Research on Microgrid Power Direct Transaction Behavior Based on Blockchain
DOI: 10.23977/jeis.2020.51005 | Downloads: 11 | Views: 684
BaoYi Wang 1, Tian Tian 1, ShaoMin Zhang 1
1 School of Control and Computer Engineering, North China Electric Power University, China
Corresponding AuthorBaoYi Wang
Because data mining has a good processing capacity for data, the clustering analysis of the historical data which is direct transactions of microgrid power provides a reliable technical support. In order to solve the problem of the imbalance between the power demand of users and the power supply of the grid in the direct transaction of microgrid power, an algorithm based on spectral clustering combined with empirical rules is proposed in this paper. The historical data (such as electricity energy, quotation submission time, and transaction price) between users and power suppliers who complete transaction settlement through the blockchain is used for clustering analysis by this method. By analyzing the clustering results, a reliable adjustment scheme to control the balance of supply and demand in microgrid power market is obtained. Through simulation experiments, the feasibility of the direct power trading model based on blockchain and the effectiveness of the algorithm based on spectral clustering combined with empirical rules are verified, so as to obtain the variation law of electricity demand and electricity price in different time periods.
KEYWORDSMicrogrid,Blockchain,Cluster Analysis,Spectral Clustering
CITE THIS PAPER
BaoYi Wang, Tian Tian and ShaoMin Zhang. Research on Microgrid Power Direct Transaction Behavior Based on Blockchain. Journal of Electronics and Information Science (2020) 5: 23-28. DOI: http://dx.doi.org/10.23977/jeis.2020.51005.
 Wang, J., Wang, Q., Zhou, N. and Chi, Y. (2017). A Novel Electricity Transaction Mode of Microgrids Based on Blockchain and Continuous Double Auction. Energies, 10(12), 1971.
 Huang, J. and Liu, J.(2018).Summary of blockchain technology research. Journal of Beijing University of Posts and Telecommunications, v.41(02), 1-8.
 Cheng, S., Zeng, B. and Huang, Y. Z. (2017). Research on application model of blockchain technology in distributed electricity market. IOP Conference Series: Earth and Environmental Science, 93, 12065.
 Reza,R., Elaheh, M., Chelsea, D., Joobin, G. and Michael, P. (2016). Scalable Daily Human Behavioral Pattern Mining from Multivariate Temporal Data. IEEE Transactions on Knowledge and Data Engineering, 28(11), 3098-3112.
 Wang, Y., Chen, Q., Kang, C. and Xia, Q. (2016). Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications. IEEE Transactions on Smart Grid, 7(5), 2437-2447.
 Vercamer, D., Steurtewagen, B., Van Den Poel, D. and Vermeulen, F. (2016). Predicting Consumer Load Profiles Using Commercial and Open Data. IEEE Transactions on Power Systems, 31(5), 3693-3701.
 Wang, C., Qiu, J., Cao, Y. and Wang, Y. (2018). Clustering algorithm for urban express customers based on spectral clustering algorithm. Journal of Wuhan University of Technology (Information and Management Engineering Edition), 40(05), 566-570.
 She, W., Hu Y., Yang X., et al. Virtual Power Plant Operation and Scheduling Model Based on Energy Blockchain Network [J]. Proceedings of the CSEE, 2017 (13): 69-76.
 Yang, D., Zhao, X., Xu, Z., et al. Analysis and Prospect of the Application of Blockchain in Energy Internet[J]. Proceedings of the CSEE, 2017, 37(13): 3664-3671.
 Gong, G., Wang, H., Zhang, T., et al.. Research on Electricity Spot Trading Market Based on Blockchain [J]. Proceedings of the CSEE, 2018, 38 (23): 6955 -6966 + 7129.
 Wang, B., Hu, H., Zhang, S.. Cluster analysis of power consumption data for massive users under differential privacy protection [J]. Automation of Electric Power Systems, 2018, 42 (02): 121-127.
 Luo, J., Jiao, L., Lozano, J. A. . A Sparse Spectral Clustering Framework via Multi-Objective Evolutionary Algorithm[J]. IEEE Transactions on Evolutionary Computation, 2015, 20(3):1-1.