data.datasets.power_plants.pv_ground_mounted.insert()

Complexity

Conditions

44

Size

Total Lines	1283
Code Lines	734

Duplication

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Ratio	0 %

Importance

Changes

0

from shapely import wkb
def insert():
    def mastr_existing_pv(pow_per_area):

        """Import MaStR data from csv-files.

        Parameters
        ----------
        pow_per_area: int
            Assumption for areas of existing pv farms and power of new built pv farms depending on area in kW/m²

        """
        # get config
        cfg = egon.data.config.datasets()["power_plants"]

        # import MaStR data: locations, grid levels and installed capacities

        # get relevant pv plants: ground mounted
        df = pd.read_csv(
            cfg["sources"]["mastr_pv"],
            usecols=[
                "Lage",
                "Laengengrad",
                "Breitengrad",
                "Nettonennleistung",
                "EinheitMastrNummer",
                "LokationMastrNummer"
            ],
        )
        df = df[df["Lage"] == "Freifläche"]

        ### examine data concerning geographical locations and drop NaNs
        x1 = df["Laengengrad"].isnull().sum()
        x2 = df["Breitengrad"].isnull().sum()
        print(" ")
        print("Examination of MaStR data set:")
        print("original number of rows in the data set: " + str(len(df)))
        print("NaNs for longitude and latitude: " + str(x1) + " & " + str(x2))
        df.dropna(inplace=True)
        print("Number of rows after neglecting NaNs: " + str(len(df)))
        print(" ")

        # derive dataframe for locations
        mastr = gpd.GeoDataFrame(
            index=df.index,
            geometry=gpd.points_from_xy(df["Laengengrad"], df["Breitengrad"]),
            crs={"init": "epsg:4326"},
        )
        mastr = mastr.to_crs(3035)

        # derive installed capacities
        mastr["installed capacity in kW"] = df["Nettonennleistung"]

        # create buffer around locations

        # calculate bufferarea and -radius considering installed capacity
        df_radius = (
            mastr["installed capacity in kW"].div(pow_per_area * np.pi) ** 0.5
        )  # in m

        # create buffer
        mastr["buffer"] = mastr["geometry"].buffer(df_radius)
        mastr["buffer"].crs = 3035

        # derive MaStR-Nummer
        mastr["LokationMastrNummer"] = df["LokationMastrNummer"]

        # derive voltage level

        mastr["voltage_level"] = pd.Series(dtype=int)
        lvl = pd.read_csv(
            cfg["sources"]["mastr_location"],
            usecols=["Spannungsebene", "MaStRNummer"],
        )    

        # assign voltage_level to MaStR-unit:
            
        vlevel_mapping = {
        "Höchstspannung": 1,
        "UmspannungZurHochspannung": 2,
        "Hochspannung": 3,
        "UmspannungZurMittelspannung": 4,
        "Mittelspannung": 5,
        "UmspannungZurNiederspannung": 6,
        "Niederspannung": 7,
        }            
            
        mastr = mastr.merge(
            lvl[["MaStRNummer", "Spannungsebene"]],
            left_on="LokationMastrNummer",
            right_on="MaStRNummer",
            how="left",
        )
        
        mastr["voltage_level"] = mastr.Spannungsebene.replace(vlevel_mapping)
        
        mastr.drop(["MaStRNummer", "Spannungsebene"], axis=1, inplace=True)

        ### examine data concerning voltage level
        x1 = mastr["voltage_level"].isnull().sum()
        print(" ")
        print("Examination of voltage levels in MaStR data set:")
        print("Original number of rows in MaStR: " + str(len(mastr)))
        print(
            "NaNs in voltage level caused by a) a missing assignment to the number or b) insufficient data: "
            + str(x1)
        )
        # drop PVs with missing values due to a) no assignment of MaStR-numbers or b) missing data in row
        mastr.dropna(inplace=True)
        print("Number of rows after neglecting NaNs: " + str(len(mastr)))

        # drop PVs in low voltage level
        index_names = mastr[mastr["voltage_level"] == "Niederspannung"].index
        x2 = len(index_names)
        mastr.drop(index_names, inplace=True)
        index_names = mastr[
            mastr["voltage_level"] == "UmspannungZurNiederspannung"
        ].index
        x3 = len(index_names)
        mastr.drop(index_names, inplace=True)

        ### further examination
        print("Number of PVs in low voltage level: " + str(x2))
        print("Number of PVs in LVMV level: " + str(x3))
        print(
            "Number of rows after dropping entries assigned to these levels: "
            + str(len(mastr))
        )
        print(" ")

        return mastr

    def potential_areas(con, join_buffer):

        """Import potential areas and choose and prepare areas suitable for PV ground mounted.

        Parameters
        ----------
        con:
            Connection to database
        join_buffer: int
            Maximum distance for joining of potential areas (only small ones to big ones) in m

        """

        # import potential areas: railways and roads & agriculture

        # roads and railway
        sql = "SELECT id, geom FROM supply.egon_re_potential_area_pv_road_railway"
        potentials_rora = gpd.GeoDataFrame.from_postgis(sql, con)
        potentials_rora = potentials_rora.set_index("id")

        # agriculture
        sql = (
            "SELECT id, geom FROM supply.egon_re_potential_area_pv_agriculture"
        )
        potentials_agri = gpd.GeoDataFrame.from_postgis(sql, con)
        potentials_agri = potentials_agri.set_index("id")

        # add areas < 1 ha to bigger areas if they are very close, otherwise exclude areas < 1 ha

        # calculate area
        potentials_rora["area"] = potentials_rora.area
        potentials_agri["area"] = potentials_agri.area

