Receding horizon optimization for distributed control of electric vehicle charging stations

Anna Malkova, Jan Martin Zepter, Mattia Marinelli, Herbert Amezquita, Hugo Morais

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

37 Downloads (Pure)

Abstract

With the continuous increase of electric vehicle (EV) adoption, deploying smart charging techniques offer a practical solution to mitigate the impact of grid overloading caused by simultaneous EV charging. At the same time, smart charging can help to stabilize the fluctuations in the production from local renewable energy sources (RES). This article introduces a receding horizon optimization model for the distributed control of EV chargers at charging stations, focusing on maximizing the profit of the charging station, while enhancing the utilization of local PV generation. The proposed model operates in 5-minute intervals, determining the power reference for the EV cluster at the charging station. Results demonstrate that the proposed model effectively lowers electricity cost for charging stations, while ensuring more than 90% energy delivery for charging EVs. Future research will be focused on integrating wind energy and refining the model in controlled lab tests for practical implementation and validation.
Original languageEnglish
Title of host publicationProceedings of IEEE PES ISGT Europe 2024
Number of pages5
PublisherIEEE
Publication statusAccepted/In press - 2024
EventIEEE PES ISGT Europe 2024 - Zagreb, Croatia
Duration: 14 Oct 202417 Oct 2024

Conference

ConferenceIEEE PES ISGT Europe 2024
Country/TerritoryCroatia
CityZagreb
Period14/10/202417/10/2024

Keywords

  • Electric vehicles
  • Receding horizon
  • Distributed control
  • EV charging station

Fingerprint

Dive into the research topics of 'Receding horizon optimization for distributed control of electric vehicle charging stations'. Together they form a unique fingerprint.

Cite this