Fix substation coordinate handling for cable crossing detection

ga.py

@@ -0,0 +1,193 @@
+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
+
+
+def design_with_ga(
+    turbines,
+    substation,
+    cable_specs=None,
+    voltage=66000,
+    power_factor=0.95,
+    system_params=None,
+    pop_size=50,
+    generations=50,
+    evaluate_func=None,
+    total_invest_func=None,
+    get_max_capacity_func=None,
+):
+    """
+    使用遗传算法优化集电线路布局
+    :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 pop_size: 种群大小
+    :param generations: 迭代代数
+    :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()
+    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)
+
+    def fitness(chromosome):
+        cluster_assign = chromosome
+        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)))
+
+        eval_res = evaluate_func(
+            turbines,
+            connections,
+            substation,
+            cable_specs,
+            is_offshore=False,
+            method_name="GA",
+            voltage=voltage,
+            power_factor=power_factor,
+        )
+        if system_params and total_invest_func:
+            res_list = total_invest_func(
+                [
+                    {
+                        "cost": eval_res["total_cost"],
+                        "loss": eval_res["total_loss"],
+                        "eval": eval_res,
+                    }
+                ],
+                system_params,
+            )
+            return res_list[0]["total_cost_npv"]
+        return eval_res["total_cost"]
+
+    def init_individual():
+        assign = np.zeros(n_turbines, dtype=int)
+        cluster_powers = np.zeros(max_clusters)
+        for i in range(n_turbines):
+            p = turbines.iloc[i]["power"]
+            possible = [
+                c for c in range(max_clusters) if cluster_powers[c] + p <= max_mw
+            ]
+            if possible:
+                c = random.choice(possible)
+            else:
+                c = random.randint(0, max_clusters - 1)
+            assign[i] = c
+            cluster_powers[c] += p
+        return assign.tolist()
+
+    population = [init_individual() for _ in range(pop_size)]
+    best = None
+    best_fitness = float("inf")
+
+    for gen in range(generations):
+        fitnesses = [fitness(ind) for ind in population]
+        min_fit = min(fitnesses)
+        if min_fit < best_fitness:
+            best_fitness = min_fit
+            best = population[fitnesses.index(min_fit)].copy()
+
+        def tournament(size=3):
+            candidates = random.sample(list(zip(population, fitnesses)), size)
+            return min(candidates, key=lambda x: x[1])[0]
+
+        selected = [tournament() for _ in range(pop_size)]
+
+        new_pop = []
+        for i in range(0, pop_size, 2):
+            p1 = selected[i]
+            p2 = selected[i + 1] if i + 1 < pop_size else selected[0]
+            if random.random() < 0.8:
+                point = random.randint(1, n_turbines - 1)
+                child1 = p1[:point] + p2[point:]
+                child2 = p2[:point] + p1[point:]
+            else:
+                child1, child2 = p1.copy(), p2.copy()
+            new_pop.extend([child1, child2])
+
+        for ind in new_pop:
+            if random.random() < 0.1:
+                idx = random.randint(0, n_turbines - 1)
+                old_c = ind[idx]
+                new_c = random.randint(0, max_clusters - 1)
+                ind[idx] = new_c
+                cluster_powers = defaultdict(float)
+                for j, c in enumerate(ind):
+                    cluster_powers[c] += turbines.iloc[j]["power"]
+                if max(cluster_powers.values()) > max_mw:
+                    ind[idx] = max_clusters
+                    max_clusters += 1
+
+        elites = sorted(zip(population, fitnesses), key=lambda x: x[1])[
+            : int(0.1 * pop_size)
+        ]
+        new_pop[: len(elites)] = [e[0] for e in elites]
+        population = new_pop[:pop_size]
+
+    # 解码最佳个体
+    cluster_assign = best
+    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],
+                            )
+                        )
+        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

gui.py

@@ -90,6 +90,9 @@ def index():
         "run_btn": None,
         "current_file_container": None,  # 替换 label 为 container
         "info_container": None,  # 新增信息展示容器
+        "ga_switch": None,  # 遗传算法开关
+        "mip_switch": None,  # MIP开关
+        "log_content": "",  # 存储计算日志内容
     }
 
     def update_info_panel():
@@ -340,6 +343,16 @@ def index():
 
             update_info_panel()
 
+            # 清空方案对比结果和拓扑可视化
+            state["results"] = []
+            if refs["results_table"]:
+                refs["results_table"].rows = []
+                refs["results_table"].selected = []
+            if refs["plot_container"]:
+                refs["plot_container"].clear()
+            if refs["export_row"]:
+                refs["export_row"].clear()
+
             # 清空上传组件列表，以便下次选择（配合 .no-list CSS 使用）
             if refs["upload_widget"]:
                 refs["upload_widget"].reset()
@@ -347,7 +360,9 @@ def index():
         except Exception as ex:
             ui.notify(f"上传处理失败: {ex}", type="negative")
 
-    async def save_file_with_dialog(filename, callback, file_filter="All files (*.*)", sender=None):
+    async def save_file_with_dialog(
+        filename, callback, file_filter="All files (*.*)", sender=None
+    ):
         """
         跨平台文件保存助手。
         如果是原生模式，弹出系统保存对话框。
@@ -409,7 +424,7 @@ def index():
                         ["powershell", "-Command", ps_script],
                         capture_output=True,
                         text=True,
-                        startupinfo=startupinfo
+                        startupinfo=startupinfo,
                     )
                     save_path = result.stdout.strip()
                     if save_path:
@@ -477,7 +492,10 @@ def index():
 
             async def on_click_excel(e):
                 await save_file_with_dialog(
-                    default_excel_name, save_excel, "Excel Files (*.xlsx)", sender=e.sender
+                    default_excel_name,
+                    save_excel,
+                    "Excel Files (*.xlsx)",
+                    sender=e.sender,
                 )
 
             ui.button(
@@ -607,12 +625,71 @@ def index():
                             except:
                                 pass
 
-                await save_file_with_dialog(default_name, save_zip, "ZIP Files (*.zip)", sender=e.sender)
+                await save_file_with_dialog(
+                    default_name, save_zip, "ZIP Files (*.zip)", sender=e.sender
+                )
 
             ui.button("导出全部方案 DXF (ZIP)", on_click=on_click_all_dxf).props(
                 "icon=folder_zip color=secondary"
             )
 
