GGlueVaR‐based participation of electric vehicles in automatic demand response for two‐stage scheduling

Abstract

Due to the uncertainty of the external situation and the varied ability of electric vehicle (EV) owners to understand and process information, the demand response optimization method is not timely and flexible enough. This article puts forward a two-stage electric vehicle automatic demand response (ADR) optimization method based on generalized Glue value-at-risk (GGlueVaR) to solve existing problems. First, a two-stage electric vehicle ADR optimization method is proposed considering both the EV owner's benefit and network load fluctuation. In the process of ADR, different risk preferences of electric vehicle owners affect the EV owner participation in ADR. Second, the GGlueVaRbased EV owner willingness decision model is adopted to measure an EV group's risk attitude. Finally, a case study is provided to verify the effectiveness of the proposed method. Results show that the proposed model reduced the average charging cost of EV owners by 45% and increased the profit resulting from DR by 91% compared with the price-based demand response model. Therefore, the proposed model is more efficient than disorder charging model. The method is timelier and more flexible compared with other prior demand response optimization methods.

Demand Response Management via Real-Time Pricing for Microgrid with Electric Vehicles under Cyber-Attack

“…Demand response (DR) is an effective tool for integrating EVs, which can realize synchronous optimization of multiple aspects of coordinated operation and improve the electricity market framework [8][9][10][11][12][13]. In [14], a two-stage EV automatic DR optimization method based on generalized Glue value-at-risk was put forward to solve existing problems. In order to obtain the ideal cost and profit, a robust method for MG scheduling and optimization of EVs was provided in [15] by Bender decomposition and the Lagrange method.…”