Source code for edisgo.flex_opt.costs

import pandas as pd
import os
import numpy as np

if "READTHEDOCS" not in os.environ:
    from shapely.ops import transform

from import proj2equidistant

[docs]def grid_expansion_costs( edisgo_obj, without_generator_import=False ): """ Calculates topology expansion costs for each reinforced transformer and line in kEUR. Attributes ---------- edisgo_obj : :class:`~.self.edisgo.EDisGo` without_generator_import : Boolean If True excludes lines that were added in the generator import to connect new generators to the topology from calculation of topology expansion costs. Default: False. mode : :obj:`str` Specifies topology levels reinforcement was conducted for to only return costs in the considered topology level. Specify * None to return costs in MV and LV topology levels. None is the default. * 'mv' to return costs of MV topology level only, including MV/LV stations. Costs to connect LV generators are excluded as well. Returns ------- `pandas.DataFrame<DataFrame>` DataFrame containing type and costs plus in the case of lines the line length and number of parallel lines of each reinforced transformer and line. Index of the DataFrame is the name of either line or transformer. Columns are the following: type: String Transformer size or cable name total_costs: float Costs of equipment in kEUR. For lines the line length and number of parallel lines is already included in the total costs. quantity: int For transformers quantity is always one, for lines it specifies the number of parallel lines. line_length: float Length of line or in case of parallel lines all lines in km. voltage_level : :obj:`str` {'lv' | 'mv' | 'mv/lv'} Specifies voltage level the equipment is in. mv_feeder : :class:`` First line segment of half-ring used to identify in which feeder the network expansion was conducted in. Notes ------- Total network expansion costs can be obtained through self.grid_expansion_costs.total_costs.sum(). """ def _get_transformer_costs(trafos): hvmv_trafos = trafos[ trafos.index.isin(edisgo_obj.topology.transformers_hvmv_df.index) ].index mvlv_trafos = trafos[ trafos.index.isin(edisgo_obj.topology.transformers_df.index) ].index costs_trafos = pd.DataFrame( { "costs_transformers": len(hvmv_trafos) * [float(edisgo_obj.config["costs_transformers"]["mv"])] }, index=hvmv_trafos, ) costs_trafos = costs_trafos.append( pd.DataFrame( { "costs_transformers": len(mvlv_trafos) * [float(edisgo_obj.config["costs_transformers"]["lv"])] }, index=mvlv_trafos, ) ) return costs_trafos.loc[trafos.index, "costs_transformers"].values def _get_line_costs(lines_added): costs_lines = line_expansion_costs(edisgo_obj, lines_added.index) costs_lines["costs"] = costs_lines.apply( lambda x: x.costs_earthworks + x.costs_cable * lines_added.loc[, "quantity"], axis=1, ) return costs_lines[["costs", "voltage_level"]] costs = pd.DataFrame() if without_generator_import: equipment_changes = edisgo_obj.results.equipment_changes.loc[ edisgo_obj.results.equipment_changes.iteration_step > 0 ] else: equipment_changes = edisgo_obj.results.equipment_changes # costs for transformers if not equipment_changes.empty: transformers = equipment_changes[ equipment_changes.index.isin(edisgo_obj.topology._grids) ] added_transformers = transformers[transformers["change"] == "added"] removed_transformers = transformers[ transformers["change"] == "removed" ] # check if any of the added transformers were later removed added_removed_transformers = added_transformers.loc[ added_transformers["equipment"].isin( removed_transformers["equipment"] ) ] added_transformers = added_transformers[ ~added_transformers["equipment"].isin( ) ] # calculate costs for transformers all_trafos = edisgo_obj.topology.transformers_hvmv_df.append( edisgo_obj.topology.transformers_df ) trafos = all_trafos.loc[added_transformers["equipment"]] # calculate costs for each transformer costs = costs.append( pd.DataFrame( { "type": trafos.type_info.values, "total_costs": _get_transformer_costs(trafos), "quantity": len(trafos) * [1], "voltage_level": len(trafos) * ["mv/lv"], }, index=trafos.index, ) ) # costs for lines # get changed lines lines = equipment_changes.loc[ equipment_changes.index.isin(edisgo_obj.topology.lines_df.index) ] lines_added = lines.iloc[ ( == edisgo_obj.topology.lines_df.loc[lines.index, "type_info"] ).values ]["quantity"].to_frame() lines_added_unique = lines_added.index.unique() lines_added = ( lines_added.groupby(axis=0, level=0) .sum() .loc[lines_added_unique, ["quantity"]] ) lines_added["length"] = edisgo_obj.topology.lines_df.loc[ lines_added.index, "length" ] if not lines_added.empty: line_costs = _get_line_costs(lines_added) costs = costs.append( pd.DataFrame( { "type": edisgo_obj.topology.lines_df.loc[ lines_added.index, "type_info" ].values, "total_costs": line_costs.costs.values, "length": ( lines_added.quantity * lines_added.length ).values, "quantity": lines_added.quantity.values, "voltage_level": line_costs.voltage_level.values, }, index=lines_added.index, ) ) # if no costs incurred write zero costs to DataFrame if costs.empty: costs = costs.append( pd.DataFrame( { "type": ["N/A"], "total_costs": [0], "length": [0], "quantity": [0], "voltage_level": "", "mv_feeder": "", }, index=["No reinforced equipment."], ) ) return costs
[docs]def line_expansion_costs(edisgo_obj, lines_names): """ Returns costs for earthworks and per added cable as well as voltage level for chosen lines in edisgo_obj. Parameters ----------- edisgo_obj : :class:`~.edisgo.EDisGo` eDisGo object of which lines of lines_df are part lines_names: list of str List of names of evaluated lines Returns ------- costs: :pandas:`pandas.DataFrame<DataFrame>` Dataframe with names of lines as index and entries for 'costs_earthworks', 'costs_cable', 'voltage_level' for each line """ lines_df = edisgo_obj.topology.lines_df.loc[lines_names, ["length"]] mv_lines = lines_df[ lines_df.index.isin(edisgo_obj.topology.mv_grid.lines_df.index) ].index lv_lines = lines_df[~lines_df.index.isin(mv_lines)].index # get population density in people/km^2 # transform area to calculate area in km^2 projection = proj2equidistant(int(edisgo_obj.config["geo"]["srid"])) sqm2sqkm = 1e6 population_density = edisgo_obj.topology.grid_district["population"] / ( transform(projection, edisgo_obj.topology.grid_district["geom"]).area / sqm2sqkm ) if population_density <= 500: population_density = "rural" else: population_density = "urban" costs_cable_mv = float(edisgo_obj.config["costs_cables"]["mv_cable"]) costs_cable_lv = float(edisgo_obj.config["costs_cables"]["lv_cable"]) costs_cable_earthwork_mv = float( edisgo_obj.config["costs_cables"][ "mv_cable_incl_earthwork_{}".format(population_density) ] ) costs_cable_earthwork_lv = float( edisgo_obj.config["costs_cables"][ "lv_cable_incl_earthwork_{}".format(population_density) ] ) costs_lines = pd.DataFrame( { "costs_earthworks": (costs_cable_earthwork_mv - costs_cable_mv) * lines_df.loc[mv_lines].length, "costs_cable": costs_cable_mv * lines_df.loc[mv_lines].length, "voltage_level": ["mv"] * len(mv_lines), }, index=mv_lines, ) costs_lines = costs_lines.append( pd.DataFrame( { "costs_earthworks": (costs_cable_earthwork_lv - costs_cable_lv) * lines_df.loc[lv_lines].length, "costs_cable": costs_cable_lv * lines_df.loc[lv_lines].length, "voltage_level": ["lv"] * len(lv_lines), }, index=lv_lines, ) ) return costs_lines.loc[lines_df.index]