IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0246718.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0246718
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0246718&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0246718?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    2. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
    3. Mughees, Neelam & Jaffery, Mujtaba Hussain & Mughees, Anam & Ansari, Ejaz Ahmad & Mughees, Abdullah, 2023. "Reinforcement learning-based composite differential evolution for integrated demand response scheme in industrial microgrids," Applied Energy, Elsevier, vol. 342(C).
    4. Marques, Vítor & Costa, Paulo Moisés & Bento, Nuno, 2022. "Greater than the sum: On regulating innovation in electricity distribution networks with externalities," Utilities Policy, Elsevier, vol. 79(C).
    5. Dai, Yeming & Yang, Xinyu & Leng, Mingming, 2022. "Forecasting power load: A hybrid forecasting method with intelligent data processing and optimized artificial intelligence," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    6. Xuefeng Liu & Li Ma, 2023. "Sustainable Development of Business Economy Based on Big Data Algorithm under the Background of Low-Carbon Economy," Sustainability, MDPI, vol. 15(7), pages 1-16, March.
    7. Tsao, Yu-Chung & Thanh, Vo-Van & Lu, Jye-Chyi, 2021. "Sustainable advanced distribution management system design considering differential pricing schemes and carbon emissions," Energy, Elsevier, vol. 219(C).
    8. Chenhui Xu & Yunkai Huang, 2023. "Integrated Demand Response in Multi-Energy Microgrids: A Deep Reinforcement Learning-Based Approach," Energies, MDPI, vol. 16(12), pages 1-19, June.
    9. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    10. Neetzow, Paul & Mendelevitch, Roman & Siddiqui, Sauleh, 2019. "Modeling coordination between renewables and grid: Policies to mitigate distribution grid constraints using residential PV-battery systems," Energy Policy, Elsevier, vol. 132(C), pages 1017-1033.
    11. Lu, Xin & Qiu, Jing & Zhang, Cuo & Lei, Gang & Zhu, Jianguo, 2024. "Seizing unconventional arbitrage opportunities in virtual power plants: A profitable and flexible recruitment approach," Applied Energy, Elsevier, vol. 358(C).
    12. Bo Wang & Yanjing Li & Fei Yang & Xiaohua Xia, 2019. "A Competitive Swarm Optimizer-Based Technoeconomic Optimization with Appliance Scheduling in Domestic PV-Battery Hybrid Systems," Complexity, Hindawi, vol. 2019, pages 1-15, October.
    13. Satoshi Nakano & Ayu Washizu, 2021. "Analysis of inter-regional effects caused by the wide-area operation of the power grid in Japan: an implication for carbon pricing schemes," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 23(3), pages 535-556, July.
    14. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    15. Ma, Siyu & Liu, Hui & Wang, Ni & Huang, Lidong & Goh, Hui Hwang, 2023. "Incentive-based demand response under incomplete information based on the deep deterministic policy gradient," Applied Energy, Elsevier, vol. 351(C).
    16. Miguel Manuel de Villena & Raphael Fonteneau & Axel Gautier & Damien Ernst, 2019. "Evaluating the Evolution of Distribution Networks under Different Regulatory Frameworks with Multi-Agent Modelling," Energies, MDPI, vol. 12(7), pages 1-15, March.
    17. Yang, Jiawei & Paudel, Amrit & Gooi, Hoay Beng & Nguyen, Hung Dinh, 2021. "A Proof-of-Stake public blockchain based pricing scheme for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 298(C).
    18. Jiajia Li & Jinfu Liu & Peigang Yan & Xingshuo Li & Guowen Zhou & Daren Yu, 2021. "Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review," Energies, MDPI, vol. 14(4), pages 1-36, February.
    19. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).
    20. Diestelmeier, Lea, 2019. "Changing power: Shifting the role of electricity consumers with blockchain technology – Policy implications for EU electricity law," Energy Policy, Elsevier, vol. 128(C), pages 189-196.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0246718. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.