Gurobi giving wrong results (not optimal solution)

here is my code for electricity biding strategy:

from gurobipy import *
# Define your functions

def f(x):

    return x / P_batt_max  # Adjust P_batt_max as needed

def f_inv(x):

    return x * P_batt_max  # Adjust P_batt_max as needed

def sing(x):

    if x >= 0:

        return 1

    else:

        return 0

# Create a new model

model = Model("energy_optimization")

# Decision variables

T = 6

I = 1

P_batt_max = 4

P_pv = {}

P_FlexibleEnergyDemand = {}

P_FlexibleEnergyExcess = {}

P_INFlexibleEnergyDemand = {}

P_INFlexibleEnergyExcess = {}

battCharge ={}

battDischarge ={}

x = {}

y = {}

a = {}

b = {}

SoC = {}

SoC_min = 0.2

SoC_max = 1

# Inflexible energy demand values

D_InflexibleDemand_values = [3, 6, 4, 10, 6, 18]

D_InflexibleDemand = {(t, i): D_InflexibleDemand_values[t] for t in range(T) for i in range(I)}

# Solar PV generation values

PV_generation_values = [5, 6, 4, 6, 6, 8]

PV_generation = {(t, i): PV_generation_values[t] for t in range(T) for i in range(I)}

# Cost parameters

C_lowBidImport = 5

C_HighBidImport = 18

C_lowOfferPrice = 3

C_HighOfferPrice = 4

for t in range(T):

    for i in range(I):

        P_pv[t, i] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"P_pv_{t}_{i}")

        P_FlexibleEnergyDemand[t, i] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"P_FlexibleEnergyDemand_{t}_{i}")

        P_FlexibleEnergyExcess[t, i] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"P_FlexibleEnergyExcess_{t}_{i}")

        P_INFlexibleEnergyDemand[t, i] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"P_INFlexibleEnergyDemand_{t}_{i}")

        P_INFlexibleEnergyExcess[t, i] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"P_INFlexibleEnergyExcess_{t}_{i}")

       

        battCharge[t,i] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"battCharge_{t}_{i}")

        battDischarge[t,i] = model.addVar(lb=0, vtype=GRB.CONTINUOUS, name=f"battDischarge_{t}_{i}")

        x[t, i] = model.addVar(vtype=GRB.BINARY, name=f"x_{t}_{i}")

        y[t, i] = model.addVar(vtype=GRB.BINARY, name=f"y_{t}_{i}")

        a[t, i] = model.addVar(vtype=GRB.BINARY, name=f"a_{t}_{i}")

        b[t, i] = model.addVar(vtype=GRB.BINARY, name=f"b_{t}_{i}")

        # Constraint: Set PV generation to given values

       

for t in range(T+1):

    for i in range(I):        

        SoC[t, i] = model.addVar(lb=0.2, ub=1, vtype=GRB.CONTINUOUS, name=f"SoC_{t}_{i}")

# Constraint: SoC at t = 0 is 0.2

for i in range(I):

    model.addConstr(SoC[0, i] == SoC_min)
   

for t in range(T):

    for i in range(I):

        if PV_generation[t,i]-D_InflexibleDemand[t,i]>=0:

            model.addConstr(b[t, i] == 1)

        else:

            model.addConstr(a[t, i] == 1)

# Constraints

for t in range(T):

    for i in range(I):

        model.addConstr(P_pv[t, i] == PV_generation[t, i])

        model.addConstr(P_FlexibleEnergyDemand[t, i] <= f_inv(SoC_max - SoC[t, i]-a[t,i]*f(battDischarge[t,i])+b[t,i]*f(battCharge[t,i])))

        model.addConstr(P_FlexibleEnergyExcess[t, i] <= f_inv(SoC[t, i] - SoC_min-a[t,i]*f(battDischarge[t,i])+b[t,i]*f(battCharge[t,i])))

        model.addConstr(P_FlexibleEnergyExcess[t, i] >= 0)

        model.addConstr(P_FlexibleEnergyDemand[t, i] >= 0)

        model.addConstr(battCharge[t, i] <= abs(PV_generation[t,i]-D_InflexibleDemand[t,i]))

        model.addConstr(battDischarge[t, i] <= abs(PV_generation[t,i]-D_InflexibleDemand[t,i]))

        model.addConstr(0 <= P_INFlexibleEnergyDemand[t, i])

        model.addConstr(0 <= P_INFlexibleEnergyExcess[t, i])


for t in range(T+1):

    for i in range(I):        

        model.addConstr(SoC[t, i] >= SoC_min)

        model.addConstr(SoC[t, i] <= SoC_max)

