A novel method for optimal placement of vehicle-to-grid charging stations in distribution power system using a quantum binary lightning search algorithm. (April 2018)

• Record Type: Journal Article
• Authors: Aljanad, Ahmed; Mohamed, Azah; Shareef, Hussain; Khatib, Tamer
• Is Part Of: Sustainable cities and society. Volume 38(2018)
• Page Start: 174
• Page End: 183
• Publication Date: 2018-04
• Subjects: Vehicle-to-grid -- Plug-in hybrid electric vehicle -- Quantum binary lightning search algorithm -- Charging stations -- Line loading; Sustainable urban development -- Periodicals; Sustainable buildings -- Periodicals; Urban ecology (Sociology) -- Periodicals; 307.76
• DOI: 10.1016/j.scs.2017.12.035
• Languages: English
• ISSNs: 2210-6707

Abstract

Highlights: We proposed novel algorithm for optimal placement of EV charging stations. The optimization is based on power system loading and quality. Results are compared with other optimization algorithm. The impact of EV charging stations on power system is also studied. Abstract: Vehicle-to-Grid (V2G) technology is currently used in plug-in hybrid electric vehicles (PHEV) during the discharging mode. This technology has become a considerable mechanism to provide a backup power source for distribution networks. In the meanwhile, these vehicles need Charging Stations (CSs) to receive power from the grid. Therefore, determining the optimal CS placement in the distribution network to utilize the V2G technology of PHEV has become an essential component so as to enhance the performance of the distribution network in the time of peak demand. This paper discusses a generalized methodology to assess the optimal placement when installing the CS in the distribution network. This optimization is done using a novel heuristic optimization technique called quantum binary lightning search algorithm. A multi-objective function is set to utilize the V2G technology to minimize line loading, voltage deviation, and circuit power loss. The performance of the proposed method is compared to the performance of other heuristic optimization techniques. As a result, the proposed algorithm has fewer limitations in terms of premature convergence than other heuristic optimization techniques.