Vehicle-to-grid technology for load balancing and energy management: A comprehensive review of technical, economic and environmental perspectives

Anis Ur Rehman, Junwei Lu, Bo Du, Feifei Bai, Mohammad J. Sanjari and Md Alamgir Hossain

Applied Energy, 2026, vol. 402, issue PB, No S0306261925017040

Abstract: The large-scale integration of vehicle-to-grid (V2G) technology presents both opportunities and challenges for power grid energy management. When effectively implemented, V2G technology can balance grid demand, lower energy costs, improve load factors, and facilitate renewable energy integration. To achieve these benefits through V2G, the paper presents a comprehensive review of global research and pilot projects, analysing algorithmic approaches and artificial intelligence-based methods for intelligent scheduling and energy optimization, mathematical and decision models for cost-efficient dispatch and system planning, and market-oriented strategies for managing uncertainty and enabling economic participation. It also examines advanced control and communication infrastructures essential for real-time energy management and secure bidirectional power exchange. The study also identifies key challenges such as the limited scalability of current optimization models, difficulties in capturing correlated uncertainties, heavy reliance on accurate forecasting within energy management systems, poor coordination between control layers, misalignment with real market behaviours, and constraints in communication and cybersecurity. To address these challenges, it proposes various solutions, including hybrid optimization frameworks, adaptive and self-correcting energy management system, chemistry-specific battery degradation models, coordinated hierarchical dispatch, behavioural-economic integration, multi-service market platforms, and standardized secure communication protocols © 2017 Elsevier Inc. All rights reserved.

Keywords: Demand management; Load balancing; Economic benefits; Global projects; Vehicle-to-grid; Power Grid Optimization; Artificial intelligence in Energy; Renewable energy integration (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1016/j.apenergy.2025.126974

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