pv_out, grid_import, grid_export, self_sufficiency_ratio) = [variables[name] for name in variable_names]
-    for i in range(len(self.hourly_electricity_demand)):
-      pv_out[i] = module_efficiency * self.installed_pv_area * self.solar_radiation[i] / cte.WATTS_HOUR_TO_JULES
-      if pv_out[i] < self.hourly_electricity_demand[i]:
-        grid_import[i] = self.hourly_electricity_demand[i] - pv_out[i]
-      else:
-        grid_export[i] = pv_out[i] - self.hourly_electricity_demand[i]
-      self_sufficiency_ratio[i] = pv_out[i] / self.hourly_electricity_demand[i]
-    self.results['Electricity Demand (W)'] = self.hourly_electricity_demand
-    self.results['PV Output (W)'] = pv_out
-    self.results['Imported from Grid (W)'] = grid_import
-    self.results['Exported to Grid (W)'] = grid_export
-    self.results['Self Sufficiency Ratio'] = self_sufficiency_ratio
-    return self.results
-
-  def enrich(self):
-    """
-    Enrich the city given to the class using the class given handler
-    :return: None
-    """
-    return getattr(self, self._model_type, lambda: None)()

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_sizing.py

@@ -1,70 +0,0 @@
-import math
-import hub.helpers.constants as cte
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_angles import CitySolarAngles
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.radiation_tilted import RadiationTilted
-
-
-class PVSizing(CitySolarAngles):
-  def __init__(self, city, tilt_angle, surface_azimuth=180, maintenance_factor=0.1, mechanical_equipment_factor=0.3,
-               orientation_factor=0.1, system_type='rooftop'):
-    super().__init__(location_latitude=city.latitude,
-                     location_longitude=city.longitude,
-                     tilt_angle=tilt_angle,
-                     surface_azimuth_angle=surface_azimuth)
-    self.city = city
-    self.maintenance_factor = maintenance_factor
-    self.mechanical_equipment_factor = mechanical_equipment_factor
-    self.orientation_factor = orientation_factor
-    self.angles = self.calculate
-    self.system_type = system_type
-
-  def rooftop_sizing(self):
-    results = {}
-    # Available Roof Area
-    for building in self.city.buildings:
-      for energy_system in building.energy_systems:
-        for generation_system in energy_system.generation_systems:
-          if generation_system.system_type == cte.PHOTOVOLTAIC:
-            module_width = float(generation_system.width)
-            module_height = float(generation_system.height)
-            roof_area = 0
-            for roof in building.roofs:
-              roof_area += roof.perimeter_area
-            pv_module_area = module_width * module_height
-            available_roof = ((self.maintenance_factor + self.orientation_factor + self.mechanical_equipment_factor) *
-                              roof_area)
-        # Inter-Row Spacing
-            winter_solstice = self.angles[(self.angles['AST'].dt.month == 12) &
-                                          (self.angles['AST'].dt.day == 21) &
-                                          (self.angles['AST'].dt.hour == 12)]
-            solar_altitude = winter_solstice['solar altitude'].values[0]
-            solar_azimuth = winter_solstice['solar azimuth'].values[0]
-            distance = ((module_height * abs(math.cos(math.radians(solar_azimuth)))) /
-                        math.tan(math.radians(solar_altitude)))
-            distance = float(format(distance, '.1f'))
-        # Calculation of the number of panels
-            space_dimension = math.sqrt(available_roof)
-            space_dimension = float(format(space_dimension, '.2f'))
-            panels_per_row = math.ceil(space_dimension / module_width)
-            number_of_rows = math.ceil(space_dimension / distance)
-            total_number_of_panels = panels_per_row * number_of_rows
-            total_pv_area = panels_per_row * number_of_rows * pv_module_area
-            building.roofs[0].installed_solar_collector_area = total_pv_area
-            results[f'Building {building.name}'] = {'total_roof_area': roof_area,
-                                                    'PV dedicated area': available_roof,
-                                                    'total_pv_area': total_pv_area,
-                                                    'total_number_of_panels': total_number_of_panels,
-                                                    'number_of_rows': number_of_rows,
-                                                    'panels_per_row': panels_per_row}
-    return results
-
-  def rooftop_tilted_radiation(self):
-    for building in self.city.buildings:
-      RadiationTilted(building=building,
-                      solar_angles=self.angles,
-                      tilt_angle=self.tilt_angle,
-                      ghi=building.roofs[0].global_irradiance[cte.HOUR],
-                      ).enrich()
-
-  def facade_sizing(self):
-    pass

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_sizing_and_simulation.py

@@ -1,59 +0,0 @@
-import math
-
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.radiation_tilted import RadiationTilted
-import hub.helpers.constants as cte
-from hub.helpers.monthly_values import MonthlyValues
-
-
-class PVSizingSimulation(RadiationTilted):
-  def __init__(self, building, solar_angles, tilt_angle, module_height, module_width, ghi):
-    super().__init__(building, solar_angles, tilt_angle, ghi)
-    self.module_height = module_height
-    self.module_width = module_width
-    self.total_number_of_panels = 0
-    self.enrich()
-
-  def available_space(self):
-    roof_area = self.building.roofs[0].perimeter_area
-    maintenance_factor = 0.1
-    orientation_factor = 0.2
-    if self.building.function == cte.RESIDENTIAL:
-      mechanical_equipment_factor = 0.2
-    else:
-      mechanical_equipment_factor = 0.3
-    available_roof = (maintenance_factor + orientation_factor + mechanical_equipment_factor) * roof_area
-    return available_roof
-
-  def inter_row_spacing(self):
-    winter_solstice = self.df[(self.df['AST'].dt.month == 12) &
-                              (self.df['AST'].dt.day == 21) &
-                              (self.df['AST'].dt.hour == 12)]
-    solar_altitude = winter_solstice['solar altitude'].values[0]
-    solar_azimuth = winter_solstice['solar azimuth'].values[0]
-    distance = ((self.module_height * abs(math.cos(math.radians(solar_azimuth)))) /
-                math.tan(math.radians(solar_altitude)))
-    distance = float(format(distance, '.1f'))
-    return distance
-
-  def number_of_panels(self, available_roof, inter_row_distance):
-    space_dimension = math.sqrt(available_roof)
-    space_dimension = float(format(space_dimension, '.2f'))
-    panels_per_row = math.ceil(space_dimension / self.module_width)
-    number_of_rows = math.ceil(space_dimension / inter_row_distance)
-    self.total_number_of_panels = panels_per_row * number_of_rows
-    return panels_per_row, number_of_rows
-
-  def pv_output_constant_efficiency(self):
-    radiation = self.total_radiation_tilted
-    pv_module_area = self.module_width * self.module_height
-    available_roof = self.available_space()
-    inter_row_spacing = self.inter_row_spacing()
-    self.number_of_panels(available_roof, inter_row_spacing)
-    self.building.roofs[0].installed_solar_collector_area = pv_module_area * self.total_number_of_panels
-    system_efficiency = 0.2
-    pv_hourly_production = [x * system_efficiency * self.total_number_of_panels * pv_module_area *
-                            cte.WATTS_HOUR_TO_JULES for x in radiation]
-    self.building.onsite_electrical_production[cte.HOUR] = pv_hourly_production
-    self.building.onsite_electrical_production[cte.MONTH] = (
-      MonthlyValues.get_total_month(self.building.onsite_electrical_production[cte.HOUR]))
-    self.building.onsite_electrical_production[cte.YEAR] = [sum(self.building.onsite_electrical_production[cte.MONTH])]

