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

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 GGlueVaR-based 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.

Automated summary

The article proposes a two-stage electric vehicle automatic demand response optimization method based on generalized Glue value-at-risk, considering both the EV owner's benefit and network load fluctuation. By measuring an EV group's risk attitude, the method effectively reduces charging costs and increases DR revenue.