Pyomo:Pyomo 约束中的 for 循环

Question

我正在努力在Python Pyomo中使用for循环进行约束。我的代码概念如下：

model.Q = Set()
model.sbase=Param(model.Q, within = PositiveReals)

我的代码可以使用一个 Sbase 正常运行。但是如果我得到 3 个不同数量的 Sbase。我想做的是使用这 3 个数字输出 3 个不同的图像。

set Q := 1 2 3;
param sbase:=
1 800
2 1000 
3 1200;

到目前为止，我所做的是：(真的不知道该怎么做..(

from pyomo.environ import *
import matplotlib.pyplot as plt
import numpy as np
import matplotlib.pyplot as plt
import random
# create a model
model = AbstractModel()
# declare decision variables
model.Q = Set()
model.J = Set()
model.A = Param(model.J, within = PositiveReals)
model.B = Param(model.J, within = PositiveReals)
model.C = Param(model.J, within = PositiveReals)
model.P_min = Param(model.J, within = PositiveReals)
model.P_max= Param(model.J, within = PositiveReals)
model.P = Var(model.J, within = NonNegativeReals)
model.sbase=Param(model.Q, within = PositiveReals)
# declare objective
def obj_expression(model):
return sum(model.A[j] * model.P[j]**2 + model.B[j] * model.P[j] + model.C[j] for j in model.J)
model.profit = Objective(rule = obj_expression, sense=minimize)
# declare constraints
def lower_bound(model,j):
return model.P_min[j] <= model.P[j]
model.laborA = Constraint(model.J, rule = lower_bound)
def upper_bound(model, j):
return model.P[j] <= model.P_max[j]
model.laborB = Constraint(model.J, rule = upper_bound)
for q in range(2):    #from this line ，really confused about this
def sum_labor(model,i,q):???
if q==1:??
return sum(model.P[j] for j in model.J) >= (model.sbase[Q] for q in model.Q)??
elif q==2:??
reture sum(model.P[j] for j in model.J) > = (model.sbase[Q] for q in model.Q)

model.laborC = Constraint(model.sbase,rule = sum_labor)
instance = model.create_instance("E:pycharm_projectPDpd.dat")
opt = SolverFactory('Ipopt')
results = opt.solve(instance)

plt.figure(figsize=(8,6))
plt.subplot(121)
for i in instance.J:
plt.bar(i,value(instance.P[i]))
plt.xlabel('Generators')
plt.ylabel('Power')
plt.title('Power distribution') 
plt.subplot(122)
x=[0.1]
plt.bar(x,value(instance.profit),alpha=0.7,width=0.015)
plt.xlim(0,0.2)
plt.xlabel('')
plt.ylabel('Cost')
plt.title('minimum cost') 
plt.show()
print('nn---------------------------')
print('Cost: ',value(instance.profit))

#DATA 
set Q := 1 2 3;
set J := g1 g2 g3 g4 g5 g6 g7 g8 g9 g10  ;
param : A B C P_min P_max:=
g1   0.0148 12.1  82  80  200
g2   0.0289 12.6  49  120 320
g3   0.0135 13.2  100 50  150
g4   0.0127 13.9  105 250 520
g5   0.0261 13.5  72  80   280
g6   0.0212 15.4  29  50   150
g7   0.0382 14.0  32  30   120
g8   0.0393 13.5  40  30   110
g9   0.0396 15.0  25  20   80
g10  0.0510 14.3  15  20   60
;
param sbase:=
1 800
2 1000 
3 1200;

你对如何做到这一点有什么建议吗？

谢谢！

Answer 1

这是一个玩具示例，我认为它回答了您的问题。 您可以使用这样的东西，或者仍然从数据中读取 sbase 变量，然后只需索引它即可在循环中以类似的方式设置值。

from pyomo.environ import *
m = ConcreteModel()
m.I = Set(initialize=[1,])      # mixed type set for demo only.
m.sbase = Param(mutable=True)   # for the purposes of each instantiation, sbase is FIXED, so no index reqd.
m.x = Var(m.I, domain=NonNegativeReals )
## constraint
m.C1 = Constraint(expr=sum(m.sbase*m.x[i] for i in m.I) <= 10 )
## objective
m.OBJ = Objective(expr=sum(m.sbase*m.x[i] for i in m.I), sense=maximize)
solver = SolverFactory('cbc')
sbase_values = {1, 2, 5}
for sbase in sbase_values:
m.sbase = sbase
results = solver.solve(m)
print(f'n ***** solved for sbase = {sbase} *****')
m.display()