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/pv_system_assessment.py

@@ -0,0 +1,225 @@
+import math
+import csv
+import hub.helpers.constants as cte
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import \
+  HourlyElectricityDemand
+from hub.catalog_factories.energy_systems_catalog_factory import EnergySystemsCatalogFactory
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class PvSystemAssessment:
+  def __init__(self, building=None, pv_system=None, battery=None, electricity_demand=None, tilt_angle=None,
+               solar_angles=None, pv_installation_type=None, simulation_model_type=None, module_model_name=None,
+               inverter_efficiency=None, system_catalogue_handler=None, roof_percentage_coverage=None,
+               facade_coverage_percentage=None, csv_output=False, output_path=None):
+    """
+    :param building:
+    :param tilt_angle:
+    :param solar_angles:
+    :param simulation_model_type:
+    :param module_model_name:
+    :param inverter_efficiency:
+    :param system_catalogue_handler:
+    :param roof_percentage_coverage:
+    :param facade_coverage_percentage:
+    """
+    self.building = building
+    self.electricity_demand = electricity_demand
+    self.tilt_angle = tilt_angle
+    self.solar_angles = solar_angles
+    self.pv_installation_type = pv_installation_type
+    self.simulation_model_type = simulation_model_type
+    self.module_model_name = module_model_name
+    self.inverter_efficiency = inverter_efficiency
+    self.system_catalogue_handler = system_catalogue_handler
+    self.roof_percentage_coverage = roof_percentage_coverage
+    self.facade_coverage_percentage = facade_coverage_percentage
+    self.pv_hourly_generation = None
+    self.t_cell = None
+    self.results = {}
+    self.csv_output = csv_output
+    self.output_path = output_path
+    if pv_system is not None:
+      self.pv_system = pv_system
+    else:
+      for energy_system in self.building.energy_systems:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.system_type == cte.PHOTOVOLTAIC:
+            self.pv_system = generation_system
+    if battery is not None:
+      self.battery = battery
+    else:
+      for energy_system in self.building.energy_systems:
+        for generation_system in energy_system.generation_systems:
+          if generation_system.system_type == cte.PHOTOVOLTAIC and generation_system.energy_storage_systems is not None:
+            for storage_system in generation_system.energy_storage_systems:
+              if storage_system.type_energy_stored == cte.ELECTRICAL:
+                self.battery = storage_system
+
+  @staticmethod
+  def explicit_model(pv_system, inverter_efficiency, number_of_panels, irradiance, outdoor_temperature):
+    inverter_efficiency = inverter_efficiency
+    stc_power = float(pv_system.standard_test_condition_maximum_power)
+    stc_irradiance = float(pv_system.standard_test_condition_radiation)
+    cell_temperature_coefficient = float(pv_system.cell_temperature_coefficient) / 100 if (
+        pv_system.cell_temperature_coefficient is not None) else None
+    stc_t_cell = float(pv_system.standard_test_condition_cell_temperature)
+    nominal_condition_irradiance = float(pv_system.nominal_radiation)
+    nominal_condition_cell_temperature = float(pv_system.nominal_cell_temperature)
+    nominal_t_out = float(pv_system.nominal_ambient_temperature)
+    g_i = irradiance
+    t_out = outdoor_temperature
+    t_cell = []
+    pv_output = []
+    for i in range(len(g_i)):
+      t_cell.append((t_out[i] + (g_i[i] / nominal_condition_irradiance) *
+                     (nominal_condition_cell_temperature - nominal_t_out)))
+      pv_output.append((inverter_efficiency * number_of_panels * (stc_power * (g_i[i] / stc_irradiance) *
+                                                                  (1 - cell_temperature_coefficient *
+                                                                   (t_cell[i] - stc_t_cell)))))
+    return pv_output
+
+  def rooftop_sizing(self):
+    pv_system = self.pv_system
+    if self.module_model_name is not None:
+      self.system_assignation()
+    # System Sizing
+    module_width = float(pv_system.width)
+    module_height = float(pv_system.height)
+    roof_area = 0
+    for roof in self.building.roofs:
+      roof_area += roof.perimeter_area
+    pv_module_area = module_width * module_height
+    available_roof = (self.roof_percentage_coverage * roof_area)
+    # Inter-Row Spacing
+    winter_solstice = self.solar_angles[(self.solar_angles['AST'].dt.month == 12) &
+                                        (self.solar_angles['AST'].dt.day == 21) &
+                                        (self.solar_angles['AST'].dt.hour == 12)]
+    solar_altitude = winter_solstice['solar altitude'].values[0]
+    solar_azimuth = winter_solstice['solar azimuth'].values[0]
+    distance = ((module_height * math.sin(math.radians(self.tilt_angle)) * abs(
+      math.cos(math.radians(solar_azimuth)))) / math.tan(math.radians(solar_altitude)))
+    distance = float(format(distance, '.2f'))
+    # Calculation of the number of panels
+    space_dimension = math.sqrt(available_roof)
+    space_dimension = float(format(space_dimension, '.2f'))
+    panels_per_row = math.ceil(space_dimension / module_width)
+    number_of_rows = math.ceil(space_dimension / distance)
+    total_number_of_panels = panels_per_row * number_of_rows
+    total_pv_area = total_number_of_panels * pv_module_area
+    self.building.roofs[0].installed_solar_collector_area = total_pv_area
+    return panels_per_row, number_of_rows
+
+  def system_assignation(self):
+    generation_units_catalogue = EnergySystemsCatalogFactory(self.system_catalogue_handler).catalog
+    catalog_pv_generation_equipments = [component for component in
+                                        generation_units_catalogue.entries('generation_equipments') if
+                                        component.system_type == 'photovoltaic']
+    selected_pv_module = None
+    for pv_module in catalog_pv_generation_equipments:
+      if self.module_model_name == pv_module.model_name:
+        selected_pv_module = pv_module
+    if selected_pv_module is None:
+      raise ValueError("No PV module with the provided model name exists in the catalogue")
+    for energy_system in self.building.energy_systems:
+      for idx, generation_system in enumerate(energy_system.generation_systems):
+        if generation_system.system_type == cte.PHOTOVOLTAIC:
+          new_system = selected_pv_module
+        # Preserve attributes that exist in the original but not in the new system
+          for attr in dir(generation_system):
+            # Skip private attributes and methods
+            if not attr.startswith('__') and not callable(getattr(generation_system, attr)):
+              if not hasattr(new_system, attr):
+                setattr(new_system, attr, getattr(generation_system, attr))
+        # Replace the old generation system with the new one
+          energy_system.generation_systems[idx] = new_system
+
+  def grid_tied_system(self):
+    if self.electricity_demand is not None:
+      electricity_demand = self.electricity_demand
+    else:
+      electricity_demand = [demand / cte.WATTS_HOUR_TO_JULES for demand in
+                                          HourlyElectricityDemand(self.building).calculate()]
+    rooftop_pv_output = [0] * 8760
+    facade_pv_output = [0] * 8760
+    rooftop_number_of_panels = 0
+    if 'rooftop' in self.pv_installation_type.lower():
+      np, ns = self.rooftop_sizing()
+      if self.simulation_model_type == 'explicit':
+        rooftop_number_of_panels = np * ns
+        rooftop_pv_output = self.explicit_model(pv_system=self.pv_system,
+                                                inverter_efficiency=self.inverter_efficiency,
+                                                number_of_panels=rooftop_number_of_panels,
+                                                irradiance=self.building.roofs[0].global_irradiance_tilted[
+                                                  cte.HOUR],
+                                                outdoor_temperature=self.building.external_temperature[
+                                                  cte.HOUR])
+
+    total_hourly_pv_output = [rooftop_pv_output[i] + facade_pv_output[i] for i in range(8760)]
+    imported_electricity = [0] * 8760
+    exported_electricity = [0] * 8760
+    for i in range(len(electricity_demand)):
+      transfer = total_hourly_pv_output[i] - electricity_demand[i]
+      if transfer > 0:
+        exported_electricity[i] = transfer
+      else:
+        imported_electricity[i] = abs(transfer)
+
+    results = {'building_name': self.building.name,
+               'total_floor_area_m2': self.building.thermal_zones_from_internal_zones[0].total_floor_area,
+               'roof_area_m2': self.building.roofs[0].perimeter_area, 'rooftop_panels': rooftop_number_of_panels,
+               'rooftop_panels_area_m2': self.building.roofs[0].installed_solar_collector_area,
+               'yearly_rooftop_ghi_kW/m2': self.building.roofs[0].global_irradiance[cte.YEAR][0] / 1000,
+               f'yearly_rooftop_tilted_radiation_{self.tilt_angle}_degree_kW/m2':
+                 self.building.roofs[0].global_irradiance_tilted[cte.YEAR][0] / 1000,
+               'yearly_rooftop_pv_production_kWh': sum(rooftop_pv_output) / 1000,
+               'yearly_total_pv_production_kWh': sum(total_hourly_pv_output) / 1000,
+               'specific_pv_production_kWh/kWp': sum(rooftop_pv_output) / (
+                   float(self.pv_system.standard_test_condition_maximum_power) * rooftop_number_of_panels),
+               'hourly_rooftop_poa_irradiance_W/m2': self.building.roofs[0].global_irradiance_tilted[cte.HOUR],
+               'hourly_rooftop_pv_output_W': rooftop_pv_output, 'T_out': self.building.external_temperature[cte.HOUR],
+               'building_electricity_demand_W': electricity_demand,
+               'total_hourly_pv_system_output_W': total_hourly_pv_output, 'import_from_grid_W': imported_electricity,
+               'export_to_grid_W': exported_electricity}
+    return results
+
+  def enrich(self):
+    system_archetype_name = self.building.energy_systems_archetype_name
+    archetype_name = '_'.join(system_archetype_name.lower().split())
+    if 'grid_tied' in archetype_name:
+      self.results = self.grid_tied_system()
+    hourly_pv_output = self.results['total_hourly_pv_system_output_W']
+    self.building.onsite_electrical_production[cte.HOUR] = hourly_pv_output
+    self.building.onsite_electrical_production[cte.MONTH] = MonthlyValues.get_total_month(hourly_pv_output)
+    self.building.onsite_electrical_production[cte.YEAR] = [sum(hourly_pv_output)]
+    if self.csv_output:
+      self.save_to_csv(self.results, self.output_path, f'{self.building.name}_pv_system_analysis.csv')
+
+  @staticmethod
+  def save_to_csv(data, output_path, filename='rooftop_system_results.csv'):
+    # Separate keys based on whether their values are single values or lists
+    single_value_keys = [key for key, value in data.items() if not isinstance(value, list)]
+    list_value_keys = [key for key, value in data.items() if isinstance(value, list)]
+
+    # Check if all lists have the same length
+    list_lengths = [len(data[key]) for key in list_value_keys]
+    if not all(length == list_lengths[0] for length in list_lengths):
+      raise ValueError("All lists in the dictionary must have the same length")
+
+    # Get the length of list values (assuming all lists are of the same length, e.g., 8760 for hourly data)
+    num_rows = list_lengths[0] if list_value_keys else 1
+
+    # Open the CSV file for writing
+    with open(output_path / filename, mode='w', newline='') as csv_file:
+      writer = csv.writer(csv_file)
+    # Write single-value data as a header section
+      for key in single_value_keys:
+        writer.writerow([key, data[key]])
+    # Write an empty row for separation
+      writer.writerow([])
+    # Write the header for the list values
+      writer.writerow(list_value_keys)
+    # Write each row for the lists
+      for i in range(num_rows):
+        row = [data[key][i] for key in list_value_keys]
+        writer.writerow(row)