+            # --- 导出计算日志 ---
+            async def on_click_export_log(e):
+                # 尝试多种方式获取日志内容
+                log_content = ""
+                method_used = "unknown"
+
+                # 方法1: 首先尝试从保存的日志内容获取
+                if refs.get("log_content") and refs["log_content"].strip():
+                    log_content = refs["log_content"]
+                    method_used = "saved_memory"
+
+                # 方法2: 如果保存的日志为空，尝试使用 JavaScript 获取 log 组件的内容
+                if not log_content.strip() and refs["log_box"]:
+                    try:
+                        log_id = refs["log_box"].id
+                        js_code = f"""
+                        (function() {{
+                            const logElement = document.querySelector("#c{log_id}");
+                            if (logElement) {{
+                                console.log("Found log element:", logElement);
+                                return logElement.innerText || logElement.textContent || "";
+                            }}
+                            console.log("Log element not found for ID: c{log_id}");
+                            return "";
+                        }})()
+                        """
+                        result = await ui.run_javascript(js_code)
+                        if result and result.strip():
+                            log_content = result
+                            method_used = "javascript"
+                    except Exception as js_error:
+                        print(f"JavaScript method failed: {js_error}")
+
+                if not log_content.strip():
+                    ui.notify(
+                        "没有可导出的日志内容。请先运行计算任务。", type="warning"
+                    )
+                    print(f"Log export failed. Method tried: {method_used}")
+                    return
+
+                print(
+                    f"Successfully exported log using method: {method_used}, length: {len(log_content)}"
+                )
+                default_name = f"{file_prefix}_calculation_log.txt"
+
+                async def save_log(path):
+                    with open(path, "w", encoding="utf-8") as f:
+                        f.write(log_content)
+
+                await save_file_with_dialog(
+                    default_name, save_log, "Text Files (*.txt)", sender=e.sender
+                )
+
+            ui.button("导出计算日志", on_click=on_click_export_log).props(
+                "icon=description color=info"
+            )
+
     def update_plot(result):
         if refs["plot_container"]:
             refs["plot_container"].clear()
@@ -667,8 +744,15 @@ def index():
             return
         if refs["log_box"]:
             refs["log_box"].clear()
+        # 重置日志内容
+        refs["log_content"] = ""
         log_queue = queue.Queue()
 
+        # 获取开关状态
+        use_ga = refs["ga_switch"].value if refs["ga_switch"] else False
+        use_mip = refs["mip_switch"].value if refs["mip_switch"] else False
+        print(f"Switch values: GA={use_ga}, MIP={use_mip}")
+
         class QueueLogger(io.StringIO):
             def write(self, message):
                 if message and message.strip():
@@ -682,6 +766,8 @@ def index():
                     try:
                         msg = log_queue.get_nowait()
                         refs["log_box"].push(msg)
+                        # 同时保存到日志内容中
+                        refs["log_content"] += msg + "\n"
                         new_msg = True
                         if msg.startswith("--- Scenario"):
                             scenario_name = msg.replace("---", "").strip()
@@ -716,6 +802,8 @@ def index():
                         n_clusters_override=None,
                         interactive=False,
                         plot_results=False,
+                        use_ga=use_ga,
+                        use_mip=use_mip,
                     )
 
             # 在后台线程运行计算任务
@@ -782,7 +870,11 @@ def index():
                     total_length_km = total_length_m / 1000
 
                     # 获取回路数 (通过统计从升压站发出的连接)
-                    n_circuits = sum(1 for d in res["eval"]["details"] if d["source"] == "substation" or d["target"] == "substation")
+                    n_circuits = sum(
+                        1
+                        for d in res["eval"]["details"]
+                        if d["source"] == "substation" or d["target"] == "substation"
+                    )
 
                     row_dict = {
                         "name": name_display,
@@ -875,7 +967,10 @@ def index():
                             raise FileNotFoundError("无法生成模板文件")
 
                     await save_file_with_dialog(
-                        "coordinates.xlsx", save_template, "Excel Files (*.xlsx)", sender=e.sender
+                        "coordinates.xlsx",
+                        save_template,
+                        "Excel Files (*.xlsx)",
+                        sender=e.sender,
                     )
 
                 ui.button("导出 Excel 模板", on_click=export_template).classes(
@@ -910,6 +1005,17 @@ def index():
                     .classes("flex-1 py-4")
                     .props("icon=play_arrow color=secondary")
                 )
+                # 4. 遗传算法开关
+                with ui.column().classes("flex-1 gap-0 justify-center items-center"):
+                    refs["ga_switch"] = ui.switch("启用遗传算法", value=False).props(
+                        "color=orange"
+                    )
+
+                # 5. MIP开关
+                with ui.column().classes("flex-1 gap-0 justify-center items-center"):
+                    refs["mip_switch"] = ui.switch("启用MIP", value=False).props(
+                        "color=blue"
+                    )
 
         with ui.column().classes("w-full gap-4"):
             # 新增：信息展示卡片

main.py

@@ -1,6 +1,7 @@
 import argparse
 import math
 import os
+import random
 from collections import defaultdict
 
 import matplotlib.pyplot as plt
@@ -12,6 +13,8 @@ from scipy.spatial import distance_matrix
 from sklearn.cluster import KMeans
 
 from esau_williams import design_with_esau_williams
+from ga import design_with_ga
+from mip import design_with_mip
 
 # 设置matplotlib支持中文显示
 plt.rcParams["font.sans-serif"] = ["Microsoft YaHei", "SimHei", "Arial"]
@@ -28,6 +31,7 @@ ANNUAL_LOSS_HOURS = 1400  # 年损耗小时数（小时）
 
 # 1. 生成风电场数据（实际应用中替换为真实坐标）
 def generate_wind_farm_data(n_turbines=30, seed=42, layout="random", spacing=800):
+    pass  # 实际应用中从Excel读取真实坐标，此函数保留用于测试
     """
     生成模拟风电场数据
     :param layout: 'random' (随机) 或 'grid' (规则行列)
@@ -654,9 +658,163 @@ def design_with_rotational_sweep(
 
