def test_prod2(self):
model = ConcreteModel()
model.A = Set(initialize=[1,2,3], doc='set A')
model.x = Var(model.A)
expr = prod(model.x[i] for i in model.x)
baseline = "x[1]*x[2]*x[3]"
self.assertEqual( str(expr), baseline )
expr = prod(model.x)
self.assertEqual( expr, 6)
def R_J_ub_init(model, j): # !!!
if pe.value(model.R_J_func_type) == 1:
return (model.R_J_base[j] + (sum(
model.z_ub[g] * model.upsilon_tilde[g, pe.value(model.Hz)] *
(model.D_J_base[j]) + (1 / model.z_lb[g]) *
model.upsilon_tilde[g, pe.value(model.sec)] *
(model.D_J_base[j]) for g in model.G)) / 1000 + 1)
if pe.value(model.R_J_func_type) is 2:
return (
model.R_J_base[j] +
sum(model.z_J_ub[g, j] *
model.upsilon_tilde[g, pe.value(model.Hz)] * (
model.D_J_base[j] + pe.prod(model.z_J_ub[g, j] #!!!
* model.upsilon_r[g]
for g in model.G)) +
(1 / model.z_J_lb[g, j]) *
model.upsilon_tilde[g, pe.value(model.sec)] * (
model.D_J_base[j] + pe.prod(model.z_J_ub[g, j] #!!!
* model.upsilon_r[g]
for g in model.G))
for g in model.G) / 1000 + 1)
def new_load_rule(mdl):
temp = sum([mdl.phi[l, u, h] * mdl.alpha[l, u, h] *
pyo.prod([(1 - sum([(mdl.x[b, l, u, h] / mdl.alpha[l, u, h]) * mdl.eta[b, l, u]
if ((pyo.value(mdl.alpha[l, u, h]) > 1e-6) &
((b, gamma) in mdl.BMPGRPING) &
((b, u) in mdl.BMPSRCLINKS))
else 0
for b in mdl.BMPS]))
if (gamma, u) in mdl.BMPGRPSRCLINKS
else 1
for gamma in mdl.BMPGRPS])
for l, u, h in mdl.PARCELS])
return temp
def Compute_R_J_rule(model, j):
if pe.value(model.R_J_func_type) == 1:
return (model.R_J[j] == model.R_J_base[j] +
(sum(model.z_kappa[g, j] *
model.upsilon_tilde[g, pe.value(model.Hz)] *
(model.D_J_base[j]) + (1 / model.z_kappa[g, j]) *
model.upsilon_tilde[g, pe.value(model.sec)] *
(model.D_J_base[j]) for g in model.G)) / 1000)
if pe.value(model.R_J_func_type) is 2:
return (
model.R_J[j] == model.R_J_base[j] +
sum(model.z_kappa[g, j] *
model.upsilon_tilde[g, pe.value(model.Hz)] * (
model.D_J_base[j] + pe.prod(model.z_kappa[g, j] #!!!
* model.upsilon_r[g]
for g in model.G)) +
(1 / model.z_kappa[g, j]) *
model.upsilon_tilde[g, pe.value(model.sec)] * (
model.D_J_base[j] + pe.prod(model.z_kappa[g, j] #!!!
* model.upsilon_r[g]
for g in model.G))
for g in model.G) / 1000)
def new_load_rule(mdl, p):
newload = pyo.quicksum(
mdl.phi[s, u, h, p] * mdl.alpha[s, u, h] *
pyo.prod([(1 - sum((mdl.x[b, s, u, h] / mdl.alpha[s, u, h]) *
mdl.eta[b, s, u, p] if (
(pyo.value(mdl.alpha[s, u, h]) > 1e-6) & (
(b, G) in mdl.BMPGRPING) & (
(b, u) in mdl.BMPSRCLINKS)) else 0
for b in mdl.BMPS)) if
(G, u) in mdl.BMPGRPSRCLINKS else 1
for G in mdl.BMPGRPS]) for s, u, h in mdl.PARCELS)
return newload
def new_load_rule_for_each_lrseg(mdl, thisl, p):
newload = pyo.quicksum(
mdl.phi[s, u, h, p] * mdl.alpha[s, u, h] *
pyo.prod([(1 - sum((mdl.x[b, s, u, h] / mdl.alpha[s, u, h]) *
mdl.eta[b, s, u, p] if (
(pyo.value(mdl.alpha[s, u, h]) > 1e-6) & (
(b, gamma) in mdl.BMPGRPING) & (
(b, u) in mdl.BMPSRCLINKS)) else 0
for b in mdl.BMPS)) if
(gamma, u) in mdl.BMPGRPSRCLINKS else 1
for gamma in mdl.BMPGRPS]) if (s == thisl) else 0
for s, u, h in mdl.PARCELS)
return newload
def test_prod1(self):
self.assertEqual(prod([1,2,3,5]),30)
def a2Category_rule(model, n):
return sum(
pyomo.prod([model.DSA[t, n] for t in range(q, model.Req[n] + q)])
for q in range(len(model.T) - model.Req[n])) >= 1
def lowere_rule(mdl, f, l):
temp_val = pyo.prod([
1 - mdl.eta[(bmp_tuple, l)] for bmp_tuple in fi_dict_fkeys[f]
])
# lowerecalc[(fi_dict_fkeys[(b1, b2)], lrsegid)] =
return temp_val