IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4705-d1170991.html
   My bibliography  Save this article

Temperature Regulation Strategy of Heterogeneous Air Conditioning Loads for Renewable Energy Consumption

Author

Listed:
  • Shu Zhang

    (School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Liping Zhou

    (School of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Dejin Fan

    (Suzhou Power Supply Company, State Grid Jiangsu Electric Power Co., Ltd., Suzhou 215004, China)

  • Jie Tang

    (Sichuan Vocational and Technical College of Communications, Chengdu 611130, China)

Abstract

In a power system with a high proportion of renewable energy, sudden increases in wind power or photovoltaic output can lead to huge challenges, such as difficulties in accommodating excess renewable energy and imbalances between supply and demand on the grid. As an important adjustable resource on the demand side, air conditioning load is a flexible load for realizing output consumption. In this paper, a heterogeneous air conditioning load regulation strategy for renewable energy consumption is proposed. Each air conditioning load regulation quantity is obtained based on the day-ahead dispatching mode. Then, the temperature setting value, rated power, and duty cycle are selected as the indexes. The load regulation sequence is obtained by the entropy weight method. Finally, the load regulation time of each air conditioning load is obtained based on the constraint of the quantity of loads during the possible adjustment time. The simulation analysis shows that the temperature regulation strategy presented in this paper can effectively reduce the power fluctuations of air conditioning loads, while ensuring that users with lower temperature settings are selected in the adjustment process.

Suggested Citation

  • Shu Zhang & Liping Zhou & Dejin Fan & Jie Tang, 2023. "Temperature Regulation Strategy of Heterogeneous Air Conditioning Loads for Renewable Energy Consumption," Energies, MDPI, vol. 16(12), pages 1-13, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4705-:d:1170991
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4705/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/12/4705/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Rama Curiel, José Adrián & Thakur, Jagruti, 2022. "A novel approach for Direct Load Control of residential air conditioners for Demand Side Management in developing regions," Energy, Elsevier, vol. 258(C).
    2. Kleidaras, Alexandros & Kiprakis, Aristides E. & Thompson, John S., 2018. "Human in the loop heterogeneous modelling of thermostatically controlled loads for demand side management studies," Energy, Elsevier, vol. 145(C), pages 754-769.
    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. Meng, Qinglong & Wei, Ying'an & Fan, Jingjing & Li, Yanbo & Zhao, Fan & Lei, Yu & Sun, Hang & Jiang, Le & Yu, Lingli, 2024. "Peak regulation strategies for ground source heat pump demand response of based on load forecasting: A case study of rural building in China," Renewable Energy, Elsevier, vol. 224(C).
    2. Wagner, Lukas Peter & Reinpold, Lasse Matthias & Kilthau, Maximilian & Fay, Alexander, 2023. "A systematic review of modeling approaches for flexible energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Joaquín Garrido-Zafra & Antonio Moreno-Munoz & Aurora Gil-de-Castro & Emilio J. Palacios-Garcia & Carlos D. Moreno-Moreno & Tomás Morales-Leal, 2019. "A Novel Direct Load Control Testbed for Smart Appliances," Energies, MDPI, vol. 12(17), pages 1-16, August.
    4. Wang, Jingjie & Qiu, Rujia & Xu, Bin & Wu, Hongbin & Tang, Longjiang & Zhang, Mingxing & Ding, Ming, 2023. "Aggregated large-scale air-conditioning load: Modeling and response capability evaluation of virtual generator units," Energy, Elsevier, vol. 276(C).
    5. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
    6. Cerna, Fernando V. & Dantas, Jamile T. & Naderi, Ehsan & Contreras, Javier, 2024. "Optimal strategy to reduce energy waste in an electricity distribution network through direct/indirect bulk load control," Energy, Elsevier, vol. 294(C).
    7. Yang, Shubo & Jahanger, Atif & Balsalobre-Lorente, Daniel, 2024. "Sustainable resource management in China's energy mining sector: A synthesis of development and conservation in the FinTech era," Resources Policy, Elsevier, vol. 89(C).
    8. Navid Rezaei & Abdollah Ahmadi & Mohammadhossein Deihimi, 2022. "A Comprehensive Review of Demand-Side Management Based on Analysis of Productivity: Techniques and Applications," Energies, MDPI, vol. 15(20), pages 1-28, October.
    9. Kanakaraj Parangusam & Ramesh Lekshmana & Tomas Gono & Radomir Gono, 2023. "Evolution of a Summer Peak Intelligent Controller (SPIC) for Residential Distribution Networks," Energies, MDPI, vol. 16(18), pages 1-18, September.
    10. Li, Li & Dong, Mi & Song, Dongran & Yang, Jian & Wang, Qibing, 2022. "Distributed and real-time economic dispatch strategy for an islanded microgrid with fair participation of thermostatically controlled loads," Energy, Elsevier, vol. 261(PB).
    11. Fahad R. Albogamy, 2022. "Optimal Energy Consumption Scheduler Considering Real-Time Pricing Scheme for Energy Optimization in Smart Microgrid," Energies, MDPI, vol. 15(21), pages 1-31, October.
    12. Wanlei Xue & Xin Zhao & Yan Li & Ying Mu & Haisheng Tan & Yixin Jia & Xuejie Wang & Huiru Zhao & Yihang Zhao, 2023. "Research on the Optimal Design of Seasonal Time-of-Use Tariff Based on the Price Elasticity of Electricity Demand," Energies, MDPI, vol. 16(4), pages 1-17, February.
    13. Alrobaian, Abdulrahman A. & Alsagri, Ali Sulaiman, 2023. "Multi-agent-based energy management for a fully electrified residential consumption," Energy, Elsevier, vol. 282(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:16:y:2023:i:12:p:4705-:d:1170991. 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.