# SPDX-FileCopyrightText: : 2017-2020 The PyPSA-Eur Authors
#
# SPDX-License-Identifier: MIT
# coding: utf-8
"""
Lifts electrical transmission network to a single 380 kV voltage layer,
removes dead-ends of the network,
and reduces multi-hop HVDC connections to a single link.
Relevant Settings
-----------------
.. code:: yaml
costs:
USD2013_to_EUR2013:
discountrate:
marginal_cost:
capital_cost:
electricity:
max_hours:
renewables: (keys)
{technology}:
potential:
lines:
length_factor:
links:
p_max_pu:
solving:
solver:
name:
.. seealso::
Documentation of the configuration file ``config.yaml`` at
:ref:`costs_cf`, :ref:`electricity_cf`, :ref:`renewable_cf`,
:ref:`lines_cf`, :ref:`links_cf`, :ref:`solving_cf`
Inputs
------
- ``data/costs.csv``: The database of cost assumptions for all included technologies for specific years from various sources; e.g. discount rate, lifetime, investment (CAPEX), fixed operation and maintenance (FOM), variable operation and maintenance (VOM), fuel costs, efficiency, carbon-dioxide intensity.
- ``resources/regions_onshore.geojson``: confer :ref:`busregions`
- ``resources/regions_offshore.geojson``: confer :ref:`busregions`
- ``networks/elec.nc``: confer :ref:`electricity`
Outputs
-------
- ``resources/regions_onshore_elec_s{simpl}.geojson``:
.. image:: ../img/regions_onshore_elec_s.png
:scale: 33 %
- ``resources/regions_offshore_elec_s{simpl}.geojson``:
.. image:: ../img/regions_offshore_elec_s .png
:scale: 33 %
- ``resources/busmap_elec_s{simpl}.csv``: Mapping of buses from ``networks/elec.nc`` to ``networks/elec_s{simpl}.nc``;
- ``networks/elec_s{simpl}.nc``:
.. image:: ../img/elec_s.png
:scale: 33 %
Description
-----------
The rule :mod:`simplify_network` does up to four things:
1. Create an equivalent transmission network in which all voltage levels are mapped to the 380 kV level by the function ``simplify_network(...)``.
2. DC only sub-networks that are connected at only two buses to the AC network are reduced to a single representative link in the function ``simplify_links(...)``. The components attached to buses in between are moved to the nearest endpoint. The grid connection cost of offshore wind generators are added to the captial costs of the generator.
3. Stub lines and links, i.e. dead-ends of the network, are sequentially removed from the network in the function ``remove_stubs(...)``. Components are moved along.
4. Optionally, if an integer were provided for the wildcard ``{simpl}`` (e.g. ``networks/elec_s500.nc``), the network is clustered to this number of clusters with the routines from the ``cluster_network`` rule with the function ``cluster_network.cluster(...)``. This step is usually skipped!
"""
import logging
from _helpers import configure_logging, update_p_nom_max
from cluster_network import clustering_for_n_clusters, cluster_regions
from add_electricity import load_costs
import pandas as pd
import numpy as np
import scipy as sp
from scipy.sparse.csgraph import connected_components, dijkstra
from functools import reduce
import pypsa
from pypsa.io import import_components_from_dataframe, import_series_from_dataframe
from pypsa.networkclustering import busmap_by_stubs, aggregategenerators, aggregateoneport, get_clustering_from_busmap, _make_consense
logger = logging.getLogger(__name__)
def simplify_network_to_380(n):
## All goes to v_nom == 380
logger.info("Mapping all network lines onto a single 380kV layer")
n.buses['v_nom'] = 380.
linetype_380, = n.lines.loc[n.lines.v_nom == 380., 'type'].unique()
lines_v_nom_b = n.lines.v_nom != 380.
n.lines.loc[lines_v_nom_b, 'num_parallel'] *= (n.lines.loc[lines_v_nom_b, 'v_nom'] / 380.)**2
n.lines.loc[lines_v_nom_b, 'v_nom'] = 380.
n.lines.loc[lines_v_nom_b, 'type'] = linetype_380
n.lines.loc[lines_v_nom_b, 's_nom'] = (
np.sqrt(3) * n.lines['type'].map(n.line_types.i_nom) *
n.lines.bus0.map(n.buses.v_nom) * n.lines.num_parallel
)
# Replace transformers by lines
trafo_map = pd.Series(n.transformers.bus1.values, index=n.transformers.bus0.values)
trafo_map = trafo_map[~trafo_map.index.duplicated(keep='first')]
several_trafo_b = trafo_map.isin(trafo_map.index)
trafo_map.loc[several_trafo_b] = trafo_map.loc[several_trafo_b].map(trafo_map)
missing_buses_i = n.buses.index.difference(trafo_map.index)
missing = pd.Series(missing_buses_i, missing_buses_i)
trafo_map = pd.concat([trafo_map, missing])
for c in n.one_port_components|n.branch_components:
df = n.df(c)
for col in df.columns:
if col.startswith('bus'):
df[col] = df[col].map(trafo_map)
n.mremove("Transformer", n.transformers.index)
n.mremove("Bus", n.buses.index.difference(trafo_map))
return n, trafo_map
def _prepare_connection_costs_per_link(n, costs, config):
if n.links.empty: return {}
connection_costs_per_link = {}
for tech in config['renewable']:
if tech.startswith('offwind'):
connection_costs_per_link[tech] = (
n.links.length * config['lines']['length_factor'] *
(n.links.underwater_fraction * costs.at[tech + '-connection-submarine', 'capital_cost'] +
(1. - n.links.underwater_fraction) * costs.at[tech + '-connection-underground', 'capital_cost'])
)
return connection_costs_per_link
def _compute_connection_costs_to_bus(n, busmap, costs, config, connection_costs_per_link=None, buses=None):
if connection_costs_per_link is None:
connection_costs_per_link = _prepare_connection_costs_per_link(n, costs, config)
if buses is None:
buses = busmap.index[busmap.index != busmap.values]
connection_costs_to_bus = pd.DataFrame(index=buses)
for tech in connection_costs_per_link:
adj = n.adjacency_matrix(weights=pd.concat(dict(Link=connection_costs_per_link[tech].reindex(n.links.index),
Line=pd.Series(0., n.lines.index))))
costs_between_buses = dijkstra(adj, directed=False, indices=n.buses.index.get_indexer(buses))
connection_costs_to_bus[tech] = costs_between_buses[np.arange(len(buses)),
n.buses.index.get_indexer(busmap.loc[buses])]
return connection_costs_to_bus
def _adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, output):
connection_costs = {}
for tech in connection_costs_to_bus:
tech_b = n.generators.carrier == tech
costs = n.generators.loc[tech_b, "bus"].map(connection_costs_to_bus[tech]).loc[lambda s: s>0]
if not costs.empty:
n.generators.loc[costs.index, "capital_cost"] += costs
logger.info("Displacing {} generator(s) and adding connection costs to capital_costs: {} "
.format(tech, ", ".join("{:.0f} Eur/MW/a for `{}`".format(d, b) for b, d in costs.iteritems())))
connection_costs[tech] = costs
pd.DataFrame(connection_costs).to_csv(output.connection_costs)
def _aggregate_and_move_components(n, busmap, connection_costs_to_bus, output, aggregate_one_ports={"Load", "StorageUnit"}):
def replace_components(n, c, df, pnl):
n.mremove(c, n.df(c).index)
import_components_from_dataframe(n, df, c)
for attr, df in pnl.items():
if not df.empty:
import_series_from_dataframe(n, df, c, attr)
_adjust_capital_costs_using_connection_costs(n, connection_costs_to_bus, output)
generators, generators_pnl = aggregategenerators(n, busmap, custom_strategies={'p_nom_min': np.sum})
replace_components(n, "Generator", generators, generators_pnl)
for one_port in aggregate_one_ports:
df, pnl = aggregateoneport(n, busmap, component=one_port)
replace_components(n, one_port, df, pnl)
buses_to_del = n.buses.index.difference(busmap)
