Merge pull request 'feat: PV sizing module is added' (#7) from pv_sizing into main
Reviewed-on: https://nextgenerations-cities.encs.concordia.ca/gitea/s_ranjbar/energy_system_modelling_workflow/pulls/7
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1e34687496
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@ -1,3 +1,4 @@
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import pandas as pd
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from scripts.geojson_creator import process_geojson
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from pathlib import Path
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import subprocess
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@ -8,11 +9,12 @@ from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.imports.results_factory import ResultFactory
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from scripts.solar_angles import CitySolarAngles
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from scripts.radiation_tilted import RadiationTilted
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from scripts.ep_run_enrich import energy_plus_workflow
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import hub.helpers.constants as cte
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from hub.exports.exports_factory import ExportsFactory
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from scripts.pv_sizing_and_simulation import PVSizingSimulation
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# Specify the GeoJSON file path
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geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
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geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0005)
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file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.geojson')
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# Specify the output path for the PDF file
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output_path = (Path(__file__).parent / 'out_files').resolve()
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@ -32,8 +34,40 @@ ExportsFactory('sra', city, output_path).export()
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sra_path = (output_path / f'{city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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ResultFactory('sra', city, output_path).enrich()
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energy_plus_workflow(city)
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solar_angles = CitySolarAngles(city.name,
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city.latitude,
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city.longitude,
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tilt_angle=45,
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surface_azimuth_angle=180).calculate
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df = pd.DataFrame()
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df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
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'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
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for building in city.buildings:
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roof_horizontal = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
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solar_angles = (
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CitySolarAngles(city.name, city.latitude, city.longitude, tilt_angle=45, surface_azimuth_angle=180).calculate)
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RadiationTilted(building, solar_angles, tilt_angle=45, ghi=roof_horizontal).enrich()
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ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
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pv_sizing_simulation = PVSizingSimulation(building,
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solar_angles,
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tilt_angle=45,
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module_height=1,
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module_width=2,
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ghi=ghi)
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pv_sizing_simulation.pv_output()
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yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
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yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
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yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
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yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
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yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
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roof_area = building.roofs[0].perimeter_area
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used_roof = pv_sizing_simulation.available_space()
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number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
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yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / 1000
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df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
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used_roof, number_of_pv_panels, yearly_pv]
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df.to_csv(output_path / 'pv.csv')
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@ -1,21 +1,66 @@
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import math
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from scripts.radiation_tilted import RadiationTilted
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from scripts.solar_angles import CitySolarAngles
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import hub.helpers.constants as cte
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from hub.helpers.monthly_values import MonthlyValues
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class PVSizingSimulation(RadiationTilted):
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def __init__(self, building, solar_angles, tilt_angle, module_height, module_width, ghi):
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super().__init__(building, solar_angles, tilt_angle, ghi)
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self.module_height = module_height
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self.module_width = module_width
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self.total_number_of_panels = 0
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self.enrich()
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def available_space(self):
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roof_area = self.building.roofs[0].perimeter_area
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maintenance_factor = 0.1
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orientation_factor = 0.2
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if self.building.function == cte.RESIDENTIAL:
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mechanical_equipment_factor = 0.2
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else:
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mechanical_equipment_factor = 0.3
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available_roof = (maintenance_factor + orientation_factor + mechanical_equipment_factor) * roof_area
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return available_roof
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def inter_row_spacing(self):
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winter_solstice = self.df[(self.df['AST'].dt.month == 12) &
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(self.df['AST'].dt.day == 21) &
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(self.df['AST'].dt.hour == 12)]
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solar_altitude = winter_solstice['solar altitude'].values[0]
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solar_azimuth = winter_solstice['solar azimuth'].values[0]
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distance = ((self.module_height * abs(math.cos(math.radians(solar_azimuth)))) /
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math.tan(math.radians(solar_altitude)))
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distance = float(format(distance, '.1f'))
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return distance
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def number_of_panels(self, available_roof, inter_row_distance):
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space_dimension = math.sqrt(available_roof)
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space_dimension = float(format(space_dimension, '.2f'))
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panels_per_row = math.ceil(space_dimension / self.module_width)
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number_of_rows = math.ceil(space_dimension / inter_row_distance)
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self.total_number_of_panels = panels_per_row * number_of_rows
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return panels_per_row, number_of_rows
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def pv_output(self):
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radiation = self.total_radiation_tilted
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pv_module_area = self.module_width * self.module_height
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available_roof = self.available_space()
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inter_row_spacing = self.inter_row_spacing()
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self.number_of_panels(available_roof, inter_row_spacing)
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system_efficiency = 0.2
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pv_hourly_production = [x * system_efficiency * self.total_number_of_panels * pv_module_area for x in radiation]
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self.building.onsite_electrical_production[cte.HOUR] = pv_hourly_production
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self.building.onsite_electrical_production[cte.MONTH] = (
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MonthlyValues.get_total_month(self.building.onsite_electrical_production[cte.HOUR]))
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self.building.onsite_electrical_production[cte.YEAR] = [sum(self.building.onsite_electrical_production[cte.MONTH])]
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class PVSizingSimulation:
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def __init__(self, city, tilt_angle):
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self.city = city
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self.tilt_angle = tilt_angle
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self.solar_angles = CitySolarAngles(file_name=self.city.name,
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location_latitude=self.city.latitude,
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location_longitude=self.city.longitude,
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tilt_angle=self.tilt_angle,
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surface_azimuth_angle=180,
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standard_meridian=-75).calculate
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self.enrich_radiation_data()
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def enrich_radiation_data(self):
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for building in self.city.buildings:
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roof_horizontal = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
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RadiationTilted(building, self.solar_angles, tilt_angle=self.tilt_angle, ghi=roof_horizontal).enrich()
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@ -13,9 +13,14 @@ class RadiationTilted:
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self.zeniths = solar_angles['zenith'].tolist()[:-1]
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self.incidents = solar_angles['incident angle'].tolist()[:-1]
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self.date_time = solar_angles['DateTime'].tolist()[:-1]
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data = {'DateTime': self.date_time, 'zenith': self.zeniths, 'incident angle': self.incidents, 'ghi': self.ghi}
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self.ast = solar_angles['AST'].tolist()[:-1]
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self.solar_azimuth = solar_angles['solar azimuth'].tolist()[:-1]
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self.solar_altitude = solar_angles['solar altitude'].tolist()[:-1]
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data = {'DateTime': self.date_time, 'AST': self.ast, 'solar altitude': self.solar_altitude, 'zenith': self.zeniths,
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'solar azimuth': self.solar_azimuth, 'incident angle': self.incidents, 'ghi': self.ghi}
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self.df = pd.DataFrame(data)
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self.df['DateTime'] = pd.to_datetime(self.df['DateTime'])
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self.df['AST'] = pd.to_datetime(self.df['AST'])
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self.df.set_index('DateTime', inplace=True)
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self.solar_constant = solar_constant
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self.maximum_clearness_index = maximum_clearness_index
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