        # roads and railways

        ### counting variable for examination
        before = len(potentials_rora)

        # get small areas and create buffer for joining around them
        small_areas = potentials_rora[potentials_rora["area"] < 10000]
        small_buffers = small_areas.copy()
        small_buffers["geom"] = small_areas["geom"].buffer(join_buffer)

        # drop small areas in potential areas
        index_names = potentials_rora[potentials_rora["area"] < 10000].index
        potentials_rora.drop(index_names, inplace=True)

        # check intersection of small areas with other potential areas
        overlay = gpd.sjoin(potentials_rora, small_buffers)
        o = overlay["index_right"]
        o.drop_duplicates(inplace=True)

        # add small areas to big ones if buffer intersects
        for i in range(0, len(o)):
            index_potentials = o.index[i]
            index_small = o.iloc[i]
            x = potentials_rora["geom"].loc[index_potentials]
            y = small_areas["geom"].loc[index_small]
            join = gpd.GeoSeries(data=[x, y])
            potentials_rora["geom"].loc[index_potentials] = join.unary_union

        ### examination of joining of areas
        count_small = len(small_buffers)
        count_join = len(o)
        count_delete = count_small - count_join
        print(" ")
        print(
            "Examination of potential areas in category 'Roads and Railways'"
        )
        print("Length of original data frame: " + str(before))
        print("Number of small areas: " + str(count_small))
        print("Number of joins: " + str(count_join))
        print("Deleted areas (not joined): " + str(count_delete))
        print("Length of resulting data frame: " + str(len(potentials_rora)))
        print(" ")

        # agriculture

        ### counting variable for examination
        before = len(potentials_agri)

        # get small areas and create buffer for joining around them
        small_areas = potentials_agri[potentials_agri["area"] < 10000]
        small_buffers = small_areas.copy()
        small_buffers["geom"] = small_areas["geom"].buffer(join_buffer)

        # drop small areas in potential areas
        index_names = potentials_agri[potentials_agri["area"] < 10000].index
        potentials_agri.drop(index_names, inplace=True)

        # check intersection of small areas with other potential areas
        overlay = gpd.sjoin(potentials_agri, small_buffers)
        o = overlay["index_right"]
        o.drop_duplicates(inplace=True)

        # add small areas to big ones if buffer intersects
        for i in range(0, len(o)):
            index_potentials = o.index[i]
            index_small = o.iloc[i]
            x = potentials_agri["geom"].loc[index_potentials]
            y = small_areas["geom"].loc[index_small]
            join = gpd.GeoSeries(data=[x, y])
            potentials_agri["geom"].loc[index_potentials] = join.unary_union

        ### examination of joining of areas
        count_small = len(small_buffers)
        count_join = len(o)
        count_delete = count_small - count_join
        print(" ")
        print("Examination of potential areas in category 'Agriculture'")
        print("Length of original data frame: " + str(before))
        print("Number of small areas: " + str(count_small))
        print("Number of joins: " + str(count_join))
        print("Deleted areas (not joined): " + str(count_delete))
        print("Length of resulting data frame: " + str(len(potentials_agri)))
        print(" ")

        # calculate new areas
        potentials_rora["area"] = potentials_rora.area
        potentials_agri["area"] = potentials_agri.area

        # check intersection of potential areas

        ### counting variable
        agri_vorher = len(potentials_agri)

        # if areas intersect, keep road & railway potential areas and drop agricultural ones
        overlay = gpd.sjoin(potentials_rora, potentials_agri)
        o = overlay["index_right"]
        o.drop_duplicates(inplace=True)
        for i in range(0, len(o)):
            index = o.iloc[i]
            potentials_agri.drop([index], inplace=True)

        ### examination of intersection of areas
        print(" ")
        print("Review function to avoid intersection of potential areas:")
        print("Initial length potentials_agri: " + str(agri_vorher))
        print("Number of occurred cases: " + str(len(o)))
        print("Resulting length potentials_agri: " + str(len(potentials_agri)))
        print(" ")

        return potentials_rora, potentials_agri

    def select_pot_areas(mastr, potentials_pot):

        """Select potential areas where there are existing pv parks (MaStR-data).

        Parameters
        ----------
        mastr: gpd.GeoDataFrame()
            MaStR-DataFrame with existing pv parks
        potentials_pot: gpd.GeoDataFrame()
            Suitable potential areas

        """

        # select potential areas with existing pv parks
        # (potential areas intersect buffer around existing plants)

        # prepare dataframes to check intersection
        pvs = gpd.GeoDataFrame()
        pvs["geom"] = mastr["buffer"].copy()
        pvs.crs = 3035
        pvs = pvs.set_geometry("geom")
        potentials = gpd.GeoDataFrame()
        potentials["geom"] = potentials_pot["geom"].copy()
        potentials.crs = 3035
        potentials = potentials.set_geometry("geom")

        # check intersection of potential areas with exisiting PVs (MaStR)
        overlay = gpd.sjoin(pvs, potentials)
        o = overlay["index_right"]
        o.drop_duplicates(inplace=True)

        # define selected potentials areas
        pot_sel = potentials_pot.copy()
        pot_sel["selected"] = pd.Series()
        pot_sel["voltage_level"] = pd.Series(dtype=int)
        for i in range(0, len(o)):
            index_pot = o.iloc[i]
            pot_sel["selected"].loc[index_pot] = True
            # get voltage level of existing PVs
            index_pv = o.index[i]
            pot_sel["voltage_level"] = mastr["voltage_level"].loc[index_pv]
        pot_sel = pot_sel[pot_sel["selected"] == True]
        pot_sel.drop("selected", axis=1, inplace=True)

        # drop selected existing pv parks from mastr
        mastr.drop(index=o.index, inplace=True)

        return (pot_sel, mastr)

    def build_pv(pv_pot, pow_per_area):

        """Build new pv parks in selected potential areas.

