Smart Charging of plug-in Vehicles under Driving Behavior Uncertainty

An upcoming introduction of plug-in hybrid electric vehicles and electric vehicles could put power systems’ infrastructure under strain in the absence of charging control. The charging of electric vehicles could be managed centrally by a socalled aggregator, which would take advantage of the flexibility of these loads. To determine optimal charging profiles day-ahead, the aggregator needs information on vehicles’ driving behavior, such as departure and arrival time, parking location and energy consumption, none of which can be perfectly forecasted. In this paper we derive charging profiles by aggregating vehicles at each network node into virtual battery resources and dispatching them with a multiperiod Optimal Power Flow (OPF) which has the objective to minimize system costs while implicitly taking into account network loading limits and constraints on the virtual batteries. Different possible realizations of individual driving patterns are generated with a Monte Carlo simulation, modeling individual driving behavior with non-Markov chains. This information is integrated into the OPF in the form of chance constraints on the parameters of each virtual battery. Compared with a deterministic approach, this framework increases the chances of not violating these stochastic constraints.