pypsa-eur/scripts/cluster_network.py

# coding: utf-8

import pandas as pd
idx = pd.IndexSlice

import logging
logger = logging.getLogger(__name__)

import os
import numpy as np
import geopandas as gpd
import shapely
import matplotlib.pyplot as plt
import seaborn as sns

from six.moves import reduce

import pyomo.environ as po

import pypsa
from pypsa.networkclustering import (busmap_by_kmeans, busmap_by_louvain,
                                     busmap_by_spectral_clustering,
                                     _make_consense, get_clustering_from_busmap)

def normed(x):
    return (x/x.sum()).fillna(0.)

def weighting_for_country(n, x):
    conv_carriers = {'OCGT','CCGT','PHS', 'hydro'}
    gen = (n
           .generators.loc[n.generators.carrier.isin(conv_carriers)]
           .groupby('bus').p_nom.sum()
           .reindex(n.buses.index, fill_value=0.) +
           n
           .storage_units.loc[n.storage_units.carrier.isin(conv_carriers)]
           .groupby('bus').p_nom.sum()
           .reindex(n.buses.index, fill_value=0.))
    load = n.loads_t.p_set.mean().groupby(n.loads.bus).sum()

    b_i = x.index
    g = normed(gen.reindex(b_i, fill_value=0))
    l = normed(load.reindex(b_i, fill_value=0))

    w= g + l
    return (w * (100. / w.max())).clip(lower=1.).astype(int)


## Plot weighting for Germany

def plot_weighting(n, country, country_shape=None):
    n.plot(bus_sizes=(2*weighting_for_country(n.buses.loc[n.buses.country == country])).reindex(n.buses.index, fill_value=1))
    if country_shape is not None:
        plt.xlim(country_shape.bounds[0], country_shape.bounds[2])
        plt.ylim(country_shape.bounds[1], country_shape.bounds[3])


# # Determining the number of clusters per country

def distribute_clusters(n, n_clusters, solver_name=None):
    if solver_name is None:
        solver_name = snakemake.config['solver']['solver']['name']

    L = (n.loads_t.p_set.mean()
         .groupby(n.loads.bus).sum()
         .groupby([n.buses.country, n.buses.sub_network]).sum()
         .pipe(normed))

    N = n.buses.groupby(['country', 'sub_network']).size()

    assert n_clusters >= len(N) and n_clusters <= N.sum(), \
        "Number of clusters must be {} <= n_clusters <= {} for this selection of countries.".format(len(N), N.sum())

    m = po.ConcreteModel()
    def n_bounds(model, *n_id):
        return (1, N[n_id])
    m.n = po.Var(list(L.index), bounds=n_bounds, domain=po.Integers)
    m.tot = po.Constraint(expr=(po.summation(m.n) == n_clusters))
    m.objective = po.Objective(expr=sum((m.n[i] - L.loc[i]*n_clusters)**2 for i in L.index),
                               sense=po.minimize)

    opt = po.SolverFactory(solver_name)
    if not opt.has_capability('quadratic_objective'):
        logger.warn(f'The configured solver `{solver_name}` does not support quadratic objectives. Falling back to `ipopt`.')
        opt = po.SolverFactory('ipopt')

    results = opt.solve(m)
    assert results['Solver'][0]['Status'].key == 'ok', "Solver returned non-optimally: {}".format(results)

    return pd.Series(m.n.get_values(), index=L.index).astype(int)

def busmap_for_n_clusters(n, n_clusters, solver_name, algorithm="kmeans", **algorithm_kwds):
    if algorithm == "kmeans":
        algorithm_kwds.setdefault('n_init', 1000)
        algorithm_kwds.setdefault('max_iter', 30000)
        algorithm_kwds.setdefault('tol', 1e-6)

    n.determine_network_topology()

    n_clusters = distribute_clusters(n, n_clusters, solver_name=solver_name)

    def reduce_network(n, buses):
        nr = pypsa.Network()
        nr.import_components_from_dataframe(buses, "Bus")
        nr.import_components_from_dataframe(n.lines.loc[n.lines.bus0.isin(buses.index) & n.lines.bus1.isin(buses.index)], "Line")
        return nr

    def busmap_for_country(x):
        prefix = x.name[0] + x.name[1] + ' '
        logger.debug("Determining busmap for country {}".format(prefix[:-1]))
        if len(x) == 1:
            return pd.Series(prefix + '0', index=x.index)
        weight = weighting_for_country(n, x)

        if algorithm == "kmeans":
            return prefix + busmap_by_kmeans(n, weight, n_clusters[x.name], buses_i=x.index, **algorithm_kwds)
        elif algorithm == "spectral":
            return prefix + busmap_by_spectral_clustering(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
        elif algorithm == "louvain":
            return prefix + busmap_by_louvain(reduce_network(n, x), n_clusters[x.name], **algorithm_kwds)
        else:
            raise ArgumentError("`algorithm` must be one of 'kmeans', 'spectral' or 'louvain'")

    return (n.buses.groupby(['country', 'sub_network'], group_keys=False, squeeze=True)
            .apply(busmap_for_country).rename('busmap'))

def plot_busmap_for_n_clusters(n, n_clusters=50):
    busmap = busmap_for_n_clusters(n, n_clusters)
    cs = busmap.unique()
    cr = sns.color_palette("hls", len(cs))
    n.plot(bus_colors=busmap.map(dict(zip(cs, cr))))
    del cs, cr

