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0b89e9c a65650a 0b89e9c e1a474a 0b89e9c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 | from __future__ import annotations
import math
from collections import defaultdict
from typing import Any, Sequence
import networkx as nx
import numpy as np
def analyze_grid_topology(
obs,
line_or_to_subid: Sequence[int],
line_ex_to_subid: Sequence[int],
n_sub: int,
) -> dict[str, Any]:
"""Build topology intelligence from a raw Grid2Op observation."""
energy_graph = obs.get_energy_graph()
line_status = [bool(x) for x in obs.line_status.tolist()]
rho = [float(x) for x in obs.rho.tolist()]
overflow = [int(x) for x in obs.timestep_overflow.tolist()]
connected_line_ids = [line_id for line_id, status in enumerate(line_status) if status]
bus_graph = nx.MultiGraph()
for sub_id in range(n_sub):
bus_graph.add_node(sub_id)
for line_id in connected_line_ids:
u = int(line_or_to_subid[line_id])
v = int(line_ex_to_subid[line_id])
bus_graph.add_edge(
u,
v,
key=line_id,
line_id=line_id,
rho=rho[line_id],
timestep_overflow=overflow[line_id],
)
active_bus_graph = nx.Graph()
active_bus_graph.add_nodes_from(
node for node in bus_graph.nodes if bus_graph.degree(node) > 0
)
for u, v, data in bus_graph.edges(data=True):
if active_bus_graph.has_edge(u, v):
continue
active_bus_graph.add_edge(u, v, rho=float(data["rho"]))
pair_to_lines: dict[tuple[int, int], list[int]] = defaultdict(list)
for line_id in connected_line_ids:
u = int(line_or_to_subid[line_id])
v = int(line_ex_to_subid[line_id])
key = tuple(sorted((u, v)))
pair_to_lines[key].append(line_id)
parallel_groups = {
str(line_id): sorted(other for other in line_ids if other != line_id)
for line_ids in pair_to_lines.values()
if len(line_ids) > 1
for line_id in line_ids
}
bridge_lines: list[int] = []
safe_to_disconnect: list[int] = []
for line_id in connected_line_ids:
trial_graph = bus_graph.copy()
u = int(line_or_to_subid[line_id])
v = int(line_ex_to_subid[line_id])
if not trial_graph.has_edge(u, v, key=line_id):
continue
trial_graph.remove_edge(u, v, key=line_id)
active_nodes = [node for node in trial_graph.nodes if trial_graph.degree(node) > 0]
if not active_nodes:
bridge_lines.append(line_id)
continue
reduced_graph = nx.Graph(trial_graph.subgraph(active_nodes))
if nx.number_connected_components(reduced_graph) > 1:
bridge_lines.append(line_id)
else:
safe_to_disconnect.append(line_id)
components = [
sorted(component)
for component in nx.connected_components(active_bus_graph)
] if active_bus_graph.number_of_nodes() else []
islanded_clusters = components[1:] if len(components) > 1 else []
centrality_graph = active_bus_graph.copy()
centrality_scores = (
nx.betweenness_centrality(centrality_graph)
if centrality_graph.number_of_nodes() > 0
else {}
)
high_centrality_buses = [
int(node)
for node, score in sorted(
centrality_scores.items(),
key=lambda item: item[1],
reverse=True,
)
if score > 0.0
][:3]
flow_matrix = _extract_flow_matrix(obs.flow_bus_matrix(active_flow=True))
exporter_buses, importer_buses = _rank_flow_buses(flow_matrix)
stressed_lines = [
{
"line_id": line_id,
"rho": round(rho[line_id], 4),
"overflow": overflow[line_id],
"from_sub": int(line_or_to_subid[line_id]),
"to_sub": int(line_ex_to_subid[line_id]),
}
for line_id in sorted(connected_line_ids, key=lambda idx: rho[idx], reverse=True)[:5]
]
congestion_corridor = "none"
if stressed_lines:
corridor_lines = [entry["line_id"] for entry in stressed_lines[:3]]
congestion_corridor = (
f"export buses {exporter_buses or ['unknown']} -> "
f"import buses {importer_buses or ['unknown']} via lines {corridor_lines}"
)
return {
"num_buses": int(active_bus_graph.number_of_nodes()),
"num_connected_lines": len(connected_line_ids),
"bridge_lines": sorted(bridge_lines),
"safe_to_disconnect": sorted(safe_to_disconnect),
"n_minus_1_critical_lines": sorted(bridge_lines),
"parallel_groups": parallel_groups,
"high_centrality_buses": high_centrality_buses,
"islanded_clusters": islanded_clusters,
"congestion_corridor": congestion_corridor,
"flow_clusters": {
"export_buses": exporter_buses,
"import_buses": importer_buses,
},
"stressed_lines": stressed_lines,
"graph_density": round(nx.density(active_bus_graph), 6)
if active_bus_graph.number_of_nodes() > 1
else 0.0,
"energy_graph_summary": {
"nodes": energy_graph.number_of_nodes(),
"edges": energy_graph.number_of_edges(),
},
}
def _extract_flow_matrix(raw_flow_output) -> np.ndarray:
if isinstance(raw_flow_output, np.ndarray):
return raw_flow_output
if isinstance(raw_flow_output, tuple) and raw_flow_output:
first = raw_flow_output[0]
if isinstance(first, np.ndarray):
return first
raise TypeError(f"Unsupported flow_bus_matrix output type: {type(raw_flow_output)!r}")
def _rank_flow_buses(flow_matrix: np.ndarray) -> tuple[list[int], list[int]]:
matrix = np.asarray(flow_matrix, dtype=float)
if matrix.ndim != 2 or matrix.shape[0] != matrix.shape[1]:
raise ValueError(f"Expected square flow matrix, got shape {matrix.shape}")
# In Grid2Op's flow_bus_matrix, the diagonal carries the nodal net active injection
# while off-diagonal entries represent inter-bus transfers. Summing rows therefore
# tends to zero by construction and hides the exporter/importer ranking.
net_exports = np.diag(matrix)
exporters = [
int(idx)
for idx in np.argsort(-net_exports)
if math.isfinite(net_exports[idx]) and net_exports[idx] > 1.0
][:3]
importers = [
int(idx)
for idx in np.argsort(net_exports)
if math.isfinite(net_exports[idx]) and net_exports[idx] < -1.0
][:3]
return exporters, importers
|