Reactive power compensation using electric vehicles considering drivers' reasons. Issue 20 (12th March 2018)

• Record Type: Journal Article
• Authors: Su, Su; Hu, Yong; Wang, Shidan; Wang, Wei; Ota, Yutaka; Yamashita, Koji; Xia, Mingchao; Nie, Xiaobo; Chen, Lijiang; Mao, Xia
• Is Part Of: IET generation, transmission & distribution. Volume 12:Issue 20(2018)
• Page Start: 4407
• Page End: 4418
• Publication Date: 2018-03-12
• Subjects: reactive power control -- electric vehicle charging -- battery powered vehicles -- nonlinear programming; electric vehicle charging load -- driver reasons -- node voltage profile -- EV charging load -- EV batteries -- flexible reactive power compensation -- substation level -- reactive power compensation strategy -- charging demand -- charging opportunity loss -- profit -- EV chargers -- unregulated charging -- power factors -- nonlinear optimisation problem -- incentive method -- node voltage -- distribution network data -- Beijing -- China -- voltage regulation -- voltages deviation; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2017.1114
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : The node voltage profile is more likely to be violated as the electric vehicles (EVs) charging load spreads in distribution network. Due to the stochastic nature of EV charging load spatially, more flexible reactive power compensation in different locations becomes important. However, the conventional reactive power compensation equipment has no flexibility spatially. Therefore, two kinds of reactive power compensation strategies using EVs considering drivers' reasons are proposed. Drivers' reasons contain charging demand, charging opportunity loss (time) and profit. In Strategy 1, EV chargers are used to fully compensate reactive power after finishing the unregulated charging. Thus, drivers' charging behaviour is not influenced at all. In Strategy 2, the operating power factors of EV chargers are treated as variables for the optimisation. The constraint of operating power factors is derived from analysing the charging demand and the charging opportunity loss (time) for drivers. Then, in order to motivate drivers, an incentive method is introduced based on the quantification of each driver's contribution to the voltage. The case study shows that Strategy 1 performs well at nodes having the non‐significant voltage deviation without any constraint on driver's charging behaviour, while Strategy 2 performs well at nodes where the voltages deviation is significant.