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Change MIP objective function to minimize total investment

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This commit is contained in:
dmy
2026-01-08 13:01:36 +08:00
parent 09b2ada5df
commit 41ac6f3963
1 changed files with 179 additions and 6 deletions
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177
mip.py
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@@ -7,8 +7,167 @@ 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,
)
y = pyo_env.Var(
max_clusters_list,
within=pyo_env.BinarySet,
initialize=False,
name="use_cluster",
)
prob.constraints += pyo_env.SummizationRule(
pyo_env.Summ(x[i, k] for i in n_turbines_list for k in max_clusters_list) == 1,
name="assign_one_turbine",
)
cluster_powers = [
pyo_env.Summ([turbines.iloc[i]["power"] * x[i, k] for i in n_turbines_list])
for k in max_clusters_list
]
prob += pyo_env.SummizationRule(
pyo_env.Summ(y[k] * max_mw for k in max_clusters_list), name="cluster_capacity"
)
for k in max_clusters_list:
prob += pyo_env.SummizationRule(
pyo_env.Summ(x[i, k] * turbines.iloc[i]["power"] for i in n_turbines_list),
name=f"turbine_assign_{k}",
)
for k in max_clusters_list:
prob += y[k] <= pyo_env.summation(x[i, k] for i in n_turbines_list)
prob += pyo_env.Minimize(pyo_env.Summ([y[k] for k in max_clusters_list]))
print(
f"Pyomo Model: {len(prob.variables)} variables, {len(prob.constraints)} constraints"
)
solver = pyo_env.SolverFactory("cbc")
print("Pyomo: Starting to solve...")
result = solver.solve(prob, time_limit=time_limit)
print(
f"Pyomo: Solver status={result.solver.status}, Termination condition={result.solver.termination_condition}"
)
if result.solver.status == pyo_env.SolverStatus.ok:
cluster_assign = [-1] * n_turbines
active_clusters = []
for k in range(max_clusters):
if pyo_env.value(y[k]) > 0.5:
active_clusters.append(k)
for i in n_turbines_list:
assigned = False
for k in active_clusters:
if pyo_env.value(x[i, k]) > 0.5:
cluster_assign[i] = k
assigned = True
break
if not assigned and active_clusters:
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
print(
f"Pyomo optimization completed successfully, {len(connections)} connections generated"
)
return connections, turbines
def design_with_mip(
@@ -37,7 +196,7 @@ def design_with_mip(
:param get_max_capacity_func: 获取最大容量函数
:return: 连接列表和带有簇信息的turbines
"""
if pulp is None:
if not pulp_available:
print(
"WARNING: PuLP library not available. MIP optimization skipped, falling back to MST."
)
@@ -69,7 +228,14 @@ def design_with_mip(
def cluster_var(k):
return pulp.LpVariable(f"cluster_{k}", cat="Binary")
prob += pulp.lpSum([cluster_var(k) for k in range(max_clusters)])
# 目标函数：最小化总投资（近似为风机到升压站的总距离）
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
@@ -90,10 +256,17 @@ def design_with_mip(
print("MIP: Starting to solve...")
solver = pulp.PULP_CBC_CMD(timeLimit=time_limit, msg=0, warmStart=False, path=None)
try:
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: Solver execution failed: {e}, 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(