        Parameters
        ----------
        pv_pot: gpd.GeoDataFrame()
            Selected potential areas
        pow_per_area: int
            Assumption for areas of existing pv farms and power of new built pv farms depending on area in kW/m²

        """

        # build pv farms in selected areas

        # calculation of centroids
        pv_pot["centroid"] = pv_pot["geom"].representative_point()

        # calculation of power in kW
        pv_pot["installed capacity in kW"] = pd.Series()
        pv_pot["installed capacity in kW"] = pv_pot["area"] * pow_per_area

        # check for maximal capacity for PV ground mounted
        limit_cap = 120000  # in kW
        pv_pot["installed capacity in kW"] = pv_pot[
            "installed capacity in kW"
        ].apply(lambda x: x if x < limit_cap else limit_cap)

        return pv_pot

    def adapt_grid_level(pv_pot, max_dist_hv, con):

        """Check and if needed adapt grid level of newly built pv parks.

        Parameters
        ----------
        pv_pot: gpd.GeoDataFrame()
            Newly built pv parks on selected potential areas
        max_dist_hv: int
            Assumption for maximum distance of park with hv-power to next substation in m
        con:
            Connection to database

        """

        # divide dataframe in MV and HV
        pv_pot_mv = pv_pot[pv_pot["voltage_level"] == 5]
        pv_pot_hv = pv_pot[pv_pot["voltage_level"] == 4]

        # check installed capacity in MV

        max_cap_mv = 5500  # in kW

        # find PVs which need to be HV or to have reduced capacity
        pv_pot_mv_to_hv = pv_pot_mv[
            pv_pot_mv["installed capacity in kW"] > max_cap_mv
        ]

        if len(pv_pot_mv_to_hv) > 0:

            # import data for HV substations

            sql = "SELECT point, voltage FROM grid.egon_hvmv_substation"
            hvmv_substation = gpd.GeoDataFrame.from_postgis(
                sql, con, geom_col="point"
            )
            hvmv_substation = hvmv_substation.to_crs(3035)
            hvmv_substation["voltage"] = hvmv_substation["voltage"].apply(
                lambda x: int(x.split(";")[0])
            )
            hv_substations = hvmv_substation[
                hvmv_substation["voltage"] >= 110000
            ]
            hv_substations = (
                hv_substations.unary_union
            )  # join all the hv_substations

            # check distance to HV substations of PVs with too high installed capacity for MV

            # calculate distance to substations
            pv_pot_mv_to_hv["dist_to_HV"] = (
                pv_pot_mv_to_hv["geom"].to_crs(3035).distance(hv_substations)
            )

            # adjust grid level and keep capacity if transmission lines are close
            pv_pot_mv_to_hv = pv_pot_mv_to_hv[
                pv_pot_mv_to_hv["dist_to_HV"] <= max_dist_hv
            ]
            pv_pot_mv_to_hv = pv_pot_mv_to_hv.drop(columns=["dist_to_HV"])
            pv_pot_hv = pv_pot_hv.append(pv_pot_mv_to_hv)

            # delete PVs which are now HV from MV dataframe
            for index, pot in pv_pot_mv_to_hv.iterrows():
                pv_pot_mv = pv_pot_mv.drop([index])
            pv_pot_hv["voltage_level"] = 4

            # keep grid level adjust capacity if transmission lines are too far
            pv_pot_mv["installed capacity in kW"] = pv_pot_mv[
                "installed capacity in kW"
            ].apply(lambda x: x if x < max_cap_mv else max_cap_mv)
            pv_pot_mv["voltage_level"] = 5

            pv_pot = pv_pot_mv.append(pv_pot_hv)

        return pv_pot

    def build_additional_pv(potentials, pv, pow_per_area, con):

        """Build additional pv parks if pv parks on selected potential areas do not hit the target value.

         Parameters
         ----------
         potenatials: gpd.GeoDataFrame()
             All suitable potential areas
         pv: gpd.GeoDataFrame()
             Newly built pv parks on selected potential areas
        pow_per_area: int
             Assumption for areas of existing pv farms and power of new built pv farms depending on area in kW/m²
         con:
             Connection to database

        """

        # get MV grid districts
        sql = "SELECT bus_id, geom FROM grid.egon_mv_grid_district"
        distr = gpd.GeoDataFrame.from_postgis(sql, con)
        distr = distr.set_index("bus_id")

        # identify potential areas where there are no PV parks yet
        for index, pv in pv.iterrows():
            potentials = potentials.drop([index])

        # aggregate potential area per MV grid district
        pv_per_distr = gpd.GeoDataFrame()
        pv_per_distr["geom"] = distr["geom"].copy()
        centroids = potentials.copy()
        centroids["geom"] = centroids["geom"].representative_point()

        overlay = gpd.sjoin(centroids, distr)

        ### examine potential area per grid district
        anz = len(overlay)
        anz_distr = len(overlay["index_right"].unique())
        size = 137500  # m2 Fläche für > 5,5 MW: (5500 kW / (0,04 kW/m2))
        anz_big = len(overlay[overlay["area"] >= size])
        anz_small = len(overlay[overlay["area"] < size])

        print(" ")
        print(
            "Examination of remaining potential areas in MV grid districts: "
        )
        print("Number of potential areas: " + str(anz))
        print(" -> distributed to " + str(anz_distr) + " districts")
        print("Number of areas with a potential >= 5,5 MW: " + str(anz_big))
        print("Number of areas with a potential < 5,5 MW: " + str(anz_small))
        print(" ")

        for index, dist in distr.iterrows():
            pots = overlay[overlay["index_right"] == index]["geom"].index
            p = gpd.GeoSeries(index=pots)
            for i in pots:
                p.loc[i] = potentials["geom"].loc[i]
            pv_per_distr["geom"].loc[index] = p.unary_union

        # calculate area per MV grid district and linearly distribute needed capacity considering pow_per_area
        pv_per_distr["area"] = pv_per_distr["geom"].area
        pv_per_distr["installed capacity in kW"] = (
            pv_per_distr["area"] * pow_per_area
        )

        # calculate centroid
        pv_per_distr["centroid"] = pv_per_distr["geom"].representative_point()

        return pv_per_distr

    def check_target(
        pv_rora_i,
        pv_agri_i,
        pv_exist_i,
        potentials_rora_i,
        potentials_agri_i,
        target_power,
        pow_per_area,
        con,
    ):

        """Check target value per scenario and per state.

