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

Shahid Hussain, Ki-Beom Lee, Mohamed A. Ahmed, Barry Hayes and Young-Chon Kim
Additional contact information
Shahid Hussain: Division of Electronic and Information, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea
Ki-Beom Lee: Division of Electronic and Information, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea
Mohamed A. Ahmed: Department of Electronic Engineering, Universidad Técnica Federico Santa María, Valparaíso 2390123, Chile
Barry Hayes: School of Engineering, University College Cork, Cork T12K8AF, Ireland
Young-Chon Kim: Division of Electronic and Information, Department of Computer Science and Engineering, Jeonbuk National University, Jeonju 54896, Korea

Energies, 2020, vol. 13, issue 18, 1-31

Abstract: 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.

Keywords: electric vehicles; fuzzy logic inference; quality of experience; quality of performance; parking lot (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/18/4634/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/18/4634/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:18:p:4634-:d:409717

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().