Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection

TL;DR

This paper introduces a distributed P2P energy trading model resilient to cyberattacks, utilizing a novel spatial-temporal anomaly detection method based on tensor learning to identify malicious faults effectively.

Contribution

It develops a fully distributed energy trading framework with an innovative online anomaly detection approach and provides theoretical guarantees for optimality and convergence.

Findings

Effective detection of Byzantine faults demonstrated

Enhanced robustness and scalability of P2P energy trading

Theoretical conditions ensure optimality and convergence

Abstract

Distributed peer-to-peer (P2P) energy trading mandates an escalating coupling between the physical power network and communication network, necessitating high-frequency sharing of real-time data among prosumers. However, this data-sharing scheme renders the system vulnerable to various malicious behaviors, as Byzantine agents can initiate cyberattacks by injecting sophisticated false data. To better investigate the impacts of malicious Byzantine faults, this paper develops a fully distributed P2P energy trading model by accounting for the high-fidelity physical network constraints. To further detect Byzantine faults and mitigate their impacts on distributed P2P energy trading problem, we propose an online spatial-temporal anomaly detection approach by leveraging the tensor learning method, which is informed by the domain knowledge to enable awesome detection performance. Moreover, to enhance its computational efficiency, we further develop closed-form solutions for the proposed detection approach. Subsequently, we derive theoretical conditions for guaranteeing optimality and convergence of the distributed P2P energy trading problem with anomaly detection mechanisms. Results from numerical simulations validate the effectiveness, optimality, and scalability of the proposed approach.