         Parameters
         ----------
         pv_rora_i: gpd.GeoDataFrame()
             Newly built pv parks on selected potential areas of road and railways p
         pv_agri_i: gpd.GeoDataFrame()
             Newly built pv parks on selected potential areas of agriculture
         pv_exist_i: gpd.GeoDataFrame()
             existing pv parks that don't intercept any potential area
         potenatials_rora_i: gpd.GeoDataFrame()
             All suitable potential areas of road and railway
         potenatials_rora_i: gpd.GeoDataFrame()
             All suitable potential areas of agriculture
         target_power: int
             Target for installed capacity of pv ground mounted in referenced state
        pow_per_area: int
             Assumption for areas of existing pv farms and power of new built pv farms depending on area in kW/m²
         con:
             Connection to database

        """

        # sum overall installed capacity for MV and HV

        total_pv_power = (
            pv_rora_i["installed capacity in kW"].sum()
            + pv_agri_i["installed capacity in kW"].sum()
            + pv_exist_i["installed capacity in kW"].sum()
        )

        pv_per_distr_i = gpd.GeoDataFrame()

        # check target value

        ###
        print(" ")
        print(
            "Installed capacity on areas with existing plants: "
            + str(total_pv_power / 1000)
            + " MW"
        )

        # linear scale farms to meet target if sum of installed capacity is too high
        if total_pv_power >= target_power:

            scale_factor = target_power / total_pv_power
            pv_rora_i["installed capacity in kW"] = (
                pv_rora_i["installed capacity in kW"] * scale_factor
            )
            pv_agri_i["installed capacity in kW"] = (
                pv_agri_i["installed capacity in kW"] * scale_factor
            )
            pv_exist_i["installed capacity in kW"] = (
                pv_exist_i["installed capacity in kW"] * scale_factor
            )

            pv_per_distr_i["grid_district"] = pd.Series()
            pv_per_distr_i["installed capacity in kW"] = pd.Series(0)

            ###
            print(
                "Expansion of existing PV parks on potential areas to achieve target capacity is sufficient."
            )
            print(
                "Installed power is greater than the target value, scaling is applied:"
            )
            print("Scaling factor: " + str(scale_factor))

        # build new pv parks if sum of installed capacity is below target value
        elif total_pv_power < target_power:

            rest_cap = target_power - total_pv_power

            ###
            print(
                "Expansion of existing PV parks on potential areas to achieve target capacity is unsufficient:"
            )
            print("Residual capacity: " + str(rest_cap / 1000) + " MW")
            print(
                "Residual capacity will initially be distributed via remaining potential areas 'Road & Railway'."
            )

            # build pv parks in potential areas road & railway
            pv_per_distr_i = build_additional_pv(
                potentials_rora_i, pv_rora_i, pow_per_area, con
            )
            # change index to add different Dataframes in the end
            pv_per_distr_i["grid_district"] = pv_per_distr_i.index.copy()
            pv_per_distr_i.index = range(0, len(pv_per_distr_i))
            # delete empty grid districts
            index_names = pv_per_distr_i[
                pv_per_distr_i["installed capacity in kW"].isna()
            ].index
            pv_per_distr_i.drop(index_names, inplace=True)

            if pv_per_distr_i["installed capacity in kW"].sum() > rest_cap:
                scale_factor = (
                    rest_cap / pv_per_distr_i["installed capacity in kW"].sum()
                )
                pv_per_distr_i["installed capacity in kW"] = (
                    pv_per_distr_i["installed capacity in kW"] * scale_factor
                )

                ###
                print(
                    "Residual capacity got distributed via scaling factor "
                    + str(scale_factor)
                    + " to remaining potential areas 'Road & Railway'."
                )

            # build pv parks on potential areas agriculture if still necessary
            elif pv_per_distr_i["installed capacity in kW"].sum() < rest_cap:

                rest_cap = (
                    target_power
                    - total_pv_power
                    - pv_per_distr_i["installed capacity in kW"].sum()
                )

                ###
                print(
                    "Distribution via potential areas Road & Railway unsufficient to achieve target capacity:"
                )
                print("Residual capacity: " + str(rest_cap / 1000) + " MW")
                print(
                    "Residual capacity is distributed to remaining potential areas 'Agriculture'."
                )

                pv_per_distr_i_2 = build_additional_pv(
                    potentials_agri_i, pv_agri_i, pow_per_area, con
                )
                # change index to add different Dataframes in the end
                pv_per_distr_i_2["grid_district"] = pv_per_distr_i_2.index
                pv_per_distr_i_2.index = range(len(pv_per_distr_i_2))