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/radiation_tilted.py

@@ -1,110 +0,0 @@
-import pandas as pd
-import math
-import hub.helpers.constants as cte
-from hub.helpers.monthly_values import MonthlyValues
-
-
-class RadiationTilted:
-  def __init__(self, building, solar_angles, tilt_angle, ghi, solar_constant=1366.1, maximum_clearness_index=1,
-               min_cos_zenith=0.065, maximum_zenith_angle=87):
-    self.building = building
-    self.ghi = ghi
-    self.tilt_angle = tilt_angle
-    self.zeniths = solar_angles['zenith'].tolist()[:-1]
-    self.incidents = solar_angles['incident angle'].tolist()[:-1]
-    self.date_time = solar_angles['DateTime'].tolist()[:-1]
-    self.ast = solar_angles['AST'].tolist()[:-1]
-    self.solar_azimuth = solar_angles['solar azimuth'].tolist()[:-1]
-    self.solar_altitude = solar_angles['solar altitude'].tolist()[:-1]
-    data = {'DateTime': self.date_time, 'AST': self.ast, 'solar altitude': self.solar_altitude, 'zenith': self.zeniths,
-            'solar azimuth': self.solar_azimuth, 'incident angle': self.incidents, 'ghi': self.ghi}
-    self.df = pd.DataFrame(data)
-    self.df['DateTime'] = pd.to_datetime(self.df['DateTime'])
-    self.df['AST'] = pd.to_datetime(self.df['AST'])
-    self.df.set_index('DateTime', inplace=True)
-    self.solar_constant = solar_constant
-    self.maximum_clearness_index = maximum_clearness_index
-    self.min_cos_zenith = min_cos_zenith
-    self.maximum_zenith_angle = maximum_zenith_angle
-    self.i_on = []
-    self.i_oh = []
-    self.k_t = []
-    self.fraction_diffuse = []
-    self.diffuse_horizontal = []
-    self.beam_horizontal = []
-    self.dni = []
-    self.beam_tilted = []
-    self.diffuse_tilted = []
-    self.total_radiation_tilted = []
-    self.calculate()
-
-  def dni_extra(self):
-    for i in range(len(self.df)):
-      self.i_on.append(self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * self.df.index.dayofyear[i] / 365))))
-
-    self.df['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
-
-  def clearness_index(self):
-    for i in range(len(self.df)):
-      self.i_oh.append(self.i_on[i] * max(math.cos(math.radians(self.zeniths[i])), self.min_cos_zenith))
-      self.k_t.append(self.ghi[i] / self.i_oh[i])
-      self.k_t[i] = max(0, self.k_t[i])
-      self.k_t[i] = min(self.maximum_clearness_index, self.k_t[i])
-    self.df['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
-    self.df['clearness index'] = self.k_t
-
-  def diffuse_fraction(self):
-    for i in range(len(self.df)):
-      if self.k_t[i] <= 0.22:
-        self.fraction_diffuse.append(1 - 0.09 * self.k_t[i])
-      elif self.k_t[i] <= 0.8:
-        self.fraction_diffuse.append(0.9511 - 0.1604 * self.k_t[i] + 4.388 * self.k_t[i] ** 2 -
-                                     16.638 * self.k_t[i] ** 3 + 12.336 * self.k_t[i] ** 4)
-      else:
-        self.fraction_diffuse.append(0.165)
-      if self.zeniths[i] > self.maximum_zenith_angle:
-        self.fraction_diffuse[i] = 1
-
-    self.df['diffuse fraction'] = self.fraction_diffuse
-
-  def radiation_components_horizontal(self):
-    for i in range(len(self.df)):
-      self.diffuse_horizontal.append(self.ghi[i] * self.fraction_diffuse[i])
-      self.beam_horizontal.append(self.ghi[i] - self.diffuse_horizontal[i])
-      self.dni.append((self.ghi[i] - self.diffuse_horizontal[i]) / math.cos(math.radians(self.zeniths[i])))
-      if self.zeniths[i] > self.maximum_zenith_angle or self.dni[i] < 0:
-        self.dni[i] = 0
-
-    self.df['diffuse horizontal (Wh/m2)'] = self.diffuse_horizontal
-    self.df['dni (Wh/m2)'] = self.dni
-    self.df['beam horizontal (Wh/m2)'] = self.beam_horizontal
-
-  def radiation_components_tilted(self):
-    for i in range(len(self.df)):
-      self.beam_tilted.append(self.dni[i] * math.cos(math.radians(self.incidents[i])))
-      self.beam_tilted[i] = max(self.beam_tilted[i], 0)
-      self.diffuse_tilted.append(self.diffuse_horizontal[i] * ((1 + math.cos(math.radians(self.tilt_angle))) / 2))
-      self.total_radiation_tilted.append(self.beam_tilted[i] + self.diffuse_tilted[i])
-
-    self.df['beam tilted (Wh/m2)'] = self.beam_tilted
-    self.df['diffuse tilted (Wh/m2)'] = self.diffuse_tilted
-    self.df['total radiation tilted (Wh/m2)'] = self.total_radiation_tilted
-
-  def calculate(self) -> pd.DataFrame:
-    self.dni_extra()
-    self.clearness_index()
-    self.diffuse_fraction()
-    self.radiation_components_horizontal()
-    self.radiation_components_tilted()
-    return self.df
-
-  def enrich(self):
-    tilted_radiation = self.total_radiation_tilted
-    self.building.roofs[0].global_irradiance_tilted[cte.HOUR] = tilted_radiation
-    self.building.roofs[0].global_irradiance_tilted[cte.MONTH] = (
-      MonthlyValues.get_total_month(self.building.roofs[0].global_irradiance_tilted[cte.HOUR]))
-    self.building.roofs[0].global_irradiance_tilted[cte.YEAR] = \
-        [sum(self.building.roofs[0].global_irradiance_tilted[cte.MONTH])]
-
-
-

