I have a variable in Pyomo called model.variable_objectiveCosts and I would like to print this value to a file. Basically it works. But when I would like to divide the value by 100 and then round to 2 decimal digits, I get an error when using the following code "%.2f" % model.variable_objectiveCosts/100
The error message is
"TypeError: Implicit conversion of Pyomo NumericValue type `variable_objectiveCosts' to a 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."
Now it tells me to use the Pyomo-provided math functions. However, I could not find a round function in Pyomo. Do you know how I can still round the result to 2 digits?
Here is a part of my code:
# Imports
import pyomo.environ as pyo
import pandas as pd
from pyomo.util.infeasible import log_infeasible_constraints
from pyomo.opt import SolverStatus, TerminationCondition
import sys
#Check if the problem is solved or infeasible
if (solution.solver.status == SolverStatus.ok) and (solution.solver.termination_condition == TerminationCondition.optimal):
# Do something when the solution in optimal and feasible
print("Result Status: Optimal")
#Create pandas dataframe for displaying the results
outputVariables_list = [model.variable_heatGenerationCoefficient_SpaceHeating, model.variable_heatGenerationCoefficient_DHW, model.variable_help_OnlyOneStorage, model.variable_temperatureBufferStorage, model.variable_usableVolumeDHWTank, model.variable_surplusPowerTotal, model.variable_surplusPowerPositivePart, model.variable_surplusPowerNegativePart, model.variable_help_isSurplusPowerPositive, model.variable_electricalPowerTotal, model.variable_RESGenerationTotal, model.variable_pvGeneration, model.variable_costsPerTimeSlotPositivePart, model.variable_revenuePerTimeSlotPositivePart, model.variable_currentChargingPowerEV, model.variable_energyLevelEV, model.variable_SOC_EV, model.param_heatDemand_In_W, model.param_DHWDemand_In_W, model.param_electricalDemand_In_W, model.param_pvGenerationNominal, model.param_outSideTemperature_In_C, model.param_windAssignedNominal, model.param_electricityPrice_In_Cents, model.param_availabilityPerTimeSlotOfEV, model.param_energyConsumptionEV, model.param_COPHeatPump_SpaceHeating, model.param_COPHeatPump_DHW, model.param_BigM_Surplus_Positive, model.param_BigM_Surplus_Negative, model.param_BigM_Costs_Positive, model.param_BigM_Costs_Negative, model.variable_objectiveMaximumLoad, model.variable_objectiveSurplusEnergy ,model.variable_objectiveCosts, model.objective_combined_general, model.set_timeslots]
optimal_values_list = [[pyo.value(model_item[key]) for key in model_item] for model_item in outputVariables_list]
results = pd.DataFrame(optimal_values_list)
results= results.T
results = results.rename(columns = {0:'variable_heatGenerationCoefficient_SpaceHeating', 1:'variable_heatGenerationCoefficient_DHW', 2:'variable_help_OnlyOneStorage', 3:'variable_temperatureBufferStorage', 4:'variable_usableVolumeDHWTank', 5:'variable_surplusPowerTotal', 6:'variable_surplusPowerPositivePart', 7:'variable_surplusPowerNegativePart', 8:'variable_help_isSurplusPowerPositive', 9:'variable_electricalPowerTotal', 10:'variable_RESGenerationTotal', 11:'variable_pvGeneration', 12:'variable_costsPerTimeSlot', 13:'variable_revenuePerTimeSlot', 14:'variable_currentChargingPowerEV', 15:'variable_energyLevelEV', 16:'variable_SOC_EV', 17:'param_heatDemand_In_W', 18:'param_DHWDemand_In_W', 19:'param_electricalDemand_In_W', 20:'param_pvGenerationNominal', 21:'param_outSideTemperature_In_C', 22:'param_windAssignedNominal', 23:'param_electricityPrice_In_Cents', 24:'param_availabilityPerTimeSlotOfEV', 25:'param_energyConsumptionEV', 26:'param_COPHeatPump_SpaceHeating', 27:'param_COPHeatPump_DHW', 28:'param_BigM_Surplus_Positive', 29:'param_BigM_Surplus_Negative', 30:'param_BigM_Costs_Positive', 31:'param_BigM_Costs_Negative', 32:'variable_objectiveMaximumLoad_kW', 33:'variable_objectiveSurplusEnergy_kWH', 34:'variable_objectiveCosts_Euro', 35:'objective_combined_general', 36:'set_timeslots'})
cols = ['set_timeslots']
results[cols]= results [cols].round(0).astype(int)
results.set_index('set_timeslots', inplace=True)
results.to_csv("C:/Users/wi9632/Desktop/Result_BT1.csv", index=True, sep =";")
elif (solution.solver.termination_condition == TerminationCondition.infeasible):
# Do something when model in infeasible
print ("Result Status: Infeasible")
else:
# Something else is wrong
print("Solver Status: ", solution.solver.status)
from pyomo.environ import *, then you should have the math functions you need for rounding. So, make sure you don't use round function from other packages (likemathornumpy) or have those imports to override what pyomo is importing. $\endgroup$from pyomo.environ import *, then I believe the round function is already there. What I meant is to test it like that and make sure you're not rounding withnp.roundormath.round. This seems to be a common issue. If you do a quick search on the web, you'll see some similar questions with the same error. $\endgroup$np.round(model.variable_objectiveCosts/100, 2)leads to the following error message "TypeError: loop of ufunc does not support argument 0 of type MonomialTermExpression which has no callable rint method". When just usingnp.round(model.variable_objectiveCosts, 2)I get a error "TypeError: unsupported operand type(s) for *: 'NoneType' and 'float'" $\endgroup$