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A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

Yue Yu1, Junhua Wu1,*, Guangshun Li1, Wangang Wang2

1 Qufu Normal University, Rizhao, 276800, China
2 Shandong Zhongsheng Data Co., Ltd., Rizhao, 276800, China

* Corresponding Author: Junhua Wu. Email: email

Intelligent Automation & Soft Computing 2023, 37(1), 583-598.


As an emerging hot technology, smart grids (SGs) are being employed in many fields, such as smart homes and smart cities. Moreover, the application of artificial intelligence (AI) in SGs has promoted the development of the power industry. However, as users’ demands for electricity increase, traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities. This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation, electrical security, and other aspects. Accordingly, this study proposes a distributed power trading scheme based on blockchain and AI. To protect the legitimate rights and interests of consumers and producers, credibility is used as an indicator to restrict untrustworthy behavior. Simultaneously, the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency, and a weighted communication tree construction algorithm is designed to achieve superior data forwarding. Finally, AI sensors are set up in power equipment to detect electricity generation and transmission, which alert users when security hazards occur, such as thunderstorms or typhoons. The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.


Cite This Article

Y. Yu, J. Wu, G. Li and W. Wang, "A distributed power trading scheme based on blockchain and artificial intelligence in smart grids," Intelligent Automation & Soft Computing, vol. 37, no.1, pp. 583–598, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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