IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i20p5146-d1499860.html
   My bibliography  Save this article

A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control

Author

Listed:
  • Yan Li

    (Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Zhiwen Liu

    (Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Chong Shao

    (Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Bingjun Lin

    (Beihai Power Supply Bureau Guangxi Power Grid Co., Ltd., Beihai 536000, China)

  • Jiayu Rong

    (Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Nan Dong

    (Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Buyun Su

    (Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China)

  • Yuejia Hong

    (Energy Development Research Institute of China Southern Power Grid, Guangzhou 510663, China)

Abstract

Demand response (DR) can ensure electricity supply security by shifting or shedding loads, which plays an important role in a power system with a high proportion of renewable energy sources. Industrial loads are vital participants in DR, but it is difficult to assess DR potential because of many complex factors. In this paper, a new method based on fuzzy control is given to assess the DR potential of industrial loads. A complete assessment framework including four steps is presented. Firstly, the industrial load data are preprocessed to mitigate the influence of noisy and transmission losses, and then the K-means algorithm considering the optimal cluster number is used to calculate baseline load of industrial load. Subsequently, an open-loop fuzzy controller is designed to predict the response factor of different industrial loads. Three strongly correlated indicators, namely peak load rate, electricity intensity, and load flexibility, are selected as the input of fuzzy control, which represents response willingness. Finally, the baseline load of diverse clustering scenarios and the response factor are used to calculate the DR potential of different industrial loads. The proposed method takes into account both economic and technical factors comprehensively, and thus, the results better represent the available DR potential in real-world situations. To demonstrate the effectiveness of the proposed method, the case of a medium-sized city in China is studied. The simulation focuses on the top eight industrial types, and the results show they can contribute about 189 MW available DR potential.

Suggested Citation

  • Yan Li & Zhiwen Liu & Chong Shao & Bingjun Lin & Jiayu Rong & Nan Dong & Buyun Su & Yuejia Hong, 2024. "A Method for Evaluating Demand Response Potential of Industrial Loads Based on Fuzzy Control," Energies, MDPI, vol. 17(20), pages 1-14, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5146-:d:1499860
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/20/5146/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/20/5146/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mert Kompil & H. Murat Celik, 2013. "Modelling trip distribution with fuzzy and genetic fuzzy systems," Transportation Planning and Technology, Taylor & Francis Journals, vol. 36(2), pages 170-200, April.
    2. Kwon, Pil Seok & Østergaard, Poul, 2014. "Assessment and evaluation of flexible demand in a Danish future energy scenario," Applied Energy, Elsevier, vol. 134(C), pages 309-320.
    3. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
    4. Paulus, Moritz & Borggrefe, Frieder, 2011. "The potential of demand-side management in energy-intensive industries for electricity markets in Germany," Applied Energy, Elsevier, vol. 88(2), pages 432-441, February.
    5. Wohlfarth, Katharina & Klobasa, Marian & Gutknecht, Ralph, 2020. "Demand response in the service sector – Theoretical, technical and practical potentials," Applied Energy, Elsevier, vol. 258(C).
    6. Ahmad Faruqui & Sanem Sergici, 2010. "Household response to dynamic pricing of electricity: a survey of 15 experiments," Journal of Regulatory Economics, Springer, vol. 38(2), pages 193-225, October.
    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. Feuerriegel, Stefan & Neumann, Dirk, 2014. "Measuring the financial impact of demand response for electricity retailers," Energy Policy, Elsevier, vol. 65(C), pages 359-368.
    2. Dranka, Géremi Gilson & Ferreira, Paula, 2019. "Review and assessment of the different categories of demand response potentials," Energy, Elsevier, vol. 179(C), pages 280-294.
    3. Katz, Jonas & Andersen, Frits Møller & Morthorst, Poul Erik, 2016. "Load-shift incentives for household demand response: Evaluation of hourly dynamic pricing and rebate schemes in a wind-based electricity system," Energy, Elsevier, vol. 115(P3), pages 1602-1616.
    4. Wenhui Zhao & Zilin Wu & Bo Zhou & Jiaoqian Gao, 2024. "Wind and PV Power Consumption Strategy Based on Demand Response: A Model for Assessing User Response Potential Considering Differentiated Incentives," Sustainability, MDPI, vol. 16(8), pages 1-22, April.
    5. Cosmo, Valeria Di & O’Hora, Denis, 2017. "Nudging electricity consumption using TOU pricing and feedback: evidence from Irish households," Journal of Economic Psychology, Elsevier, vol. 61(C), pages 1-14.
    6. Durmaz, Tunç, 2016. "Precautionary Storage in Electricity Markets," Discussion Papers 2016/5, Norwegian School of Economics, Department of Business and Management Science.
    7. Carsten Helm & Mathias Mier, 2020. "Steering the Energy Transition in a World of Intermittent Electricity Supply: Optimal Subsidies and Taxes for Renewables Storage," ifo Working Paper Series 330, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
    8. Weiss, Mariana & Chueca, J. Enrique & Jacob, Jorge & Gonçalves, Felipe & Azevedo, Marina & Gouvêa, Adriana & Ravillard, Pauline & Carvalho Metanias Hallack, Michelle, 2022. "Empowering Electricity Consumers through Demand Response Approach: Why and How," IDB Publications (Working Papers) 12133, Inter-American Development Bank.
    9. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    10. McPherson, Madeleine & Stoll, Brady, 2020. "Demand response for variable renewable energy integration: A proposed approach and its impacts," Energy, Elsevier, vol. 197(C).
    11. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    12. Fürsch, Michaela & Hagspiel, Simeon & Jägemann, Cosima & Nagl, Stephan & Lindenberger, Dietmar & Tröster, Eckehard, 2013. "The role of grid extensions in a cost-efficient transformation of the European electricity system until 2050," Applied Energy, Elsevier, vol. 104(C), pages 642-652.
    13. Xu, Xiaojing & Chen, Chien-fei, 2019. "Energy efficiency and energy justice for U.S. low-income households: An analysis of multifaceted challenges and potential," Energy Policy, Elsevier, vol. 128(C), pages 763-774.
    14. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    15. Hui Wang & Yao Xu, 2024. "Optimized Decision-Making for Multi-Market Green Power Transactions of Electricity Retailers under Demand-Side Response: The Chinese Market Case Study," Energies, MDPI, vol. 17(11), pages 1-15, May.
    16. Mattias Vesterberg and Chandra Kiran B. Krishnamurthy, 2016. "Residential End-use Electricity Demand: Implications for Real Time Pricing in Sweden," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    17. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    18. Luo, Shihua & Hu, Weihao & Liu, Wen & Liu, Zhou & Huang, Qi & Chen, Zhe, 2022. "Flexibility enhancement measures under the COVID-19 pandemic – A preliminary comparative analysis in Denmark, the Netherlands, and Sichuan of China," Energy, Elsevier, vol. 239(PC).
    19. Bernadeta Gołębiowska & Anna Bartczak & Mikołaj Czajkowski, 2020. "Energy Demand Management and Social Norms," Energies, MDPI, vol. 13(15), pages 1-20, July.
    20. Helm, Carsten & Mier, Mathias, 2021. "Steering the energy transition in a world of intermittent electricity supply: Optimal subsidies and taxes for renewables and storage," Journal of Environmental Economics and Management, Elsevier, vol. 109(C).

    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:gam:jeners:v:17:y:2024:i:20:p:5146-:d:1499860. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.