def clustering_for_n_clusters(n, n_clusters, aggregate_carriers=None,
                              line_length_factor=1.25, potential_mode='simple',
                              solver_name="cbc", algorithm="kmeans"):

    if potential_mode == 'simple':
        p_nom_max_strategy = np.sum
    elif potential_mode == 'conservative':
        p_nom_max_strategy = np.min
    else:
        raise AttributeError("potential_mode should be one of 'simple' or 'conservative', "
                             "but is '{}'".format(potential_mode))

    clustering = get_clustering_from_busmap(
        n, busmap_for_n_clusters(n, n_clusters, solver_name, algorithm),
        bus_strategies=dict(country=_make_consense("Bus", "country")),
        aggregate_generators_weighted=True,
        aggregate_generators_carriers=aggregate_carriers,
        aggregate_one_ports=["Load", "StorageUnit"],
        line_length_factor=line_length_factor,
        generator_strategies={'p_nom_max': p_nom_max_strategy}
    )

    nc = clustering.network
    nc.links['underwater_fraction'] = (n.links.eval('underwater_fraction * length')
                                       .div(nc.links.length).dropna())
#    nc.links['capital_cost'] += (costs.at['HVDC overhead', 'capital_cost'] *
#                                 (nc.links.length - n.links.length)
#                                 .dropna().clip(lower=0))
    return clustering

def save_to_geojson(s, fn):
    if os.path.exists(fn):
        os.unlink(fn)
    df = s.reset_index()
    schema = {**gpd.io.file.infer_schema(df), 'geometry': 'Unknown'}
    df.to_file(fn, driver='GeoJSON', schema=schema)

def cluster_regions(busmaps, input=None, output=None):
    if input is None: input = snakemake.input
    if output is None: output = snakemake.output

    busmap = reduce(lambda x, y: x.map(y), busmaps[1:], busmaps[0])

    for which in ('regions_onshore', 'regions_offshore'):
        regions = gpd.read_file(getattr(input, which)).set_index('name')
        geom_c = regions.geometry.groupby(busmap).apply(shapely.ops.cascaded_union)
        regions_c = gpd.GeoDataFrame(dict(geometry=geom_c))
        regions_c.index.name = 'name'
        save_to_geojson(regions_c, getattr(output, which))

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(
            wildcards=Dict(network='elec', simpl='', clusters='45'),
            input=Dict(
                network='networks/{network}_s{simpl}.nc',
                regions_onshore='resources/regions_onshore_{network}_s{simpl}.geojson',
                regions_offshore='resources/regions_offshore_{network}_s{simpl}.geojson',
                clustermaps='resources/clustermaps_{network}_s{simpl}.h5'
            ),
            output=Dict(
                network='networks/{network}_s{simpl}_{clusters}.nc',
                regions_onshore='resources/regions_onshore_{network}_s{simpl}_{clusters}.geojson',
                regions_offshore='resources/regions_offshore_{network}_s{simpl}_{clusters}.geojson',
                clustermaps='resources/clustermaps_{network}_s{simpl}_{clusters}.h5'
            )
        )

    logging.basicConfig(level=snakemake.config['logging_level'])

    n = pypsa.Network(snakemake.input.network)

    renewable_carriers = pd.Index([tech
                                   for tech in n.generators.carrier.unique()
                                   if tech.split('-', 2)[0] in snakemake.config['renewable']])

    if snakemake.wildcards.clusters.endswith('m'):
        n_clusters = int(snakemake.wildcards.clusters[:-1])
        aggregate_carriers = pd.Index(n.generators.carrier.unique()).difference(renewable_carriers)
    else:
        n_clusters = int(snakemake.wildcards.clusters)
        aggregate_carriers = None # All

    if n_clusters == len(n.buses):
        # Fast-path if no clustering is necessary
        busmap = n.buses.index.to_series()
        linemap = n.lines.index.to_series()
        clustering = pypsa.networkclustering.Clustering(n, busmap, linemap, linemap, pd.Series(dtype='O'))
    else:
        line_length_factor = snakemake.config['lines']['length_factor']

        def consense(x):
            v = x.iat[0]
            assert ((x == v).all() or x.isnull().all()), (
                "The `potential` configuration option must agree for all renewable carriers, for now!"
            )
            return v
        potential_mode = consense(pd.Series([snakemake.config['renewable'][tech]['potential']
                                             for tech in renewable_carriers]))
        clustering = clustering_for_n_clusters(n, n_clusters, aggregate_carriers,
                                               line_length_factor=line_length_factor,
                                               potential_mode=potential_mode,
                                               solver_name=snakemake.config['solving']['solver']['name'])

    clustering.network.export_to_netcdf(snakemake.output.network)
    with pd.HDFStore(snakemake.output.clustermaps, mode='w') as store:
        with pd.HDFStore(snakemake.input.clustermaps, mode='r') as clustermaps:
            for attr in clustermaps.keys():
                store.put(attr, clustermaps[attr], format="table", index=False)
        for attr in ('busmap', 'linemap', 'linemap_positive', 'linemap_negative'):
            store.put(attr, getattr(clustering, attr), format="table", index=False)

    cluster_regions((clustering.busmap,))