# coding: utf-8 import yaml import pandas as pd import numpy as np import scipy as sp, scipy.spatial from scipy.sparse import csgraph from operator import attrgetter from six import iteritems from six.moves import filter from itertools import count, chain import shapely, shapely.prepared, shapely.wkt from shapely.geometry import Point from vresutils import shapes as vshapes from vresutils.graph import BreadthFirstLevels import logging logger = logging.getLogger(__name__) import pypsa def _load_buses_from_eg(): buses = (pd.read_csv(snakemake.input.eg_buses, quotechar="'", true_values='t', false_values='f', dtype=dict(bus_id="str", under_construction='bool')) .set_index("bus_id") .drop(['station_id'], axis=1) .rename(columns=dict(voltage='v_nom'))) buses['carrier'] = buses.pop('dc').map({True: 'DC', False: 'AC'}) buses['under_construction'] = buses['under_construction'].fillna(False).astype(bool) # remove all buses outside of all countries including exclusive economic zones (offshore) europe_shape = vshapes.country_cover(snakemake.config['countries']) europe_shape_exterior = shapely.geometry.Polygon(shell=europe_shape.exterior) # no holes europe_shape_exterior_prepped = shapely.prepared.prep(europe_shape_exterior) buses_in_europe_b = buses[['x', 'y']].apply(lambda p: europe_shape_exterior_prepped.contains(Point(p)), axis=1) buses_with_v_nom_to_keep_b = buses.v_nom.isin(snakemake.config['electricity']['voltages']) | buses.v_nom.isnull() logger.info("Removing buses with voltages {}".format(pd.Index(buses.v_nom.unique()).dropna().difference(snakemake.config['electricity']['voltages']))) return pd.DataFrame(buses.loc[buses_in_europe_b & buses_with_v_nom_to_keep_b]) def _load_transformers_from_eg(buses): transformers = (pd.read_csv(snakemake.input.eg_transformers, quotechar="'", true_values='t', false_values='f', dtype=dict(transformer_id='str', bus0='str', bus1='str')) .set_index('transformer_id')) transformers = _remove_dangling_branches(transformers, buses) return transformers def _load_converters_from_eg(buses): converters = (pd.read_csv(snakemake.input.eg_converters, quotechar="'", true_values='t', false_values='f', dtype=dict(converter_id='str', bus0='str', bus1='str')) .set_index('converter_id')) converters = _remove_dangling_branches(converters, buses) converters['carrier'] = 'B2B' return converters def _load_links_from_eg(buses): links = (pd.read_csv(snakemake.input.eg_links, quotechar="'", true_values='t', false_values='f', dtype=dict(link_id='str', bus0='str', bus1='str', under_construction="bool")) .set_index('link_id')) links['length'] /= 1e3 links = _remove_dangling_branches(links, buses) # Add DC line parameters links['carrier'] = 'DC' return links def _load_lines_from_eg(buses): lines = (pd.read_csv(snakemake.input.eg_lines, quotechar="'", true_values='t', false_values='f', dtype=dict(line_id='str', bus0='str', bus1='str', underground="bool", under_construction="bool")) .set_index('line_id') .rename(columns=dict(voltage='v_nom', circuits='num_parallel'))) lines['length'] /= 1e3 lines = _remove_dangling_branches(lines, buses) return lines def _apply_parameter_corrections(n): with open(snakemake.input.parameter_corrections) as f: corrections = yaml.load(f) for component, attrs in iteritems(corrections): df = n.df(component) for attr, repls in iteritems(attrs): for i, r in iteritems(repls): if i == 'oid': df["oid"] = df.tags.str.extract('"oid"=>"(\d+)"', expand=False) r = df.oid.map(repls["oid"]).dropna() elif i == 'index': r = pd.Series(repls["index"]) else: raise NotImplementedError() df.loc[r.index, attr] = r.astype(df[attr].dtype) def _set_electrical_parameters_lines(lines): v_noms = snakemake.config['electricity']['voltages'] linetypes = snakemake.config['lines']['types'] for v_nom in v_noms: lines.loc[lines["v_nom"] == v_nom, 'type'] = linetypes[v_nom] lines['s_max_pu'] = snakemake.config['lines']['s_max_pu'] lines.loc[lines.under_construction.astype(bool), 'num_parallel'] = 0. return lines def _set_lines_s_nom_from_linetypes(n): n.lines['s_nom'] = ( np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) * n.lines['v_nom'] * n.lines.num_parallel ) def _set_electrical_parameters_links(links): links['p_max_pu'] = snakemake.config['links']['s_max_pu'] links['p_min_pu'] = -1. * snakemake.config['links']['s_max_pu'] links_p_nom = pd.read_csv(snakemake.input.links_p_nom) tree = sp.spatial.KDTree(np.vstack([ links_p_nom[['x1', 'y1', 'x2', 'y2']], links_p_nom[['x2', 'y2', 'x1', 'y1']] ])) dist, ind = tree.query( np.asarray([np.asarray(shapely.wkt.loads(s))[[0, -1]].flatten() for s in links.geometry]), distance_upper_bound=1.5 ) links_p_nom["j"] =( pd.DataFrame(dict(D=dist, i=links_p_nom.index[ind % len(links_p_nom)]), index=links.index) .groupby('i').D.idxmin() ) p_nom = links_p_nom.dropna(subset=["j"]).set_index("j")["Power (MW)"] links.loc[p_nom.index, "p_nom"] = p_nom links.loc[links.under_construction.astype(bool), "p_nom"] = 0. return links def _set_electrical_parameters_converters(converters): converters['p_max_pu'] = snakemake.config['links']['s_max_pu'] converters['p_min_pu'] = -1. * snakemake.config['links']['s_max_pu'] converters['p_nom'] = 2000 # Converters are combined with links converters['under_construction'] = False converters['underground'] = False return converters def _set_electrical_parameters_transformers(transformers): config = snakemake.config['transformers'] ## Add transformer parameters transformers["x"] = config.get('x', 0.1) transformers["s_nom"] = config.get('s_nom', 2000) transformers['type'] = config.get('type', '') return transformers def _remove_dangling_branches(branches, buses): return pd.DataFrame(branches.loc[branches.bus0.isin(buses.index) & branches.bus1.isin(buses.index)]) def _remove_unconnected_components(network): _, labels = csgraph.connected_components(network.adjacency_matrix(), directed=False) component = pd.Series(labels, index=network.buses.index) component_sizes = component.value_counts() components_to_remove = component_sizes.iloc[1:] logger.info("Removing {} unconnected network components with less than {} buses. In total {} buses." .format(len(components_to_remove), components_to_remove.max(), components_to_remove.sum())) return network[component == component_sizes.index[0]] def _set_countries_and_substations(n): buses = n.buses def buses_in_shape(shape): shape = shapely.prepared.prep(shape) return pd.Series( np.fromiter((shape.contains(Point(x, y)) for x, y in buses.loc[:,["x", "y"]].values), dtype=bool, count=len(buses)), index=buses.index ) countries = snakemake.config['countries'] country_shapes = vshapes.countries(subset=countries, add_KV_to_RS=True, tolerance=0.01, minarea=0.1) offshore_shapes = vshapes.eez(subset=countries, tolerance=0.01) substation_b = buses['symbol'].str.contains('substation', case=False) def prefer_voltage(x, which): index = x.index if len(index) == 1: return pd.Series(index, index) key = (x.index[0] if x['v_nom'].isnull().all() else getattr(x['v_nom'], 'idx' + which)()) return pd.Series(key, index) gb = buses.loc[substation_b].groupby(['x', 'y'], as_index=False, group_keys=False, sort=False) bus_map_low = gb.apply(prefer_voltage, 'min') lv_b = (bus_map_low == bus_map_low.index).reindex(buses.index, fill_value=False) bus_map_high = gb.apply(prefer_voltage, 'max') hv_b = (bus_map_high == bus_map_high.index).reindex(buses.index, fill_value=False) onshore_b = pd.Series(False, buses.index) offshore_b = pd.Series(False, buses.index) for country in countries: onshore_shape = country_shapes[country] onshore_country_b = buses_in_shape(onshore_shape) onshore_b |= onshore_country_b buses.loc[onshore_country_b, 'country'] = country if country not in offshore_shapes: continue offshore_country_b = buses_in_shape(offshore_shapes[country]) offshore_b |= offshore_country_b buses.loc[offshore_country_b, 'country'] = country buses['substation_lv'] = lv_b & onshore_b & (~ buses['under_construction']) buses['substation_off'] = (offshore_b | (hv_b & onshore_b)) & (~ buses['under_construction']) # Nearest country in numbers of hops defines country of homeless buses c_nan_b = buses.country.isnull() c = n.buses['country'] graph = n.graph() n.buses.loc[c_nan_b, 'country'] = \ [(next(filter(len, map(lambda x: c.loc[x].dropna(), BreadthFirstLevels(graph, [b])))) .value_counts().index[0]) for b in buses.index[c_nan_b]] return buses def base_network(): buses = _load_buses_from_eg() links = _load_links_from_eg(buses) converters = _load_converters_from_eg(buses) lines = _load_lines_from_eg(buses) transformers = _load_transformers_from_eg(buses) lines = _set_electrical_parameters_lines(lines) transformers = _set_electrical_parameters_transformers(transformers) links = _set_electrical_parameters_links(links) converters = _set_electrical_parameters_converters(converters) n = pypsa.Network() n.name = 'PyPSA-Eur' n.set_snapshots(pd.date_range(freq='h', **snakemake.config['snapshots'])) n.import_components_from_dataframe(buses, "Bus") n.import_components_from_dataframe(lines, "Line") n.import_components_from_dataframe(transformers, "Transformer") n.import_components_from_dataframe(links, "Link") n.import_components_from_dataframe(converters, "Link") n = _remove_unconnected_components(n) _set_lines_s_nom_from_linetypes(n) _apply_parameter_corrections(n) _set_countries_and_substations(n) return n if __name__ == "__main__": # Detect running outside of snakemake and mock snakemake for testing if 'snakemake' not in globals(): from vresutils.snakemake import MockSnakemake, Dict snakemake = MockSnakemake( path='..', wildcards={}, input=Dict( eg_buses='data/entsoegridkit/buses.csv', eg_lines='data/entsoegridkit/lines.csv', eg_links='data/entsoegridkit/links.csv', eg_converters='data/entsoegridkit/converters.csv', eg_transformers='data/entsoegridkit/transformers.csv', parameter_corrections='data/parameter_corrections.yaml', links_p_nom='data/links_p_nom.csv' ), output = ['networks/base_LC.nc'] ) logging.basicConfig(level=snakemake.config['logging_level']) n = base_network() n.export_to_netcdf(snakemake.output[0])