Electric Vehicle Charging Station Optimization using Game Theory

Electric vehicle (EV) charging station optimization is a critical area of research aimed at improving the efficiency, fairness, and security of charging networks. As EV adoption grows, issues such as resource monopolization, unpredictable usage patterns, and potential system abuse have emerged. To address these challenges, this thesis proposes a framework that integrates game theory, dynamic pricing, anomaly detection, and secure data processing to manage EV charging resources effectively. The model uses a Stackelberg-inspired pricing strategy to dynamically adjust costs based on demand, encouraging equitable resource distribution among users. It also introduces a privacy-preserving anomaly detection method that combines AES encryption with the Isolation Forest algorithm, so that user data stays secure while malicious behaviors, such as resource hoarding, are detected.