Energy management for plug-in hybrid electric vehicles via vehicle-to-grid

Abstract

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