• Explains V2G Systems: Breaks down the essential hardware, communication, and energy management parts of Vehicle-to-Grid technology. • Compares Bidirectional Chargers: Analyzes and compares different types of Bidirectional chargers. • Covers Control Methods: Discusses everything from traditional control techniques to advanced AI and machine learning for smarter energy management. • Analyzes Battery Impact: Investigates how V2G affects EV battery health, performance, and the overall cost-effectiveness. • Highlights Digital Twin Technology: Shows how creating virtual models of V2G systems helps test, predict performance, and check financial viability. • Addresses Cybersecurity: Outlines the critical security risks in V2G and the measures needed to protect the system. • Reviews Global Projects & Future Trends: Shares lessons from real-world V2G case studies and looks ahead to future research involving IoT, AI, and blockchain. The market for electric vehicles is booming. This surge poses challenges for power systems as the simultaneous charging of electric vehicles occurs without enough organization. This situation may slow our transition to clean energy. An innovative approach in Electric Vehicles (EVs) is Vehicle-to-Grid (V2G) technology, which allows EVs to interact with the power grid and become active participants in the energy system rather than passive consumers. By transferring unused battery power from vehicles to the grid, this technology can help balance electricity supply and demand, particularly during peak periods. This review examines the functionality of V2G systems, including their architecture, communication protocols, converter technology, battery performance and degradation rates, and various control methods (from traditional techniques to machine learning approaches), as well as security issues. Digital twins are highly significant. They are utilized for virtual replicas, real-time observation, estimating battery health, and assessing feasibility within Distributed Energy Resource Management Systems (DERMS). This assessment contrasts traditional methods with emerging technologies such as machine learning for predictive analytics, IoT, and blockchain, considering current offerings and anticipated market introductions by 2026. These viewpoints offer crucial guidance to stakeholders in building sustainable, intelligent energy systems.
Munusamy et al. (Sun,) studied this question.