Smart Energy Transactions in Vehicle-to-Grid Networks: A Deep Q-Network Approach with Blockchain
Ali Hasan Ali · Fusion Practice and Applications · 2024
Electric vehicles (EVs) have gained significant traction due to their environmental benefits and potential to revolutionize the transportation sector. Integrating EVs into the Vehicle-to-Grid (V2G) network presents an innovative solution for optimizing energy transactions and grid stability. However, managing energy transactions during peak hours poses a challenge. This research proposes a novel approach that combines the Deep Q-Network (DQN) algorithm with block chain technology to enhance energy transactions in the V2G network. In this study, a V2G network model is introduced consisting of EVs, charging stations, a grid control center, and a block chain infrastructure. The block chain ensures transparency, security, and decentralized energy transactions. The DQN algorithm learns optimal action policies based on current states and rewards, contributing to grid stability. To incentivize EV owners for peak-hour energy contributions, a block chain-enabled rewarding mechanism is implemented. The proposed methodology is rigorously evaluated through simulations conducted in a custom environment that emulates V2G network dynamics. Performance metrics such as load shifting efficiency, peak demand reduction, and energy efficiency are employed for comprehensive assessment. The proposed method showcases superior performance compared to traditional load shifting and demand response strategies. Furthermore, comparative analyses are conducted against different state-of-the-art methods, demonstrating the effectiveness of our approach. The results underscore the potential of integrating DQN-based energy management with block chain technology to achieve grid stability and incentivize sustainable energy behaviors. This research contributes to the advancement of smart grid technologies, paving the way for a more sustainable and efficient energy ecosystem.