Bidirectional smart charging of electric vehicles considering user preferences, peer to peer energy trade, and provision of grid ancillary services. (January 2021)

• Record Type: Journal Article
• Authors: Al-Obaidi, Abdullah; Khani, Hadi; Farag, Hany E.Z.; Mohamed, Moataz
• Is Part Of: International journal of electrical power & energy systems. Volume 124(2021)
• Publication Date: 2021-01
• Subjects: Smart charging -- Electric vehicles -- Ancillary services -- Peer–to–peer -- Economical operation -- Energy storage -- Smart contracts -- Blockchain; Electrical engineering -- Periodicals; Electric power systems -- Periodicals; Électrotechnique -- Périodiques; Réseaux électriques (Énergie) -- Périodiques; Electric power systems; Electrical engineering; Periodicals; 621.3
• DOI: 10.1016/j.ijepes.2020.106353
• Languages: English
• ISSNs: 0142-0615

Abstract

Highlights: A Survey is conducted on 693 EV owners in Canada pertaining to the EV usage. Blockchain's smart contracts allow decentralized energy trading among EVs. User preferences make the charging scheduling model adaptive to various conditions. Peer-to-peer trading and ancillary services provision are increased by up to 90%. Charging costs could be offset by external transactions revenue by up to 100%. Abstract: Electric vehicles (EVs) are witnessing increased utilization throughout the world as an alternative to fossil–fueled vehicles. The extensive deployment of EVs can bring challenges to the grid if not properly integrated. Such challenges, however, could be exploited as opportunities if the huge unused capacity of the battery storage in millions of EVs are utilized for ancillary services to the grid and peer–to–peer (PtP) energy trade. Given that there is at least one human user per vehicle, human input must be considered to improve the scheduling process. To that end, this paper presents a new algorithm for bidirectional smart charging of EVs considering user preferences, PtP energy trade, and provision of ancillary services to the grid. The preferences of an EV user as input to the model are embedded into the scheduling process enabling the model to be adaptive to various conditions. Optimization slack variables are utilized for optimal management of EV battery SOC and energy allocation for multiple services. New indices are developed and introduced for quantification of the EV participation in ancillary services and PtP transactions. Real–world data has been collected and utilized for model specification and simulation to make the assumptions more realistic. The efficacy and feasibility of the proposed model are validated using numerical studies. The results indicate that incorporating users' preferences into the scheduling process would improve the aggregated revenue generated by the EV scheduling model which in turn could offset the charging costs by up to 100%. Further, an increase of about 90% in peer-to-peer energy transactions among EVs and 11% in ancillary services provision to the grid are achieved through the developed user-centric smart charging model.