     return final_connections, 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)
+
+    def fitness(chromosome):
+        cluster_assign = chromosome
+        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)))
+
+        eval_res = evaluate_design(
+            turbines,
+            connections,
+            substation,
+            cable_specs,
+            is_offshore=False,
+            method_name="GA",
+            voltage=voltage,
+            power_factor=power_factor,
+        )
+        if system_params:
+            res_list = total_investment(
+                [
+                    {
+                        "cost": eval_res["total_cost"],
+                        "loss": eval_res["total_loss"],
+                        "eval": eval_res,
+                    }
+                ],
+                system_params,
+            )
+            return res_list[0]["total_cost_npv"]
+        return eval_res["total_cost"]
+
+    def init_individual():
+        assign = np.zeros(n_turbines, dtype=int)
+        cluster_powers = np.zeros(max_clusters)
+        for i in range(n_turbines):
+            p = turbines.iloc[i]["power"]
+            possible = [
+                c for c in range(max_clusters) if cluster_powers[c] + p <= max_mw
+            ]
+            if possible:
+                c = random.choice(possible)
+            else:
+                c = random.randint(0, max_clusters - 1)
+            assign[i] = c
+            cluster_powers[c] += p
+        return assign.tolist()
+
+    population = [init_individual() for _ in range(pop_size)]
+    best = None
+    best_fitness = float("inf")
+
+    for gen in range(generations):
+        fitnesses = [fitness(ind) for ind in population]
+        min_fit = min(fitnesses)
+        if min_fit < best_fitness:
+            best_fitness = min_fit
+            best = population[fitnesses.index(min_fit)].copy()
+
+        def tournament(size=3):
+            candidates = random.sample(list(zip(population, fitnesses)), size)
+            return min(candidates, key=lambda x: x[1])[0]
+
+        selected = [tournament() for _ in range(pop_size)]
+
+        new_pop = []
+        for i in range(0, pop_size, 2):
+            p1 = selected[i]
+            p2 = selected[i + 1] if i + 1 < pop_size else selected[0]
+            if random.random() < 0.8:
+                point = random.randint(1, n_turbines - 1)
+                child1 = p1[:point] + p2[point:]
+                child2 = p2[:point] + p1[point:]
+            else:
+                child1, child2 = p1.copy(), p2.copy()
+            new_pop.extend([child1, child2])
+
+        for ind in new_pop:
+            if random.random() < 0.1:
+                idx = random.randint(0, n_turbines - 1)
+                old_c = ind[idx]
+                new_c = random.randint(0, max_clusters - 1)
+                ind[idx] = new_c
+                cluster_powers = defaultdict(float)
+                for j, c in enumerate(ind):
+                    cluster_powers[c] += turbines.iloc[j]["power"]
+                if max(cluster_powers.values()) > max_mw:
+                    ind[idx] = max_clusters
+                    max_clusters += 1
+
+        elites = sorted(zip(population, fitnesses), key=lambda x: x[1])[
+            : int(0.1 * pop_size)
+        ]
+        new_pop[: len(elites)] = [e[0] for e in elites]
+        population = new_pop[:pop_size]
+
+    # 解码最佳个体
+    cluster_assign = best
+    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],
+                            )
+                        )
+        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
+
 
 # 4. 获取电缆最大容量(MW)
-def get_max_cable_capacity_mw(cable_specs, voltage=VOLTAGE_LEVEL, power_factor=POWER_FACTOR):
+def get_max_cable_capacity_mw(
+    cable_specs, voltage=VOLTAGE_LEVEL, power_factor=POWER_FACTOR
+):
     """
     根据电缆规格计算最大承载功率
     :param cable_specs: 电缆规格列表 list of tuples，或者直接是最大功率数值(MW)
@@ -991,7 +1149,11 @@ def export_to_excel(connections_details, filename):
     df = pd.DataFrame(data)
 
     # 汇总统计
-    n_circuits = sum(1 for conn in connections_details if conn["source"] == "substation" or conn["target"] == "substation")
+    n_circuits = sum(
+        1
+        for conn in connections_details
+        if conn["source"] == "substation" or conn["target"] == "substation"
+    )
     summary = {
         "Total Cost (¥)": df["Cost (¥)"].sum(),
         "Total Effective Length (m)": df["Effective Length (m)"].sum(),
@@ -1023,7 +1185,11 @@ def export_all_scenarios_to_excel(results, filename):
             summary_data = []
             for res in results:
                 # 获取回路数 (通过统计从升压站发出的连接)
-                n_circuits = sum(1 for conn in res["eval"]["details"] if conn["source"] == "substation" or conn["target"] == "substation")
+                n_circuits = sum(
+                    1
+                    for conn in res["eval"]["details"]
+                    if conn["source"] == "substation" or conn["target"] == "substation"
+                )
 
                 summary_data.append(
                     {
@@ -1230,7 +1396,12 @@ def visualize_design(
 
 # 7. 主函数：比较两种设计方法
 def compare_design_methods(
-    excel_path=None, n_clusters_override=None, interactive=True, plot_results=True
+    excel_path=None,
+    n_clusters_override=None,
+    interactive=True,
+    plot_results=True,
+    use_ga=False,
+    use_mip=False,
 ):
     """
     比较MST和三种电缆方案下的K-means设计方法
@@ -1411,7 +1582,11 @@ def compare_design_methods(
             voltage=voltage,
             power_factor=power_factor,
         )
-        n_circuits_base = sum(1 for d in eval_base["details"] if d["source"] == "substation" or d["target"] == "substation")
+        n_circuits_base = sum(
+            1
+            for d in eval_base["details"]
+            if d["source"] == "substation" or d["target"] == "substation"
+        )
         comparison_results.append(
             {
                 "name": base_name,
@@ -1441,7 +1616,11 @@ def compare_design_methods(
             voltage=voltage,
             power_factor=power_factor,
         )
-        n_circuits_rot = sum(1 for d in eval_rot["details"] if d["source"] == "substation" or d["target"] == "substation")
+        n_circuits_rot = sum(
+            1
+            for d in eval_rot["details"]
+            if d["source"] == "substation" or d["target"] == "substation"
+        )
         comparison_results.append(
             {
                 "name": rot_name,
@@ -1471,7 +1650,11 @@ def compare_design_methods(
             voltage=voltage,
             power_factor=power_factor,
         )
-        n_circuits_ew = sum(1 for d in eval_ew["details"] if d["source"] == "substation" or d["target"] == "substation")
+        n_circuits_ew = sum(
+            1
+            for d in eval_ew["details"]
+            if d["source"] == "substation" or d["target"] == "substation"
+        )
         comparison_results.append(
             {
                 "name": ew_name,
@@ -1486,6 +1669,93 @@ def compare_design_methods(
             f"  [Esau-Williams] Cost: ¥{eval_ew['total_cost']:,.2f} | Loss: {eval_ew['total_loss']:.2f} kW | Circuits: {n_circuits_ew}"
         )
 