                # delete empty grid districts
                index_names = pv_per_distr_i_2[
                    pv_per_distr_i_2["installed capacity in kW"].isna()
                ].index
                pv_per_distr_i_2.drop(index_names, inplace=True)

                if (
                    pv_per_distr_i_2["installed capacity in kW"].sum()
                    > rest_cap
                ):
                    scale_factor = (
                        rest_cap
                        / pv_per_distr_i_2["installed capacity in kW"].sum()
                    )
                    pv_per_distr_i_2["installed capacity in kW"] = (
                        pv_per_distr_i_2["installed capacity in kW"]
                        * scale_factor
                    )

                    ###
                    print(
                        "Residual capacity got distributed via scaling factor "
                        + str(scale_factor)
                        + " to remaining potential areas 'Road & Railway' and 'Agriculture'."
                    )

                pv_per_distr_i = pv_per_distr_i.append(
                    pv_per_distr_i_2, ignore_index=True
                )

            # assign grid level to pv_per_distr
            v_lvl = pd.Series(dtype=int, index=pv_per_distr_i.index)
            for index, distr in pv_per_distr_i.iterrows():
                if distr["installed capacity in kW"] > 5500:  # > 5 MW
                    v_lvl[index] = 4
                else:
                    v_lvl[index] = 5
            pv_per_distr_i["voltage_level"] = v_lvl

            # new overall installed capacity
            total_pv_power = (
                pv_rora_i["installed capacity in kW"].sum()
                + pv_agri_i["installed capacity in kW"].sum()
                + pv_exist_i["installed capacity in kW"].sum()
                + pv_per_distr_i["installed capacity in kW"].sum()
            )

            ###
            print(
                "Total installed capacity of PV farms: "
                + str(total_pv_power / 1000)
                + " MW"
            )
            print(" ")

        pv_rora_i = pv_rora_i[pv_rora_i["installed capacity in kW"] > 0]
        pv_agri_i = pv_agri_i[pv_agri_i["installed capacity in kW"] > 0]
        pv_exist_i = pv_exist_i[pv_exist_i["installed capacity in kW"] > 0]
        pv_per_distr_i = pv_per_distr_i[
            pv_per_distr_i["installed capacity in kW"] > 0
        ]

        return pv_rora_i, pv_agri_i, pv_exist_i, pv_per_distr_i

    def keep_existing_pv(mastr, con):
        pv_exist = mastr[
            [
                "geometry",
                "installed capacity in kW",
                "voltage_level",
            ]
        ]
        pv_exist.rename(columns={"geometry": "centroid"}, inplace=True)
        pv_exist = gpd.GeoDataFrame(pv_exist, geometry="centroid", crs=3035)

        # German states
        sql = "SELECT geometry as geom, gf FROM boundaries.vg250_lan"
        land = gpd.GeoDataFrame.from_postgis(sql, con).to_crs(3035)
        land = land[(land["gf"] != 1) & (land["gf"] != 2)]
        land = land.unary_union
        pv_exist = gpd.clip(pv_exist, land)

        return pv_exist

    def run_methodology(
        con=db.engine(),
        pow_per_area=0.04,
        join_buffer=10,
        max_dist_hv=20000,
        show_map=False,
    ):

        """Execute methodology to distribute pv ground mounted.

         Parameters
         ----------
         con:
             Connection to database
         pow_per_area: int, default 0.4
             Assumption for areas of existing pv farms and power of new built pv farms depending on area in kW/m²
         join_buffer : int, default 10
             Maximum distance for joining of potential areas (only small ones to big ones) in m
         max_dist_hv : int, default 20000
             Assumption for maximum distance of park with hv-power to next substation in m
        show_map:  boolean
            Optional creation of map to show distribution of installed capacity

        """

        ###
        print(" ")
        print("MaStR-Data")
        print(" ")

        # MaStR-data: existing PV farms
        mastr = mastr_existing_pv(pow_per_area)

        ###
        print(" ")
        print("potential area")
        print(" ")

        # database-data: potential areas for new PV farms
        potentials_rora, potentials_agri = potential_areas(con, join_buffer)

        ###
        print(" ")
        print("select potentials area")
        print(" ")

        # select potential areas with existing PV farms to build new PV farms
        pv_rora, mastr = select_pot_areas(mastr, potentials_rora)
        pv_agri, mastr = select_pot_areas(mastr, potentials_agri)

        ###
        print(" ")
        print(
            "build PV parks where there is PV ground mounted already (-> MaStR) on potential area"
        )
        print(" ")

        # build new PV farms
        pv_rora = build_pv(pv_rora, pow_per_area)
        pv_agri = build_pv(pv_agri, pow_per_area)

        # keep the existing pv_farms that don't intercept potential areas
        exist = keep_existing_pv(mastr, con)

        ###
        print(" ")
        print("adapt grid level of PV parks")
        print(" ")

        # adapt grid level to new farms
        rora = adapt_grid_level(pv_rora, max_dist_hv, con)
        agri = adapt_grid_level(pv_agri, max_dist_hv, con)

        ###
        print(" ")
        print(
            "check target value and build more PV parks on potential area if necessary"
        )
        print(" ")

        # 1) scenario: eGon2035

        ###
        print(" ")
        print("scenario: eGon2035")
        print(" ")

        # German states
        sql = "SELECT geometry as geom, nuts FROM boundaries.vg250_lan"
        states = gpd.GeoDataFrame.from_postgis(sql, con)

        # assumption for target value of installed capacity
        sql = "SELECT capacity,scenario_name,nuts FROM supply.egon_scenario_capacities WHERE carrier='solar'"
        target = pd.read_sql(sql, con)
        target = target[target["scenario_name"] == "eGon2035"]
        nuts = np.unique(target["nuts"])

        # initialize final dataframe
        pv_rora = gpd.GeoDataFrame()
        pv_agri = gpd.GeoDataFrame()
        pv_exist = gpd.GeoDataFrame()
        pv_per_distr = gpd.GeoDataFrame()

