Construction of a Power Market Trading Platform Based on Regional Blockchain Technology

Authors

  • Hongxi Wang State Grid Hebei Markting Service Center, Hebei Shijiazhuang, 050021, China
  • Xudong Zhang State Grid Hebei Electric Power Co., Ltd, Hebei Shijiazhuang, 050021, China
  • Fei Li State Grid Hebei Markting Service Center, Hebei Shijiazhuang, 050021, China
  • Lun Shi State Grid Hebei Markting Service Center, Hebei Shijiazhuang, 050021, China
  • Yidi Wu State Grid Hebei Electric Power Co., Ltd, Hebei Shijiazhuang, 050021, China
  • Chunhai Li Shijiazhuang Kelin Electric Co.,Ltd, Hebei Shijiazhuang, 050000, China

DOI:

https://doi.org/10.12694/scpe.v26i3.4080

Keywords:

Blockchain; Electric energy; Credit evaluation; Consensus algorithm

Abstract

In order to solve the problems of traditional centralized trading platforms being difficult to handle rapidly increasing transaction data, achieving cross regional information sharing and resource unified optimization configuration, the author proposes the construction of a power market trading platform based on regional blockchain technology. This technology is based on the regional power energy trading architecture, designs a decentralized cloud energy storage blockchain trading model, and further refines the three-layer technical architecture of the business layer, middleware, and open license chain, thereby ensuring effective collaboration between the client and the distributed backend. In order to further improve the computational efficiency of the system, the author proposes a consensus algorithm optimization scheme based on transaction credit evaluation. The evaluation of blockchain nodes is achieved through the joint evaluation of multidimensional indicators, and the credit ranking of nodes is completed based on the defined transaction priority weight. The experimental results indicate that: Compared with the comparison algorithm, the clustering accuracy obtained by the proposed algorithm is higher, with a maximum value of 91.56%, indicating that the proposed algorithm correctly clusters more electricity sales information. The algorithm proposed by the author can reduce the probability of malicious transactions compared to existing algorithms, while improving the processing power and response speed of the trading system.

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Published

2025-04-01

Issue

Section

Special Issue - High-performance Computing Algorithms for Material Sciences