Optimizing Charging Schedules for Vehicle-to-Grid Systems: A Comparative Study of Different Algorithmic Approaches
Publish Year: 1403
نوع سند: مقاله کنفرانسی
زبان: English
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شناسه ملی سند علمی:
ICPCONF10_015
تاریخ نمایه سازی: 4 آذر 1403
Abstract:
The growing trend of electric vehicle (EV) adoption and vehicle-to-grid (V۲G) technology has emerged as a promising solution for optimizing the use of electrical energy in the transportation sector. V۲G systems enable EVs to supply energy back to the grid during periods of low demand, reducing the strain on the power grid and providing a cleaner, more sustainable source of energy. However, the efficiency of V۲G systems depends heavily on the charging schedules employed. In this study, we conduct a comparative analysis of different algorithmic approaches for optimizing charging schedules in V۲G systems.We consider three categories of algorithms: traditional time-based, machine learning techniques, and game theoretical approaches. For each category, we present a detailed description of the algorithms and their underlying assumptions, followed by a comprehensive comparison of their strengths and weaknesses. Our evaluation criteria include computational complexity, scalability, adaptability to changing conditions, and economic performance. Our results show that machine learning techniques outperform traditional methods in terms of adaptability and economic performance, while game theoretical approaches offer the best scalability and computational efficiency. However, no single approach dominates across all criteria, indicating that the choice of algorithm depends on the specific needs and constraints of the V۲G system. We conclude by discussing the implications of our findings for practitioners and policymakers and highlighting several promising areas for future research. Overall, this study provides valuable insights into the tradeoffs between different algorithmic approaches for optimizing charging schedules in V۲G systems, shedding light on the most effective strategies for leveraging this promising technology to create a more sustainable and efficient energy landscape.
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Authors
Sina Samadi Gharehveran
Department of Electrical and Computer Engineering, University of Tabriz
Nima Saei
Department of Electrical and Computer Engineering, University of Tabriz
Kimia Shirini
Department of Electrical and Computer Engineering, University of Tabriz
Mohammad Mahdi Hejazi
Department of Electrical and Computer Engineering, University of Tabriz