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