Pyomo dae 具有目标函数的离散输入集

Question

我是 Python 和 Pyomo 的新手，包括用于根据简单微分方程确定电容器大小的代码。参考是一个离散数组，经过插值后可用于问题公式化。为了验证我的结果，我使用了这一行，但它给出了错误。

Pref = [pyo.value(m.intPload[i]) for i in m.t]

Error：ValueError：评估参数值时出错（intPload [0.03569]）： Param 值当前设置为无效值。这是 通常来自标量参数或可变索引参数，无需 初始值或默认值。

**完整代码：

import numpy as np
from scipy import integrate
import math
import matplotlib.pyplot as plt
import pyomo.environ as pyo
from pyomo.dae import ContinuousSet, DerivativeVar
model = m = pyo.ConcreteModel()


# power and time data
t1 = np.array([0, 1, 2, 3, 4, 5]) #s
P1 = np.array([0, 10, 20, 30, 40, 0]) # W
# resample the data
dt      = 1 # delta time, resampled every one second
tFinal  = math.floor(t1[t1.size-1])
numSims = math.ceil(tFinal/dt)
tsecs   = np.linspace(0, tFinal, numSims)
Pload   = np.interp(tsecs, t1, P1)
# Capacitor rating
Cc      = 1
Rsc     = 2.4e-3 # capacitor internal resistance
VcMax   = 3 # maximum capacitor cell voltage
IcMax   = 50

# Pyomo problem formulation
m.t     =  ContinuousSet(initialize=tsecs)
m.Vc    = pyo.Var(m.t, bounds =(0.5*VcMax, VcMax))
m.VcT   = pyo.Var(m.t, bounds =(0.5*VcMax, VcMax))
m.Ic    = pyo.Var(m.t, bounds=(-IcMax, IcMax))
m.N_sc  = pyo.Var(bounds=(0, 100))
m.N_pc  = pyo.Var(bounds=(0, 100))
m.dVcdt = DerivativeVar(m.Vc, wrt=m.t)
m.intPload = pyo.Param(m.t, mutable=True)

timepoints  = list(m.t)
Pload_Int   = np.interp(timepoints, t1, P1)
for i,t in enumerate(timepoints):
     m.intPload[t] = Pload_Int[i]

m.obj = pyo.Objective(expr=(m.N_sc+m.N_pc)**2 + sum( (m.VcT[i]*m.N_sc*m.Ic[i]*m.N_pc - m.intPload[i])**2 for i in m.t), sense=pyo.minimize)

def _Vcdot(m, t):
    return m.dVcdt[t] == -m.Ic[t]/Cc
m.Vcdot = pyo.Constraint(m.t,rule=_Vcdot)

def _VcTR(m, t):
    return m.VcT[t] == m.Vc[t] -m.Ic[t]*Rsc
m.VcTR = pyo.Constraint(m.t,rule=_VcTR)

def _init_conditions(m):
    yield m.Vc[0] == VcMax
    #yield m.VcT[0] == VcMax
m.init_conditions = pyo.ConstraintList(rule=_init_conditions)

# Discretize model using Orthogonal Collocation
discretizer = pyo.TransformationFactory('dae.collocation')
discretizer.apply_to(model, nfe=8, ncp=5)


solver=pyo.SolverFactory('ipopt')
results = solver.solve(model,tee=True)


x1 = [pyo.value(m.Ic[i]) for i in m.t]
Pl = [pyo.value(m.Ic[i]*m.VcT[i]*m.N_sc*m.N_pc) for i in m.t]
Pref = [pyo.value(m.intPload[i]) for i in m.t]

#Pl2 = [pyo.value(m.intPload[i]) for i in m.t]


diffPl = [pyo.value(m.Ic[i]*m.VcT[i]*m.N_sc*m.N_pc - m.intPload[i]) for i in m.t]


plt.plot(x1)
plt.show( )

错误信息： ValueError：评估参数值时出错（intPload [0.03569]）： Param 值当前设置为无效值。这是 通常来自标量参数或可变索引参数，无需 初始值或默认值。

Answer 1

您需要将

intPload

的插值线移动到调用离散化变换之后。离散化变换正在向

m.t

添加插值代码缺失的时间点。