Battery Status Sensing Software-Defined Multicast for V2G Regulation in Smart Grid

During the bidirectional exchange of electricity between electric vehicles (EVs) and smart grid, plenty of sensors have been deployed to sense the EVs' battery status and monitor the electricity regulation requirements. When electricity consumption at power grid demand-side sharply increases/decreases, charging points (CPs) access these sensors by multicast technology to aggregate the EVs' battery status, publish electricity regulation requests and assign the state of charge (SOC) transition instructs in vehicle-to-grid (V2G). However, intermittent connections arising from distributed and mobile energy storage bring many challenging problems on multicast scheduling. First, in smart grid, peak shaving and load shifting require V2G to sense and regulate the EVs' SOC according to their battery status precisely, while the traditional multicast only considers their mutable network locations (e.g., WLAN, IP, and MAC). Second, as the number of EVs that participate into V2G increases, V2G needs to schedule the massive multicast traffic with priority to satisfy the dynamic and real-time regulation requirements. To address these problems, in this paper, we first aim to demonstrate that the appreciable battery status is more adaptive than network location to act as a multicast primitive in V2G. We propose a battery status sensing software-defined multicast (BSS-SDM) scheme to reduce the latency of V2G regulation services. In the BSS-SDM scheme, the battery status of each EV is identified during SOC transitions and maintained by a centralized controller. Besides, we propose a battery-status-based multicast scheduling algorithm to implement the V2G regulation optimization. Simulation results verify the effectiveness of proposed schemes.