Dividend Redistribution Mechanism and Consensus Algorithm for Joint Clearing in Regional Power Markets

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  • (1. Key Lab of Control of Power Transmission and Conversion of the Ministry of Education , Shanghai Jiao Tong University, Shanghai 200240, China;2. College of Smart Energy, Shanghai Jiao Tong University, Shanghai 200240, China;3. Shanghai Non-Carbon Energy Conversion and Utilization Institute, Shanghai 200240, China)

Online published: 2025-01-08

Abstract

The regional power market, where electricity generation and consumption resources from various provinces and cities compete on the same platform and clear jointly, can achieve the free flow and optimal allocation of power resources. However, its implementation faces two technical bottlenecks: joint clearing alters the existing interest distribution among market participants, potentially harming the interests and enthusiasm of some participants; and the joint clearing and settlement process is complex, opaque, and lacks supervisory measures, making it difficult to ensure fairness. This paper proposes a dividend redistribution mechanism for regional joint clearing and a joint clearing and settlement consensus algorithm that combines the POSO consensus mechanism with the traditional BFT consensus mechanism. The calculation results show that adopting regional joint clearing can significantly reduce the total power supply cost in the region and improve the efficiency of power resource allocation. The proposed dividend redistribution mechanism can achieve Pareto improvement, ensuring the enthusiasm of various market participants to engage in joint clearing. The proposed consensus algorithm allows market participants to reach a consensus on the clearing results and settlement scheme, ensuring the fairness, feasibility, and transparency of joint clearing.

Cite this article

FANG Shengzhe1 , CHEN Sijie1 , QIN Yuyao1 , PING Jian2 , YAN Zheng3 . Dividend Redistribution Mechanism and Consensus Algorithm for Joint Clearing in Regional Power Markets[J]. Journal of Shanghai Jiaotong University, 0 : 0 . DOI: 10.16183/j.cnki.jsjtu.2024.364

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