def xcharge_battery(model_in,i):
"""
Adjusting the charging/discharging capacity based on the current SOC of the vehicle.
"""
model_in.xcharging_power[i] = aml.Expr_if(model_in.soc_temp[i] > 0.8,
- 5 * model_in.xcharging_power[i] * (model_in.soc_temp[i]
- 1.0), aml.Expr_if(model_in.soc_temp[i] < 0.2, 5 *
model_in.xcharging_power[i] *
model_in.soc_temp[i], model_in.xcharging_power[i]))
def test_expr_if(self):
m = pe.ConcreteModel()
m.x = pe.Var()
m.y = pe.Var()
e = pe.Expr_if(m.x <= 0, m.y + m.x == 0, m.y - m.x == 0)
pn = convert_expression_to_prefix_notation(e)
expected = [(Expr_ifExpression, 3), (InequalityExpression, 2), m.x, 0,
(EqualityExpression, 2), (SumExpression, 2), m.y, m.x, 0,
(EqualityExpression, 2), (SumExpression, 2), m.y,
(MonomialTermExpression, 2), -1, m.x, 0]
self.assertEqual(pn, expected)
def demand_rule(model_in, i):
"""
Adjust vehicle energy demand based on either parking time and maximal charging power or SOC target.
:param model_in: Pyomo optimization model_in.
:param i: Model instance index.
:type i: int
:return: Energy demand.
"""
connection_time = min(data[ev_set.index(i)]['parking_time'], len(hours) / 60 * tau)
return sum(
getattr(model_in, 'power_' + str(j))[i] for j in model_in.time) / 60 / model_in.battery_size[i] + \
model_in.soc[i] >= aml.Expr_if(
model_in.soc_target[i] <= connection_time * model_in.xcharging_power[i] / model_in.battery_size[i] +
model_in.soc[i], model_in.soc_target[i], connection_time * model_in.xcharging_power[i] /
model_in.battery_size[i] + model_in.soc[i])