Energy Management Strategy for Plug-In Hybrid Electric Vehicles via Bidirectional Vehicle-to-Grid

As a paradigm of the incoming smart grid, vehicle-to-grid (V2G) has been proposed as a promising solution to increase the adoption rate of plug-in hybrid electric vehicles (PHEVs). In this paper, we investigate the energy management strategies for PHEVs via bidirectional V2G. We first follow a cost-conscious approach from the PHEV owner point of view. To minimize the daily energy cost, we formulate the energy management problem via dynamic programming (DP). However, the “well-known” complexity in solving DP poses a computational challenge even for a small number of iterations. Therefore, we propose a state-independent four-threshold
$(s,S,s^{\prime},S^{\prime})$
battery charging/discharging policy and theoretically prove the optimality of the proposed energy management strategy based on stochastic inventory theory. A backward iteration algorithm is further developed to practically implement the
$(s,S,s^{\prime},S^{\prime})$
feedback policy. Second, from the distribution system operator's perspective, we aim to shave the peak load and flatten the overall load profile. To this end, we propose an optimal PHEV charging scheme and further derive a reminiscent “water-filling” solution for this scenario. Realistic PHEV battery models, time-of-use electricity pricing rate, and real data of household demand are integrated into our formulated V2G system model. The theoretical analysis and proofs are instrumental to the future large-scale PHEV adoption in smart grids.