Electric Vehicle Charging Schedules in Workplace Parking Lots Based on Evolutionary Optimization Algorithm

Stavros Poniris and Anastasios I. Dounis ()
Additional contact information
Stavros Poniris: Department of Biomedical Engineering, University of West Attica, Egaleo Park Campus, Ag. Spyridonos Str., Egaleo, 12243 Athens, Greece
Anastasios I. Dounis: Department of Biomedical Engineering, University of West Attica, Egaleo Park Campus, Ag. Spyridonos Str., Egaleo, 12243 Athens, Greece

Energies, 2022, vol. 16, issue 1, 1-16

Abstract: The electrification of vehicles is considered to be the means of reducing the greenhouse gas (GHG) emissions of the transport sector, but “range anxiety” makes most people reluctant to adopt electric vehicles (EVs) as their main method of transportation. Workplace charging has been proven to counter range anxiety and workplace charging is becoming quite common. A workplace parking lot can house hundreds of EVs. In this paper, a program has been developed in MATLAB that uses the well-known evolutionary optimization algorithm, the genetic algorithm (GA), to optimize the charging schedule of fifty EVs that aims at achieving three goals: (a) keeping the electricity demand low, (b) reducing the cost of charging and (c) applying load shifting. Three schedules were developed for three scenarios. The results demonstrate that each schedule was successful in achieving its goal, which means that scheduling the charging of a fleet of EVs can be used as a method of demand-side management (DSM) in workplace parking lots and at the same time reduce the energy cost of charging. In the scenarios examined in this paper, cost was reduced by approximately 2%.

Keywords: electric vehicle; genetic algorithm; charging schedule; workplace charging; evolutionary optimization; demand-side management (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/1/221/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/1/221/ (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:16:y:2022:i:1:p:221-:d:1014481

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 ().