+        if use_ga:
+            # --- Run 4: Genetic Algorithm ---
+            ga_name = f"{name} (GA)"
+            conns_ga, turbines_ga = design_with_ga(
+                turbines.copy(),
+                substation,
+                current_specs,
+                voltage,
+                power_factor,
+                system_params,
+                evaluate_func=evaluate_design,
+                total_invest_func=total_investment,
+                get_max_capacity_func=get_max_cable_capacity_mw,
+            )
+            eval_ga = evaluate_design(
+                turbines,
+                conns_ga,
+                substation,
+                cable_specs=current_specs,
+                is_offshore=is_offshore,
+                method_name=ga_name,
+                voltage=voltage,
+                power_factor=power_factor,
+            )
+            n_circuits_ga = sum(
+                1
+                for d in eval_ga["details"]
+                if d["source"] == "substation" or d["target"] == "substation"
+            )
+            comparison_results.append(
+                {
+                    "name": ga_name,
+                    "cost": eval_ga["total_cost"],
+                    "loss": eval_ga["total_loss"],
+                    "eval": eval_ga,
+                    "turbines": turbines_ga,
+                    "specs": current_specs,
+                }
+            )
+            print(
+                f"  [GA] Cost: ¥{eval_ga['total_cost']:,.2f} | Loss: {eval_ga['total_loss']:.2f} kW | Circuits: {n_circuits_ga}"
+            )
+
+        if use_mip:
+            print(f"Starting MIP optimization for {name}")
+            # --- Run 5: Mixed Integer Programming ---
+            mip_name = f"{name} (MIP)"
+            conns_mip, turbines_mip = design_with_mip(
+                turbines.copy(),
+                substation,
+                current_specs,
+                voltage,
+                power_factor,
+                system_params,
+                evaluate_func=evaluate_design,
+                total_invest_func=total_investment,
+                get_max_capacity_func=get_max_cable_capacity_mw,
+            )
+            eval_mip = evaluate_design(
+                turbines,
+                conns_mip,
+                substation,
+                cable_specs=current_specs,
+                is_offshore=is_offshore,
+                method_name=mip_name,
+                voltage=voltage,
+                power_factor=power_factor,
+            )
+            n_circuits_mip = sum(
+                1
+                for d in eval_mip["details"]
+                if d["source"] == "substation" or d["target"] == "substation"
+            )
+            comparison_results.append(
+                {
+                    "name": mip_name,
+                    "cost": eval_mip["total_cost"],
+                    "loss": eval_mip["total_loss"],
+                    "eval": eval_mip,
+                    "turbines": turbines_mip,
+                    "specs": current_specs,
+                }
+            )
+            print(
+                f"  [MIP] Cost: ¥{eval_mip['total_cost']:,.2f} | Loss: {eval_mip['total_loss']:.2f} kW | Circuits: {n_circuits_mip}"
+            )
+
         # 记录最佳
         if eval_rot["total_cost"] < best_cost:
             best_cost = eval_rot["total_cost"]
@@ -1500,7 +1770,11 @@ def compare_design_methods(
         # 可视化 (只画 Base 版本)
         ax_idx = i + 1
         if plot_results and ax_idx < 4:
-            n_circuits = sum(1 for d in eval_base["details"] if d["source"] == "substation" or d["target"] == "substation")
+            n_circuits = sum(
+                1
+                for d in eval_base["details"]
+                if d["source"] == "substation" or d["target"] == "substation"
+            )
             title = f"{base_name} ({n_circuits} circuits)\nCost: ¥{eval_base['total_cost'] / 10000:.2f}万"
             visualize_design(
                 turbines_base, substation, eval_base["details"], title, ax=axes[ax_idx]
@@ -1542,9 +1816,15 @@ def compare_design_methods(
                 best_idx = i
 
             # 获取回路数 (通过统计从升压站发出的连接)
-            n_circuits = sum(1 for conn in res["eval"]["details"] if conn["source"] == "substation" or conn["target"] == "substation")
+            n_circuits = sum(
+                1
+                for conn in res["eval"]["details"]
+                if conn["source"] == "substation" or conn["target"] == "substation"
+            )
 
-            print(f"  {i + 1}. {res['name']} - Cost: ¥{res['cost']:,.2f} | Circuits: {n_circuits}")
+            print(
+                f"  {i + 1}. {res['name']} - Cost: ¥{res['cost']:,.2f} | Circuits: {n_circuits}"
+            )
 
         print(f"推荐方案: {comparison_results[best_idx]['name']} (默认)")
 

