Add MIP module for collector layout optimization

mip.py

@@ -0,0 +1,142 @@
+import numpy as np
+import pandas as pd
+from scipy.spatial import distance_matrix
+from scipy.sparse.csgraph import minimum_spanning_tree
+from collections import defaultdict
+import pulp
+
+
+def design_with_mip(
+    turbines,
+    substation,
+    cable_specs=None,
+    voltage=66000,
+    power_factor=0.95,
+    system_params=None,
+    max_clusters=None,
+    time_limit=300,  # seconds
+    evaluate_func=None,
+    total_invest_func=None,
+    get_max_capacity_func=None,
+):
+    """
+    使用混合整数规划(MIP)优化集电线路布局
+    :param turbines: 风机DataFrame
+    :param substation: 升压站坐标
+    :param cable_specs: 电缆规格
+    :param system_params: 系统参数（用于NPV计算）
+    :param max_clusters: 最大簇数，默认基于功率计算
+    :param time_limit: 求解时间限制（秒）
+    :param evaluate_func: 评估函数
+    :param total_invest_func: 总投资计算函数
+    :param get_max_capacity_func: 获取最大容量函数
+    :return: 连接列表和带有簇信息的turbines
+    """
+    if get_max_capacity_func:
+        max_mw = get_max_capacity_func(cable_specs, voltage, power_factor)
+    else:
+        max_mw = 100.0  # 默认值
+
+    total_power = turbines["power"].sum()
+    if max_clusters is None:
+        max_clusters = int(np.ceil(total_power / max_mw))
+    n_turbines = len(turbines)
+
+    # 预计算距离矩阵
+    all_coords = np.vstack([substation, turbines[["x", "y"]].values])
+    dist_matrix_full = distance_matrix(all_coords, all_coords)
+
+    # MIP 模型
+    prob = pulp.LpProblem("WindFarmCollectorMIP", pulp.LpMinimize)
+
+    # 决策变量：风机分配到簇 (binary)
+    x = pulp.LpVariable.dicts(
+        "assign", (range(n_turbines), range(max_clusters)), cat="Binary"
+    )
+
+    # 簇使用变量 (binary)
+    y = pulp.LpVariable.dicts("use_cluster", range(max_clusters), cat="Binary")
+
+    # 目标函数：最小化总成本 (简化版：距离成本)
+    # 这里使用简化成本：簇内距离 + 到升压站距离
+    prob += pulp.lpSum(
+        [
+            dist_matrix_full[i + 1, j + 1] * x[i][k] * x[j][k]
+            for i in range(n_turbines)
+            for j in range(n_turbines)
+            for k in range(max_clusters)
+            if i < j
+        ]
+    ) + pulp.lpSum(
+        [
+            dist_matrix_full[0, i + 1] * y[k]  # 假设每个簇连接到升压站
+            for i in range(n_turbines)
+            for k in range(max_clusters)
+        ]
+    )
+
+    # 约束：每个风机分配到一个簇
+    for i in range(n_turbines):
+        prob += pulp.lpSum([x[i][k] for k in range(max_clusters)]) == 1
+
+    # 簇功率约束
+    for k in range(max_clusters):
+        prob += (
+            pulp.lpSum([turbines.iloc[i]["power"] * x[i][k] for i in range(n_turbines)])
+            <= max_mw * y[k]
+        )
+
+    # 如果簇未使用，则无分配
+    for k in range(max_clusters):
+        for i in range(n_turbines):
+            prob += x[i][k] <= y[k]
+
+    # 求解
+    solver = pulp.PULP_CBC_CMD(timeLimit=time_limit)
+    status = prob.solve(solver)
+
+    if pulp.LpStatus[prob.status] != "Optimal":
+        print(f"MIP not optimal: {pulp.LpStatus[prob.status]}")
+        # 返回默认方案，如 MST
+        from main import design_with_mst
+
+        return design_with_mst(turbines, substation)
+
+    # 提取结果
+    cluster_assign = [-1] * n_turbines
+    for i in range(n_turbines):
+        for k in range(max_clusters):
+            if pulp.value(x[i][k]) > 0.5:
+                cluster_assign[i] = k
+                break
+
+    # 构建连接
+    clusters = defaultdict(list)
+    for i, c in enumerate(cluster_assign):
+        clusters[c].append(i)
+
+    connections = []
+    for c, members in clusters.items():
+        if len(members) == 0:
+            continue
+        coords = turbines.iloc[members][["x", "y"]].values
+        if len(members) > 1:
+            dm = distance_matrix(coords, coords)
+            mst = minimum_spanning_tree(dm).toarray()
+            for i in range(len(members)):
+                for j in range(len(members)):
+                    if mst[i, j] > 0:
+                        connections.append(
+                            (
+                                f"turbine_{members[i]}",
+                                f"turbine_{members[j]}",
+                                mst[i, j],
+                            )
+                        )
+        # 连接到升压站
+        dists = [dist_matrix_full[0, m + 1] for m in members]
+        closest = members[np.argmin(dists)]
+        connections.append((f"turbine_{closest}", "substation", min(dists)))
+
+    turbines["cluster"] = cluster_assign
+    return connections, turbines