Real-Time Optimal Energy Management Controller for Electric Vehicle Integration in Workplace Microgrid

Abstract

Electric vehicles (EVs) that are connected to the charging station of a parking lot in a workplace can be considered as potential power sources. These EVs can be operated in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) modes of power transfer. However, an intelligent energy management (EM) controller is required for providing optimal schedule for V2G and G2V modes of operation. This paper proposes a real-time optimal EM controller for EV to grid integration for workplace microgrid system. In this proposed EM scheme, charging and discharging of EV battery are scheduled by forecasting the EV travel pattern using random forest methodology. In addition, the EM scheme is designed as optimization problem that utilizes this predicted EV travel data to minimize the cost energy consumption at the workplace. The EM scheme is developed by considering economic benefits to both the EV owner and the workplace. The EM controller is developed using Java Agent Development framework, and it is tested with real-world data in real-time simulation testbed. The real-time testbed is developed by interfacing two Typhoon hardware-in-loop units emulating two EV batteries, and real-time digital simulator emulating the grid-side power network.