TokenGreen: A Versatile NFT Framework for Peer-to-Peer Energy Trading and Asset Ownership of Electric Vehicles

The rapid increase in the adoption of electric vehicles (EVs) and the installation of charging stations (CSs) are key components for bidirectional energy transfer between EVs and CSs. However, the traditional techniques of energy trading have issues of trust, scalability, traceability, provenance, and authenticity among energy prosumers. To address these challenges, particularly information imbalances between energy buyers and sellers, we propose TokenGreen, a novel framework that leverages blockchain and nonfungible tokens (NFTs) to enable participants to have ownership of energy assets through investments in distributed energy generation, distribution, and clean energy infrastructure, leading to trust and transparency management among the participants. The proposed framework uses Ethereum virtual machine (EVM), ERC-721 NFT, interplanetary file system (IPFS), and solidity smart contracts to develop an NFT-based energy marketplace. Various smart contracts, contract events, functions, and algorithms have been designed and integrated into the energy marketplace to facilitate the minting, creation, purchase, and resale of NFT tokens, including energy trading. To assess the performance of the proposal, experiments are performed using tools, such as Geth, Hyperledger Caliper, and the Ethereum SDK. The obtained results indicate that the average maximum latency for CreateToken reached 12.39 s, while BuyToken and ResellToken reached 11.02 s. Additionally, the average minimum latency for CreateToken, BuyToken, and ResellToken reached 10.46, 10.03, and 9.14 s, respectively. On average, memory consumption ranged from 640 to 775 MB, while CPU usage averaged between 30% and 55% for each function. The performance analysis indicates that CreateToken has low throughput, while BuyToken shows higher, and ResellToken exhibits the highest throughput due to fewer write operations. TokenGreen demonstrates superior performance compared to the existing state of the art, considering the mentioned parameters.