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/solar_angles.py

@@ -1,145 +0,0 @@
-import math
-import pandas as pd
-from datetime import datetime
-from pathlib import Path
-
-
-class CitySolarAngles:
-  def __init__(self, location_latitude, location_longitude, tilt_angle, surface_azimuth_angle,
-               standard_meridian=-75):
-    self.location_latitude = location_latitude
-    self.location_longitude = location_longitude
-    self.location_latitude_rad = math.radians(location_latitude)
-    self.surface_azimuth_angle = surface_azimuth_angle
-    self.surface_azimuth_rad = math.radians(surface_azimuth_angle)
-    self.tilt_angle = tilt_angle
-    self.tilt_angle_rad = math.radians(tilt_angle)
-    self.standard_meridian = standard_meridian
-    self.longitude_correction = (location_longitude - standard_meridian) * 4
-    self.timezone = 'Etc/GMT+5'
-
-    self.eot = []
-    self.ast = []
-    self.hour_angles = []
-    self.declinations = []
-    self.solar_altitudes = []
-    self.solar_azimuths = []
-    self.zeniths = []
-    self.incidents = []
-    self.beam_tilted = []
-    self.factor = []
-    self.times = pd.date_range(start='2023-01-01', end='2024-01-01', freq='h', tz=self.timezone)
-    self.df = pd.DataFrame(index=self.times)
-    self.day_of_year = self.df.index.dayofyear
-
-  def solar_time(self, datetime_val, day_of_year):
-    b = (day_of_year - 81) * 2 * math.pi / 364
-    eot = 9.87 * math.sin(2 * b) - 7.53 * math.cos(b) - 1.5 * math.sin(b)
-    self.eot.append(eot)
-
-    # Calculate Local Solar Time (LST)
-    lst_hour = datetime_val.hour
-    lst_minute = datetime_val.minute
-    lst_second = datetime_val.second
-    lst = lst_hour + lst_minute / 60 + lst_second / 3600
-
-    # Calculate Apparent Solar Time (AST) in decimal hours
-    ast_decimal = lst + eot / 60 + self.longitude_correction / 60
-    ast_hours = int(ast_decimal)
-    ast_minutes = round((ast_decimal - ast_hours) * 60)
-
-    # Ensure ast_minutes is within valid range
-    if ast_minutes == 60:
-      ast_hours += 1
-      ast_minutes = 0
-    elif ast_minutes < 0:
-      ast_minutes = 0
-    ast_time = datetime(year=datetime_val.year, month=datetime_val.month, day=datetime_val.day,
-                        hour=ast_hours, minute=ast_minutes)
-    self.ast.append(ast_time)
-    return ast_time
-
-  def declination_angle(self, day_of_year):
-    declination = 23.45 * math.sin(math.radians(360 / 365 * (284 + day_of_year)))
-    declination_radian = math.radians(declination)
-    self.declinations.append(declination)
-    return declination_radian
-
-  def hour_angle(self, ast_time):
-    hour_angle = ((ast_time.hour * 60 + ast_time.minute) - 720) / 4
-    hour_angle_radian = math.radians(hour_angle)
-    self.hour_angles.append(hour_angle)
-    return hour_angle_radian
-
-  def solar_altitude(self, declination_radian, hour_angle_radian):
-    solar_altitude_radians = math.asin(math.cos(self.location_latitude_rad) * math.cos(declination_radian) *
-                                       math.cos(hour_angle_radian) + math.sin(self.location_latitude_rad) *
-                                       math.sin(declination_radian))
-    solar_altitude = math.degrees(solar_altitude_radians)
-    self.solar_altitudes.append(solar_altitude)
-    return solar_altitude_radians
-
-  def zenith(self, solar_altitude_radians):
-    solar_altitude = math.degrees(solar_altitude_radians)
-    zenith_degree = 90 - solar_altitude
-    zenith_radian = math.radians(zenith_degree)
-    self.zeniths.append(zenith_degree)
-    return zenith_radian
-
-  def solar_azimuth_analytical(self, hourangle, declination, zenith):
-    numer = (math.cos(zenith) * math.sin(self.location_latitude_rad) - math.sin(declination))
-    denom = (math.sin(zenith) * math.cos(self.location_latitude_rad))
-    if math.isclose(denom, 0.0, abs_tol=1e-8):
-      cos_azi = 1.0
-    else:
-      cos_azi = numer / denom
-
-    cos_azi = max(-1.0, min(1.0, cos_azi))
-
-    sign_ha = math.copysign(1, hourangle)
-    solar_azimuth_radians = sign_ha * math.acos(cos_azi) + math.pi
-    solar_azimuth_degrees = math.degrees(solar_azimuth_radians)
-    self.solar_azimuths.append(solar_azimuth_degrees)
-    return solar_azimuth_radians
-
-  def incident_angle(self, solar_altitude_radians, solar_azimuth_radians):
-    incident_radian = math.acos(math.cos(solar_altitude_radians) *
-                                math.cos(abs(solar_azimuth_radians - self.surface_azimuth_rad)) *
-                                math.sin(self.tilt_angle_rad) + math.sin(solar_altitude_radians) *
-                                math.cos(self.tilt_angle_rad))
-    incident_angle_degrees = math.degrees(incident_radian)
-    self.incidents.append(incident_angle_degrees)
-    return incident_radian
-
-  @property
-  def calculate(self) -> pd.DataFrame:
-    for i in range(len(self.times)):
-      datetime_val = self.times[i]
-      day_of_year = self.day_of_year[i]
-      declination_radians = self.declination_angle(day_of_year)
-      ast_time = self.solar_time(datetime_val, day_of_year)
-      hour_angle_radians = self.hour_angle(ast_time)
-      solar_altitude_radians = self.solar_altitude(declination_radians, hour_angle_radians)
-      zenith_radians = self.zenith(solar_altitude_radians)
-      solar_azimuth_radians = self.solar_azimuth_analytical(hour_angle_radians, declination_radians, zenith_radians)
-      incident_angle_radian = self.incident_angle(solar_altitude_radians, solar_azimuth_radians)
-
-    self.df['DateTime'] = self.times
-    self.df['AST'] = self.ast
-    self.df['hour angle'] = self.hour_angles
-    self.df['eot'] = self.eot
-    self.df['declination angle'] = self.declinations
-    self.df['solar altitude'] = self.solar_altitudes
-    self.df['zenith'] = self.zeniths
-    self.df['solar azimuth'] = self.solar_azimuths
-    self.df['incident angle'] = self.incidents
-
-    return self.df
-
-
-
-
-
-
-
-

energy_system_modelling_package/energy_system_modelling_factories/pv_assessment/solar_calculator.py

