347 lines
11 KiB
Python
347 lines
11 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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pulp_available = True
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except ImportError:
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pulp = None
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pulp_available = False
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try:
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import pyomo.environ as pyo_env
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pyomo_available = True
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except (ImportError, AttributeError):
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pyomo_available = False
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print("Pyomo not available, falling back to PuLP")
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def design_with_pyomo(
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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,
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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Pyomo求解器优化集电线路布局
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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 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
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total_power = turbines["power"].sum()
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if max_clusters is None:
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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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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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# Simple fallback for now - use PuLP instead
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print("Pyomo not fully implemented, falling back to PuLP")
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return design_with_mip(
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turbines,
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substation,
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cable_specs,
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voltage,
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power_factor,
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system_params,
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max_clusters,
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time_limit,
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evaluate_func,
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total_invest_func,
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get_max_capacity_func,
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)
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y = pyo_env.Var(
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max_clusters_list,
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within=pyo_env.BinarySet,
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initialize=False,
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name="use_cluster",
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)
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prob.constraints += pyo_env.SummizationRule(
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pyo_env.Summ(x[i, k] for i in n_turbines_list for k in max_clusters_list) == 1,
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name="assign_one_turbine",
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)
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cluster_powers = [
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pyo_env.Summ([turbines.iloc[i]["power"] * x[i, k] for i in n_turbines_list])
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for k in max_clusters_list
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]
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prob += pyo_env.SummizationRule(
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pyo_env.Summ(y[k] * max_mw for k in max_clusters_list), name="cluster_capacity"
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)
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for k in max_clusters_list:
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prob += pyo_env.SummizationRule(
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pyo_env.Summ(x[i, k] * turbines.iloc[i]["power"] for i in n_turbines_list),
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name=f"turbine_assign_{k}",
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)
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for k in max_clusters_list:
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prob += y[k] <= pyo_env.summation(x[i, k] for i in n_turbines_list)
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prob += pyo_env.Minimize(pyo_env.Summ([y[k] for k in max_clusters_list]))
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print(
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f"Pyomo Model: {len(prob.variables)} variables, {len(prob.constraints)} constraints"
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)
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solver = pyo_env.SolverFactory("cbc")
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print("Pyomo: Starting to solve...")
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result = solver.solve(prob, time_limit=time_limit)
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print(
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f"Pyomo: Solver status={result.solver.status}, Termination condition={result.solver.termination_condition}"
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)
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if result.solver.status == pyo_env.SolverStatus.ok:
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cluster_assign = [-1] * n_turbines
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active_clusters = []
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for k in range(max_clusters):
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if pyo_env.value(y[k]) > 0.5:
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active_clusters.append(k)
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for i in n_turbines_list:
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assigned = False
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for k in active_clusters:
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if pyo_env.value(x[i, k]) > 0.5:
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cluster_assign[i] = k
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assigned = True
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break
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if not assigned and active_clusters:
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dists = [dist_matrix_full[0, i + 1] for k in active_clusters]
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cluster_assign[i] = active_clusters[np.argmin(dists)]
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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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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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print(
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f"Pyomo optimization completed successfully, {len(connections)} connections generated"
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)
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return connections, turbines
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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,
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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 not pulp_available:
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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
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if max_clusters is None:
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max_clusters = int(np.ceil(turbines["power"].sum() / max_mw))
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n_turbines = len(turbines)
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print(
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f"MIP Model Setup: n_turbines={n_turbines}, max_clusters={max_clusters}, max_mw={max_mw:.2f} MW"
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)
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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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prob = pulp.LpProblem("WindFarmCollectorMIP", pulp.LpMinimize)
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def assign_var(i, k):
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return pulp.LpVariable(f"assign_{i}_{k}", cat="Binary")
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def cluster_var(k):
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return pulp.LpVariable(f"cluster_{k}", cat="Binary")
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# 目标函数：最小化总投资（近似为风机到升压站的总距离）
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prob += pulp.lpSum(
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[
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dist_matrix_full[0, i + 1] * assign_var(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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for i in range(n_turbines):
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prob += pulp.lpSum([assign_var(i, k) for k in range(max_clusters)]) == 1
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for k in range(max_clusters):
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cluster_power = pulp.lpSum(
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[turbines.iloc[i]["power"] * assign_var(i, k) for i in range(n_turbines)]
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)
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prob += cluster_power <= max_mw * 1.2 * cluster_var(k)
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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 += assign_var(i, k) <= cluster_var(k)
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print(
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f"MIP Model: {len(prob.variables())} variables, {len(prob.constraints)} constraints"
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)
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print("MIP: Starting to solve...")
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solver = pulp.PULP_CBC_CMD(timeLimit=time_limit, msg=0, warmStart=False, path=None)
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try:
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status = prob.solve(solver)
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print(
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f"MIP: Solver status={pulp.LpStatus[prob.status]}, Objective value={pulp.value(prob.objective):.4f}"
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)
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except Exception as e:
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print(f"MIP: Solver execution failed: {e}, falling back to 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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if pulp.LpStatus[prob.status] != "Optimal":
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print(
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f"MIP solver status: {pulp.LpStatus[prob.status]}, solution not found, falling back to MST"
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)
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print("Model feasibility check:")
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print(f"Total power: {turbines['power'].sum():.2f} MW")
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print(f"Max cluster capacity: {max_mw:.2f} MW")
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print(f"Number of clusters: {max_clusters}, Number of turbines: {n_turbines}")
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for k in range(max_clusters):
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cluster_power = pulp.value(
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pulp.lpSum(
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[
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turbines.iloc[i]["power"] * assign_var(i, k)
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for i in range(n_turbines)
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]
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)
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)
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cluster_used = pulp.value(cluster_var(k))
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print(
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f"Cluster {k}: Power={cluster_power:.2f} MW (max {max_mw * 1.2:.2f}), Used={cluster_used}"
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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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cluster_assign = [-1] * n_turbines
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active_clusters = []
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for k in range(max_clusters):
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if pulp.value(cluster_var(k)) > 0.5:
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active_clusters.append(k)
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for i in range(n_turbines):
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assigned = False
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for k in active_clusters:
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if pulp.value(assign_var(i, k)) > 0.5:
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cluster_assign[i] = k
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assigned = True
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break
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if not assigned:
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dists = [dist_matrix_full[0, i + 1] for k in active_clusters]
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cluster_assign[i] = active_clusters[np.argmin(dists)]
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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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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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print(
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f"MIP optimization completed successfully, {len(connections)} connections generated"
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)
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return connections, turbines
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