# Sum of decision variables

for t in range(T):

    model.addConstr(quicksum(x[t, i] + y[t, i] for i in range(I)) == 1)

    model.addConstr(quicksum(a[t, i] + b[t, i] for i in range(I)) == 1)

# Constraints related to inflexible energy demand and SoC update equation

for i in range(I):

    for t in range(T):

        model.addConstr(SoC[t+1, i] == SoC[t, i] - f(-x[t,i]*P_FlexibleEnergyDemand[t, i] + y[t,i]*P_FlexibleEnergyExcess[t, i]+a[t,i]*battDischarge[t,i]-b[t,i]*battCharge[t,i]))

        model.addConstr((SoC[t, i] - f(a[t,i]*battDischarge[t,i]-b[t,i]*battCharge[t,i]))<=SoC_max)

        model.addConstr((SoC[t, i] - f(a[t,i]*battDischarge[t,i]-b[t,i]*battCharge[t,i]))>=SoC_min)

        model.addConstr(SoC[t+1, i] >= 0.2)

        model.addConstr(SoC[t+1, i] <= 1)

        model.addConstr(P_pv[t, i] + a[t,i]* battDischarge[t,i] + x[t,i]* P_INFlexibleEnergyDemand[t, i]  == D_InflexibleDemand[t, i] + y[t,i]* P_INFlexibleEnergyExcess[t, i]+ b[t,i]* battCharge[t,i])

model.addConstr(

    quicksum(P_pv[t, i] + a[t,i]* battDischarge[t,i] + x[t,i]* P_INFlexibleEnergyDemand[t, i]   for t in range(T) for i in range(I)) ==

    quicksum(D_InflexibleDemand[t, i] + y[t,i]* P_INFlexibleEnergyExcess[t, i]+ b[t,i]* battCharge[t,i] for t in range(T) for i in range(I))

)

       

model.setObjective(

    quicksum(

        0*(a[t,i]*battDischarge[t,i])-20*(b[t,i]*battCharge[t,i])+

        x[t, i] * (C_lowBidImport * P_FlexibleEnergyDemand[t, i] + C_HighBidImport * P_INFlexibleEnergyDemand[t,i]) +

        y[t, i] * (-C_HighOfferPrice * P_FlexibleEnergyExcess[t, i] - C_lowOfferPrice * P_INFlexibleEnergyExcess[t,i])

       

        for t in range(T) for i in range(I)

    ),

    GRB.MINIMIZE

)

# Solve the optimization problem

model.optimize()

# Access the solution

if model.status == GRB.OPTIMAL:

    for t in range(T):

        for i in range(I):

            print(f"********Results for: ", t,"**************")

            print(f"P_pv_{t}_{i}:", P_pv[t, i].x)

            print(f"D_InflexibleDemand_{t}_{i}:", D_InflexibleDemand[t, i])

            print(f"P_FlexibleEnergyDemand_{t}_{i}:", P_FlexibleEnergyDemand[t, i].x)

            print(f"P_FlexibleEnergyExcess_{t}_{i}:", P_FlexibleEnergyExcess[t, i].x)

            print(f"battDisch{t}_{i}:", battDischarge[t, i].x)

            print(f"battCharge{t}_{i}:", battCharge[t, i].x)

            print(f"P_INFlexibleEnergyDemand_{t}_{i}:", P_INFlexibleEnergyDemand[t, i].x)

            print(f"P_INFlexibleEnergyExcess_{t}_{i}:", P_INFlexibleEnergyExcess[t, i].x)

            print(f"x_{t}_{i}:", x[t, i].x)

            print(f"y_{t}_{i}:", y[t, i].x)

            print(f"a_{t}_{i}:", x[t, i].x)

            print(f"b_{t}_{i}:", y[t, i].x)

            print(f"SoC_{t}_{i}:", SoC[t, i].x)

            obj = model.getObjective()

            print(obj.getValue())

else:

    print("Optimization problem is infeasible or unbounded.")

However i could get manually results that give better solution that the one proposed by solver. Can any one help me. Thank you

正式なコメント

Simranjit Kaur

Gurobi Staff

2026年11月13日 01:30

This post is more than two years old. Some information may not be up to date. For current information, please check the Gurobi Documentation or Knowledge Base. If you need more help, please create a new post in the community forum, or try Gurobot, our chatbot interface offering instant, expert-level support.

David Torres Sanchez

2024年02月13日 08:11

If you can obtain a better solution you could try to fix the variables to these values (set LB and UB values) and see what happens.

Cheers,
David

Gwyneth Butera

2024年02月13日 17:00

On addition, you may want to refer to the Knowledge Base article : How do I diagnose a wrong result?

Gurobi giving wrong results (not optimal solution)

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