I am trying to run an optimization model with Pyomo. To build the constraints, it is necessary complext formulation using the decision variable. I am getting the error below.
"Implicit conversion of Pyomo numeric value (730.0*Vol_CT1) to float is disabled. This error is often the result of using Pyomo components as arguments to one of the Python built-in math module functions when defining expressions. Avoid this error by using Pyomo-provided math functions or explicitly resolving the numeric value using the Pyomo value() function."
How to solve this problem?
!pip install -q pyomo
!apt-get install -y -qq glpk-utils
import numpy as np
import pyomo.environ as pyo
from pyomo.environ import *
from pyomo.opt import SolverFactory
# Input data
Consumo_MWm = [3.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000,
1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000, 1.000]
PLD = [100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00,
100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00, 100.00]
Horas = [730., 730., 730., 730., 730., 730., 730., 730., 730., 730., 730., 730.,
730., 730., 730., 730., 730., 730., 730., 730., 730., 730., 730., 730.]
# Contrato 1
Inic_vig_CT1 = 1
Fim_vig_CT1 = 12
Preço_CT1 = 200.00
# Contrato 2
Inic_vig_CT2 = 13
Fim_vig_CT2 = 24
Preço_CT2 = 200.00
# Contrato 3
Inic_vig_CT3 = 1
Fim_vig_CT3 = 24
Preço_CT3 = 200.00
# Transformation for np.array
Consumo_MWm = np.array(Consumo_MWm)
PLD = np.array(PLD)
Horas = np.array(Horas)
# Previous Calculation
Consumo_MWh = Consumo_MWm * Horas
# Creating a model
model = pyo.ConcreteModel()
# Declaring variables
model.Vol_CT1 = pyo.Var(domain=NonNegativeReals) # Volume do Contrato 1 em MWm
model.Vol_CT2 = pyo.Var(domain=NonNegativeReals) # Volume do Contrato 2 em MWm
model.Vol_CT3 = pyo.Var(domain=NonNegativeReals) # Volume do Contrato 3 em MWm
# Making variables use easier
Vol_CT1 = model.Vol_CT1
Vol_CT2 = model.Vol_CT2
Vol_CT3 = model.Vol_CT3
# Constraints
Vol_CT1_MWh = np.zeros(24)
for i in range(24):
if i + 1 >= Inic_vig_CT1 and i + 1 <= Fim_vig_CT1:
Vol_CT1_MWh[i] = Vol_CT1 * Horas[i]
Custo_CT1 = Vol_CT1_MWh * PLD
Vol_CT2_MWh = np.zeros(24)
for i in range(24):
if i + 1 >= Inic_vig_CT2 and i + 1 <= Fim_vig_CT2:
Vol_CT2_MWh[i] = Vol_CT2 * Horas[i]
Custo_CT2 = Vol_CT2_MWh * PLD
Vol_CT3_MWh = np.zeros(24)
for i in range(24):
if i + 1 >= Inic_vig_CT3 and i + 1 <= Fim_vig_CT3:
Vol_CT3_MWh[i] = Vol_CT3 * Horas[i]
Custo_CT3 = Vol_CT3_MWh * PLD
Custo_CT_ALL = sum(Custo_CT1) + sum(Custo_CT2) + sum(Custo_CT3)
Volume_MCP_MWh = Consumo_MWh - Vol_CT1_MWh - Vol_CT2_MWh - Vol_CT3_MWh
Custo_MCP = Volume_MCP_MWh * PLD
# Defining objective function
model.obj = pyo.Objective(expr= Custo_CT_ALL + Custo_MCP, sense=minimize)
model.pprint()
print('=======================================================')
# Solving
opt = SolverFactory ('glpk', executable='/usr/bin/glpsol')
opt.solve(model).write()
# Results presentation
Vol_CT1_value = pyo.value(Vol_CT1)
Vol_CT2_value = pyo.value(Vol_CT2)
Vol_CT3_value = pyo.value(Vol_CT3)
print('=======================================================')
print('Volume Contrato 1 = ', Vol_CT1_value)
print('Volume Contrato 2 = ', Vol_CT2_value)
print('Volume Contrato 3 = ', Vol_CT3_value)
The problem is the line:
Vol_CT1_MWh[i] = Vol_CT1 * Horas[i]
You are declaring Vol_CT1_MWh as a NumPy array with dtype=float64. When you assign things to the NumPy array, NumPy will attempt to convert the value into the dtype (in this case by casting the Pyomo expression Vol_CT1 * Horas[i] to a float, thereby triggering the error).
Remember that algebraic modeling languages (like Pyomo) construct expression trees to represent the objective and constraints. A better approach is to not use intermediate NumPy objects when constructing the expressions. For example, you could construct Custo_CT1 directly:
Custo_CT1 = sum(
Vol_CT1 * Horas[i]
for i in range(24)
if i + 1 >= Inic_vig_CT1 and i + 1 <= Fim_vig_CT1
) * PLD