Adaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand

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dc.contributor.authorPitchaya Jamjuntr
dc.contributor.authorChanchai Techawatcharapaikul
dc.contributor.authorPannee Suanpang
dc.contributor.correspondenceP. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th
dc.date.accessioned2025-03-10T07:34:21Z
dc.date.available2025-03-10T07:34:21Z
dc.date.issued2024
dc.description.abstractThe rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of ThailandÕs evolving electric mobility landscape. © 2024 by the authors.
dc.identifier.citationWorld Electric Vehicle Journal
dc.identifier.doi10.3390/wevj15100453
dc.identifier.issn20326653
dc.identifier.scopus2-s2.0-85207349990
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4502
dc.languageEnglish
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.rightsAll Open Access; Gold Open Access
dc.rights.holderScopus
dc.subjectadaptive multi-agent systems
dc.subjectdynamic optimization
dc.subjectelectric vehicle charging
dc.subjectreinforcement learning
dc.titleAdaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand
dc.typeArticle
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85207349990&doi=10.3390%2fwevj15100453&partnerID=40&md5=3e2c602d1f98776e57c0200f639c3739
oaire.citation.issue10
oaire.citation.volume15
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