V2G Capacity Estimation Using Dynamic EV Scheduling

Abstract

An aggregated number of electric vehicles (EVs) provides a platform for smart energy storage (SES) in buildings which can be used during periods of maximum demand or high electricity price as well as for leveling the intermittent outputs of renewable energy sources (RESs). However, the vehicle to grid (V2G) capacity varies over time based on the availability of the EVs and their individual state of charge (SOC). Hence a real-time V2G capacity estimation is important for utilizing EVs as SES efficiently. In this paper, an algorithm for half-hourly V2G capacity estimation using real-time EV scheduling is proposed. The algorithm is implemented as part of the Building Energy Management System (BEMS). The BEMS uses forecasted building load demand without EVs and predicted charging profiles of the EVs connected to the building for estimating the V2G capacity. The estimated V2G capacity and the availability of RESs are considered by the BEMS to schedule the EV charging/discharging. The proposed algorithm is applied to study three case scenarios using BEMS on residential, office, and commercial buildings in Singapore. The results obtained clearly show that a group of EVs connected to any high-rise building can be effectively used as a distributed storage system.