n.mremove("Bus", buses_to_del)
for c in n.branch_components:
df = n.df(c)
n.mremove(c, df.index[df.bus0.isin(buses_to_del) | df.bus1.isin(buses_to_del)])
def simplify_links(n, costs, config, output):
## Complex multi-node links are folded into end-points
logger.info("Simplifying connected link components")
if n.links.empty:
return n, n.buses.index.to_series()
# Determine connected link components, ignore all links but DC
adjacency_matrix = n.adjacency_matrix(branch_components=['Link'],
weights=dict(Link=(n.links.carrier == 'DC').astype(float)))
_, labels = connected_components(adjacency_matrix, directed=False)
labels = pd.Series(labels, n.buses.index)
G = n.graph()
def split_links(nodes):
nodes = frozenset(nodes)
seen = set()
supernodes = {m for m in nodes
if len(G.adj[m]) > 2 or (set(G.adj[m]) - nodes)}
for u in supernodes:
for m, ls in G.adj[u].items():
if m not in nodes or m in seen: continue
buses = [u, m]
links = [list(ls)] #[name for name in ls]]
while m not in (supernodes | seen):
seen.add(m)
for m2, ls in G.adj[m].items():
if m2 in seen or m2 == u: continue
buses.append(m2)
links.append(list(ls)) # [name for name in ls])
break
else:
# stub
break
m = m2
if m != u:
yield pd.Index((u, m)), buses, links
seen.add(u)
busmap = n.buses.index.to_series()
connection_costs_per_link = _prepare_connection_costs_per_link(n, costs, config)
connection_costs_to_bus = pd.DataFrame(0., index=n.buses.index, columns=list(connection_costs_per_link))
for lbl in labels.value_counts().loc[lambda s: s > 2].index:
for b, buses, links in split_links(labels.index[labels == lbl]):
if len(buses) <= 2: continue
logger.debug('nodes = {}'.format(labels.index[labels == lbl]))
logger.debug('b = {}\nbuses = {}\nlinks = {}'.format(b, buses, links))
m = sp.spatial.distance_matrix(n.buses.loc[b, ['x', 'y']],
n.buses.loc[buses[1:-1], ['x', 'y']])
busmap.loc[buses] = b[np.r_[0, m.argmin(axis=0), 1]]
connection_costs_to_bus.loc[buses] += _compute_connection_costs_to_bus(n, busmap, costs, config, connection_costs_per_link, buses)
all_links = [i for _, i in sum(links, [])]
p_max_pu = config['links'].get('p_max_pu', 1.)
lengths = n.links.loc[all_links, 'length']
name = lengths.idxmax() + '+{}'.format(len(links) - 1)
params = dict(
carrier='DC',
bus0=b[0], bus1=b[1],
length=sum(n.links.loc[[i for _, i in l], 'length'].mean() for l in links),
p_nom=min(n.links.loc[[i for _, i in l], 'p_nom'].sum() for l in links),
underwater_fraction=sum(lengths/lengths.sum() * n.links.loc[all_links, 'underwater_fraction']),
p_max_pu=p_max_pu,
p_min_pu=-p_max_pu,
underground=False,
under_construction=False
)
logger.info("Joining the links {} connecting the buses {} to simple link {}".format(", ".join(all_links), ", ".join(buses), name))
n.mremove("Link", all_links)
static_attrs = n.components["Link"]["attrs"].loc[lambda df: df.static]
for attr, default in static_attrs.default.iteritems(): params.setdefault(attr, default)
n.links.loc[name] = pd.Series(params)
# n.add("Link", **params)
logger.debug("Collecting all components using the busmap")
_aggregate_and_move_components(n, busmap, connection_costs_to_bus, output)
return n, busmap
def remove_stubs(n, costs, config, output):
logger.info("Removing stubs")
busmap = busmap_by_stubs(n) # ['country'])
connection_costs_to_bus = _compute_connection_costs_to_bus(n, busmap, costs, config)
_aggregate_and_move_components(n, busmap, connection_costs_to_bus, output)
return n, busmap
def aggregate_to_substations(n, buses_i=None):
