Optimal Dynamic Scheduling of Electric Vehicles in a Parking Lot Using Particle Swarm Optimization and Shuffled Frog Leaping Algorithm

Energies

Adel El-Shahat¹  JagabarS. Ali²  GeorgeS. Fernandez²  Selvakumar Kuppusamy²  Vijayakumar Krishnasamy²  ZiadM. Ali³  ShadyH. E. Abdel Aleem⁴ 

[1] Department of Electrical and Computer Engineering, Georgia Southern University (GSU), Statesboro, GA 30460-7995, USA;Department of Electrical and Electronics Engineering, SRM University, Kattankulathur 603-203, India;Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, Al-Kharj 11991, Saudi Arabia;Power Quality Department, ETA Electric Company, El Omraniya, Giza 12111, Egypt;

关键词: charging cost;    dynamic charging;    economics;    electric vehicles;    optimization;    parking lots;   

DOI  :  10.3390/en13236384

来源: DOAJ

【 摘 要 】

In this paper, the optimal dynamic scheduling of electric vehicles (EVs) in a parking lot (PL) is proposed to minimize the charging cost. In static scheduling, the PL operator can make the optimal scheduling if the demand, arrival, and departure time of EVs are known well in advance. If not, a static charging scheme is not feasible. Therefore, dynamic charging is preferred. A dynamic scheduling scheme means the EVs may come and go at any time, i.e., EVs’ arrival is dynamic in nature. The EVs may come to the PL with prior appointments or not. Therefore, a PL operator requires a mechanism to charge the EVs that arrive with or without reservation, and the demand for EVs is unknown to the PL operator. In general, the PL uses the first-in-first serve (FIFS) method for charging the EVs. The well-known optimization techniques such as particle swarm optimization and shuffled frog leaping algorithms are used for the EVs’ dynamic scheduling scheme to minimize the grid’s charging cost. Moreover, a microgrid is also considered to reduce the charging cost further. The results obtained show the effectiveness of the proposed solution methods.

【 授权许可】

Unknown