Blockchain-enabled transformation: Decentralized planning and secure peer-to-peer trading in local energy networks

被引:0
|
作者
Wang, Bingkun [1 ,2 ,3 ]
Guo, Xiaolin [4 ]
机构
[1] Zhengzhou Coll Finance & Econ, Sch Informat Engn, Zhengzhou 450000, Henan, Peoples R China
[2] Zhengzhou Coll Finance & Econ, Intelligent Classroom Teaching Evaluat Res Lab Hen, Zhengzhou 450000, Henan, Peoples R China
[3] Zhengzhou Coll Finance & Econ, Cyberspace Secur Governance Engn Res Ctr, Zhengzhou 450000, Henan, Peoples R China
[4] Hubei Univ Econ, Sch Informat Engn, Wuhan 430205, Hubei, Peoples R China
关键词
Blockchain; Automatic Load Response; Local Energy Grids; Decentralized Programming; Peer-to-Peer Trading; MANAGEMENT;
D O I
10.1016/j.segan.2024.101556
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
This paper introduces a novel blockchain-based automatic load response architecture for local energy networks, focusing on secure peer-to-peer (P2P) energy trading and decentralized planning. Departing from traditional centralized methods, the proposed system leverages non-cooperative game theory for pricing-based decentralized planning, enabling efficient resource distribution without a central authority. A key contribution is the integration of a machine-governed smart contract mechanism, which ensures secure, transparent, and consistent transactions in P2P energy trading. Additionally, an adaptive evaluation system for transaction nodes enhances the system's responsiveness to dynamic energy demands. A distributed algorithm is developed to optimize the implementation of this architecture, ensuring practical efficiency. Case studies confirm significant improvements in operational efficiency, security, and economic outcomes, marking a substantial advancement in decentralized energy management. Key findings demonstrate that the proposed automatic load response strategy significantly enhances load curve stability, achieving a 99.16% reduction in net load fluctuations and an 8.24% reduction in operational costs compared to traditional methods. Additionally, the framework improves the self-consumption rate of renewable energy by up to 14.62% and reduces the average cost for electric vehicle (EV) users by 26.12%. These results highlight the framework's effectiveness in fostering a more balanced supply-demand relationship within local energy networks while ensuring economic and computational efficiency. The study underscores the potential to revolutionize decentralized energy management, offering a sustainable and costeffective solution for future energy systems.
引用
收藏
页数:11
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