New technologies for optimal scheduling of electric vehicles in renewable energy‐oriented power systems: A review of deep learning, deep reinforcement learning and blockchain technology. Issue 6 (22nd December 2022)

• Record Type: Journal Article
• Authors: Ma, Wenshuai; Hu, Junjie; Yao, Li; Fu, Zhuoming; Morais, Hugo; Marinelli, Mattia
• Is Part Of: Energy conversion and economics. Volume 3:Issue 6(2022)
• Page Start: 345
• Page End: 359
• Publication Date: 2022-12-22
• Subjects: electric vehicles -- optimal scheduling -- artificial intelligence applications -- blockchain applications ; Energy conversion -- Periodicals; Energy conversion; Periodicals; 621.3124
• DOI: 10.1049/enc2.12071
• Languages: English
• ISSNs: 2634-1581

Abstract

Abstract: With global concerns about carbon emissions, the proportion of renewable energy generation worldwide is increasing, and the demand for flexible resources in power systems is growing. In recent years, as a clean means of transportation, the number of electric vehicles has increased, and the optimal scheduling of electric vehicles has become a research hotspot. The rise of artificial intelligence, blockchain, and other innovative technologies has enriched research on optimal scheduling of electric vehicles. To reveal the latest developments in electric vehicle optimal scheduling studies, this paper summarises the application of state‐of‐the‐art technologies, including deep learning, deep reinforcement learning, and blockchain technology in the optimal scheduling of electric vehicles. Moreover, the advantages and disadvantages of various technical applications are highlighted. Finally, considering the shortcomings and developmental status of applications of the above three technologies, some suggestions for future research directions are proposed.