mip.py

@@ -0,0 +1,472 @@
+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
+
+    pulp_available = True
+except ImportError:
+    pulp = None
+    pulp_available = False
+
+try:
+    import pyomo.environ as pyo_env
+
+    pyomo_available = True
+except (ImportError, AttributeError):
+    pyomo_available = False
+    print("Pyomo not available, falling back to PuLP")
+
+
+def design_with_pyomo(
+    turbines,
+    substation,
+    cable_specs=None,
+    voltage=66000,
+    power_factor=0.95,
+    system_params=None,
+    max_clusters=None,
+    time_limit=300,
+    evaluate_func=None,
+    total_invest_func=None,
+    get_max_capacity_func=None,
+):
+    """
+    使用Pyomo求解器优化集电线路布局
+    :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
+    total_power = turbines["power"].sum()
+    if max_clusters is None:
+        max_clusters = int(np.ceil(total_power / max_mw))
+    n_turbines = len(turbines)
+
+    all_coords = np.vstack([substation, turbines[["x", "y"]].values])
+    dist_matrix_full = distance_matrix(all_coords, all_coords)
+
+    # Simple fallback for now - use PuLP instead
+    print("Pyomo not fully implemented, falling back to PuLP")
+    return design_with_mip(
+        turbines,
+        substation,
+        cable_specs,
+        voltage,
+        power_factor,
+        system_params,
+        max_clusters,
+        time_limit,
+        evaluate_func,
+        total_invest_func,
+        get_max_capacity_func,
+    )
+
+
+def design_with_mip(
+    turbines,
+    substation,
+    cable_specs=None,
+    voltage=66000,
+    power_factor=0.95,
+    system_params=None,
+    max_clusters=None,
+    time_limit=300,
+    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 not pulp_available:
+        print(
+            "WARNING: PuLP library not available. MIP optimization skipped, falling back to MST."
+        )
+        from main import design_with_mst
+
+        connections = design_with_mst(turbines, substation)
+        return connections, turbines
+
+    if get_max_capacity_func:
+        max_mw = get_max_capacity_func(cable_specs, voltage, power_factor)
+    else:
+        max_mw = 100.0
+    if max_clusters is None:
+        max_clusters = int(np.ceil(turbines["power"].sum() / max_mw))
+    n_turbines = len(turbines)
+
+    print(
+        f"MIP Model Setup: n_turbines={n_turbines}, max_clusters={max_clusters}, max_mw={max_mw:.2f} MW"
+    )
+
+    all_coords = np.vstack([substation, turbines[["x", "y"]].values])
+    dist_matrix_full = distance_matrix(all_coords, all_coords)
+
+    prob = pulp.LpProblem("WindFarmCollectorMIP", pulp.LpMinimize)
+
+    # Create all decision variables upfront to avoid duplicates
+    assign_vars = {}
+    for i in range(n_turbines):
+        for k in range(max_clusters):
+            assign_vars[(i, k)] = pulp.LpVariable(f"assign_{i}_{k}", cat="Binary")
+
+    cluster_vars = {}
+    for k in range(max_clusters):
+        cluster_vars[k] = pulp.LpVariable(f"cluster_{k}", cat="Binary")
+
+    # Helper functions to access variables
+    def assign_var(i, k):
+        return assign_vars[(i, k)]
+
+    def cluster_var(k):
+        return cluster_vars[k]
+
+    # Simplified objective function: minimize total distance
+    prob += pulp.lpSum(
+        [
+            dist_matrix_full[0, i + 1] * assign_var(i, k)
+            for i in range(n_turbines)
+            for k in range(max_clusters)
+        ]
+    )
+
+    for i in range(n_turbines):
+        prob += pulp.lpSum([assign_var(i, k) for k in range(max_clusters)]) == 1
+
+    for k in range(max_clusters):
+        cluster_power = pulp.lpSum(
+            [turbines.iloc[i]["power"] * assign_var(i, k) for i in range(n_turbines)]
+        )
+        prob += cluster_power <= max_mw * 1.2 * cluster_var(k)
+
+    for k in range(max_clusters):
+        for i in range(n_turbines):
+            prob += assign_var(i, k) <= cluster_var(k)
+
+    print(
+        f"MIP Model: {len(prob.variables())} variables, {len(prob.constraints)} constraints"
+    )
+
+    # Debug: Print model structure
+    print("MIP model structure check:")
+    print(f"  Variables: {len(prob.variables())}")
+    print(f"  Constraints: {len(prob.constraints)}")
+    print(f"  Time limit: {time_limit}s")
+    print(f"  Turbines: {n_turbines}, Clusters: {max_clusters}")
+
+    # Test solver availability
+    try:
+        import subprocess
+
+        test_solver = subprocess.run(
+            [
+                r"D:\code\windfarm\.venv\Lib\site-packages\pulp\apis\..\solverdir\cbc\win\i64\cbc.exe",
+                "-version",
+            ],
+            capture_output=True,
+            text=True,
+            timeout=5,
+        )
+        print(
+            f"CBC solver test: {test_solver.stdout[:100] if test_solver.stdout else 'No output'}"
+        )
+    except Exception as solver_test_error:
+        print(f"CBC solver test failed: {solver_test_error}")
+
+    print("MIP: Starting to solve...")
+    try:
+        # Try to use CBC solver with different configurations
+        solver = pulp.PULP_CBC_CMD(
+            timeLimit=time_limit,
+            msg=False,
+            warmStart=False,
+        )
+        print(f"Using CBC solver with time limit: {time_limit}s")
+        status = prob.solve(solver)
+        print(
+            f"MIP: Solver status={pulp.LpStatus[prob.status]}, Objective value={pulp.value(prob.objective):.4f}"
+        )
+    except Exception as e:
+        print(f"MIP: CBC solver execution failed: {e}")
+        # Try alternative solver configurations
+        try:
+            print("MIP: Trying alternative solver configuration...")
+            solver = pulp.PULP_CBC_CMD(
+                msg=True,  # Enable messages for debugging
+                threads=1,  # Single thread
+                timeLimit=time_limit,
+            )
+            status = prob.solve(solver)
+            print(
+                f"MIP: Alternative solver status={pulp.LpStatus[prob.status]}, Objective value={pulp.value(prob.objective):.4f}"
+            )
+        except Exception as e2:
+            print(f"MIP: All solver attempts failed: {e2}, falling back to MST")
+            from main import design_with_mst
+
+            connections = design_with_mst(turbines, substation)
+            return connections, turbines
+
+    if pulp.LpStatus[prob.status] != "Optimal":
+        print(
+            f"MIP solver status: {pulp.LpStatus[prob.status]}, solution not found, falling back to MST"
+        )
+        print("Model feasibility check:")
+        print(f"Total power: {turbines['power'].sum():.2f} MW")
+        print(f"Max cluster capacity: {max_mw:.2f} MW")
+        print(f"Number of clusters: {max_clusters}, Number of turbines: {n_turbines}")
+
+        for k in range(max_clusters):
+            cluster_power = pulp.value(
+                pulp.lpSum(
+                    [
+                        turbines.iloc[i]["power"] * assign_var(i, k)
+                        for i in range(n_turbines)
+                    ]
+                )
+            )
+            cluster_used = pulp.value(cluster_var(k))
+            print(
+                f"Cluster {k}: Power={cluster_power:.2f} MW (max {max_mw * 1.2:.2f}), Used={cluster_used}"
+            )
+
+        from main import design_with_mst
+
+        connections = design_with_mst(turbines, substation)
+        return connections, turbines
+
+    cluster_assign = [-1] * n_turbines
+    active_clusters = []
+    for k in range(max_clusters):
+        if pulp.value(cluster_var(k)) > 0.5:
+            active_clusters.append(k)
+
+    for i in range(n_turbines):
+        assigned = False
+        for k in active_clusters:
+            if pulp.value(assign_var(i, k)) > 0.5:
+                cluster_assign[i] = k
+                assigned = True
+                break
+        if not assigned:
+            dists = [dist_matrix_full[0, i + 1] for k in active_clusters]
+            cluster_assign[i] = active_clusters[np.argmin(dists)]
+
+    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],
+                            )
+                        )
+        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
+
+    # Check cluster distances
+    min_cluster_distance = check_cluster_distances(clusters, turbines)
+    if min_cluster_distance is not None:
+        print(
+            f"Cluster validation: Minimum distance between clusters = {min_cluster_distance:.2f} m"
+        )
+        if min_cluster_distance < 1000:
+            print(
+                f"WARNING: Clusters are very close to each other ({min_cluster_distance:.2f} m < 1000 m)"
+            )
+        elif min_cluster_distance < 2000:
+            print(
+                f"NOTICE: Clusters are relatively close ({min_cluster_distance:.2f} m)"
+            )
+
+    # Check for cable crossings
+    cable_crossings = check_cable_crossings(connections, turbines, substation)
+    if cable_crossings:
+        print(
+            f"WARNING: Found {len(cable_crossings)} cable crossing(s) in the solution"
+        )
+        for i, (idx1, idx2, p1, p2, p3, p4) in enumerate(cable_crossings):
+            conn1 = connections[idx1]
+            conn2 = connections[idx2]
+            print(
+                f"  Crossing {i + 1}: Connection {conn1[0]}-{conn1[1]} crosses {conn2[0]}-{conn2[1]}"
+            )
+    else:
+        print("No cable crossings detected in the solution")
+
+    print(
+        f"MIP optimization completed successfully, {len(connections)} connections generated"
+    )
+    return connections, turbines
+
+
+def calculate_cluster_centroids(clusters, turbines):
+    """Calculate the centroid coordinates for each cluster."""
+    centroids = {}
+    for c, members in clusters.items():
+        if len(members) == 0:
+            centroids[c] = (0, 0)
+        else:
+            coords = turbines.iloc[members][["x", "y"]].values
+            centroid_x = np.mean(coords[:, 0])
+            centroid_y = np.mean(coords[:, 1])
+            centroids[c] = (centroid_x, centroid_y)
+    return centroids
+
+
+def check_cluster_distances(clusters, turbines, min_distance_threshold=1000):
+    """Check if any clusters are too close to each other."""
+    if len(clusters) < 2:
+        return None
+
+    centroids = calculate_cluster_centroids(clusters, turbines)
+    active_clusters = [c for c, members in clusters.items() if len(members) > 0]
+
+    min_distance = float("inf")
+    min_pair = None
+
+    for i in range(len(active_clusters)):
+        for j in range(i + 1, len(active_clusters)):
+            c1, c2 = active_clusters[i], active_clusters[j]
+            centroid1 = np.array(centroids[c1])
+            centroid2 = np.array(centroids[c2])
+            distance = np.linalg.norm(centroid1 - centroid2)
+
+            if distance < min_distance:
+                min_distance = distance
+                min_pair = (c1, c2)
+
+    return min_distance
+
+
+def check_cable_crossings(connections, turbines, substation):
+    """Check if there are cable crossings in the solution."""
+    crossings = []
+
+    def line_intersection(p1, p2, p3, p4):
+        """Check if line segments (p1,p2) and (p3,p4) intersect."""
+        x1, y1 = p1
+        x2, y2 = p2
+        x3, y3 = p3
+        x4, y4 = p4
+
+        denom = (y4 - y3) * (x2 - x1) - (x4 - x3) * (y2 - y1)
+
+        if abs(denom) < 1e-10:
+            return False
+
+        ua = ((x4 - x3) * (y1 - y3) - (y4 - y3) * (x1 - x3)) / denom
+        ub = ((x2 - x1) * (y1 - y3) - (y2 - y1) * (x1 - x3)) / denom
+
+        return 0 <= ua <= 1 and 0 <= ub <= 1
+
+    def get_turbine_coord(connection_part):
+        """Get coordinates from connection part (turbine_# or substation)."""
+        if connection_part == "substation":
+            # Handle different substation formats robustly
+            if isinstance(substation, np.ndarray):
+                if substation.ndim == 1:
+                    # 1D array [x, y]
+                    return (substation[0], substation[1])
+                elif substation.ndim == 2:
+                    # 2D array [[x, y]] or shape (n, 2)
+                    if substation.shape[0] == 1:
+                        return (substation[0, 0], substation[0, 1])
+                    else:
+                        # Multiple points, use first one
+                        return (substation[0, 0], substation[0, 1])
+                else:
+                    # Unexpected dimension, try fallback
+                    return (substation.flat[0], substation.flat[1])
+            elif isinstance(substation, (list, tuple)):
+                # List or tuple format
+                # Handle nested lists like [[x, y]]
+                if (
+                    isinstance(substation[0], (list, tuple, np.ndarray))
+                    and len(substation[0]) >= 2
+                ):
+                    return (substation[0][0], substation[0][1])
+                elif len(substation) >= 2:
+                    return (substation[0], substation[1])
+                else:
+                    return (float("inf"), float("inf"))
+            else:
+                # Unexpected format, try to convert
+                try:
+                    sub_array = np.array(substation)
+                    if sub_array.ndim == 1:
+                        return (sub_array[0], sub_array[1])
+                    else:
+                        return (sub_array.flat[0], sub_array.flat[1])
+                except:
+                    return (float("inf"), float("inf"))
+        else:
+            turbine_idx = int(connection_part.split("_")[1])
+            return (
+                turbines.iloc[turbine_idx]["x"],
+                turbines.iloc[turbine_idx]["y"],
+            )
+
+    for i in range(len(connections)):
+        for j in range(i + 1, len(connections)):
+            conn1 = connections[i]
+            conn2 = connections[j]
+
+            p1 = get_turbine_coord(conn1[0])
+            p2 = get_turbine_coord(conn1[1])
+            p3 = get_turbine_coord(conn2[0])
+            p4 = get_turbine_coord(conn2[1])
+
+            if (
+                np.array_equal(p1, p3)
+                or np.array_equal(p1, p4)
+                or np.array_equal(p2, p3)
+                or np.array_equal(p2, p4)
+            ):
+                continue
+
+            if line_intersection(p1, p2, p3, p4):
+                crossings.append((i, j, p1, p2, p3, p4))
+
+    return crossings