@@ -0,0 +1,221 @@
+import math
+import pandas as pd
+from datetime import datetime
+import hub.helpers.constants as cte
+from hub.helpers.monthly_values import MonthlyValues
+
+
+class SolarCalculator:
+  def __init__(self, city, tilt_angle, surface_azimuth_angle, standard_meridian=-75,
+               solar_constant=1366.1, maximum_clearness_index=1, min_cos_zenith=0.065, maximum_zenith_angle=87):
+    """
+    A class to calculate the solar angles and solar irradiance on a tilted surface in the City
+    :param city: An object from the City class -> City
+    :param tilt_angle: tilt angle of surface -> float
+    :param surface_azimuth_angle: The orientation of the surface. 0 is North -> float
+    :param standard_meridian: A standard meridian is the meridian whose mean solar time is the basis of the time of day
+    observed in a time zone -> float
+    :param solar_constant: The amount of energy received by a given area one astronomical unit away from the Sun. It is
+    constant and must not be changed
+    :param maximum_clearness_index: This is used to calculate the diffuse fraction of the solar irradiance -> float
+    :param min_cos_zenith: This is needed to avoid unrealistic values in tilted irradiance calculations -> float
+    :param maximum_zenith_angle: This is needed to avoid negative values in tilted irradiance calculations -> float
+    """
+    self.city = city
+    self.location_latitude = city.latitude
+    self.location_longitude = city.longitude
+    self.location_latitude_rad = math.radians(self.location_latitude)
+    self.surface_azimuth_angle = surface_azimuth_angle
+    self.surface_azimuth_rad = math.radians(surface_azimuth_angle)
+    self.tilt_angle = tilt_angle
+    self.tilt_angle_rad = math.radians(tilt_angle)
+    self.standard_meridian = standard_meridian
+    self.longitude_correction = (self.location_longitude - standard_meridian) * 4
+    self.solar_constant = solar_constant
+    self.maximum_clearness_index = maximum_clearness_index
+    self.min_cos_zenith = min_cos_zenith
+    self.maximum_zenith_angle = maximum_zenith_angle
+    timezone_offset = int(-standard_meridian / 15)
+    self.timezone = f'Etc/GMT{"+" if timezone_offset < 0 else "-"}{abs(timezone_offset)}'
+    self.eot = []
+    self.ast = []
+    self.hour_angles = []
+    self.declinations = []
+    self.solar_altitudes = []
+    self.solar_azimuths = []
+    self.zeniths = []
+    self.incidents = []
+    self.i_on = []
+    self.i_oh = []
+    self.times = pd.date_range(start='2023-01-01', end='2023-12-31 23:00', freq='h', tz=self.timezone)
+    self.solar_angles = pd.DataFrame(index=self.times)
+    self.day_of_year = self.solar_angles.index.dayofyear
+
+  def solar_time(self, datetime_val, day_of_year):
+    b = (day_of_year - 81) * 2 * math.pi / 364
+    eot = 9.87 * math.sin(2 * b) - 7.53 * math.cos(b) - 1.5 * math.sin(b)
+    self.eot.append(eot)
+
+    # Calculate Local Solar Time (LST)
+    lst_hour = datetime_val.hour
+    lst_minute = datetime_val.minute
+    lst_second = datetime_val.second
+    lst = lst_hour + lst_minute / 60 + lst_second / 3600
+
+    # Calculate Apparent Solar Time (AST) in decimal hours
+    ast_decimal = lst + eot / 60 + self.longitude_correction / 60
+    ast_hours = int(ast_decimal) % 24  # Adjust hours to fit within 0–23 range
+    ast_minutes = round((ast_decimal - ast_hours) * 60)
+
+    # Ensure ast_minutes is within valid range
+    if ast_minutes == 60:
+      ast_hours += 1
+      ast_minutes = 0
+    elif ast_minutes < 0:
+      ast_minutes = 0
+    ast_time = datetime(year=datetime_val.year, month=datetime_val.month, day=datetime_val.day,
+                        hour=ast_hours, minute=ast_minutes)
+    self.ast.append(ast_time)
+    return ast_time
+
+  def declination_angle(self, day_of_year):
+    declination = 23.45 * math.sin(math.radians(360 / 365 * (284 + day_of_year)))
+    declination_radian = math.radians(declination)
+    self.declinations.append(declination)
+    return declination_radian
+
+  def hour_angle(self, ast_time):
+    hour_angle = ((ast_time.hour * 60 + ast_time.minute) - 720) / 4
+    hour_angle_radian = math.radians(hour_angle)
+    self.hour_angles.append(hour_angle)
+    return hour_angle_radian
+
+  def solar_altitude(self, declination_radian, hour_angle_radian):
+    solar_altitude_radians = math.asin(math.cos(self.location_latitude_rad) * math.cos(declination_radian) *
+                                       math.cos(hour_angle_radian) + math.sin(self.location_latitude_rad) *
+                                       math.sin(declination_radian))
+    solar_altitude = math.degrees(solar_altitude_radians)
+    self.solar_altitudes.append(solar_altitude)
+    return solar_altitude_radians
+
+  def zenith(self, solar_altitude_radians):
+    solar_altitude = math.degrees(solar_altitude_radians)
+    zenith_degree = 90 - solar_altitude
+    zenith_radian = math.radians(zenith_degree)
+    self.zeniths.append(zenith_degree)
+    return zenith_radian
+
+  def solar_azimuth_analytical(self, hourangle, declination, zenith):
+    numer = (math.cos(zenith) * math.sin(self.location_latitude_rad) - math.sin(declination))
+    denom = (math.sin(zenith) * math.cos(self.location_latitude_rad))
+    if math.isclose(denom, 0.0, abs_tol=1e-8):
+      cos_azi = 1.0
+    else:
+      cos_azi = numer / denom
+
+    cos_azi = max(-1.0, min(1.0, cos_azi))
+
+    sign_ha = math.copysign(1, hourangle)
+    solar_azimuth_radians = sign_ha * math.acos(cos_azi) + math.pi
+    solar_azimuth_degrees = math.degrees(solar_azimuth_radians)
+    self.solar_azimuths.append(solar_azimuth_degrees)
+    return solar_azimuth_radians
+
+  def incident_angle(self, solar_altitude_radians, solar_azimuth_radians):
+    incident_radian = math.acos(math.cos(solar_altitude_radians) *
+                                math.cos(abs(solar_azimuth_radians - self.surface_azimuth_rad)) *
+                                math.sin(self.tilt_angle_rad) + math.sin(solar_altitude_radians) *
+                                math.cos(self.tilt_angle_rad))
+    incident_angle_degrees = math.degrees(incident_radian)
+    self.incidents.append(incident_angle_degrees)
+    return incident_radian
+
+  def dni_extra(self, day_of_year, zenith_radian):
+    i_on = self.solar_constant * (1 + 0.033 * math.cos(math.radians(360 * day_of_year / 365)))
+    i_oh = i_on * max(math.cos(zenith_radian), self.min_cos_zenith)
+    self.i_on.append(i_on)
+    self.i_oh.append(i_oh)
+    return i_on, i_oh
+
+  def clearness_index(self, ghi, i_oh):
+    k_t = ghi / i_oh
+    k_t = max(0, k_t)
+    k_t = min(self.maximum_clearness_index, k_t)
+    return k_t
+
+  def diffuse_fraction(self, k_t, zenith):
+    if k_t <= 0.22:
+      fraction_diffuse = 1 - 0.09 * k_t
+    elif k_t <= 0.8:
+      fraction_diffuse = (0.9511 - 0.1604 * k_t + 4.388 * k_t ** 2 - 16.638 * k_t ** 3 + 12.336 * k_t ** 4)
+    else:
+      fraction_diffuse = 0.165
+    if zenith > self.maximum_zenith_angle:
+      fraction_diffuse = 1
+    return fraction_diffuse
+
+  def radiation_components_horizontal(self, ghi, fraction_diffuse, zenith):
+    diffuse_horizontal = ghi * fraction_diffuse
+    dni = (ghi - diffuse_horizontal) / math.cos(math.radians(zenith))
+    if zenith > self.maximum_zenith_angle or dni < 0:
+      dni = 0
+    return diffuse_horizontal, dni
+
+  def radiation_components_tilted(self, diffuse_horizontal, dni, incident_angle):
+    beam_tilted = dni * math.cos(math.radians(incident_angle))
+    beam_tilted = max(beam_tilted, 0)
+    diffuse_tilted = diffuse_horizontal * ((1 + math.cos(math.radians(self.tilt_angle))) / 2)
+    total_radiation_tilted = beam_tilted + diffuse_tilted
+    return total_radiation_tilted
+
+  def solar_angles_calculator(self, csv_output=False):
+    for i in range(len(self.times)):
+      datetime_val = self.times[i]
+      day_of_year = self.day_of_year[i]
+      declination_radians = self.declination_angle(day_of_year)
+      ast_time = self.solar_time(datetime_val, day_of_year)
+      hour_angle_radians = self.hour_angle(ast_time)
+      solar_altitude_radians = self.solar_altitude(declination_radians, hour_angle_radians)
+      zenith_radians = self.zenith(solar_altitude_radians)
+      solar_azimuth_radians = self.solar_azimuth_analytical(hour_angle_radians, declination_radians, zenith_radians)
+      self.incident_angle(solar_altitude_radians, solar_azimuth_radians)
+      self.dni_extra(day_of_year=day_of_year, zenith_radian=zenith_radians)
+    self.solar_angles['DateTime'] = self.times
+    self.solar_angles['AST'] = self.ast
+    self.solar_angles['hour angle'] = self.hour_angles
+    self.solar_angles['eot'] = self.eot
+    self.solar_angles['declination angle'] = self.declinations
+    self.solar_angles['solar altitude'] = self.solar_altitudes
+    self.solar_angles['zenith'] = self.zeniths
+    self.solar_angles['solar azimuth'] = self.solar_azimuths
+    self.solar_angles['incident angle'] = self.incidents
+    self.solar_angles['extraterrestrial normal radiation (Wh/m2)'] = self.i_on
+    self.solar_angles['extraterrestrial radiation on horizontal (Wh/m2)'] = self.i_oh
+    if csv_output:
+      self.solar_angles.to_csv('solar_angles_new.csv')
+
+  def tilted_irradiance_calculator(self):
+    if self.solar_angles.empty:
+      self.solar_angles_calculator()
+    for building in self.city.buildings:
+      hourly_tilted_irradiance = []
+      roof_ghi = building.roofs[0].global_irradiance[cte.HOUR]
+      for i in range(len(roof_ghi)):
+        k_t = self.clearness_index(ghi=roof_ghi[i], i_oh=self.i_oh[i])
+        fraction_diffuse = self.diffuse_fraction(k_t, self.zeniths[i])
+        diffuse_horizontal, dni = self.radiation_components_horizontal(ghi=roof_ghi[i],
+                                                                       fraction_diffuse=fraction_diffuse,
+                                                                       zenith=self.zeniths[i])
+        hourly_tilted_irradiance.append(int(self.radiation_components_tilted(diffuse_horizontal=diffuse_horizontal,
+                                                                             dni=dni,
+                                                                             incident_angle=self.incidents[i])))
+
+      building.roofs[0].global_irradiance_tilted[cte.HOUR] = hourly_tilted_irradiance
+      building.roofs[0].global_irradiance_tilted[cte.MONTH] = (MonthlyValues.get_total_month(
+        building.roofs[0].global_irradiance_tilted[cte.HOUR]))
+      building.roofs[0].global_irradiance_tilted[cte.YEAR] = [sum(building.roofs[0].global_irradiance_tilted[cte.MONTH])]
+
+
+
+
+

