Overload Mitigation in Electric Vehicle Smart Charging Algorithms Using Photovoltaic Generation Forecasting

Lucas Zenichi Terada*, Juan C. Cortez, Juan Camilo López, João Soares, Zita Vale, Marcos J. Rider

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Recently, the growing use of electric vehicles (EVs) and distributed energy resources (DERs) has brought about the emergence of new stakeholders in the public EV charging market. This has resulted in a need for innovative techniques in electric vehicle smart charging (EVSC) to lower charging costs and optimize renewable energy sources (RES) usage. However, an unsupervised EVSC system may experience overloads or violations of the maximum power defined by the charging point operator (CPO) due to fluctuations in photovoltaic (PV) generation. To address this issue, this study proposes a heuristic method to prevent overloads in the electric vehicle aggregated system (EVAS), considering various 15-minute interval PV generation prediction methods. Specifically, this research compares the effectiveness of different prediction models, such as ARIMA, LSTM, and CNN-LSTM, in minimizing overloads during a 4-week evaluation period. The study's outcomes are expected to offer valuable insights into creating more efficient and reliable EVSC systems. Both Machine Learning (ML) methods succeeded in reducing the occurrences of overloads by approximately 50%. However, ARIMA method yielded even more impressive results, reducing the occurrence of violations by approximately 87%.

Original languageEnglish
Title of host publication2023 International Conference on Future Energy Solutions, FES 2023
Place of PublicationPiscataway, NJ
PublisherIEEE
Number of pages6
ISBN (Electronic)979-8-3503-3230-8
ISBN (Print)979-8-3503-3231-5
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event2023 International Conference on Future Energy Solutions, FES 2023 - Vaasa, Finland
Duration: 12 Jun 202314 Jun 2023

Conference

Conference2023 International Conference on Future Energy Solutions, FES 2023
Abbreviated titleFES
Country/TerritoryFinland
CityVaasa
Period12/06/2314/06/23

Keywords

  • Electric vehicles smart charging
  • Machine Learning (ML)
  • Overload mitigation
  • Photovoltaic forecasting
  • n/a OA procedure

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