Pyomo 中用于解决 MILP 的非固定边界错误

Question

我是 Pyomo 的新手。我正在尝试解决以下 MILP 问题：

我尝试使用以下脚本：

import numpy as np
from pyomo.environ import *
from pyomo.gdp import *
import pyomo.environ as aml

OMEGA = ['Bus 01','Bus 06','Bus 32']

K = ['G 03','Line 15-16']

MARGINS = {
         'G 03' : 0.28,
         'Line 15-16': 0.30,
         }

BMAX = {
        'Bus 01':3,
        'Bus 06':3,
        'Bus 32':3,
        }

BMIN = {
        'Bus 01':0.001,
        'Bus 06':0.001,
        'Bus 32':0.001,
        }

SENSITIVITIES = {
    ('Bus 01','G 03') : {'S': 0.001},
    ('Bus 06','G 03') : {'S': 0.016},
    ('Bus 32','G 03') : {'S': 0.008},
    ('Bus 01','Line 15-16') : {'S': 0.004},
    ('Bus 06','Line 15-16') : {'S': 0.010},
    ('Bus 32','Line 15-16') : {'S': 0.015},
    }
Cv = 0.41
Cf = 1.3
Mr = 0.35

model = ConcreteModel()

model.Omega = Set(initialize = (i for i in OMEGA))
model.K = Set(initialize = (i for i in K))

model.M = Param(model.K,initialize = MARGINS)

model.b = Var(model.Omega, within = NonNegativeReals)
model.q = Var(model.Omega, within = Binary)
model.b_k = Var(model.Omega,model.K, within = NonNegativeReals)

model.Bmax = Param(model.Omega, initialize=BMAX)
model.Bmin = Param(model.Omega, initialize=BMIN)

def obj_rule(model):
    return sum(Cv*model.b[i] + Cf*model.q[i] for i in model.Omega)
model.obj = Objective(rule = obj_rule, sense = minimize)

def margin_rule(model,k):
    value = sum(SENSITIVITIES[(i,k)]['S']*model.b_k[i,k] for i in model.Omega) + model.M[k]
    return value >= Mr
model.margin = Constraint(model.K,rule=margin_rule)

def minmargin_rule(model,i,k):
    return aml.inequality(model.Bmin[i],model.b_k[i,k],model.b[i])
model.minmargin = Constraint(model.Omega,model.K, rule=minmargin_rule)

def powerlimits_rule(model,i):
    return  aml.inequality(model.Bmin[i]*model.q[i],model.b[i],model.Bmax[i]*model.q[i])
model.powerlimits = Constraint(model.Omega,rule=powerlimits_rule)

results = SolverFactory('glpk').solve(model)
results.write()

但是 returns "ValueError: non-fixed bound or weight: b[Bus 01]" 用于 "minmargin" 约束，"ValueError: No value for uninitialized NumericValue object q[Bus 01]" 用于 "powerlimits" 约束。如果能提供一些帮助或建议来解决这些问题，我将不胜感激。

Answer 1

出于某种原因，pyomo 似乎不喜欢具有多个变量引用的链式不等式约束。我只是做了一些修补。

这将在解决时失败，就像您的模型一样：

from pyomo.environ import *

m = ConcreteModel()

m.A = Set(initialize = [1,2,3])

m.X = Var(m.A, domain=NonNegativeReals)
m.Y = Var(m.A, domain=NonNegativeReals)

def x_sandwich(m, a):
    return inequality(5, m.X[a], m.Y[a])
m.c2 = Constraint(m.A, rule=x_sandwich)

然而，这工作得很好：

def x_sandwich(m, a):
    return inequality(5, m.X[a], 10)
m.c2 = Constraint(m.A, rule=x_sandwich)

也许了解链式不等式函数的内幕的人可以发表评论。我在快速搜索中没有找到任何会说这些都是酸的东西。

我能够通过将链式不等式分解成独立的约束来让你的模型达到 process/solve（你可能需要对你的 powerlimits 约束做同样的事情，我已经注释掉了）：

def minmargin_rule_lower(model, i, k):
    return model.Bmin[i] <= model.b_k[i, k]
model.mm_l = Constraint(model.Omega, model.K, rule=minmargin_rule_lower)

def minmargin_rule_upper(model, i, k):
    return model.b_k[i, k] <= model.b[i]
model.mm_u = Constraint(model.Omega, model.K, rule=minmargin_rule_upper)

Pyomo 中用于解决 MILP 的非固定边界错误

Non-fixed bound Error in Pyomo to solve MILP

python

pyomo