energy_system_modelling_package/random_assignation.py

@@ -29,19 +29,41 @@ 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,
-    'Rooftop PV System': 0
+  'Central Hydronic Air and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 100,
+  'Central Hydronic Air and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
+  'Central Hydronic Ground and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
+  'Central Hydronic Ground and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+  'and Grid Tied PV': 0,
+  'Central Hydronic Water and Gas Source Heating System with Unitary Split Cooling and Air Source HP DHW and Grid Tied PV': 0,
+  'Central Hydronic Water and Electricity Source Heating System with Unitary Split Cooling and Air Source HP DHW '
+  'and Grid Tied 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,
+  'Grid Tied PV System': 0,
+  'system 1 gas': 0,
+  'system 1 gas grid tied pv': 0,
+  'system 1 electricity': 0,
+  'system 1 electricity grid tied pv': 0,
+  'system 2 gas': 0,
+  'system 2 gas grid tied pv': 0,
+  'system 2 electricity': 0,
+  'system 2 electricity grid tied pv': 0,
+  'system 3 and 4 gas': 0,
+  'system 3 and 4 gas grid tied pv': 0,
+  'system 3 and 4 electricity': 0,
+  'system 3 and 4 electricity grid tied pv': 0,
+  'system 6 gas': 0,
+  'system 6 gas grid tied pv': 0,
+  'system 6 electricity': 0,
+  'system 6 electricity grid tied pv': 0,
+  'system 8 gas': 0,
+  'system 8 gas grid tied pv': 0,
+  'system 8 electricity': 0,
+  'system 8 electricity grid tied pv': 0,
 }
 
 non_residential_systems_percentage = {'system 1 gas': 0,
@@ -118,4 +140,3 @@ def call_random(_buildings: [Building], _systems_percentage):
       _buildings[_selected_buildings[_position]].energy_systems_archetype_name = case['system']
       _position += 1
   return _buildings
-

energy_system_retrofit.py

@@ -3,8 +3,10 @@ import subprocess
 from building_modelling.ep_run_enrich import energy_plus_workflow
 from energy_system_modelling_package.energy_system_modelling_factories.montreal_energy_system_archetype_modelling_factory import \
   MontrealEnergySystemArchetypesSimulationFactory
-from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_feasibility import \
-  pv_feasibility
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_system_assessment import \
+  PvSystemAssessment
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
+  SolarCalculator
 from hub.imports.geometry_factory import GeometryFactory
 from hub.helpers.dictionaries import Dictionaries
 from hub.imports.construction_factory import ConstructionFactory
@@ -22,9 +24,10 @@ from costing_package.constants import SYSTEM_RETROFIT_AND_PV, CURRENT_STATUS
 from hub.exports.exports_factory import ExportsFactory
 
 # Specify the GeoJSON file path
+main_path = Path(__file__).parent.resolve()
 input_files_path = (Path(__file__).parent / 'input_files')
 input_files_path.mkdir(parents=True, exist_ok=True)
-geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
+geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.00006, path=main_path)
 geojson_file_path = input_files_path / 'output_buildings.geojson'
 output_path = (Path(__file__).parent / 'out_files').resolve()
 output_path.mkdir(parents=True, exist_ok=True)
@@ -34,6 +37,8 @@ simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_res
 simulation_results_path.mkdir(parents=True, exist_ok=True)
 sra_output_path = output_path / 'sra_outputs'
 sra_output_path.mkdir(parents=True, exist_ok=True)
+pv_assessment_path = output_path / 'pv_outputs'
+pv_assessment_path.mkdir(parents=True, exist_ok=True)
 cost_analysis_output_path = output_path / 'cost_analysis'
 cost_analysis_output_path.mkdir(parents=True, exist_ok=True)
 city = GeometryFactory(file_type='geojson',
@@ -49,7 +54,6 @@ 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)
 energy_plus_workflow(city, energy_plus_output_path)
 random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
 EnergySystemsFactory('montreal_custom', city).enrich()
@@ -65,12 +69,35 @@ for building in city.buildings:
   current_status_life_cycle_cost[f'{building.name}'] = cost_data(building, lcc_dataframe, cost_retrofit_scenario)
 random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
 EnergySystemsFactory('montreal_future', city).enrich()
-EnergySystemsSizingFactory('pv_sizing', city).enrich()
 EnergySystemsSizingFactory('peak_load_sizing', city).enrich()
+# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
+tilt_angle = 37
+solar_parameters = SolarCalculator(city=city,
+                                   surface_azimuth_angle=180,
+                                   tilt_angle=tilt_angle,
+                                   standard_meridian=-75)
+solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
+solar_parameters.tilted_irradiance_calculator()  # Calculate the solar radiation on a tilted surface
 for building in city.buildings:
   MontrealEnergySystemArchetypesSimulationFactory(f'archetype_cluster_{building.energy_systems_archetype_cluster_id}',
                                                   building,
                                                   simulation_results_path).enrich()
+  if 'PV' in building.energy_systems_archetype_name:
+    PvSystemAssessment(building=building,
+                       pv_system=None,
+                       battery=None,
+                       electricity_demand=None,
+                       tilt_angle=tilt_angle,
+                       solar_angles=solar_angles,
+                       pv_installation_type='rooftop',
+                       simulation_model_type='explicit',
+                       module_model_name=None,
+                       inverter_efficiency=0.95,
+                       system_catalogue_handler=None,
+                       roof_percentage_coverage=0.75,
+                       facade_coverage_percentage=0,
+                       csv_output=False,
+                       output_path=pv_assessment_path).enrich()
 retrofitted_energy_consumption = consumption_data(city)
 retrofitted_life_cycle_cost = {}
 for building in city.buildings:

