Dynamic load balancing and smart grid integration of fast-charging stations: A machine learning approach
Department of Automobile Engineering, Government Polytechnic, Kushalnagar, Karnataka, India.
Research Article
World Journal of Advanced Research and Reviews, 2020, 07(02), 392-400
Publication history:
Received on 17 July 2020; revised on 27 August 2020; accepted on 30 August 2020
Abstract:
The rapid proliferation of electric vehicles (EVs) has created unprecedented challenges for power grid infrastructure, particularly in managing the high-power demands of fast-charging stations. This research presents a novel approach to dynamic load balancing in EV fast-charging stations through machine learning integration with smart grid systems. The study develops and validates a predictive model that combines Long Short-Term Memory (LSTM) networks, Random Forest, and Gradient Boosting algorithms to optimize power distribution while maintaining grid stability. Using real-world data collected from 50 fast-charging stations across urban and suburban locations over a 12-month period, our proposed system demonstrates significant improvements over traditional methods. The results show a 27% improvement in load distribution efficiency and a 15% reduction in peak demand. Furthermore, the system achieves 92% accuracy in demand prediction and reduces charging waiting times by 8%. Grid stability metrics also improved substantially, with a 30% reduction in voltage fluctuations and a 20% improvement in power factor. The economic impact analysis reveals a 23% reduction in operational costs and a 35% improvement in energy utilization. The system's integration with smart grid infrastructure demonstrates robust scalability and adaptability to varying demand patterns. These findings suggest that machine learning-based load balancing can significantly enhance the reliability and efficiency of EV charging infrastructure while supporting grid stability. The proposed approach provides a promising foundation for future developments in smart charging systems, particularly in the context of increasing EV adoption rates and the growing need for sustainable transportation infrastructure.
Keywords:
Electric Vehicles; Fast-Charging Stations; Machine Learning; Load Balancing; Smart Grid Integration; LSTM Networks; Energy Management; Grid Stability
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Copyright © 2020 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0