Gurobi says the model is infeasible, yet I can come up with a solution by hand
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Hello. Here is my model. My apologies if its hard to follow. Gurobi says its infeasible.
from gurobipy import Model, GRB
import json
f = Model()
num_steps = 3
steps = [*range(num_steps)]
steps_for_C = [*range(num_steps + 1)]
A = f.addVar(0, float('inf'), name = 'A')
B = f.addVar(0, float('inf'), name = 'B')
C = {}
for step in steps_for_C:
C[step] = f.addVar(0, float('inf'), name="C[%i]" % step)
D = {}
for step in steps_for_C:
D[step] = f.addVar(0, 1, name="D[%i]" % step)
E = {}
for step in steps_for_C:
E[step] = f.addVar(0, float('inf'), name="E[%i]" % step)
F = {}
for step in steps_for_C:
F[step] = f.addVar(0, float('inf'), name="F[%i]" % step)
H = {}
for step in steps_for_C:
H[step] = f.addVar(0, float('inf'), name="H[%i]" % step)
J = {}
for step in steps_for_C:
J[step] = f.addVar(0, float('inf'), name="J[%i]" % step)
G = {}
for step in steps:
G[step] = f.addVar(0, 80, name="G[%i]" % step)
for step in steps:
L = 100
f.addConstr(D[0] == 1., name = 'C == 1')
f.addConstr(C[0] == 0.8*A, name = 'C initial')
f.addConstr(C[step + 1] == C[step] * 0.9999 + E[step+1] - F[step+1], name = 'C update')
f.addConstr(H[step] == E[step] - F[step], name = 'H constr')
f.addGenConstrAbs(J[step], H[step], name = 'J constr')
f.addConstr(D[step + 1] == D[step] - (0.75 * J[step+1] - 0.02 * C[step+1] + 0.02) * 0.001, name = 'D constr')
f.addConstr(C[step] <= 0.8*A*D[step+1], name = 'C max')
f.addConstr(C[step] >= 0.2*A, name = 'C min')
f.addConstr(E[step] <= B, name = 'E less than B')
f.addConstr(E[step+1] <= 0.8*A*D[step+1] - C[step], name = 'E limit')
f.addConstr(F[step] <= B, name = 'F less than B')
f.addConstr(F[step+1] <= C[step] - 0.2*A, name = 'F limit')
f.addConstr(-L + G[step] - E[step] + F[step] == 0, name = 'p constraint')
f.setObjective(A, GRB.MINIMIZE)
f.params.FeasibilityTol = 0.01
f.params.NonConvex = 2
f.optimize()
However, I can easily come up with a solution by hand, as in:
A = 10
B = 2
D = 1
C = 8
F = 2
E = 0
for step in [1,2,3]:
J = abs(PB_charging - PB_discharging)
D -= ((0.75 * J - 0.02 * C + 0.02) * 0.001 + step/1000)
C = C * 0.9999 + E - F
G = E - F + L
print('G is: ', G)
print('D is: ', D)
print('C is: ', C)
print('----------')
Since I can come up with a solution by hand, why is the model infeasible? Am I missing something here? Perhaps the order in which I arrange my constraints? Any help is appreciated.
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