G-PBFT Algorithm and its Application in Distributed Energy Trading

Abstract

Distributed energy trading demands high scalability, low latency, and high reliability from the underlying consensus mechanism. However, traditional algorithms represented by Practical Byzantine Fault Tolerance (PBFT), when applied to large-scale, geographically dispersed energy networks, face two core bottlenecks: first, the inherent O(N²) communication complexity results in massive network overhead and poor scalability; second, the assumption of node homogeneity ignores the heterogeneity of real-world nodes, impacting consensus efficiency and system robustness. To address these challenges, this paper proposes an improved consensus algorithm, G-PBFT (Geohash-based Practical Byzantine Fault Tolerance). To address the scalability bottleneck, the algorithm first employs a geo-aware and latency-optimized intelligent grouping mechanism to partition the large-scale network into multiple low-latency consensus groups. Furthermore, to handle node heterogeneity, the algorithm introduces a multi-dimensional reputation-based dynamic representative election mechanism. By quantitatively evaluating the comprehensive performance of nodes, it ensures the selection of optimal nodes to lead the consensus, naturally forming an efficient two-layer consensus architecture. Simulation results demonstrate that G-PBFT exhibits comprehensive advantages in scalability and robustness. Compared to standard PBFT and various mainstream improved algorithms, G-PBFT maintains a stable throughput of approximately 200 TPS and an average latency below 220ms in a heterogeneous wide-area network with up to 220 nodes. Additionally, comparative experiments in heterogeneous network environments prove that the proposed reputation-based election mechanism can effectively ensure the system's robustness and efficiency, avoiding the catastrophic performance collapse that may result from random election. In conclusion, G-PBFT provides an efficient, scalable, and robust consensus solution for large-scale, geographically dispersed BFT application scenarios.