Multi-Agent Based Simulation for Investigating Centralized Charging Strategies and their Impact on Electric Vehicle Home Charging Ecosystem

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Abstract

This paper addresses the critical integration of electric vehicles (EVs) into the electricity grid, which is essential for achieving carbon neutrality by 2050. The rapid increase in EV adoption poses significant challenges to the existing grid infrastructure, particularly in managing the increasing electricity demand and mitigating the risk of grid overloads. Centralized EV charging strategies are investigated due to their potential to optimize grid stability and efficiency, compared to decentralized approaches that may exacerbate grid stress. Utilizing a multi-agent based simulation model, the study provides a realistic representation of the electric vehicle home charging ecosystem in a case study of Strib, Denmark. The findings show that the Earliest-deadline-first and Round Robin perform best with 100% EV adoption in terms of EV user satisfaction. The simulation considers a realistic adoption curve, EV charging strategies, EV models, and driving patterns to capture the full ecosystem dynamics over a long-term period with high resolution (hourly). Additionally, the study offers detailed load profiles for future distribution grids, demonstrating how centralized charging strategies can efficiently manage grid loads and prevent overloads.

Original languageEnglish
Title of host publicationEnergy Informatics : 4th Energy Informatics Academy Conference, EI.A 2024, Kuta, Bali, Indonesia, October 23–25, 2024, Proceedings, Part I
PublisherSpringer
Publication date2025
Pages193-209
ISBN (Print)978-3-031-74737-3
ISBN (Electronic)978-3-031-74738-0
DOIs
Publication statusPublished - 2025
EventEnergy Informatics.Academy Conference 2024 - Bali, Indonesia
Duration: 23. Oct 202425. Oct 2024
https://www.energyinformatics.academy/eia-2024-conference

Conference

ConferenceEnergy Informatics.Academy Conference 2024
LocationBali
Country/TerritoryIndonesia
Period23/10/202425/10/2024
Internet address
SeriesEnergy Informatics
ISSN2520-8942

Keywords

  • agent-based modeling
  • charging algorithms
  • charging strategies
  • electric vehicle
  • multi-agent based simulation
  • multi-agent systems

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