Cooperative Evolutionary Game Strategy for Electricity Trading Stakeholders in Active Distribution Network under Consortium Blockchain Framework

被引：0
|
作者：
Ye C. [1 ,2 ]
Miao S. [1 ,2 ]
Liu H. [3 ]
Zhang D. [1 ,2 ]
Zhao J. [3 ]
机构：
[1] State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[2] Hubei Electric Power Security and High Efficiency Key Laboratory, School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan
[3] State Grid Henan Electric Power Company Electric Power Research Institute, Zhengzhou
来源：
Miao, Shihong (shmiao@mail.hust.edu.cn) | 1739年 / China Machine Press卷 / 35期
关键词：
Active distribution network; Consortium blockchain; Cooperative evolutionary game; Electricity trading; Multiobjective evolutionary algorithm;
D O I：
10.19595/j.cnki.1000-6753.tces.190092
中图分类号：
学科分类号：
摘要：
In the traditional electricity trading mode, centralized transaction data and regulatory agency will bring the issues such as low data security and regulatory trust crisis. The rise and development of blockchain technology provides new ideas and methods for solving these problems. Firstly, by analyzing behavior characteristics of electricity trading stakeholders (ETSs) in the active distribution network (ADN), a electricity market trading system is established under consortium blockchain framework. This system pre-authorizes each ETS as a semi-open local node. Through the dynamic selection of alliance node, distributed storage of transaction data can be realized without additional third-party regulatory agency. Then a detailed certification method for electricity trading in AND is proposed. Furthermore, a cooperative evolutionary game model of ETSs is established, and a multi-objective evolutionary algorithm based on decomposition is utilized to solve this model. The solution results can be used as a basis for generating smart contracts, and realize the decision-making optimization for electricity trading in ADN. Finally, the case study verify the proposed strategy. © 2020, Electrical Technology Press Co. Ltd. All right reserved.
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页码：1739 / 1753
页数：14
相关论文
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