我正在使用 GEKKO 解决 Python 中的 MINLP 问题，该问题涉及与电力系统仿真软件 PowerFactory 的联合仿真。MINLP 问题目标函数需要从内部调用 PowerFactory 并在将值返回给 MILP 问题定义之前执行特定任务。在等式约束定义中，我还需要使用两个 Pandas 数据框（具有 10000x2 单元格的表）来获取与约束表达式中决策变量的值相对应的特定值。但是，由于这些函数调用中 GEKKO 变量的性质存在一些问题，我在目标/约束函数中编写任何涉及循环或 Pandas 'loc' 或 'iloc' 函数的代码时遇到错误。在这方面的任何帮助将不胜感激。代码结构如下：
import powerfactory as pf
from gekko import GEKKO
import pandas as pd
# Execute the PF setup commands for Python
# Pandas dataframe in question
df1 = pd.read_csv("table1.csv")
df2 = pd.read_csv("table2.csv")
def constraint1(X):
P = 0
for i in range(length(X)):
V = X[i] * 1000
D = round(V, 1)
P += df1.loc[D, "ColumnName"]
return P
def objective(X):
for i in range(length(X)):
V = X[i] * 1000
D = round(V, 1)
I = df2.loc[D, "ColumnName"]
# A list with the different values of 'I' is generated for passing to PF
# Some PowerFactory based commands below, involving inputs, and extracting results from PF, using a list generated from the values of 'I'. PF returns some result values to the code
return results
# MINLP problem definition:
m = GEKKO(remote=False)
x = m.Array(m.Var, nInv, value=1.0, lb=0.533, ub=1.0)
m.Equation(constraint1(x) == 30)
m.Minimize(objective(x))
m.options.SOLVER = 3
m.solve(disp=False)
# Command for exporting the results to a .txt file
在另一个问题公式中，我试图在目标和约束函数中运行 MINLP 优化问题作为嵌套优化问题。但是，我也遇到了错误。代码结构如下：
import powerfactory as pf
from gekko import GEKKO
# Execute the PF setup commands for Python
def constraint1(X):
P = 0
for i in range(length(X)):
V = X[i] * 1000
# 2nd MINLP problem: Finds 'I' from value of 'V', a single element
# Calculate 'Pcal' from 'I'
P += Pcal
return P
def objective(X):
Iset = []
for i in range(length(X)):
V = X[i] * 1000
# 3rd MINLP problem: Finds 'I' from value of 'V'
# Appends 'I' to a 'Iset'
# 'Iset' list passed on to PF
# Some PowerFactory based commands below, involving inputs, and extracting results from PF, using the 'Iset' list. PF returns some result values to the code
return results
# Main MINLP problem definition:
m = GEKKO(remote=False)
x = m.Array(m.Var, nInv, value=1.0, lb=0.533, ub=1.0)
m.Equation(constraint1(x) == 30)
m.Minimize(objective(x))
m.options.SOLVER = 3
m.solve(disp=False)
# Command for exporting the results to a .txt file
