Smart Charging of Plug-in Electric Vehicles Under Driving Behavior Uncertainty

Abstract

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 so-called 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 chapter, we introduce an approach to derive day-ahead charging profiles that minimize generation costs while respecting network and drivers’ end-use constraints, as well as taking into account the uncertainty in driving patterns. The charging profiles are derived by aggregating vehicles at each network node into virtual battery resources and dispatching them with a multiperiod optimal power flow (OPF). To take driving pattern uncertainty into consideration, 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, where constraints concerning the virtual batteries are modeled as chance constraints, i.e., as constraints that may be violated with a certain probability. Compared with a deterministic approach, this framework increases the chances of not violating the constraints subject to uncertainty.