windfarm/mip.py

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 random

try:
    import pulp
except ImportError:
    pulp = None


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 pulp is None:
        print(
            "WARNING: PuLP library not available. MIP optimization skipped, falling back to MST."
        )
        from main import design_with_mst

        return design_with_mst(turbines, substation)

    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()
    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 (线性简化版：到升压站距离总和)
    # 由于MIP线性约束，简化目标为最小化风机到升压站的距离总和（通过簇）
    prob += pulp.lpSum(
        [
            dist_matrix_full[0, i + 1] * x[i][k]
            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