Hybrid optimization enabled multi‐aggregator‐based charge scheduling of electric vehicle in internet of electric vehicles. (23rd February 2023)

• Record Type: Journal Article
• Authors: Suresh, Pandian; Shobana, Selvaraj; Ramya, Ganesan; Belsam Jeba Ananth, Manasea Selvin
• Is Part Of: Concurrency and computation. Volume 35:Number 9(2023)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2023-02-23
• Subjects: charging scheduling -- electric vehicles -- fractional calculus -- sea lion optimization algorithm -- social optimization algorithm; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.7654
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: In modern days, electric vehicles are quickly industrialized as well as their penetration is also increased highly, which brings more challenges for the power system. The electric vehicle charge scheduling process is vital to encourage the daily usage of the electric vehicle. However, irregular charging methods for electric vehicles may disturb voltage security areas because of their stochastic characteristics. Moreover, an electric vehicle requires recurrent charging owing to its constrained battery capacity, but it is a time‐consuming process. In this article, an effective charge scheduling model is devised using the fractional social sea lion optimization (Fr‐SSLO) algorithm. At first, IoEV network is simulated along with charge station and electric vehicle location. Furthermore, multi aggregator‐based charge scheduling is done for increasing the profit and amount of scheduled electric vehicles. Then, routing is performed based on developed Fr‐SSLO algorithm. Moreover, several fitness measures, including distance, energy and variable energy purchase are included. Here, the devised Fr‐SSLO model is designed by integrating fractional calculus (FC) and sea lion optimization (SLnO) technique along with SOA. After the completion of routing process, charge scheduling is performed based on developed Fr‐SSLO approach. Moreover, various fitness functions are also considered for computing better performance.