example_codes/pv_system_assessment.py

@@ -0,0 +1,86 @@
+from pathlib import Path
+import subprocess
+from building_modelling.ep_run_enrich import energy_plus_workflow
+from energy_system_modelling_package import random_assignation
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.pv_system_assessment import \
+  PvSystemAssessment
+from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.solar_calculator import \
+  SolarCalculator
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from hub.imports.geometry_factory import GeometryFactory
+from hub.helpers.dictionaries import Dictionaries
+from hub.imports.construction_factory import ConstructionFactory
+from hub.imports.usage_factory import UsageFactory
+from hub.imports.weather_factory import WeatherFactory
+from hub.imports.results_factory import ResultFactory
+from building_modelling.geojson_creator import process_geojson
+from hub.exports.exports_factory import ExportsFactory
+import hub.helpers.constants as cte
+# Define paths for input and output directories, ensuring directories are created if they do not exist
+main_path = Path(__file__).parent.parent.resolve()
+input_files_path = (Path(__file__).parent.parent / 'input_files')
+input_files_path.mkdir(parents=True, exist_ok=True)
+output_path = (Path(__file__).parent.parent / 'out_files').resolve()
+output_path.mkdir(parents=True, exist_ok=True)
+# Define specific paths for outputs from EnergyPlus and SRA (Simplified Radiosity Algorith) and PV calculation processes
+energy_plus_output_path = output_path / 'energy_plus_outputs'
+energy_plus_output_path.mkdir(parents=True, exist_ok=True)
+sra_output_path = output_path / 'sra_outputs'
+sra_output_path.mkdir(parents=True, exist_ok=True)
+pv_assessment_path = output_path / 'pv_outputs'
+pv_assessment_path.mkdir(parents=True, exist_ok=True)
+# Generate a GeoJSON file for city buildings based on latitude, longitude, and building dimensions
+geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, path=main_path, diff=0.0001)
+geojson_file_path = input_files_path / 'output_buildings.geojson'
+# Initialize a city object from the geojson file, mapping building functions using a predefined dictionary
+city = GeometryFactory(file_type='geojson',
+                       path=geojson_file_path,
+                       height_field='height',
+                       year_of_construction_field='year_of_construction',
+                       function_field='function',
+                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+# Enrich city data with construction, usage, and weather information specific to the location
+ConstructionFactory('nrcan', city).enrich()
+UsageFactory('nrcan', city).enrich()
+WeatherFactory('epw', city).enrich()
+# Execute the EnergyPlus workflow to simulate building energy performance and generate output
+# energy_plus_workflow(city, energy_plus_output_path)
+# Export the city data in SRA-compatible format to facilitate solar radiation assessment
+ExportsFactory('sra', city, sra_output_path).export()
+# Run SRA simulation using an external command, passing the generated SRA XML file path as input
+sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
+subprocess.run(['sra', str(sra_path)])
+# Enrich city data with SRA simulation results for subsequent analysis
+ResultFactory('sra', city, sra_output_path).enrich()
+# Assign PV system archetype name to the buildings in city
+random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
+# Enrich city model with Montreal future systems parameters
+EnergySystemsFactory('montreal_future', city).enrich()
+# # Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
+tilt_angle = 37
+solar_parameters = SolarCalculator(city=city,
+                                   surface_azimuth_angle=180,
+                                   tilt_angle=tilt_angle,
+                                   standard_meridian=-75)
+solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
+solar_parameters.tilted_irradiance_calculator()  # Calculate the solar radiation on a tilted surface
+# # PV modelling building by building
+#List of available PV modules ['RE400CAA Pure 2', 'RE410CAA Pure 2', 'RE420CAA Pure 2', 'RE430CAA Pure 2',
+# 'REC600AA Pro M', 'REC610AA Pro M', 'REC620AA Pro M', 'REC630AA Pro M', 'REC640AA Pro M']
+for building in city.buildings:
+  PvSystemAssessment(building=building,
+                     pv_system=None,
+                     battery=None,
+                     tilt_angle=tilt_angle,
+                     solar_angles=solar_angles,
+                     pv_installation_type='rooftop',
+                     simulation_model_type='explicit',
+                     module_model_name='REC640AA Pro M',
+                     inverter_efficiency=0.95,
+                     system_catalogue_handler='montreal_future',
+                     roof_percentage_coverage=0.75,
+                     facade_coverage_percentage=0,
+                     csv_output=False,
+                     output_path=pv_assessment_path).enrich()
+
+

hub/catalog_factories/data_models/energy_systems/thermal_storage_system.py

@@ -119,7 +119,7 @@ class ThermalStorageSystem(EnergyStorageSystem):
         'height [m]': self.height,
         'layers': _layers,
         'maximum operating temperature [Celsius]': self.maximum_operating_temperature,
-        'storage_medium': self.storage_medium.to_dictionary(),
+        'storage_medium': _medias,
         'heating coil capacity [W]': self.heating_coil_capacity
       }
     }

hub/catalog_factories/energy_systems/montreal_future_system_catalogue.py

@@ -30,7 +30,8 @@ class MontrealFutureSystemCatalogue(Catalog):
     path = str(path / 'montreal_future_systems.xml')
     with open(path, 'r', encoding='utf-8') as xml:
       self._archetypes = xmltodict.parse(xml.read(),
-                                         force_list=['pv_generation_component', 'templateStorages', 'demand'])
+                                         force_list=['pv_generation_component', 'templateStorages', 'demand',
+                                                     'system', 'system_id'])
 
     self._storage_components = self._load_storage_components()
     self._generation_components = self._load_generation_components()
@@ -49,7 +50,7 @@ class MontrealFutureSystemCatalogue(Catalog):
       'non_pv_generation_component']
     if non_pv_generation_components is not None:
       for non_pv in non_pv_generation_components:
-        system_id = non_pv['system_id']
+        system_id = non_pv['generation_system_id']
         name = non_pv['name']
         system_type = non_pv['system_type']
         model_name = non_pv['model_name']
@@ -181,7 +182,7 @@ class MontrealFutureSystemCatalogue(Catalog):
       'pv_generation_component']
     if pv_generation_components is not None:
       for pv in pv_generation_components:
-        system_id = pv['system_id']
+        system_id = pv['generation_system_id']
         name = pv['name']
         system_type = pv['system_type']
         model_name = pv['model_name']