        # prepare selection per state
        rora = rora.set_geometry("centroid")
        agri = agri.set_geometry("centroid")
        potentials_rora = potentials_rora.set_geometry("geom")
        potentials_agri = potentials_agri.set_geometry("geom")

        # check target value per state
        for i in nuts:

            target_power = (
                target[target["nuts"] == i]["capacity"].iloc[0] * 1000
            )

            ###
            land = target[target["nuts"] == i]["nuts"].iloc[0]
            print(" ")
            print("Bundesland (NUTS): " + land)
            print("target power: " + str(target_power / 1000) + " MW")

            # select state
            state = states[states["nuts"] == i]
            state = state.to_crs(3035)

            # select PVs in state
            rora_i = gpd.sjoin(rora, state)
            agri_i = gpd.sjoin(agri, state)
            exist_i = gpd.sjoin(exist, state)
            rora_i.drop("index_right", axis=1, inplace=True)
            agri_i.drop("index_right", axis=1, inplace=True)
            exist_i.drop("index_right", axis=1, inplace=True)
            rora_i.drop_duplicates(inplace=True)
            agri_i.drop_duplicates(inplace=True)
            exist_i.drop_duplicates(inplace=True)

            # select potential areas in state
            potentials_rora_i = gpd.sjoin(potentials_rora, state)
            potentials_agri_i = gpd.sjoin(potentials_agri, state)
            potentials_rora_i.drop("index_right", axis=1, inplace=True)
            potentials_agri_i.drop("index_right", axis=1, inplace=True)
            potentials_rora_i.drop_duplicates(inplace=True)
            potentials_agri_i.drop_duplicates(inplace=True)

            # check target value and adapt installed capacity if necessary
            rora_i, agri_i, exist_i, distr_i = check_target(
                rora_i,
                agri_i,
                exist_i,
                potentials_rora_i,
                potentials_agri_i,
                target_power,
                pow_per_area,
                con,
            )

            if len(distr_i) > 0:
                distr_i["nuts"] = target[target["nuts"] == i]["nuts"].iloc[0]

            ### examination of built PV parks per state
            rora_i_mv = rora_i[rora_i["voltage_level"] == 5]
            rora_i_hv = rora_i[rora_i["voltage_level"] == 4]
            agri_i_mv = agri_i[agri_i["voltage_level"] == 5]
            agri_i_hv = agri_i[agri_i["voltage_level"] == 4]
            print("eGon2035: Examination of voltage level per federal state:")
            print("a) PVs on potential areas Road & Railway: ")
            print(
                "Total installed capacity: "
                + str(rora_i["installed capacity in kW"].sum() / 1000)
                + " MW"
            )
            print("Number of PV farms: " + str(len(rora_i)))
            print(" - thereof MV: " + str(len(rora_i_mv)))
            print(" - thereof HV: " + str(len(rora_i_hv)))
            print("b) PVs on potential areas Agriculture: ")
            print(
                "Total installed capacity: "
                + str(agri_i["installed capacity in kW"].sum() / 1000)
                + " MW"
            )
            print("Number of PV farms: " + str(len(agri_i)))
            print(" - thereof MV: " + str(len(agri_i_mv)))
            print(" - dthereof HV: " + str(len(agri_i_hv)))
            print("c) Existing PVs not in potential areas: ")
            print("Number of PV farms: " + str(len(exist_i)))
            print("d) PVs on additional potential areas per MV-District: ")
            if len(distr_i) > 0:
                distr_i_mv = distr_i[distr_i["voltage_level"] == 5]
                distr_i_hv = distr_i[distr_i["voltage_level"] == 4]
                print(
                    "Total installed capacity: "
                    + str(distr_i["installed capacity in kW"].sum() / 1000)
                    + " MW"
                )
                print("Number of PV farms: " + str(len(distr_i)))
                print(" - thereof MV: " + str(len(distr_i_mv)))
                print(" - thereof HV: " + str(len(distr_i_hv)))
            else:
                print(" -> No additional expansion necessary")
            print(" ")

            pv_rora = pv_rora.append(rora_i)
            pv_agri = pv_agri.append(agri_i)
            pv_exist = pv_exist.append(exist_i)
            if len(distr_i) > 0:
                pv_per_distr = pv_per_distr.append(distr_i)

        # 2) scenario: eGon100RE

        # assumption for target value of installed capacity in Germany per scenario
        sql = "SELECT capacity,scenario_name FROM supply.egon_scenario_capacities WHERE carrier='solar'"
        target_power = pd.read_sql(sql, con)
        target_power = target_power[
            target_power["scenario_name"] == "eGon100RE"
        ]
        target_power = target_power["capacity"].sum() * 1000

        ###
        print(" ")
        print("scenario: eGon100RE")
        print("target power: " + str(target_power) + " kW")
        print(" ")

        # check target value and adapt installed capacity if necessary
        (
            pv_rora_100RE,
            pv_agri_100RE,
            pv_exist_100RE,
            pv_per_distr_100RE,
        ) = check_target(
            rora,
            agri,
            exist,
            potentials_rora,
            potentials_agri,
            target_power,
            pow_per_area,
            con,
        )

        ### create map to show distribution of installed capacity
        if show_map == True:

            # 1) eGon2035

            # get MV grid districts
            sql = "SELECT bus_id, geom FROM grid.egon_mv_grid_district"
            distr = gpd.GeoDataFrame.from_postgis(sql, con)
            distr = distr.set_index("bus_id")

            # assign pv_per_distr-power to districts
            distr["capacity"] = pd.Series()
            for index, row in distr.iterrows():
                if index in np.unique(pv_per_distr["grid_district"]):
                    pv = pv_per_distr[pv_per_distr["grid_district"] == index]
                    x = pv["installed capacity in kW"].iloc[0]
                    distr["capacity"].loc[index] = x
                else:
                    distr["capacity"].loc[index] = 0
            distr["capacity"] = distr["capacity"] / 1000

