AssetMgt.add_Markovian_uncertainty(g)
for t in range(T):
m = AssetMgt[t]
now, past = m.addStateVars(2, lb=0, obj=0)
if t == 0:
m.addConstr(now[0] + now[1] == 55)
if t in [1, 2]:
m.addConstr(past[0] + past[1] == now[0] + now[1],
uncertainty_dependent={
past[0]: 0,
past[1]: 1
})
if t == 3:
y = m.addVar(obj=1)
w = m.addVar(obj=-4)
m.addConstr(past[0] + past[1] - y + w == 80,
uncertainty_dependent={
past[0]: 0,
past[1]: 1
})
AssetMgt.discretize(
n_Markov_states=25,
n_sample_paths=10000,
)
# Extensive(AssetMgt).solve()
SDDP(AssetMgt).solve(max_iterations=400)
result = Evaluation(AssetMgt)
result.run(n_simulations=1000)
resultTrue = EvaluationTrue(AssetMgt)
resultTrue.run(n_simulations=1000)
X_now, X_past = m.addStateVars(I, name='accumulated purchase')
if t == 0:
m.addConstrs(X_now[j] == 0 for j in range(J))
else:
# u_jt #
u = m.addVars(J, name='shortage', uncertainty=beta_generator)
# v_ijkt #
v = m.addVars(I, J, K, name='allocation')
# w_jt #
w = m.addVars(J, name='production')
# x_it #
x = m.addVars(I, name='purchase', uncertainty=alpha_generator)
## accumulated number of purchased tools updated ##
m.addConstrs(X_now[i] == X_past[i] + x[i] for i in range(I))
# time allocation constraint
m.addConstrs(
gurobipy.quicksum(
gurobipy.quicksum(a[i][j][k] * v[(i, j, k)] for k in range(K))
for j in range(J)) <= c[i] * X_now[i] for i in range(I))
# production allocation constraint
m.addConstrs(
gurobipy.quicksum(v[(i, j, k)] for i in range(I)) >= w[j]
for j in range(J) for k in range(K))
# demand constraint
m.addConstrs((w[j] + u[j] >= 0 for j in range(J)),
uncertainty=d_generator)
semiconductor.discretize(n_samples=20)
SDDP(semiconductor).solve(max_iterations=10)
result = EvaluationTrue(semiconductor)
result.run(n_simulations=1000)
gurobipy.quicksum(exchange[(j, 4)] for j in range(5)) -
gurobipy.quicksum(exchange[(4, j)] for j in range(5)) == 0)
m.addConstrs(stored_now[i] + spill[i] + hydro[i] -
stored_past[i] == inflow_now[i] for i in range(4))
if t == 0:
m.addConstrs(stored_past[i] == stored_initial[i] for i in range(4))
m.addConstrs(inflow_now[i] == inflow_initial[i] for i in range(4))
else:
TS = m.addConstrs(inflow_now[i] + inflow_past[i] == 0
for i in range(4))
m.add_continuous_uncertainty(
uncertainty=sampler(t - 1),
locations=([(TS[i], inflow_past[i])
for i in range(4)] + [TS[i] for i in range(4)]),
)
HydroThermal.discretize(n_samples=n_samples, random_state=seed)
HT_sddp = SDDP(HydroThermal)
HT_sddp.solve(
n_processes=6,
n_steps=6,
max_iterations=n_iterations,
)
result = Evaluation(HydroThermal)
result.run(random_state=666, n_simulations=n_simulations)
resultTrue = EvaluationTrue(HydroThermal)
resultTrue.run(random_state=666, n_simulations=n_simulations)
model = 'TS' + str(n_samples)
if model == 'TS100':
fig = HT_sddp.plot_bounds(window=1, smooth=1)
fig.tight_layout()
fig.savefig("./result/{}_bounds.png".format(model), dpi=1200)
v = m.addVar(obj=1, lb=-gurobipy.GRB.INFINITY, name='wealth')
capm = m.addVars(N, lb = -gurobipy.GRB.INFINITY, name='capm')
idio = m.addVars(N, name='idio')
m.addConstr(v == gurobipy.quicksum(now[j] for j in range(N+1)))
m.addConstrs(
now[j] == capm[j] + idio[j]
for j in range(N)
)
for j in range(N):
m.addConstr(past[j] == capm[j], uncertainty_dependent={past[j]:j})
m.addConstr(past[j] == idio[j], uncertainty={past[j]:f(alpha[j],sigma[j])})
m.addConstr(now[N] == (1+rf) * past[N])
AssetMgt.discretize(
n_samples=100,
method='input',
Markov_states=Markov_states,
transition_matrix=transition_matrix,
random_state=888,
)
AssetMgt.set_AVaR(lambda_=lambda_, alpha_=0.25)
AssetMgt_SDDP = SDDP(AssetMgt)
AssetMgt_SDDP.solve(max_iterations=50, n_steps=3, n_processes=3)
evaluation = Evaluation(AssetMgt)
evaluation.run(n_simulations=1000, random_state=666)
evaluationTrue = EvaluationTrue(AssetMgt)
evaluationTrue.run(n_simulations=1000, random_state=666)
result = {
'mean':numpy.mean(evaluation.pv)-100,
'std':numpy.std(evaluation.pv),
'VAR': numpy.quantile(evaluation.pv,0.05)-100,
'skewness': stats.skew(evaluation.pv),
for j in range(5)) == demand.iloc[t %
12][i])
m.addConstr(
gurobipy.quicksum(exchange[(j, 4)] for j in range(5)) -
gurobipy.quicksum(exchange[(4, j)] for j in range(5)) == 0)
m.addConstrs(
(stored_now[i] + spill[i] + hydro[i] - stored_past[i] == 0
for i in range(4)),
uncertainty_dependent=[0, 1, 2, 3],
)
if t == 0:
m.addConstrs(stored_past[i] == stored_initial[i] for i in range(4))
start = time.time()
HydroThermal.discretize(
method=method,
n_Markov_states=n_Markov_states,
n_sample_paths=n_sample_paths,
)
time_MCA = time.time() - start
HT_sddp = SDDP(HydroThermal)
HT_sddp.solve(
n_processes=6,
n_steps=6,
max_iterations=n_iterations,
)
result = Evaluation(HydroThermal)
result.run(random_state=666, n_simulations=3000)
resultTrue = EvaluationTrue(HydroThermal)
resultTrue.run(random_state=666, n_simulations=3000)
model = method + str(n_Markov_states)
if model == 'SA100':
for j in range(5)) == demand.iloc[t %
12][i])
m.addConstr(
gurobipy.quicksum(exchange[(j, 4)] for j in range(5)) -
gurobipy.quicksum(exchange[(4, j)] for j in range(5)) == 0)
m.addConstrs(
(stored_now[i] + spill[i] + hydro[i] - stored_past[i] == 0
for i in range(4)),
uncertainty_dependent=[0, 1, 2, 3],
)
if t == 0:
m.addConstrs(stored_past[i] == stored_initial[i] for i in range(4))
start = time.time()
HydroThermal.discretize(
method='input',
Markov_states=Markov_states,
transition_matrix=transition_matrix,
)
time_MCA = time.time() - start
HT_sddp = SDDP(HydroThermal)
HT_sddp.solve(
n_processes=6,
n_steps=6,
max_iterations=n_iterations,
)
result = Evaluation(HydroThermal)
result.run(random_state=666, n_simulations=n_simulations)
resultTrue = EvaluationTrue(HydroThermal)
resultTrue.run(random_state=666, n_simulations=n_simulations)
if model == 'SA100':
fig = HT_sddp.plot_bounds(window=1, smooth=1)
