A model predictive control-based approach for plug-in electric vehicles charging: Power tracking, renewable energy sources integration and driver preferences satisfaction

Abstract

This chapter presents a model predictive control (MPC) framework for controlling in real-time the charging processes of a set of plug-in electric vehicles (PEVs) located in a load area (LA), namely a distribution system operator (DSO)- defined portion of the grid under a secondary substation. The LA considered in the reference scenario hosts remotely controlled, IEC 61851-compliant electric vehicle supply equipment (EVSE), where the PEVs are plugged to recharge the batteries, and a share of generation from renewable energy sources (RES). The proposed framework works regardless of the EVSE technology and power level (direct current, alternating current, single phase or three phases). The controller, named load area controller (LAC), works under the requirements of: (i) seeking costs minimization while respecting drivers’ preferences on the amount of energy to recharge (or desired final state of charge) and the time flexibility for recharging specified by the driver; (ii) tracking of a LA-level power reference established by the DSO on a day-ahead basis and possibly updated intraday; (iii) integrating RES by, e.g., maximizing the share of photovoltaic power absorbed by the LA, thus ensuring economic saving while avoiding the injection into the grid of possibly intermittent power profiles. The design of the controller is based on the analysis of a possible future charging scenario in an unbundled electricity system, but is general enough to be tailored to a large number of possible regulatory frameworks and business models. Starting from the available equipment and the role of actors possibly involved, use cases are presented and controller functional requirements and technical specifications identified; based on that, the reasons for using MPC methodology are explained and the discrete time optimal control problem at its basis is presented. The issue of estimating the battery state of charge is discussed, which constitutes a delicate point for the deployment of the control system in a real environment. A set of incremental simulations is presented in order to validate the concept and show its effectiveness.