Fair energy scheduling in vehicle-to-grid networks in the smart grid

Abstract

Plug-in hybrid electric vehicles (PHEVs) are receiving growing attention to achieve a sustainable transport system and society. Due to the limited vehicle battery capacity, PHEVs perform charging and re-charging from time to time. It is visioned that the charging load with high PHEVs penetration will pose a considerable impact on the residential distribution network. Therefore, implementation of coordinated PHEVs charging becomes necessary for smart grid. For maintaining the household load, the limited energy supply may not fulfill all PHEVs charging load at any time. Thus, the fairness of energy scheduling among PHEVs should be considered. In this paper, charging fairness (CF) and discouraging-charging fairness (DCF) are proposed to guarantee the charging opportunity of each PHEV and fast recovery of PHEV driving ability. We formulate the problem of the fair energy scheduling in residential distribution network as a Semi Markov Decision Process (SMDP). The technique Neuro-Dynamic Programming (NDP) is exploited to solve the corresponding problem in SMDP. In the scheduling process, Entropy Weight Method (EWM) is proposed to consider three key metrics: CF, DCF and cable power loss. Simulation results illustrate that the proposed scheduling scheme is able to avoid a number of peak load caused by PHEVs charging and at the same time reduce power loss without affecting traveling plan.