Question

请我是Pyomo的新手。我试图运行以下代码：

from pyutilib.misc import import_file
from pyomo.environ import *


model = ConcreteModel() 
model.name = "Transmission Investment planning problem_"

#Sets
#Epoch
model.E = RangeSet(0,3) #No. of Epochs

#System nodes
model.N = ['N1', 'N2', 'N3'] #Names of Bus nodes
#model.n_name= Param(model.N)

model.G = ['G1', 'G2', 'G3'] #Names of generators
model.LI = ['L1', 'L2', 'L3'] #Nnames of Transmission lines

#Scalar Parameters
model.int_rate = 0.05 #interest rate 
model.vll = 3000 #value of loss load(£/MWh)
model.tau_period = 8760 #Time duration of demand period (hours) 
model.base = 100 #MVA base
model.ref = {'N3'} #reference node
model.vadegree = 0 #phase angle of reference node

#Discount factors
L = 5
Y= len(model.E)*L
irate = range(0,(Y-1))
def disc_factor(i):
    disc = [1/((1 + model.int_rate)**i) for i in irate]
    return disc
model.cum_disc_inv_cost = [sum(disc_factor(i)[(i*L):]) for i in model.E] #investment discount factor
model.cum_disc_op_cost = [sum(disc_factor(i)[(i*L):((i+1)*L)]) for i in model.E] #operation discount factor

#Demand Periods
model.t_demand = {'N1': 105, 'N2': 210, 'N3': 735} #demand at nodes (MW)
model.demand_curtailed = Var(model.E, model.N, initialize = 0) #curtailed demand (MW)

#Generation Units  

model.ge_max = {'G1': 200, 'G2': 200, 'G3': 1000} #maximum stable power generation(MW)

model.ge_marginal_cost = {'G1': 30, 'G2': 35, 'G3': 40} #marginal cost of generation units (£/MWh)

#Bus to generation incidence matrix
model.B = {('N1','G1'): 1, ('N1','G2'): 0, ('N1','G3'): 0, ('N2','G1'): 0, ('N2','G2'): 1, ('N2','G3'): 0, ('N3','G1'): 0, ('N3','G2'): 0, ('N3','G3'): 1,}

#Transmission lines
model.li_x = {'L1': 0.2, 'L2': 0.2, 'L3': 0.2} #reactance of transmission line(p.u)
model.li_max_f = 150 #maximum capaciy provided for line expansion (MW)
model.li_f = {'L1': 100, 'L2': 100, 'L3': 100} #initial capacity for line l (MW)
model.li_sending_bus = {'L1': 'N1', 'L2': 'N1', 'L3': 'N2'} #sending bus for line l
model.li_receiving_bus = {'L1': 'N2', 'L2': 'N3', 'L3': 'N3'} #receiving bus for line l
model.li_length = {'L1': 100, 'L2': 100, 'L3': 100} #length of line l (km)

#Expansion Options
model.inv_cost_var = 4000000 #Annuitized variable investment cost for line l (£/MW.km.yr)

#Bus to line incidence matrix
model.I = {('N1','L1'): 1, ('N1','L2'): 1, ('N1','L3'): 0, ('N2','L1'): -1, ('N2','L2'): 0, ('N2','L3'): 1, ('N3','L1'): 0, ('N3','L2'): -1, ('N3','L3'): -1,}

#Variables

#Transmission line power flow limits
def fl_inv(model, i, l):
    return (0, model.li_max_f)
model.li_f_inv = Var(model.E, model.LI, bounds = fl_inv) #transmission capacity to be built for line l (MW)

#Transmission line investment and operation contraints
model.f = Var(model.LI, model.E, initialize=0)
def fl_rule(model, l, j, i):
    if i:
        return model.f[l,j] >= -(model.li_f_inv[j,l] + model.li_f[l])
    else:
        return model.f[l,j] <= (model.li_f_inv[j,l] + model.li_f[l])
model.bound_f = Constraint(model.LI, model.E, [0,1], rule=fl_rule)

##generation limit
def fg(model, i, g):
    return (0, model.ge_max[g])
model.ge_output = Var(model.E, model.G, initialize = 0, bounds = fg)

#phase angles for the nodes
def theta(model, e, n):
    for n in model.N:
        if n == model.ref:
            model.theta[e, n].fixed = True
            return model.vadegree
        else: return 0
model.theta = Var(model.E, model.N, initialize = theta)

def line_equation(model, l, e):
    return model.f[l, e] == model.base/model.li_x[l] *(sum(model.theta[e, n] for n in model.N if n == model.li_sending_bus[l]) - sum(model.theta[e, n] for n in model.N if n == model.li_receiving_bus[l]))
model.line_equation = Constraint(model.LI, model.E, rule = line_equation)

def system_balance(model, e, n):
    return sum(model.B[n, g] * model.ge_output[e, g] for g in model.G) \
           + sum(model.I[n, l] * model.f[l, e] for l in model.LI) \
           - model.t_demand[n] + model.demand_curtailed[e, n] == 0
model.SystemBalance = Constraint(model.E, model.N, rule=system_balance)

#OBJECTIVE FUNCTION
def objective_mincost(model):
    for i in model.E:
        return sum(model.cum_disc_inv_cost[i] * sum(model.li_f_inv[i, l] * model.inv_cost_var * model.li_length[l] for l in model.LI)  + model.cum_disc_op_cost[i] * (model.tau_period * (sum(model.ge_max[g] * (model.ge_marginal_cost[g]) for g in model.G) + sum(model.demand_curtailed[i, n] for n in model.N * model.vll)))) 
model.objective = Objective(rule = objective_mincost, sense = minimize) 

opt = SolverFactory('gurobi')
results = opt.solve(model) # solves and updates instance
model.display()

我收到了以下错误代码：

错误：为客观目标生成表达式时规则失败： TypeError：'_ ShowExpression'对象不可迭代错误：从data = None构造组件'objective'失败： TypeError：'_ ShowExpression'对象不可迭代

请问有关问题可能出在何处以及可能的解决方案？

谢谢。

Answer 1

你有两个地方，其中model.E乘以一个不是一个集合的东西。你可能想要以不同的方式为你的总和括起来（即，这主要是你的总和如何组织的问题）

TypeError：'_ ShowExpression'对象不可迭代

1 个答案: