Online optimization of a workplace electric vehicle charging station under grid constraints

Anna Malkova*, Jan Martin Zepter, Mattia Marinelli

*Corresponding author for this work

Research output: Contribution to conferencePaperResearch

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Abstract

As the number of electric vehicles (EVs) on the roads continues to grow, the implementation of smart charging strategies will be a viable solution to reduce the impact of simultaneous charging of a large number of EVs and assist in mitigating the variability of local renewable energy source (RES) generation. This article presents a control model for a workplace EV charging station, focusing on maximizing the profit of the charging station while considering the presence of local PV generation. The proposed model is an online shrinking horizon optimization model that operates in 5-minute intervals, determining the power reference for the EV cluster at the charging station. In addition, the model incorporates a day-ahead PV forecast which will be updated with actual measurements throughout the day. The model demonstrates responsiveness to changing system conditions. The model is compared to perfect foresight of PV production and only using PV forecast data for the dispatch. The comparative analysis of the proposed model indicates a 5.6 % improvement in profit compared to the initial dispatch of power reference using only PV forecast data.
Original languageEnglish
Publication date2023
Number of pages7
Publication statusPublished - 2023
Event7th E-Mobility Power System Integration Symposium - Lyngby, Denmark
Duration: 25 Sept 202325 Sept 2023
https://mobilityintegrationsymposium.org/

Conference

Conference7th E-Mobility Power System Integration Symposium
Country/TerritoryDenmark
CityLyngby
Period25/09/202325/09/2023
Internet address

Keywords

  • Electric vehicles
  • Cluster management
  • Online optimization
  • Shrinking horizon
  • Model predictive control

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