SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning

In recent years, advancements in electric vehicle (EV) technology and rising petrol prices have increased the demand for EVs and also made them important for the Smart Grid (SG) economy. During the high energy demand, Vehicle to Grid (V2G) comprises a notable feature that returns the stored energy back to the grid. However, due to dynamic nature of energy prices and EVs availability, determining the best charging and discharging strategy is quite difficult. The existing approaches need a model to predict the uncertainty and optimize the scheduling problem. Further, other issues like security, scalability, and real-time data accessibility of EVs energy trading (ET) data at low cost also exist. Though many solutions exist, they are not adequate to handle the aforementioned issues. This paper proposes a Secure V2G-Energy Trading (SV2G-ET) scheme using deep Reinforcement Learning (RL) and Ethereum Blockchain Technology (EBT). The proposed SV2G-ET scheme employs a deep Q-network for EVs scheduling for charging/discharging. SV2G-ET scheme uses InterPlanetary File System (IPFS) and smart contract (SC) for secure access of EV's ET data in real time. The experimental results prove the efficacy of the proposed SV2G-ET scheme that leads to improved scalability, saving the EVs charging cost, low ET data storage cost, and increased EV owner's profit.