            # add pv_rora- and pv_agri-power to district
            pv_rora = pv_rora.set_geometry("centroid")
            pv_agri = pv_agri.set_geometry("centroid")
            overlay_rora = gpd.sjoin(pv_rora, distr)
            overlay_agri = gpd.sjoin(pv_agri, distr)

            for index, row in distr.iterrows():
                o_rora = overlay_rora[overlay_rora["index_right"] == index]
                o_agri = overlay_agri[overlay_agri["index_right"] == index]
                cap_rora = o_rora["installed capacity in kW"].sum() / 1000
                cap_agri = o_agri["installed capacity in kW"].sum() / 1000
            distr["capacity"].loc[index] = (
                distr["capacity"].loc[index] + cap_rora + cap_agri

The variable cap_rora does not seem to be defined in case the for loop on line 1009 is not entered. Are you sure this can never be the case?

The variable index does not seem to be defined in case the for loop on line 994 is not entered. Are you sure this can never be the case?

The variable cap_agri does not seem to be defined in case the for loop on line 1009 is not entered. Are you sure this can never be the case?

            )

            from matplotlib import pyplot as plt

            fig, ax = plt.subplots(1, 1)
            distr.boundary.plot(linewidth=0.2, ax=ax, color="black")
            distr.plot(
                ax=ax,
                column="capacity",
                cmap="magma_r",
                legend=True,
                legend_kwds={
                    "label": f"Installed capacity in MW",
                    "orientation": "vertical",
                },
            )
            plt.savefig("pv_per_distr_map_eGon2035.png", dpi=300)

            # 2) eGon100RE

            # get MV grid districts
            sql = "SELECT bus_id, geom FROM grid.egon_mv_grid_district"
            distr = gpd.GeoDataFrame.from_postgis(sql, con)
            distr = distr.set_index("bus_id")

            # assign pv_per_distr-power to districts
            distr["capacity"] = pd.Series()
            for index, row in distr.iterrows():
                if index in np.unique(pv_per_distr_100RE["grid_district"]):
                    pv = pv_per_distr_100RE[
                        pv_per_distr_100RE["grid_district"] == index
                    ]
                    x = pv["installed capacity in kW"].iloc[0]
                    distr["capacity"].loc[index] = x
                else:
                    distr["capacity"].loc[index] = 0
            distr["capacity"] = distr["capacity"] / 1000

            # add pv_rora- and pv_agri-power to district
            pv_rora_100RE = pv_rora_100RE.set_geometry("centroid")
            pv_agri_100RE = pv_agri_100RE.set_geometry("centroid")
            overlay_rora = gpd.sjoin(pv_rora_100RE, distr)
            overlay_agri = gpd.sjoin(pv_agri_100RE, distr)

            for index, row in distr.iterrows():
                o_rora = overlay_rora[overlay_rora["index_right"] == index]
                o_agri = overlay_agri[overlay_agri["index_right"] == index]
                cap_rora = o_rora["installed capacity in kW"].sum() / 1000
                cap_agri = o_agri["installed capacity in kW"].sum() / 1000
            distr["capacity"].loc[index] = (
                distr["capacity"].loc[index] + cap_rora + cap_agri
            )

            from matplotlib import pyplot as plt

            fig, ax = plt.subplots(1, 1)
            distr.boundary.plot(linewidth=0.2, ax=ax, color="black")
            distr.plot(
                ax=ax,
                column="capacity",
                cmap="magma_r",
                legend=True,
                legend_kwds={
                    "label": f"Installed capacity in MW",
                    "orientation": "vertical",
                },
            )
            plt.savefig("pv_per_distr_map_eGon100RE.png", dpi=300)

        pv_rora = pv_rora[pv_rora["installed capacity in kW"] > 0]
        pv_agri = pv_agri[pv_agri["installed capacity in kW"] > 0]
        pv_per_distr = pv_per_distr[
            pv_per_distr["installed capacity in kW"] > 0
        ]
        pv_rora_100RE = pv_rora_100RE[
            pv_rora_100RE["installed capacity in kW"] > 0
        ]
        pv_agri_100RE = pv_agri_100RE[
            pv_agri_100RE["installed capacity in kW"] > 0
        ]
        pv_per_distr_100RE = pv_per_distr_100RE[
            pv_per_distr_100RE["installed capacity in kW"] > 0
        ]

        return (
            pv_rora,
            pv_agri,
            pv_exist,
            pv_per_distr,
            pv_rora_100RE,
            pv_agri_100RE,
            pv_exist_100RE,
            pv_per_distr_100RE,
        )

    def insert_pv_parks(
        pv_rora, pv_agri, pv_exist, pv_per_distr, scenario_name
    ):

        """Write to database.

