# coding: utf-8 """ Creates the network topology from a `ENTSO-E map extract `_ (25 May 2018) as a PyPSA network. Relevant Settings ----------------- .. code:: yaml snapshots: countries: electricity: voltages: lines: types: s_max_pu: under_construction: links: p_max_pu: under_construction: include_tyndp: transformers: x: s_nom: type: .. seealso:: Documentation of the configuration file ``config.yaml`` at :ref:`snapshots_cf`, :ref:`toplevel_cf`, :ref:`electricity_cf`, :ref:`load_cf`, :ref:`lines_cf`, :ref:`links_cf`, :ref:`transformers_cf` Inputs ------ - ``data/entsoegridkit``: Extract from the geographical vector data of the online `ENTSO-E Interactive Map `_ by the `GridKit `_ toolkit dating back to 25 May 2018. - ``data/parameter_corrections.yaml``: Corrections for ``data/entsoegridkit`` - ``data/links_p_nom.csv``: confer :ref:`links` - ``data/links_tyndp.csv``: List of projects in the `TYNDP 2018 `_ that are at least *in permitting* with fields for start- and endpoint (names and coordinates), length, capacity, construction status, and project reference ID. - ``resources/country_shapes.geojson``: confer :ref:`shapes` - ``resources/offshore_shapes.geojson``: confer :ref:`shapes` - ``resources/europe_shape.geojson``: confer :ref:`shapes` Outputs ------- - ``networks/base.nc`` .. image:: ../img/base.png :scale: 33 % Description ----------- """ import logging logger = logging.getLogger(__name__) from _helpers import configure_logging import yaml import pandas as pd import geopandas as gpd import numpy as np import scipy as sp from scipy.sparse import csgraph from six import iteritems from itertools import product from shapely.geometry import Point, LineString import shapely, shapely.prepared, shapely.wkt import networkx as nx import pypsa def _get_oid(df): if "tags" in df.columns: return df.tags.str.extract('"oid"=>"(\d+)"', expand=False) else: return pd.Series(np.nan, df.index) def _get_country(df): if "tags" in df.columns: return df.tags.str.extract('"country"=>"([A-Z]{2})"', expand=False) else: return pd.Series(np.nan, df.index) def _find_closest_links(links, new_links, distance_upper_bound=1.5): tree = sp.spatial.KDTree(np.vstack([ new_links[['x1', 'y1', 'x2', 'y2']], new_links[['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=distance_upper_bound ) found_b = ind < 2 * len(new_links) return ( pd.DataFrame(dict(D=dist[found_b], i=new_links.index[ind[found_b] % len(new_links)]), index=links.index[found_b]) .groupby('i').D.idxmin() ) 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(['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 = gpd.read_file(snakemake.input.europe_shape).loc[0, 'geometry'] europe_shape_prepped = shapely.prepared.prep(europe_shape) buses_in_europe_b = buses[['x', 'y']].apply(lambda p: europe_shape_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 _add_links_from_tyndp(buses, links): links_tyndp = pd.read_csv(snakemake.input.links_tyndp) # remove all links from list which lie outside all of the desired countries europe_shape = gpd.read_file(snakemake.input.europe_shape).loc[0, 'geometry'] europe_shape_prepped = shapely.prepared.prep(europe_shape) x1y1_in_europe_b = links_tyndp[['x1', 'y1']].apply(lambda p: europe_shape_prepped.contains(Point(p)), axis=1) x2y2_in_europe_b = links_tyndp[['x2', 'y2']].apply(lambda p: europe_shape_prepped.contains(Point(p)), axis=1) is_within_covered_countries_b = x1y1_in_europe_b & x2y2_in_europe_b if not is_within_covered_countries_b.all(): logger.info("TYNDP links outside of the covered area (skipping): " + ", ".join(links_tyndp.loc[~ is_within_covered_countries_b, "Name"])) links_tyndp = links_tyndp.loc[is_within_covered_countries_b] if links_tyndp.empty: return buses, links has_replaces_b = links_tyndp.replaces.notnull() oids = dict(Bus=_get_oid(buses), Link=_get_oid(links)) keep_b = dict(Bus=pd.Series(True, index=buses.index), Link=pd.Series(True, index=links.index)) for reps in links_tyndp.loc[has_replaces_b, 'replaces']: for comps in reps.split(':'): oids_to_remove = comps.split('.') c = oids_to_remove.pop(0) keep_b[c] &= ~oids[c].isin(oids_to_remove) buses = buses.loc[keep_b['Bus']] links = links.loc[keep_b['Link']] links_tyndp["j"] = _find_closest_links(links, links_tyndp, distance_upper_bound=0.8) # Corresponds approximately to 60km tolerances if links_tyndp["j"].notnull().any(): logger.info("TYNDP links already in the dataset (skipping): " + ", ".join(links_tyndp.loc[links_tyndp["j"].notnull(), "Name"])) links_tyndp = links_tyndp.loc[links_tyndp["j"].isnull()] tree = sp.spatial.KDTree(buses[['x', 'y']]) _, ind0 = tree.query(links_tyndp[["x1", "y1"]]) ind0_b = ind0 < len(buses) links_tyndp.loc[ind0_b, "bus0"] = buses.index[ind0[ind0_b]] _, ind1 = tree.query(links_tyndp[["x2", "y2"]]) ind1_b = ind1 < len(buses) links_tyndp.loc[ind1_b, "bus1"] = buses.index[ind1[ind1_b]] links_tyndp_located_b = links_tyndp["bus0"].notnull() & links_tyndp["bus1"].notnull() if not links_tyndp_located_b.all(): logger.warning("Did not find connected buses for TYNDP links (skipping): " + ", ".join(links_tyndp.loc[~links_tyndp_located_b, "Name"])) links_tyndp = links_tyndp.loc[links_tyndp_located_b] logger.info("Adding the following TYNDP links: " + ", ".join(links_tyndp["Name"])) links_tyndp = links_tyndp[["bus0", "bus1"]].assign( carrier='DC', p_nom=links_tyndp["Power (MW)"], length=links_tyndp["Length (given) (km)"].fillna(links_tyndp["Length (distance*1.2) (km)"]), under_construction=True, underground=False, geometry=(links_tyndp[["x1", "y1", "x2", "y2"]] .apply(lambda s: str(LineString([[s.x1, s.y1], [s.x2, s.y2]])), axis=1)), tags=('"name"=>"' + links_tyndp["Name"] + '", ' + '"ref"=>"' + links_tyndp["Ref"] + '", ' + '"status"=>"' + links_tyndp["status"] + '"') ) links_tyndp.index = "T" + links_tyndp.index.astype(str) return buses, links.append(links_tyndp, sort=True) 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.safe_load(f) if corrections is None: return for component, attrs in iteritems(corrections): df = n.df(component) oid = _get_oid(df) if attrs is None: continue for attr, repls in iteritems(attrs): for i, r in iteritems(repls): if i == 'oid': r = oid.map(repls["oid"]).dropna() elif i == 'index': r = pd.Series(repls["index"]) else: raise NotImplementedError() inds = r.index.intersection(df.index) df.loc[inds, attr] = r[inds].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'] 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): if links.empty: return links p_max_pu = snakemake.config['links'].get('p_max_pu', 1.) links['p_max_pu'] = p_max_pu links['p_min_pu'] = -p_max_pu links_p_nom = pd.read_csv(snakemake.input.links_p_nom) links_p_nom["j"] = _find_closest_links(links, links_p_nom) p_nom = links_p_nom.dropna(subset=["j"]).set_index("j")["Power (MW)"] # Don't update p_nom if it's already set p_nom_unset = p_nom.drop(links.index[links.p_nom.notnull()], errors='ignore') if "p_nom" in links else p_nom links.loc[p_nom_unset.index, "p_nom"] = p_nom_unset return links def _set_electrical_parameters_converters(converters): p_max_pu = snakemake.config['links'].get('p_max_pu', 1.) converters['p_max_pu'] = p_max_pu converters['p_min_pu'] = -p_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 = gpd.read_file(snakemake.input.country_shapes).set_index('name')['geometry'] offshore_shapes = gpd.read_file(snakemake.input.offshore_shapes).set_index('name')['geometry'] substation_b = buses['symbol'].str.contains('substation|converter station', 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.index: continue offshore_country_b = buses_in_shape(offshore_shapes[country]) offshore_b |= offshore_country_b buses.loc[offshore_country_b, 'country'] = country # Only accept buses as low-voltage substations (where load is attached), if # they have at least one connection which is not under_construction has_connections_b = pd.Series(False, index=buses.index) for b, df in product(('bus0', 'bus1'), (n.lines, n.links)): has_connections_b |= ~ df.groupby(b).under_construction.min() buses['substation_lv'] = lv_b & onshore_b & (~ buses['under_construction']) & has_connections_b buses['substation_off'] = (offshore_b | (hv_b & onshore_b)) & (~ buses['under_construction']) c_nan_b = buses.country.isnull() if c_nan_b.sum() > 0: c_tag = _get_country(buses.loc[c_nan_b]) c_tag.loc[~c_tag.isin(countries)] = np.nan n.buses.loc[c_nan_b, 'country'] = c_tag c_tag_nan_b = n.buses.country.isnull() # Nearest country in path length defines country of still homeless buses # Work-around until commit 705119 lands in pypsa release n.transformers['length'] = 0. graph = n.graph(weight='length') n.transformers.drop('length', axis=1, inplace=True) for b in n.buses.index[c_tag_nan_b]: df = (pd.DataFrame(dict(pathlength=nx.single_source_dijkstra_path_length(graph, b, cutoff=200))) .join(n.buses.country).dropna()) assert not df.empty, "No buses with defined country within 200km of bus `{}`".format(b) n.buses.at[b, 'country'] = df.loc[df.pathlength.idxmin(), 'country'] logger.warning("{} buses are not in any country or offshore shape," " {} have been assigned from the tag of the entsoe map," " the rest from the next bus in terms of pathlength." .format(c_nan_b.sum(), c_nan_b.sum() - c_tag_nan_b.sum())) return buses def _replace_b2b_converter_at_country_border_by_link(n): # Affects only the B2B converter in Lithuania at the Polish border at the moment buscntry = n.buses.country linkcntry = n.links.bus0.map(buscntry) converters_i = n.links.index[(n.links.carrier == 'B2B') & (linkcntry == n.links.bus1.map(buscntry))] def findforeignbus(G, i): cntry = linkcntry.at[i] for busattr in ('bus0', 'bus1'): b0 = n.links.at[i, busattr] for b1 in G[b0]: if buscntry[b1] != cntry: return busattr, b0, b1 return None, None, None for i in converters_i: G = n.graph() busattr, b0, b1 = findforeignbus(G, i) if busattr is not None: comp, line = next(iter(G[b0][b1])) if comp != "Line": logger.warning("Unable to replace B2B `{}` expected a Line, but found a {}" .format(i, comp)) continue n.links.at[i, busattr] = b1 n.links.at[i, 'p_nom'] = min(n.links.at[i, 'p_nom'], n.lines.at[line, 's_nom']) n.links.at[i, 'carrier'] = 'DC' n.links.at[i, 'underwater_fraction'] = 0. n.links.at[i, 'length'] = n.lines.at[line, 'length'] n.remove("Line", line) n.remove("Bus", b0) logger.info("Replacing B2B converter `{}` together with bus `{}` and line `{}` by an HVDC tie-line {}-{}" .format(i, b0, line, linkcntry.at[i], buscntry.at[b1])) def _set_links_underwater_fraction(n): if n.links.empty: return if not hasattr(n.links, 'geometry'): n.links['underwater_fraction'] = 0. else: offshore_shape = gpd.read_file(snakemake.input.offshore_shapes).unary_union links = gpd.GeoSeries(n.links.geometry.dropna().map(shapely.wkt.loads)) n.links['underwater_fraction'] = links.intersection(offshore_shape).length / links.length def _adjust_capacities_of_under_construction_branches(n): lines_mode = snakemake.config['lines'].get('under_construction', 'undef') if lines_mode == 'zero': n.lines.loc[n.lines.under_construction, 'num_parallel'] = 0. n.lines.loc[n.lines.under_construction, 's_nom'] = 0. elif lines_mode == 'remove': n.mremove("Line", n.lines.index[n.lines.under_construction]) elif lines_mode != 'keep': logger.warning("Unrecognized configuration for `lines: under_construction` = `{}`. Keeping under construction lines.") links_mode = snakemake.config['links'].get('under_construction', 'undef') if links_mode == 'zero': n.links.loc[n.links.under_construction, "p_nom"] = 0. elif links_mode == 'remove': n.mremove("Link", n.links.index[n.links.under_construction]) elif links_mode != 'keep': logger.warning("Unrecognized configuration for `links: under_construction` = `{}`. Keeping under construction links.") if lines_mode == 'remove' or links_mode == 'remove': # We might need to remove further unconnected components n = _remove_unconnected_components(n) return n def base_network(): buses = _load_buses_from_eg() links = _load_links_from_eg(buses) if snakemake.config['links'].get('include_tyndp'): buses, links = _add_links_from_tyndp(buses, links) 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.snapshot_weightings[:] *= 8760./n.snapshot_weightings.sum() 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) _set_links_underwater_fraction(n) _replace_b2b_converter_at_country_border_by_link(n) n = _adjust_capacities_of_under_construction_branches(n) return n if __name__ == "__main__": if 'snakemake' not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake('base_network') configure_logging(snakemake) n = base_network() n.export_to_netcdf(snakemake.output[0])