pyproject.toml

@@ -12,6 +12,8 @@ dependencies = [
     "numpy>=2.4.0",
     "openpyxl>=3.1.5",
     "pandas>=2.3.3",
+    "pulp>=3.3.0",
+    "pyomo>=6.9.5",
     "pywebview>=6.1",
     "scikit-learn>=1.8.0",
     "scipy>=1.16.3",

test_cbc_solver.py

@@ -0,0 +1,146 @@
+"""
+Simple test to verify CBC solver functionality
+"""
+
+import pulp
+import sys
+import subprocess
+import os
+
+print("=== PuLP and CBC Solver Test ===")
+print(f"Python version: {sys.version}")
+print(f"PuLP version: {pulp.__version__}")
+
+# Test 1: Check PuLP installation
+print("\n1. Checking PuLP installation...")
+try:
+    from pulp import LpProblem, LpVariable, LpMinimize, LpMaximize, lpSum, value
+
+    print("[OK] PuLP imported successfully")
+except ImportError as e:
+    print(f"[FAIL] PuLP import failed: {e}")
+    sys.exit(1)
+
+# Test 2: Check CBC solver file existence
+print("\n2. Checking CBC solver file...")
+solver_dir = os.path.join(
+    os.path.dirname(pulp.__file__), "apis", "..", "solverdir", "cbc", "win", "i64"
+)
+solver_path = os.path.join(solver_dir, "cbc.exe")
+
+print(f"Looking for CBC at: {solver_path}")
+if os.path.exists(solver_path):
+    print(f"[OK] CBC solver file found")
+    file_size = os.path.getsize(solver_path)
+    print(f"  File size: {file_size:,} bytes ({file_size / 1024 / 1024:.2f} MB)")
+else:
+    print(f"[FAIL] CBC solver file not found")
+    print(f"  Checking directory contents:")
+    try:
+        parent_dir = os.path.dirname(solver_path)
+        if os.path.exists(parent_dir):
+            for item in os.listdir(parent_dir):
+                print(f"    - {item}")
+        else:
+            print(f"  Directory does not exist: {parent_dir}")
+    except Exception as e:
+        print(f"  Error listing directory: {e}")
+
+# Test 3: Try to run CBC solver directly
+print("\n3. Testing CBC solver execution...")
+if os.path.exists(solver_path):
+    try:
+        result = subprocess.run(
+            [solver_path, "-version"],
+            capture_output=True,
+            text=True,
+            timeout=10,
+            check=True,
+        )
+        print("[OK] CBC solver executed successfully")
+        print(f"  Output: {result.stdout[:200]}")
+    except subprocess.CalledProcessError as e:
+        print(f"[FAIL] CBC solver execution failed (exit code {e.returncode})")
+        print(f"  stdout: {e.stdout[:200]}")
+        print(f"  stderr: {e.stderr[:200]}")
+    except subprocess.TimeoutExpired:
+        print("[FAIL] CBC solver execution timed out")
+    except Exception as e:
+        print(f"[FAIL] CBC solver execution error: {e}")
+else:
+    print("[FAIL] Cannot test CBC execution - file not found")
+
+# Test 4: Solve a simple linear programming problem
+print("\n4. Testing simple LP problem...")
+try:
+    # Simple problem: minimize x + y subject to x + y >= 5, x >= 0, y >= 0
+    prob = LpProblem("Simple_LP_Test", LpMinimize)
+
+    x = LpVariable("x", lowBound=0, cat="Continuous")
+    y = LpVariable("y", lowBound=0, cat="Continuous")
+
+    prob += x + y  # Objective: minimize x + y
+    prob += x + y >= 5  # Constraint
+
+    print("  Created simple LP problem: minimize x + y subject to x + y >= 5")
+
+    # Try to solve with CBC
+    solver = pulp.PULP_CBC_CMD(msg=False, timeLimit=10)
+    print("  Attempting to solve with CBC...")
+
+    status = prob.solve(solver)
+
+    print(f"[OK] LP problem solved")
+    print(f"  Status: {pulp.LpStatus[prob.status]}")
+    print(f"  Objective value: {value(prob.objective)}")
+    print(f"  x = {value(x)}, y = {value(y)}")
+
+    if abs(value(prob.objective) - 5.0) < 0.01:
+        print("  [OK] Correct solution found!")
+    else:
+        print(f"  [FAIL] Unexpected solution (expected 5.0)")
+
+except Exception as e:
+    print(f"[FAIL] LP problem solving failed: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+# Test 5: Solve a simple mixed integer programming problem
+print("\n5. Testing simple MIP problem...")
+try:
+    # Simple MIP: minimize x + y subject to x + y >= 5, x, y integers >= 0
+    prob = LpProblem("Simple_MIP_Test", LpMinimize)
+
+    x = LpVariable("x", lowBound=0, cat="Integer")
+    y = LpVariable("y", lowBound=0, cat="Integer")
+
+    prob += x + y  # Objective
+    prob += x + y >= 5  # Constraint
+
+    print(
+        "  Created simple MIP problem: minimize x + y subject to x + y >= 5, x,y integers"
+    )
+
+    solver = pulp.PULP_CBC_CMD(msg=False, timeLimit=10)
+    print("  Attempting to solve with CBC...")
+
+    status = prob.solve(solver)
+
+    print(f"[OK] MIP problem solved")
+    print(f"  Status: {pulp.LpStatus[prob.status]}")
+    print(f"  Objective value: {value(prob.objective)}")
+    print(f"  x = {value(x)}, y = {value(y)}")
+
+    if abs(value(prob.objective) - 5.0) < 0.01:
+        print("  [OK] Correct solution found!")
+    else:
+        print(f"  [FAIL] Unexpected solution (expected 5.0)")
+
+except Exception as e:
+    print(f"[FAIL] MIP problem solving failed: {e}")
+    import traceback
+
+    traceback.print_exc()
+
+print("\n=== Test Complete ===")

