Model/recommendations/SolarPvRecommendations.py

import numpy as np
from recommendations.Costs import Costs
from recommendations.recommendation_utils import override_costs


class SolarPvRecommendations:
    # Solar panel specs based on Eurener 400s solar panels
    # https://midsummerwholesale.co.uk/buy/eurener/eurener-400w-mepv-zebra-ab-half-cut-mono
    # Approximate area of the solar panels
    SOLAR_PANEL_AREA = 1.79
    # Wattage per panel - this is based on the average wattage of a solar panel being between 250w and 420w
    # This was previously set to 250w, but has been upped to 400 based on the systems used by Cotswolrd Energy Group
    SOLAR_PANEL_WATTAGE = 400

    MAX_SYSTEM_WATTAGE = 6000
    MIN_SYSTEM_WATTAGE = 1000

    def __init__(self, property_instance):
        """
        :param property_instance: Instance of the Property class, for the home associated to property_id
        """

        self.property = property_instance
        self.costs = Costs(self.property)

        self.recommendation = []

    @staticmethod
    def trim_solar_wattage_options(scenarios_with_wattage):
        # Initialize the list with the first element, assuming the list is not empty
        trimmed_list = [scenarios_with_wattage[0]]

        # Iterate over the list starting from the second element
        for scenario in scenarios_with_wattage[1:]:
            # Compare the second element (index 1) of the current tuple with the last tuple in the trimmed list
            if scenario[1] > trimmed_list[-1][1]:
                trimmed_list.append(scenario)

        return trimmed_list

    def mds_recommend(self, phase=None, solar_pv_percentage=0.5):
        # For specific usage within the mds report

        solar_pv_roof_area = self.property.get_solar_pv_roof_area(solar_pv_percentage)

        number_solar_panels = np.floor(solar_pv_roof_area / self.SOLAR_PANEL_AREA)
        solar_panel_wattage = number_solar_panels * self.SOLAR_PANEL_WATTAGE

        solar_panel_wattage = np.clip(
            a=solar_panel_wattage, a_min=self.MIN_SYSTEM_WATTAGE, a_max=self.MAX_SYSTEM_WATTAGE
        )

        # We now have a property which is potentially suitable for solar PV
        roof_coverage_percent = round(solar_pv_percentage * 100)
        # Given the wattage, we estimate the cost of the solar PV system. This is based on the MCS database
        # of solar PV installations
        cost_result = self.costs.solar_pv(wattage=solar_panel_wattage, has_battery=False)
        kw = np.floor(solar_panel_wattage / 100) / 10

        description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
                       f"anel system on {round(roof_coverage_percent)}% the roof.")

        return [
            {
                "phase": phase,
                "parts": [],
                "type": "solar_pv",
                "description": description,
                "starting_u_value": None,
                "new_u_value": None,
                "sap_points": None,
                "already_installed": False,
                **cost_result,
                # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
                # back up here
                "photo_supply": roof_coverage_percent,
                "has_battery": False
            }
        ]

    def recommend_building_analysis(self, phase):
        """
        This recommendation approach handles the case of producing solar PV recommendations at the building level,
        across multiple flats. For these recommendations, we don't include the battery option since it's impractical
        from a space perspective.
        :return:
        """

        panel_performance = self.property.solar_panel_configuration["panel_performance"]
        total_roof_area = (
            self.property.solar_panel_configuration["insights_data"]["solarPotential"]["wholeRoofStats"]["areaMeters2"]
        )
        n_units = self.property.solar_panel_configuration["n_units"]

        # At a building level, we take a single configuration so that all properties a guaranteed to use
        # the same configuration
        best_configurations = panel_performance.head(1).reset_index(drop=True)

        for rank, recommendation_config in best_configurations.iterrows():
            roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / total_roof_area * 100)
            # Spread the cost to the individual units - adding a 20% contingency
            total_cost = recommendation_config["total_cost"] / n_units
            kw = np.floor(recommendation_config["array_wattage"] / 100) / 10
            # Default to a weeks work for a team of 3 people doing 8 hour days
            labour_days = 5
            labour_hours = 3 * 8 * labour_days

            description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on the roof "
                           "of the building")

            initial_ac_kwh_per_year = recommendation_config["initial_ac_kwh_per_year"]

            self.recommendation.append(
                {
                    "phase": phase,
                    "parts": [],
                    "type": "solar_pv",
                    "description": description,
                    "starting_u_value": None,
                    "new_u_value": None,
                    "sap_points": None,
                    "already_installed": False,
                    "total": total_cost,
                    "labour_days": labour_days,
                    "labour_hours": labour_hours,
                    # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we scale
                    # back up here
                    "photo_supply": roof_coverage_percent,
                    "has_battery": False,
                    "initial_ac_kwh_per_year": initial_ac_kwh_per_year,
                    "description_simulation": {"photo-supply": roof_coverage_percent},
                    "rank": rank  # Rank is used to get the representative recommendation - rank 0 will be chosen
                }
            )

    def recommend(self, phase):
        """
        We check if a property is potentially suitable for solar PV based on the following criteria:
        - The property is a house or bungalow
        - The property has a flat or pitched roof
        - The property does not have existing solar pv
        :return:
        """

        if not self.property.is_solar_pv_valid():
            return

        # If we have a buiilding level analysis, we implement separate logic
        if self.property.building_id is not None:
            self.recommend_building_analysis(phase)
            return

        panel_performance = self.property.solar_panel_configuration["panel_performance"]
        roof_area = self.property.roof_area

        solar_configurations = panel_performance.head(3).reset_index(drop=True)

        # We combine each of these configurations with estimates with and without a battery
        for rank, recommendation_config in solar_configurations.iterrows():
            roof_coverage_percent = round(recommendation_config["panneled_roof_area"] / roof_area * 100)
            for has_battery in [False, True]:
                cost_result = self.costs.solar_pv(
                    wattage=recommendation_config["array_wattage"], has_battery=has_battery
                )
                kw = np.floor(recommendation_config["array_wattage"] / 100) / 10
                if has_battery:
                    description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) panel system on "
                                   f"{round(roof_coverage_percent)}% the roof, with a battery storage system.")
                else:
                    description = (f"Install a {kw} kilowatt-peak (kWp) solar photovoltaic (PV) p"
                                   f"anel system on {round(roof_coverage_percent)}% the roof.")

                already_installed = "solar_pv" in self.property.already_installed
                if already_installed:
                    cost_result = override_costs(cost_result)

                self.recommendation.append(
                    {
                        "phase": phase,
                        "parts": [],
                        "type": "solar_pv",
                        "description": description,
                        "starting_u_value": None,
                        "new_u_value": None,
                        "sap_points": None,
                        "already_installed": already_installed,
                        **cost_result,
                        # This is required for simulating the SAP impact. solar_pv_percentage is between 0 & 1 so we
                        # scale
                        # back up here
                        "photo_supply": roof_coverage_percent,
                        "has_battery": has_battery,
                        "initial_ac_kwh_per_year": recommendation_config["initial_ac_kwh_per_year"],
                        "description_simulation": {"photo-supply": roof_coverage_percent},
                    }
                )