Research on energy blockchain architecture design and operating cost based on blockchain and edge computing technology

Qi Han; Xia Wu; Han Liu; Tian Liang

doi:10.1117/12.2641478

10 November 2022 Research on energy blockchain architecture design and operating cost based on blockchain and edge computing technology

Qi Han, Xia Wu, Han Liu, Tian Liang

Author Affiliations +

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123483Z (2022) https://doi.org/10.1117/12.2641478
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

With the development of power grid technology, the coordinated development of multiple types of source, network, load and storage is the future development trend. Advanced technologies based on blockchain and edge computing can efficiently utilize the advantages of energy blockchain and solve problems such as data security and flexible user transactions. To this end, this article formulates an important business application scenario of blockchain in the source network load storage, and proposes its design plan. At the same time, this article combines typical actual cases to calculate and analyze the relationship between the degree of decentralization and the operating efficiency of the microgrid. On this basis, this paper calculates the operating costs under different conditions, and finally puts forward the best operating strategy. The design scheme proposed in this paper can provide a new calculation reference for realizing the economic, high-efficiency and safe processing of a large amount of power transaction information.

Citation Download Citation

Qi Han, Xia Wu, Han Liu, and Tian Liang "Research on energy blockchain architecture design and operating cost based on blockchain and edge computing technology", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123483Z (10 November 2022); https://doi.org/10.1117/12.2641478

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