Real-Time Power Management of Plug-In Electric Vehicles and Renewable Energy Sources in Virtual Prosumer Networks with Integrated Physical and Network Security Using Blockchain - Nikolaos Sifakis; Konstantinos Armyras; Fotis Kanellos (Energies)
This paper presents a blockchain-enabled Multi-Agent System (MAS) for real-time power management in Virtual Prosumer (VP) Networks, integrating Plug-in Electric Vehicles (PEVs) and Renewable Energy Sources (RESs). The proposed framework addresses critical challenges related to scalability, security, and operational efficiency by developing a hierarchical MAS architecture that optimizes the scheduling and coordination of geographically distributed PEVs and RESs. Unlike conventional business management systems, this study integrates a blockchain-based security mechanism within the MAS framework, leveraging Proof of Authority (PoA) consensus to enhance transaction security while addressing scalability and energy consumption concerns. The system further employs advanced Particle Swarm Optimization (PSO) to dynamically compute optimal power set-points, enabling adaptive and efficient energy distribution. Additionally, hierarchical aggregation of transactions at lower MAS layers enhances computational efficiency and system resilience under high-traffic and partial network failure conditions. The proposed framework is validated through large-scale simulations spanning four major cities in Greece, demonstrating its scalability, reliability, and efficiency under diverse operational scenarios. Results confirm that the system effectively balances energy supply and demand while maintaining secure and transparent transactions. Despite these advancements, practical deployment challenges remain, including synchronization delays in geographically distributed agents, legacy system integration, and blockchain energy consumption. Future research directions include investigating more advanced consensus mechanisms (e.g., Proof of Task), machine learning-driven predictive optimization, real-world large-scale testing, and federated learning models for decentralized decision-making. The proposed framework offers a scalable, secure, and efficient solution for decentralized real-time energy management in Virtual Prosumer Networks.