Fault-Tolerant Peer-to-Peer Energy Trading in Energy Communities Using Blockchain and Distributed Stochastic Model Predictive Control

This work presents a novel two-layer distributed control system for energy management in an energy community, integrating stochastic and distributed model predictive control with a fault-tolerant mechanism. Unlike conventional approaches, the proposed method allows agents to autonomously detect, isolate, and adapt to faults in a fully distributed manner, without relying on a central coordinator. This is achieved through a smart contract deployed on a blockchain network, which facilitates decentralized coordination and fault management. The lower layer implements a fault-tolerant mechanism that detects, isolates, and adapts the control parameters of affected agents. It minimizes false positives through residual signal analysis and dynamically adjusts operational constraints to maintain system stability and performance. When multiple agents are affected, fault-related information is exchanged among them, enabling a coordinated response. This allows proactive actions such as disabling power lines linked to faulty agents or adjusting power transactions in response to reduced output capacity from a neighboring agent. The upper layer employs chance-constrained model predictive control to handle uncertainties in energy demand and renewable generation, ensuring robust decision-making under variability. Energy scheduling is managed through blockchain-based smart contracts, providing a secure and decentralized coordination framework. The effectiveness of the proposed scheme is validated through simulations, demonstrating its ability to optimize energy distribution while enhancing fault resilience in energy communities.