A two-stage optimization method for Vehicle to Grid coordination considering building and Electric Vehicle user expectations

Abstract

The formulation of the Vehicle to Grid (V2G) scheme should ideally consider both Electric Vehicle (EV) and building owners’ viewpoints. From the building owner’s viewpoint, the EVs should be present in the building and participate consistently to deliver the required power. From the EV owners’ viewpoint, participating in the V2G scheme should provide economic incentives while not compromising their travel needs. However, those proposed V2G algorithms up to this point in time had not considered any corrections required in response to unplanned changes in the EV travel plan. In this paper, a Two-stage optimization technique is proposed to determine the charging and discharging schedule for EVs participating in a vehicle-to-grid (V2G) programme at an office building. The EV owners’ travel convenience is focused with more attention in the proposed model by giving them two V2G options. Firstly, day-ahead optimization (DAO) is applied, based on the expected building load profile and EV behaviour, the optimal charging or discharging control of the EVs is obtained in order to save electricity bills by minimizing the maximum demand of the building. Subsequently, a real-time optimization (RTO) is performed to adjust the V2G operation based on actual vehicle behaviours which usually deviate from their estimations. Simulations are conducted and the preliminary results show that the proposed technique is able to adjust the EV charging or discharging in real-time with the aid of the day-ahead stage. The proposed model manages to capture a new ideal optimal maximum demand point and maintain the EV SOC profile as planned from the DAO stage in real-time when prediction deviation occurs. In addition, a comprehensive cost–benefit analysis in utilizing V2G in peak load reduction is also performed to gain insight into the potential savings and discharging rewards attributable to the building and EV owner respectively.