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 is_solar_pv_valid(self):

        # If the property is a flat but we are looking at building solar potential, we can include this
        if (self.property.building_id is not None) and (self.property.solar_panel_configuration is not None):
            return True

        is_valid_property_type = self.property.data["property-type"] in ["House", "Bungalow", "Maisonette"]
        is_valid_roof_type = (
            self.property.roof["is_flat"] or self.property.roof["is_pitched"] or self.property.roof["is_roof_room"]
        )
        # If there is no existing solar PV, the photo-supply field will be None or a missing value
        has_no_existing_solar_pv = self.property.data["photo-supply"] in [
            None, 0, self.property.DATA_ANOMALY_MATCHES
        ]

        return is_valid_property_type and is_valid_roof_type and has_no_existing_solar_pv

    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_warrage"] / 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.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

        solar_pv_percentage = self.property.solar_pv_percentage
        # We round up to the neaest 10%
        solar_pv_percentage = np.ceil(solar_pv_percentage * 10) / 10

        # For the solar recommendations, we produce the following scenarios:
        # 1) Solar panels only, we present a high, medium and low coverage
        # 2) With and without battery
        roof_coverage_scenarios = [
            solar_pv_percentage - 0.1, solar_pv_percentage,
        ]
        if solar_pv_percentage <= 0.4:
            roof_coverage_scenarios.append(solar_pv_percentage + 0.1)
        # We make sure we haven't gone too low or high - we allow no more than 60% coverage
        roof_coverage_scenarios = [v for v in roof_coverage_scenarios if 0 <= v <= 0.6]
        # If we only have two scenarios, we add a coverage scenario 10% less than the smallest
        if len(roof_coverage_scenarios) == 2:
            roof_coverage_scenarios.insert(0, roof_coverage_scenarios[0] - 0.1)
        battery_scenarios = [False, True]

        scenarios_with_wattage = []
        for roof_coverage in roof_coverage_scenarios:
            # We now have a property which is potentially suitable for solar PV
            solar_pv_roof_area = self.property.get_solar_pv_roof_area(roof_coverage)

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

            if solar_panel_wattage < self.MIN_SYSTEM_WATTAGE:
                continue

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

        # We trim the scenarios, so that we don't have duplicate wattages
        scenarios_with_wattage = self.trim_solar_wattage_options(scenarios_with_wattage)

        # Produce the cross product of the scenarios
        scenarios = [
            (roof, wattage, battery) for roof, wattage in scenarios_with_wattage for battery in battery_scenarios
        ]
        # We deduce the wattage of the solar panels based on the roof coverage

        for roof_coverage, solar_panel_wattage, has_battery in scenarios:
            # We now have a property which is potentially suitable for solar PV
            roof_coverage_percent = round(roof_coverage * 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=has_battery)
            kw = np.floor(solar_panel_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": 100 * roof_coverage,
                    "has_battery": has_battery
                }
            )