test_mip.py

@@ -0,0 +1,50 @@
+"""
+Test script to verify MIP functionality
+"""
+
+import numpy as np
+import pandas as pd
+from mip import design_with_mip
+
+# Create test data
+np.random.seed(42)
+n_turbines = 10
+turbines = pd.DataFrame(
+    {
+        "x": np.random.uniform(0, 2000, n_turbines),
+        "y": np.random.uniform(0, 2000, n_turbines),
+        "power": np.random.uniform(5, 10, n_turbines),
+    }
+)
+
+substation = np.array([1000, 1000])
+
+print("Test data created:")
+print(f"Number of turbines: {n_turbines}")
+print(f"Substation location: {substation}")
+print(f"Total power: {turbines['power'].sum():.2f} MW")
+
+# Test MIP function
+print("\nTesting MIP design...")
+try:
+    connections, turbines_with_clusters = design_with_mip(
+        turbines,
+        substation,
+        cable_specs=None,
+        voltage=66000,
+        power_factor=0.95,
+        system_params=None,
+        max_clusters=None,
+        time_limit=30,
+        evaluate_func=None,
+        total_invest_func=None,
+        get_max_capacity_func=None,
+    )
+    print(f"MIP test successful!")
+    print(f"Number of connections: {len(connections)}")
+    print(f"Clusters assigned: {turbines_with_clusters['cluster'].tolist()}")
+except Exception as e:
+    print(f"MIP test failed with error: {e}")
+    import traceback
+
+    traceback.print_exc()

uv.lock

@@ -1243,6 +1243,15 @@ wheels = [
     { url = "https://mirrors.pku.edu.cn/pypi/web/packages/fc/f5/68334c015eed9b5cff77814258717dec591ded209ab5b6fb70e2ae873d1d/pillow-12.1.0-cp314-cp314t-win_arm64.whl", hash = "sha256:f61333d817698bdcdd0f9d7793e365ac3d2a21c1f1eb02b32ad6aefb8d8ea831" },
 ]
 
+[[package]]
+name = "ply"
+version = "3.11"
+source = { registry = "https://mirrors.pku.edu.cn/pypi/web/simple" }
+sdist = { url = "https://mirrors.pku.edu.cn/pypi/web/packages/e5/69/882ee5c9d017149285cab114ebeab373308ef0f874fcdac9beb90e0ac4da/ply-3.11.tar.gz", hash = "sha256:00c7c1aaa88358b9c765b6d3000c6eec0ba42abca5351b095321aef446081da3" }
+wheels = [
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/a3/58/35da89ee790598a0700ea49b2a66594140f44dec458c07e8e3d4979137fc/ply-3.11-py2.py3-none-any.whl", hash = "sha256:096f9b8350b65ebd2fd1346b12452efe5b9607f7482813ffca50c22722a807ce" },
+]
+
 [[package]]
 name = "propcache"
 version = "0.4.1"
@@ -1333,6 +1342,15 @@ version = "0.1.0"
 source = { registry = "https://mirrors.pku.edu.cn/pypi/web/simple" }
 sdist = { url = "https://mirrors.pku.edu.cn/pypi/web/packages/f2/cf/77d3e19b7fabd03895caca7857ef51e4c409e0ca6b37ee6e9f7daa50b642/proxy_tools-0.1.0.tar.gz", hash = "sha256:ccb3751f529c047e2d8a58440d86b205303cf0fe8146f784d1cbcd94f0a28010" }
 
+[[package]]
+name = "pulp"
+version = "3.3.0"
+source = { registry = "https://mirrors.pku.edu.cn/pypi/web/simple" }
+sdist = { url = "https://mirrors.pku.edu.cn/pypi/web/packages/16/1c/d880b739b841a8aa81143091c9bdda5e72e226a660aa13178cb312d4b27f/pulp-3.3.0.tar.gz", hash = "sha256:7eb99b9ce7beeb8bbb7ea9d1c919f02f003ab7867e0d1e322f2f2c26dd31c8ba" }
+wheels = [
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/99/6c/64cafaceea3f99927e84b38a362ec6a8f24f33061c90bda77dfe1cd4c3c6/pulp-3.3.0-py3-none-any.whl", hash = "sha256:dd6ad2d63f196d1254eddf9dcff5cd224912c1f046120cb7c143c5b0eda63fae" },
+]
+
 [[package]]
 name = "pycparser"
 version = "2.23"
@@ -1571,6 +1589,34 @@ wheels = [
     { url = "https://mirrors.pku.edu.cn/pypi/web/packages/2d/86/637cda4983dc0936b73a385f3906256953ac434537b812814cb0b6d231a2/pyobjc_framework_webkit-12.1-cp314-cp314t-macosx_10_15_universal2.whl", hash = "sha256:1aaa3bf12c7b68e1a36c0b294d2728e06f2cc220775e6dc4541d5046290e4dc8" },
 ]
 
