Two-Stage Fuzzy Logic Inference Algorithm for Maximizing the Quality of Performance under the Operational Constraints of Power Grid in Electric Vehicle Parking Lots

Energies

MohamedA. Ahmed¹  Shahid Hussain²  Young-Chon Kim²  Ki-Beom Lee²  Barry Hayes³ 

[1] Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile;Division of Electronic and Information, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea;School of Engineering, University College Cork, Cork T12K8AF, Ireland;

关键词: electric vehicles;    fuzzy logic inference;    quality of experience;    quality of performance;    parking lot;   

DOI  :  10.3390/en13184634

来源: DOAJ

【 摘 要 】

The widespread adoption of electric vehicles (EVs) has entailed the need for the parking lot operators to satisfy the charging and discharging requirements of all the EV owners during their parking duration. Meanwhile, the operational constraints of the power grids limit the amount of simultaneous charging and discharging of all EVs. This affects the EV owner’s quality of experience (QoE) and thereby reducing the quality of performance (QoP) for the parking lot operators. The QoE represents a certain percentage of the EV battery required for its next trip distance; whereas, the QoP refers to the ratio of EVs with satisfied QoE to the total number of EVs during the operational hours of the parking lot. This paper proposes a two-stage fuzzy logic inference based algorithm (TSFLIA) to schedule the charging and discharging operations of EVs in such a way that maximizes the QoP for the parking lot operators under the operational constraints of the power grid. The first stage fuzzy inference system (FIS) of TSFLIA is modeled based on the real-time arrival and departure probability density functions in order to calculate the aggregated charging and discharging energies of EVs according to their next trip distances. The second stage FIS evaluates several dynamic and uncertain input parameters from the electric grid and from EVs to distribute the aggregated energy among the EVs by controlling their charging and discharging operations through preference variables. The feasibility and effectiveness of the proposed algorithm are demonstrated through the IEEE 34-node distribution system.

【 授权许可】

Unknown