• Volume: 2
• Issue:01
• Date: 01-01-2025

Title : Analyzing the Impact of Electric Vehicle Charging on Grid Congestion and Load Management: A Comprehensive Review

Abstract: The widespread adoption of electric vehicles (EVs) is transforming the transportation and energy sectors, presenting both opportunities and challenges for power grid infrastructure. As EV penetration increases, uncoordinated charging patterns contribute to grid congestion, voltage fluctuations, transformer overloading, and peak load surges, which can compromise grid stability and efficiency. Effective load management strategies are essential to mitigate these challenges and ensure a sustainable, resilient, and optimized power distribution system. This review paper examines the impact of EV charging on grid congestion and load management, focusing on charging demand forecasting, smart charging algorithms, demand response mechanisms, and vehicle-to-grid (V2G) technology. It explores the role of machine learning (ML), artificial intelligence (AI), blockchain-based energy trading, and dynamic pricing models in enhancing grid flexibility and efficiency. Furthermore, it discusses the policy frameworks, regulatory challenges, and infrastructure requirements necessary for seamless EV-grid integration. By analyzing recent research, real-world case studies, and emerging smart grid innovations, this study provides valuable insights for grid operators, policymakers, and energy stakeholders. The findings highlight the importance of predictive analytics, decentralized energy management, and intelligent charging strategies in minimizing grid congestion and optimizing load distribution. As EV adoption continues to grow, developing scalable, data-driven, and adaptive grid management solutions will be crucial to achieving a sustainable and resilient energy future.

Key Words: Electric Vehicle Charging, Grid Congestion, Load Management, Smart Grid, Demand Response, Vehicle-to-Grid (V2G), Renewable Energy Integration, AI in Energy, Dynamic Pricing

Area: Engineering

• Approved ISSN: ----
• Paper Id: IJREISTU21
• Page No: 1-5

• Author: Tej Pratap Singh

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• Co- Author: Ashish Bhargava