+[[package]]
+name = "pyomo"
+version = "6.9.5"
+source = { registry = "https://mirrors.pku.edu.cn/pypi/web/simple" }
+dependencies = [
+    { name = "ply" },
+]
+sdist = { url = "https://mirrors.pku.edu.cn/pypi/web/packages/87/d8/f32e0dcacc8219694709200d4402c86a6e28d3af50380a5ccf7f7e15ffae/pyomo-6.9.5.tar.gz", hash = "sha256:0734020fcd5cc03ee200fd3f79d143fbfc14e6be116e0d16bab79f3f89609879" }
+wheels = [
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/f8/63/5f163b231a924ba7a5f6c58466c751f70be88568fa446524b6e806c98e4b/pyomo-6.9.5-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:549ee4226cab6e2ff6efe5b3b9891ce1dfd866d38a024715315ea850fa1bf0ec" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/2d/bf/0cebcfce70be04d6d7aa19fbcbdeecdd5843caac617424f34ab3feb8e96e/pyomo-6.9.5-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:b382cc8c3728199c8332024d64eed8622dabb3f8aebe5874c86a036489064f7a" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/14/27/967545514a2d0f4ca5ac6b595661cb0927cdcd10c3bb2832c5aa0ee15990/pyomo-6.9.5-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:43c6e425ca5231b530cd23460e371b7ca9119224dd57237c34580e15f31e4d72" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/85/fe/691e5eb26f58ee4a072add6cc484756d9e3c367901ec6701d2c6789b394d/pyomo-6.9.5-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a1923c358e1e8009a05ada911fc72e615c9e2ce6988f0979ec1ecc75880ee1f7" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/c7/b3/ae47340790f2f1f92f76b176acf475890717f0cb7def073e504b9857a057/pyomo-6.9.5-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:694262dc2eb53ca1ab245261f432a5ed1ec30cf3e651b5a6a1c276bc2dd81076" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/29/e9/7f782864afd28a9eb53057c9d046541be6535b2da35e11c2bcb80839c6bd/pyomo-6.9.5-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:1f99ce91f2710d60b380a3a519288282d2183c44e1d66c131909313a3b63e7a2" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/cd/4d/9ca17a602e31a1c3f3148c455a5739fcbe23c102b80a12ec3e6d3bf5e847/pyomo-6.9.5-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:d22f99e0ba8e2fb7d0e806bf630b8ce9b0a41d777c51f22711adbcb905f7486e" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/44/2e/78c3ac876791b59c836338b73dc49317b01cef574b01af061999a04a064a/pyomo-6.9.5-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:d5953e490b9e9ea42d28804dd0358a9d3ef82560022c2b538e70a638790bc392" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/3c/27/3eb3db8e9ed6a01dee63219389aec761d5cc29b6dc5015b32f826f2a9225/pyomo-6.9.5-cp314-cp314-macosx_10_15_x86_64.whl", hash = "sha256:058eddde05b4354307975f1ecd25cfda9f8a282ad2e3b4f168ff8fee3c3623a1" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/9a/31/7f4750fc9bb0ec18a9534549e4c80ea63f1267aa828d495a48bbf0018f49/pyomo-6.9.5-cp314-cp314-macosx_11_0_arm64.whl", hash = "sha256:105a073c47a2d2d6e74e48ed6fc82c6f6d19027488d5003aabb7ed5d10271483" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/f8/67/639d0006eddab30cf415b0154763ccc51f3c15b934e866eb4fb07bc2b6ed/pyomo-6.9.5-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f2c636c2c640b33dde3b119f6f0941a1bbde39397c392dba55351b0438d8600f" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/67/ef/023b74b8f161f15a51febdd160354f1e3fd7e1475abbe5ccfb3d7588cf1f/pyomo-6.9.5-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:e02813b4021eeed7214a1ca5d7daecbdc78d3db7059962553a57fd138d747c22" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/a0/ca/edab1b532fd5e2d146d0cb96836eb5ae387b8a5bd255213e306793f6168e/pyomo-6.9.5-cp314-cp314t-macosx_10_15_x86_64.whl", hash = "sha256:83789ce89271da31e0ff5bbef692af1621ab1747798183a5603b6577b7074277" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/a9/3c/2745386f57030bc60b626adba002b68db3f9538d5b52900f48026a4a17d7/pyomo-6.9.5-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:1f449aaceac5078daaecc21d19b96a15529f9ac8aa90f6472e8811cc07112ecc" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/f1/93/2058af0890b13f7e1a26e4925ff8d681c23d9cbdc2ecc9db17c744941617/pyomo-6.9.5-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:f94d03f122fcf04a769c28ad48c423cd7b6d3d2c40da20bc8ea1a41bb20d0c36" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/de/30/c808931fc034851a16d3f8360d045b087ac743ea97bfe96cdb4b1df47c21/pyomo-6.9.5-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:96ff300e96cdab75e2e6983c99e3a61eaff2a6d0f5ed83acd939e74e361de537" },
+    { url = "https://mirrors.pku.edu.cn/pypi/web/packages/68/29/394967f7df51788cbdf1b4aedfb7c5a3a62e11b85b4c9d806b86cc576be4/pyomo-6.9.5-py3-none-any.whl", hash = "sha256:60326f7d3143ee7d0f5c5c4a3cbf871b53e08cc6c2b0c9e6d25568880233472f" },
+]
+
 [[package]]
 name = "pyparsing"
 version = "3.3.1"
@@ -2106,6 +2152,8 @@ dependencies = [
     { name = "numpy" },
     { name = "openpyxl" },
     { name = "pandas" },
+    { name = "pulp" },
+    { name = "pyomo" },
     { name = "pywebview" },
     { name = "scikit-learn" },
     { name = "scipy" },
@@ -2125,6 +2173,8 @@ requires-dist = [
     { name = "numpy", specifier = ">=2.4.0" },
     { name = "openpyxl", specifier = ">=3.1.5" },
     { name = "pandas", specifier = ">=2.3.3" },
+    { name = "pulp", specifier = ">=3.3.0" },
+    { name = "pyomo", specifier = ">=6.9.5" },
     { name = "pywebview", specifier = ">=6.1" },
     { name = "scikit-learn", specifier = ">=1.8.0" },
     { name = "scipy", specifier = ">=1.16.3" },