Threats, Vulnerabilities, and Mitigation in V2G Networks

Vehicle-to-grid (V2G) networks are becoming more common in smart grid systems. This makes it imperative to implement strong security measures to guard against cyberattacks and communication errors. Researchers have suggested various methods, such as fuzzy logic-based approaches and deep reinforcement learning (DRL), to overcome these security concerns. In this chapter, we compare how well these two strategies enhance the security of V2G networks. The fuzzy logic-based approach uses a rule-based system to make decisions based on input variables, while the proposed DRL-based approach uses the deep deterministic policy gradient (DDPG) algorithm to learn the best V2G network security policies. Both methods were implemented using the software packages MATLAB and Anaconda. We analyse the effectiveness of different approaches using quantitative data and evaluate how well they work in attack detection, mitigation, and communication dependability. According to our research, both methods can successfully increase the security of V2G networks, but each has its own benefits and drawbacks. Overall, this chapter adds to the body of literature by shedding light on the potential of DRL and fuzzy logic for boosting the security of V2G networks in smart grid systems. Our results are helpful for researchers and professionals looking to build efficient security measures for V2G networks.