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simplify_network: Refactor compute_distance

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Jonas Hörsch 2018-10-27 16:42:58 +02:00
parent cc7d356db0
commit 9df54c9530
1 changed files with 13 additions and 10 deletions
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23
scripts/simplify_network.py
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@ -71,7 +71,7 @@ def _adjust_costs_using_distance(n, distance):
if tech + "-grid-perlength" in costs.index: if tech + "-grid-perlength" in costs.index:
cost_perlength = costs.at[tech + "-grid-perlength", "capital_cost"] cost_perlength = costs.at[tech + "-grid-perlength", "capital_cost"]
tech_b = n.generators.carrier == tech tech_b = n.generators.carrier == tech
generator_distance = n.generators.loc[tech_b, ["bus"]].join(distance.rename("distance"), on="bus")['distance'].loc[lambda s: s>0] generator_distance = n.generators.loc[tech_b, "bus"].map(distance).loc[lambda s: s>0]
if not generator_distance.empty: if not generator_distance.empty:
n.generators.loc[generator_distance.index, "capital_cost"] += cost_perlength * generator_distance n.generators.loc[generator_distance.index, "capital_cost"] += cost_perlength * generator_distance
logger.info("Displacing generator(s) {}; capital_cost is adjusted accordingly" logger.info("Displacing generator(s) {}; capital_cost is adjusted accordingly"
@ -102,6 +102,16 @@ def _aggregate_and_move_components(n, busmap, distance, aggregate_one_ports={"Lo
df = n.df(c) df = n.df(c)
n.mremove(c, df.index[df.bus0.isin(buses_to_del) | df.bus1.isin(buses_to_del)]) n.mremove(c, df.index[df.bus0.isin(buses_to_del) | df.bus1.isin(buses_to_del)])
def _compute_distance(n, busmap, buses=None, adjacency_matrix=None):
if buses is None:
buses = busmap.index[busmap.index != busmap.values]
if adjacency_matrix is None:
adjacency_matrix = n.adjacency_matrix(weights=pd.concat(dict(Link=n.links.length, Line=pd.Series(0., n.lines.index))))
dist = dijkstra(adjacency_matrix, directed=False, indices=n.buses.index.get_indexer(buses))
return pd.Series(dist[np.arange(len(buses)), n.buses.index.get_indexer(busmap.loc[buses])], buses)
def simplify_links(n): def simplify_links(n):
## Complex multi-node links are folded into end-points ## Complex multi-node links are folded into end-points
logger.info("Simplifying connected link components") logger.info("Simplifying connected link components")
@ -159,8 +169,7 @@ def simplify_links(n):
m = sp.spatial.distance_matrix(n.buses.loc[b, ['x', 'y']], m = sp.spatial.distance_matrix(n.buses.loc[b, ['x', 'y']],
n.buses.loc[buses[1:-1], ['x', 'y']]) n.buses.loc[buses[1:-1], ['x', 'y']])
busmap.loc[buses] = b[np.r_[0, m.argmin(axis=0), 1]] busmap.loc[buses] = b[np.r_[0, m.argmin(axis=0), 1]]
dist = dijkstra(adjacency_matrix, directed=False, indices=n.buses.index.get_indexer(buses)) distance.loc[buses] += _compute_distance(n, busmap, buses)
distance.loc[buses] += [dist[i,j] for i, j in enumerate(n.buses.index.get_indexer(busmap.loc[buses]))]
all_links = [i for _, i in sum(links, [])] all_links = [i for _, i in sum(links, [])]
@ -198,14 +207,8 @@ def remove_stubs(n):
logger.info("Removing stubs") logger.info("Removing stubs")
busmap = busmap_by_stubs(n, ['country']) busmap = busmap_by_stubs(n, ['country'])
indices, = np.where(busmap.index != busmap.values)
buses = busmap.index[indices]
adjacency_matrix = n.adjacency_matrix(busorder=busmap.index, weights=pd.concat(dict(Link=n.links.length, Line=pd.Series(0., n.lines.index)))) distance = _compute_distance(n, busmap)
dist = dijkstra(adjacency_matrix, directed=False, indices=indices)
distance = pd.Series(dist[np.arange(len(indices)), busmap.index.get_indexer(busmap.iloc[indices])], buses)
logger.info("The following offshore buses are displaced: {}"
.format(", ".join("{} by {:.0f}".format(b, d) for b,d in distance.loc[offwind_buses].iteritems())))
_aggregate_and_move_components(n, busmap, distance) _aggregate_and_move_components(n, busmap, distance)