The Prediction of Electric Vehicles Load Profiles Considering Stochastic Charging and Discharging Behavior and Their Impact Assessment on a Real UK Distribution Network. (February 2019)

• Record Type: Journal Article
• Authors: Hu, Qian; Li, Haiyu; Bu, Siqi
• Is Part Of: Energy procedia. Volume 158(2019)
• Page Start: 6458
• Page End: 6465
• Publication Date: 2019-02
• Subjects: Electric Vehicles (EVs) -- Monte Carlo simulation -- Distribution network -- Impact assessment; Power resources -- Congresses; Power resources -- Periodicals; Power resources; Conference proceedings; Periodicals; 333.7905
• DOI: 10.1016/j.egypro.2019.01.134
• Languages: English
• ISSNs: 1876-6102

Abstract

Abstract: Electric vehicle (EV) as one of the most promising solutions to reduce the greenhouse emission is developing faster than ever. With the increasing number of EVs, the additional load will cause technical issues on the existing distribution network. To cope with possible challenges, the reasonable prediction of EV load profile is fundamental to the evaluation of how the distribution network responds to the potential increasing EV penetration. This paper investigates the critical issues that EVs bring into the network at various penetration levels considering the uncertainties due to stochastic charging and discharging behaviour. To deal with these uncertainties, a Monte Carlo based simulation method is utilised to create EV charging and discharging profiles. Three scenarios are proposed and their impacts on a real UK distribution network are analysed by the simulation in OpenDSS and MATLAB. The simulation results imply that EV charging process has the negative effect in regard to thermal stress, voltage drop, system efficiency and power factor of the network. Conclusions are drawn to provide the guidance for the upgrading and reinforcement of the existing network assets.