# 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 itertools import count, chain import shapely, shapely.prepared, shapely.wkt from shapely.geometry import Point from vresutils import shapes as vshapes 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")) .set_index("bus_id") .drop(['under_construction', 'station_id'], axis=1) .rename(columns=dict(voltage='v_nom'))) buses['carrier'] = buses.pop('dc').map({True: 'DC', False: 'AC'}) # 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 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'] return lines 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_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 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) # buses, lines, transformers = _split_aclines_with_several_voltages(buses, lines, transformers) lines = _set_electrical_parameters_lines(lines) links = _set_electrical_parameters_links(links) transformers = _set_electrical_parameters_transformers(transformers) n = pypsa.Network() n.name = 'PyPSA-Eur' n.set_snapshots(pd.date_range(snakemake.config['historical_year'], periods=8760, freq='h')) 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") if 'T' in snakemake.wildcards.opts.split('-'): raise NotImplemented n = _remove_unconnected_components(n) _apply_parameter_corrections(n) return n if __name__ == "__main__": # Detect running outside of snakemake and mock snakemake for testing if 'snakemake' not in globals(): from vresutils import Dict import yaml snakemake = Dict() snakemake.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' ) snakemake.wildcards = Dict(opts='LC') with open('../config.yaml') as f: snakemake.config = yaml.load(f) snakemake.output = ['../networks/base_LC.nc'] logger.setLevel(level=snakemake.config['logging_level']) n = base_network() n.export_to_netcdf(snakemake.output[0])