← Back to Volume 4, Issue 2

G-PBFT Algorithm and its Application in Distributed Energy Trading

Zichao Xu, Mingquan Zhang, Junxian Zhao
Vol.4, No.2, pp.27-43, 2025 DOI: 10.62051/ijepes.v4n2.05 September 27, 2025
Download PDF
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.
Keywords: G-PBFT, Consensus Algorithm, Byzantine Fault Tolerance, Distributed Energy Resources, P2P Energy Trading, Scalability, Reputation Mechanism
APA Citation: Zichao Xu, Mingquan Zhang, Junxian Zhao (2025). G-PBFT Algorithm and its Application in Distributed Energy Trading. International Journal of Electric Power and Energy Studies, 4(2), 27-43. https://doi.org/10.62051/ijepes.v4n2.05

References

  1. National Development and Reform Commission, “‘Fourteenth Five-Year’ Modern Energy System Plan,” [Online]. Available: https://www.gov.cn/zhengce/zhengceku/202410/content_6978315.htm.
  2. National Development and Reform Commission, National Data Administration, and Ministry of Industry and Information Technology, “Notice on the Issuance of the ‘National Data Infrastructure Construction Guidelines’,” [Online]. Available: https://www.gov.cn/zhengce/zhengceku/202501/content_6996487.htm. [Accessed: Mar. 7, 2025].
  3. K. E. Bassey, S. A. Rajput, and K. Oyewale, “Peer-to-peer energy trading: Innovations, regulatory challenges, and the future of decentralized energy systems,” World Journal of Advanced Research and Reviews, vol. 24, pp. 172-186, 2024.
  4. H. Zhu, K. Ouahada, and A. M. Abu-Mahfouz, “Peer-to-peer energy trading in smart energy communities: A lyapunov-based energy control and trading system,” IEEE Access, vol. 10, pp. 42916-42932, 2022.
  5. Y. Zhou and P. D. Lund, “Peer-to-peer energy sharing and trading of renewable energy in smart communities─ trading pricing models, decision-making and agent-based collaboration,” Renewable Energy, vol. 207, pp. 177-193, 2023.
  6. K. Fan, J. Liu, and M. Yang, “Privacy Protection of Power Transaction Based on Blockchain,” Ordnance Industry Automation, vol. 44, no. 1, pp. 65-69, 109, 2025.
  7. C. Gao, Y. Chen, K. Shi, et al., “Distributed power trading among park microgrid-based virtual power plants based on power flow distribution identification,” Distribution & Utilization, vol. 41, no. 8, pp. 112-119, 2024, doi: 10.19421/j.cnki.1006-6357.2024.08.011.
  8. A. Iqbal, A. S. Rajasekaran, G. S. Nikhil, and M. Azees, “A Secure and Decentralized Blockchain Based EV Energy Trading Model Using Smart Contract in V2G Network,” IEEE Access, vol. 9, pp. 75761-75777, 2021.
  9. B. Liskov, “From viewstamped replication to Byzantine fault tolerance,” in Replication: Theory and Practice, Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 121-149.
  10. S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf
  11. X. Feng, L. Li, D. Liu, et al., “A review of access control research for blockchain data sharing,” Journal of Frontiers of Computer Science and Technology, pp. 1-27, 2025.
  12. Z. Zhen, “Research on green bank financing decision of agricultural supply chain based on blockchain,” Logistics Technology, vol. 44, no. 3, pp. 37-48, 2025.
  13. Q. Shi, “Research on the innovative development mode of cross-border e-commerce service platform based on blockchain technology,” China Journal of Commercevol. 34, no. 5, pp. 102-107, 2025, doi: 10.19699/j.cnki.issn2096-0298.2025.05.102.
  14. Y. Fan, Z. Zhang, T. Qin, et al., “Review of blockchain-based data sharing in Internet of Vehicles,” Application Research of Computers, pp. 1-15, 2025, doi: 10.19734/j.issn.1001-3695.2024.10.0450.
  15. T. Sawa, “Blockchain technology outline and its application to field of power and energy system,” Electrical Engineering in Japan, vol. 206, no. 2, pp. 11-15, 2019.
  16. S. Rahmadika, D. R. Ramdania, and M. Harika, “Security analysis on the decentralized energy trading system using blockchain technology,” Jurnal Online Informatika, vol. 3, no. 1, pp. 44-47, 2018.
  17. Y. Li, N. Li, and Y. Xia, “Research on a data desensitization algorithm of blockchain distributed energy transaction based on differential privacy,” in 2019 IEEE 8th International Conference on Advanced Power System Automation and Protection (APAP), 2019, pp. 980-985.
  18. Z. Jing, “A Distributed Energy Transaction Method Based on Blockchain,” in E3S Web of Conferences, vol. 267, EDP Sciences, 2021, p. 01007.
  19. X. Zhu, “Application of blockchain technology in energy internet market and transaction,” in IOP Conference Series: Materials Science and Engineering, vol. 592, no. 1, IOP Publishing, 2019, p. 012159.
  20. N. Saeed, F. Wen, and M. Z. Afzal, “Decentralized peer-to-peer energy trading in microgrids: Leveraging blockchain technology and smart contracts,” Energy Reports, vol. 12, pp. 1753-1764, 2024.
  21. J. Xie, X. Zhou, S. Wang, et al., “Transaction search engine of distributed electricity market trading platform based on blockchain technology,” Energy Science & Engineering, vol. 10, no. 2, pp. 439-457, 2022.
  22. J. Song, Y. Li, and W. Hu, “Research on Weakly Centralized Trading Mechanism of Distributed Energy Based on Smart Contract of Blockchain,” in 2021 China International Conference on Electricity Distribution (CICED), 2021, pp. 997-1001.
  23. I. F. T. Alyaseen, “Consensus algorithms blockchain: A comparative study,” International Journal on Perceptive and Cognitive Computing, vol. 5, no. 2, pp. 66-71, 2019.
  24. M. S. Ferdous, M. J. M. Chowdhury, M. A. Hoque, et al., “Blockchain consensus algorithms: A survey,” arXiv preprint arXiv:2001.07091, 2020.
  25. Y. Yang, L. Tang, and H. Wang, “Dynamic multi-organizational PBFT algorithm based on k-means,” Journal of Chongqing University, vol. 47, no. 7, pp. 125-139, 2024.
  26. X. Jing and Z. Liu, “Master-Slave Multi-Chain Consensus Mechanism of Consortium Blockchain Based on Hypergraph and MuSig2,” Acta Electronica Sinica, vol. 52, no. 3, pp. 803-813, 2024.
  27. Y. Song, G. Zheng, and X. Zhang, “RG-BFT: Random grouping based Byzantine fault-tolerant algorithm,” Computer Engineering and Design, vol. 45, no. 6, pp. 1661-1667, 2024, doi: 10.16208/j.issn1000-7024.2024.06.009.
  28. H. Zhang, J. Li, K. Liu, et al., “PoRT consensus mechanism for energy trading system of new energy vehicles,” Journal on Communications, vol. 46, no. 1, pp. 157-166, 2025
  29. Z. Liu, F. Wang, and H. Jia, “Optimization Scheme of PBFT Algorithm Combining Dynamic Credit Mechanism,” Computer Engineering, vol. 49, no. 2, pp. 191-198, 2023, doi: 10.19678/j.issn.1000-3428.0063464.
  30. G. Niemeyer, “Geohash,” [Online]. Available: http://geohash.org/. [Accessed: Jun. 2008].