Blockchain and Federated Reinforcement Learning for Vehicle-to-Everything Energy Trading in Smart Grids
Md Moniruzzaman, Abdulsalam Yassine, Rachid Benlamri
Abstract
The proliferation of electric vehicles (EVs) and the advancement of vehicle-to-everything energy trading systems are expected to play a crucial role in alleviating the stress on the electric grid during peak hours. However, the wide adoption of these paradigms requires intelligent mechanisms that protect the security and privacy of EV users. This article proposes a novel federated reinforcement learning system combined with blockchain technology to maximize EV users' utility while preserving the security and privacy of trading transactions. Furthermore, we develop the concept of proof of state of charge as a consensus mechanism to determine the winning EVs and reward them as block miners in the blockchain. The proposed system is validated through comprehensive simulation experiments utilizing a real-world dataset. The model is implemented on the Avalanche blockchain platform to demonstrate its real-world feasibility. The test results show that the proposed scheme improves EV users' utility significantly compared to the existing studies. The obtained simulation results indicate the effectiveness and robustness of the proposed system.