# can be used to aggregate a selection of buses to electrically closest neighbors
# if no buses are given, nodes that are no substations or without offshore connection are aggregated
if buses_i is None:
logger.info("Aggregating buses that are no substations or have no valid offshore connection")
buses_i = list(set(n.buses.index)-set(n.generators.bus)-set(n.loads.bus))
weight = pd.concat({'Line': n.lines.length/n.lines.s_nom.clip(1e-3),
'Link': n.links.length/n.links.p_nom.clip(1e-3)})
adj = n.adjacency_matrix(branch_components=['Line', 'Link'], weights=weight)
bus_indexer = n.buses.index.get_indexer(buses_i)
dist = pd.DataFrame(dijkstra(adj, directed=False, indices=bus_indexer), buses_i, n.buses.index)
dist[buses_i] = np.inf # bus in buses_i should not be assigned to different bus in buses_i
for c in n.buses.country.unique():
incountry_b = n.buses.country == c
dist.loc[incountry_b, ~incountry_b] = np.inf
busmap = n.buses.index.to_series()
busmap.loc[buses_i] = dist.idxmin(1)
clustering = get_clustering_from_busmap(n, busmap,
bus_strategies=dict(country=_make_consense("Bus", "country")),
aggregate_generators_weighted=True,
aggregate_generators_carriers=None,
aggregate_one_ports=["Load", "StorageUnit"],
line_length_factor=1.0,
generator_strategies={'p_nom_max': 'sum'},
scale_link_capital_costs=False)
return clustering.network, busmap
def cluster(n, n_clusters, config):
logger.info(f"Clustering to {n_clusters} buses")
focus_weights = config.get('focus_weights', None)
renewable_carriers = pd.Index([tech
for tech in n.generators.carrier.unique()
if tech.split('-', 2)[0] in config['renewable']])
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([config['renewable'][tech]['potential']
for tech in renewable_carriers]))
if len(renewable_carriers) > 0 else 'conservative')
clustering = clustering_for_n_clusters(n, n_clusters, custom_busmap=False, potential_mode=potential_mode,
solver_name=config['solving']['solver']['name'],
focus_weights=focus_weights)
return clustering.network, clustering.busmap
if __name__ == "__main__":
if 'snakemake' not in globals():
from _helpers import mock_snakemake
snakemake = mock_snakemake('simplify_network', simpl='', network='elec')
configure_logging(snakemake)
n = pypsa.Network(snakemake.input.network)
n, trafo_map = simplify_network_to_380(n)
Nyears = n.snapshot_weightings.objective.sum() / 8760
technology_costs = load_costs(snakemake.input.tech_costs, snakemake.config['costs'], snakemake.config['electricity'], Nyears)
n, simplify_links_map = simplify_links(n, technology_costs, snakemake.config, snakemake.output)
n, stub_map = remove_stubs(n, technology_costs, snakemake.config, snakemake.output)
busmaps = [trafo_map, simplify_links_map, stub_map]
if snakemake.config.get('clustering', {}).get('simplify', {}).get('to_substations', False):
n, substation_map = aggregate_to_substations(n)
busmaps.append(substation_map)
if snakemake.wildcards.simpl:
n, cluster_map = cluster(n, int(snakemake.wildcards.simpl), snakemake.config)
busmaps.append(cluster_map)
# some entries in n.buses are not updated in previous functions, therefore can be wrong. as they are not needed
# and are lost when clustering (for example with the simpl wildcard), we remove them for consistency:
buses_c = {'symbol', 'tags', 'under_construction', 'substation_lv', 'substation_off'}.intersection(n.buses.columns)
n.buses = n.buses.drop(buses_c, axis=1)
update_p_nom_max(n)
n.export_to_netcdf(snakemake.output.network)
busmap_s = reduce(lambda x, y: x.map(y), busmaps[1:], busmaps[0])
busmap_s.to_csv(snakemake.output.busmap)
cluster_regions(busmaps, snakemake.input, snakemake.output)