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The role of clustering algorithm-based big data processing in information economy development

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  • Hongyan Ma

Abstract

The purposes are to evaluate the Distributed Clustering Algorithm (DCA) applicability in the power system’s big data processing and find the information economic dispatch strategy suitable for new energy consumption in power systems. A two-layer DCA algorithm is proposed based on K-Means Clustering (KMC) and Affinity Propagation (AP) clustering algorithms. Then the incentive Demand Response (DR) is introduced, and the DR flexibility of the user side is analyzed. Finally, the day-ahead dispatch and real-time dispatch schemes are combined, and a multi-period information economic dispatch model is constructed. The algorithm performance is analyzed according to case analyses of new energy consumption. Results demonstrate that the two-layer DCA’s calculation time is 5.23s only, the number of iterations is small, and the classification accuracy rate reaches 0.991. Case 2 corresponding to the proposed model can consume the new energy, and the income of the aggregator can be maximized. In short, the multi-period information economic dispatch model can consume the new energy and meet the DR of the user side.

Suggested Citation

  • Hongyan Ma, 2021. "The role of clustering algorithm-based big data processing in information economy development," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-14, March.
    • Handle: RePEc:plo:pone00:0246718
    DOI: 10.1371/journal.pone.0246718
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    References listed on IDEAS

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    1. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
    2. Yang, Fei & Xia, Xiaohua, 2017. "Techno-economic and environmental optimization of a household photovoltaic-battery hybrid power system within demand side management," Renewable Energy, Elsevier, vol. 108(C), pages 132-143.
    3. Faerber, Laura Antonia & Balta-Ozkan, Nazmiye & Connor, Peter M., 2018. "Innovative network pricing to support the transition to a smart grid in a low-carbon economy," Energy Policy, Elsevier, vol. 116(C), pages 210-219.
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