An Efficient and Secure Energy Trading Approach with Machine Learning Technique and Consortium Blockchain

Area of Science:

• Computer Science
• Electrical Engineering
• Blockchain Technology

Background:

• Vehicular energy networks (VENs) face challenges in secure energy trading and efficient electric vehicle (EV) charging.
• Traditional energy trading involves centralized systems, leading to high costs, delays, data tampering, and single points of failure.
• EVs struggle with optimal charging station selection and energy calculation for transit.

Purpose of the Study:

• To propose a secure and efficient energy trading mechanism for VENs using blockchain technology.
• To address issues of insecure trading, inefficient charging, and data integrity in EV energy networks.
• To mitigate problems associated with centralized energy trading systems.

Main Methods:

• A blockchain-based secure energy trading mechanism is developed for VENs.
• A novel branching concept divides EV data into a Fraud Chain (F-chain) and an Integrity Chain (I-chain) to optimize storage and computation.
• An attacker model is employed to test system robustness against double-spending and replay attacks; smart contract security is analyzed.

Main Results:

• The proposed system demonstrates efficient communication between EVs and aggregators via blockchain.
• The branching mechanism effectively reduces storage requirements and computational time.
• Simulation results confirm a significant reduction in EV charging costs and time within the VEN.

Conclusions:

• The blockchain-based energy trading mechanism offers a secure and efficient solution for VENs.
• The F-chain and I-chain branching strategy enhances system performance and data integrity.
• The proposed model effectively addresses key challenges in EV charging and energy trading, improving overall network efficiency and security.