hub/city_model_structure/building.py

@@ -840,53 +840,55 @@ class Building(CityObject):
     Get energy consumption of different sectors
     return: dict
     """
-    fuel_breakdown = {cte.ELECTRICITY: {cte.LIGHTING: self.lighting_electrical_demand[cte.YEAR][0],
-                                        cte.APPLIANCES: self.appliances_electrical_demand[cte.YEAR][0]}}
+    fuel_breakdown = {cte.ELECTRICITY: {cte.LIGHTING: self.lighting_electrical_demand[cte.YEAR][0] if self.lighting_electrical_demand else 0,
+                                        cte.APPLIANCES: self.appliances_electrical_demand[cte.YEAR][0] if self.appliances_electrical_demand else 0}}
     energy_systems = self.energy_systems
-    for energy_system in energy_systems:
-      demand_types = energy_system.demand_types
-      generation_systems = energy_system.generation_systems
-      for demand_type in demand_types:
-        for generation_system in generation_systems:
-          if generation_system.system_type != cte.PHOTOVOLTAIC:
-            if generation_system.fuel_type not in fuel_breakdown:
-              fuel_breakdown[generation_system.fuel_type] = {}
-            if demand_type in generation_system.energy_consumption:
-              fuel_breakdown[f'{generation_system.fuel_type}'][f'{demand_type}'] = (
-                generation_system.energy_consumption)[f'{demand_type}'][cte.YEAR][0]
-            storage_systems = generation_system.energy_storage_systems
-            if storage_systems:
-              for storage_system in storage_systems:
-                if storage_system.type_energy_stored == 'thermal' and storage_system.heating_coil_capacity is not None:
-                  fuel_breakdown[cte.ELECTRICITY][f'{demand_type}'] += storage_system.heating_coil_energy_consumption[f'{demand_type}'][cte.YEAR][0]
-    #TODO: When simulation models of all energy system archetypes are created, this part can be removed
-    heating_fuels = []
-    dhw_fuels = []
-    for energy_system in self.energy_systems:
-      if cte.HEATING in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          heating_fuels.append(generation_system.fuel_type)
-      if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-        for generation_system in energy_system.generation_systems:
-          dhw_fuels.append(generation_system.fuel_type)
-    for key in fuel_breakdown:
-      if key == cte.ELECTRICITY and cte.COOLING not in fuel_breakdown[key]:
-        for energy_system in energy_systems:
-          if cte.COOLING in energy_system.demand_types and cte.COOLING not in fuel_breakdown[key]:
-            for generation_system in energy_system.generation_systems:
-              fuel_breakdown[generation_system.fuel_type][cte.COOLING] = self.cooling_consumption[cte.YEAR][0]
-      for fuel in heating_fuels:
-        if cte.HEATING not in fuel_breakdown[fuel]:
+    if energy_systems is not None:
+      for energy_system in energy_systems:
+        demand_types = energy_system.demand_types
+        generation_systems = energy_system.generation_systems
+        for demand_type in demand_types:
+          for generation_system in generation_systems:
+            if generation_system.system_type != cte.PHOTOVOLTAIC:
+              if generation_system.fuel_type not in fuel_breakdown:
+                fuel_breakdown[generation_system.fuel_type] = {}
+              if demand_type in generation_system.energy_consumption:
+                fuel_breakdown[f'{generation_system.fuel_type}'][f'{demand_type}'] = (
+                  generation_system.energy_consumption)[f'{demand_type}'][cte.YEAR][0]
+              storage_systems = generation_system.energy_storage_systems
+              if storage_systems:
+                for storage_system in storage_systems:
+                  if storage_system.type_energy_stored == 'thermal' and storage_system.heating_coil_energy_consumption:
+                    fuel_breakdown[cte.ELECTRICITY][f'{demand_type}'] += (
+                      storage_system.heating_coil_energy_consumption)[f'{demand_type}'][cte.YEAR][0]
+      #TODO: When simulation models of all energy system archetypes are created, this part can be removed
+      heating_fuels = []
+      dhw_fuels = []
+      for energy_system in self.energy_systems:
+        if cte.HEATING in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            heating_fuels.append(generation_system.fuel_type)
+        if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+          for generation_system in energy_system.generation_systems:
+            dhw_fuels.append(generation_system.fuel_type)
+      for key in fuel_breakdown:
+        if key == cte.ELECTRICITY and cte.COOLING not in fuel_breakdown[key]:
           for energy_system in energy_systems:
-            if cte.HEATING in energy_system.demand_types:
-              for generation_system in energy_system.generation_systems:
-                fuel_breakdown[generation_system.fuel_type][cte.HEATING] = self.heating_consumption[cte.YEAR][0]
-      for fuel in dhw_fuels:
-        if cte.DOMESTIC_HOT_WATER not in fuel_breakdown[fuel]:
-          for energy_system in energy_systems:
-            if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
-              for generation_system in energy_system.generation_systems:
-                fuel_breakdown[generation_system.fuel_type][cte.DOMESTIC_HOT_WATER] = self.domestic_hot_water_consumption[cte.YEAR][0]
+            if cte.COOLING in energy_system.demand_types and cte.COOLING not in fuel_breakdown[key]:
+              if self.cooling_consumption:
+                fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.COOLING] = self.cooling_consumption[cte.YEAR][0]
+        for fuel in heating_fuels:
+          if cte.HEATING not in fuel_breakdown[fuel]:
+            for energy_system in energy_systems:
+              if cte.HEATING in energy_system.demand_types:
+                if self.heating_consumption:
+                  fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.HEATING] = self.heating_consumption[cte.YEAR][0]
+        for fuel in dhw_fuels:
+          if cte.DOMESTIC_HOT_WATER not in fuel_breakdown[fuel]:
+            for energy_system in energy_systems:
+              if cte.DOMESTIC_HOT_WATER in energy_system.demand_types:
+                if self.domestic_hot_water_consumption:
+                  fuel_breakdown[energy_system.generation_systems[0].fuel_type][cte.DOMESTIC_HOT_WATER] = self.domestic_hot_water_consumption[cte.YEAR][0]
     self._fuel_consumption_breakdown = fuel_breakdown
     return self._fuel_consumption_breakdown
 

hub/helpers/constants.py

@@ -303,6 +303,7 @@ GRID = 'Grid'
 ONSITE_ELECTRICITY = 'Onsite Electricity'
 PHOTOVOLTAIC = 'Photovoltaic'
 BOILER = 'Boiler'
+FURNACE = 'Furnace'
 HEAT_PUMP = 'Heat Pump'
 BASEBOARD = 'Baseboard'
 ELECTRICITY_GENERATOR = 'Electricity generator'

input_files/output_buildings_expanded.geojson

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-        "address": "rue Victor-Hugo (MTL) 1610",
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-        "address": "rue Victor-Hugo (MTL) 1616",
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-}

tests/test_systems_catalog.py

@@ -39,11 +39,11 @@ class TestSystemsCatalog(TestCase):
 
     catalog_categories = catalog.names()
     archetypes = catalog.names()
-    self.assertEqual(13, len(archetypes['archetypes']))
+    self.assertEqual(34, len(archetypes['archetypes']))
     systems = catalog.names('systems')
-    self.assertEqual(17, len(systems['systems']))
+    self.assertEqual(39, len(systems['systems']))
     generation_equipments = catalog.names('generation_equipments')
-    self.assertEqual(27, len(generation_equipments['generation_equipments']))
+    self.assertEqual(49, len(generation_equipments['generation_equipments']))
     with self.assertRaises(ValueError):
       catalog.names('unknown')
 
@@ -54,4 +54,4 @@ class TestSystemsCatalog(TestCase):
 
     with self.assertRaises(IndexError):
       catalog.get_entry('unknown')
-    print(catalog.entries())
+

tests/test_systems_factory.py

@@ -114,8 +114,8 @@ class TestSystemsFactory(TestCase):
     ResultFactory('insel_monthly_energy_balance', self._city, self._output_path).enrich()
 
     for building in self._city.buildings:
-      building.energy_systems_archetype_name = ('Central 4 Pipes Air to Water Heat Pump and Gas Boiler with '
-                                                'Independent Water Heating and PV')
+      building.energy_systems_archetype_name = ('Central Hydronic Air and Gas Source Heating System with Unitary Split '
+                                                'Cooling and Air Source HP DHW and Grid Tied PV')
     EnergySystemsFactory('montreal_future', self._city).enrich()
     # Need to assign energy systems to buildings:
     for building in self._city.buildings:
@@ -123,13 +123,14 @@ class TestSystemsFactory(TestCase):
       for energy_system in building.energy_systems:
         if cte.HEATING in energy_system.demand_types:
           _generation_system = cast(NonPvGenerationSystem, energy_system.generation_systems[0])
-          _generation_system.heat_power = building.heating_peak_load[cte.YEAR][0]
+          _generation_system.nominal_heat_output = building.heating_peak_load[cte.YEAR][0]
         if cte.COOLING in energy_system.demand_types:
           _generation_system = cast(NonPvGenerationSystem, energy_system.generation_systems[0])
-          _generation_system.cooling_power = building.cooling_peak_load[cte.YEAR][0]
+          _generation_system.nominal_cooling_output = building.cooling_peak_load[cte.YEAR][0]
 
     for building in self._city.buildings:
       self.assertLess(0, building.heating_consumption[cte.YEAR][0])
       self.assertLess(0, building.cooling_consumption[cte.YEAR][0])
       self.assertLess(0, building.domestic_hot_water_consumption[cte.YEAR][0])
-      self.assertLess(0, building.onsite_electrical_production[cte.YEAR][0])
+      if 'PV' in building.energy_systems_archetype_name:
+        self.assertLess(0, building.onsite_electrical_production[cte.YEAR][0])