Real power loss minimisation of smart grid with electric vehicles using distribution feeder reconfiguration. Issue 18 (9th September 2019)

• Record Type: Journal Article
• Authors: Singh, Jyotsna; Tiwari, Rajive
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 18(2019)
• Page Start: 4249
• Page End: 4261
• Publication Date: 2019-09-09
• Subjects: electric vehicles -- battery powered vehicles -- smart power grids -- genetic algorithms -- power grids -- distribution networks -- distributed power generation -- power distribution economics -- optimisation -- battery storage plants -- scheduling; optimal EV load demand -- 69‐bus distribution system -- 118‐bus distribution system -- EVs loads -- power loss minimisation -- smart grid -- electric vehicles -- distribution feeder reconfiguration -- ongoing growth -- anticipated growth -- uncoordinated charging -- random charging -- increased power loss -- vehicle‐to‐grid -- grid functionality -- stability -- EV demands; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6330
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The ongoing and anticipated growth in the use of electric vehicles (EVs) in transportation brings new opportunities for the development of new smart grids. EVs can transfer power from vehicle‐to‐grid (V2G) to potentially contribute towards improving grid functionality and stability. Coordinated charging/discharging of EVs is a possible solution to the challenges imposed by random charging and potential ways to extract benefits from the V2G functionality of EVs in the grid. Furthermore, coordinated scheduling can be augmented with effective operative tools to improve the operation of system. Towards addressing this goal, this study proposes a two‐stage optimisation to investigate the impact on the distribution system in terms of losses and voltage when distribution feeder reconfiguration (DFR) is employed with different scheduling strategies of EVs. In the first stage, the optimal charging/discharging schedule of EVs is developed on the basis of the technical and economic objective. A genetic algorithm‐based approach is used to model EV demands. The DFR problem is solved in the second stage with a new, improved version of a grey wolf optimisation algorithm considering the optimal EV load demand obtained from the first stage. The efficacy of proposed methodology is demonstrated on 69‐bus distribution system and 118‐bus distribution system.