        Parameters
        ----------
        pv_rora : gpd.GeoDataFrame()
            Pv parks on selected potential areas of raod and railway
        pv_agri : gpd.GeoDataFrame()
            Pv parks on selected potential areas of raod and railway
        pv_exist : gpd.GeoDataFrame()
            Existing Pv parks on selected areas
        pv_per_distr: gpd.GeoDataFrame()
            Additionally built pv parks on potential areas per mv grid district
        scenario_name:
            Scenario name of calculation

        """

        # prepare dataframe for integration in supply.egon_power_plants

        pv_parks = pv_rora.append(
            [pv_agri, pv_exist, pv_per_distr], ignore_index=True
        )
        pv_parks["el_capacity"] = pv_parks["installed capacity in kW"] / 1000
        pv_parks.rename(columns={"centroid": "geometry"}, inplace=True)
        pv_parks = gpd.GeoDataFrame(pv_parks, geometry="geometry", crs=3035)
        pv_parks = pv_parks[["el_capacity", "voltage_level", "geometry"]]

        # integration in supply.egon_power_plants

        con = db.engine()

        # maximum ID in egon_power_plants
        sql = "SELECT MAX(id) FROM supply.egon_power_plants"
        max_id = pd.read_sql(sql, con)
        max_id = max_id["max"].iat[0]
        if max_id == None:
            max_id = 1

        pv_park_id = max_id + 1

        # copy relevant columns from pv_parks
        insert_pv_parks = pv_parks[
            ["el_capacity", "voltage_level", "geometry"]
        ]
        insert_pv_parks = insert_pv_parks.set_geometry("geometry")
        insert_pv_parks["voltage_level"] = insert_pv_parks[
            "voltage_level"
        ].apply(int)

        # set static column values
        insert_pv_parks["carrier"] = "solar"
        insert_pv_parks["scenario"] = scenario_name

        # change name and crs of geometry column
        insert_pv_parks.set_crs(epsg=3035, allow_override=True, inplace=True)
        insert_pv_parks = (
            insert_pv_parks.rename({"geometry": "geom"}, axis=1)
            .set_geometry("geom")
            .to_crs(4326)
        )

        # reset index
        insert_pv_parks.index = pd.RangeIndex(
            start=pv_park_id, stop=pv_park_id + len(insert_pv_parks), name="id"
        )

        # insert into database
        insert_pv_parks.reset_index().to_postgis(
            "egon_power_plants",
            schema="supply",
            con=db.engine(),
            if_exists="append",
        )

        return pv_parks

    #########################################################################

    # execute methodology

    (
        pv_rora,
        pv_agri,
        pv_exist,
        pv_per_distr,
        pv_rora_100RE,
        pv_agri_100RE,
        pv_exist_100RE,
        pv_per_distr_100RE,
    ) = run_methodology(
        con=db.engine(),
        pow_per_area=0.04,
        join_buffer=10,
        max_dist_hv=20000,
        show_map=False,
    )

    ### examination of results
    if len(pv_per_distr) > 0:
        pv_per_distr_mv = pv_per_distr[pv_per_distr["voltage_level"] == 5]
        pv_per_distr_hv = pv_per_distr[pv_per_distr["voltage_level"] == 4]
    pv_rora_mv = pv_rora[pv_rora["voltage_level"] == 5]
    pv_rora_hv = pv_rora[pv_rora["voltage_level"] == 4]
    pv_agri_mv = pv_agri[pv_agri["voltage_level"] == 5]
    pv_agri_hv = pv_agri[pv_agri["voltage_level"] == 4]

    print(" ")
    print("eGon2035: Examination of overall voltage levels:")
    print("a) PVs on potential areas Road & Railway: ")
    print(
        "Total installed capacity: "
        + str(pv_rora["installed capacity in kW"].sum() / 1000)
        + " MW"
    )
    print("Number of PV farms: " + str(len(pv_rora)))
    print(" - thereof MV: " + str(len(pv_rora_mv)))
    print(" - thereof HV: " + str(len(pv_rora_hv)))
    print("b) PVs on potential areas Agriculture: ")
    print(
        "Total installed capacity: "
        + str(pv_agri["installed capacity in kW"].sum() / 1000)
        + " MW"
    )
    print("Number of PV farms: " + str(len(pv_agri)))
    print(" - thereof MV: " + str(len(pv_agri_mv)))
    print(" - thereof HV: " + str(len(pv_agri_hv)))
    print("c) Existing PVs not in potential areas: ")
    print("Number of PV farms: " + str(len(pv_exist)))
    print("d) PVs on additional potential areas per MV-District: ")
    if len(pv_per_distr) > 0:
        print(
            "Total installed capacity: "
            + str(pv_per_distr["installed capacity in kW"].sum() / 1000)
            + " MW"
        )
        print("Number of PV farms: " + str(len(pv_per_distr)))
        print(" - thereof MV: " + str(len(pv_per_distr_mv)))

The variable pv_per_distr_mv does not seem to be defined in case len(pv_per_distr) > 0 on line 1213 is False. Are you sure this can never be the case?

        print(" - thereof HV: " + str(len(pv_per_distr_hv)))

The variable pv_per_distr_hv does not seem to be defined in case len(pv_per_distr) > 0 on line 1213 is False. Are you sure this can never be the case?

    else:
        print(" -> No additional expansion needed")
    print(" ")
    ###

    # save to DB
    if (
        pv_rora["installed capacity in kW"].sum() > 0
        or pv_agri["installed capacity in kW"].sum() > 0
        or pv_per_distr["installed capacity in kW"].sum() > 0
        or pv_exist["installed capacity in kW"].sum() > 0
    ):

        pv_parks = insert_pv_parks(
            pv_rora, pv_agri, pv_exist, pv_per_distr, "eGon2035"
        )

    else:

        pv_parks = gpd.GeoDataFrame()

    if (
        pv_rora_100RE["installed capacity in kW"].sum() > 0
        or pv_agri_100RE["installed capacity in kW"].sum() > 0
        or pv_per_distr_100RE["installed capacity in kW"].sum() > 0
        or pv_exist_100RE["installed capacity in kW"].sum() > 0
    ):

        pv_parks_100RE = insert_pv_parks(
            pv_rora_100RE,
            pv_agri_100RE,
            pv_exist_100RE,
            pv_per_distr_100RE,
            "eGon100RE",
        )

    else:

        pv_parks_100RE = gpd.GeoDataFrame()

    return pv_parks, pv_parks_100RE