IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i4p683-d207662.html
   My bibliography  Save this article

Aggregate Control Strategy for Thermostatically Controlled Loads with Demand Response

Author

Listed:
  • Xiao Zhou

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan 430074, China)

  • Jing Shi

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan 430074, China)

  • Yuejin Tang

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan 430074, China)

  • Yuanyuan Li

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan 430074, China)

  • Shujian Li

    (State Key Laboratory of Advanced Electromagnetic Engineering and Technology, Huazhong University of Science and Technology, No.1037 Luoyu Road, Wuhan 430074, China)

  • Kang Gong

    (Yichang Power Supply Company, Hubei Power Supply Company of State Gird, 117 Yanjiang Road, Yichang 443000, China)

Abstract

The improvement of intelligent appliances provides the basis for the demand response (DR) of residential loads. Thermostatically controlled loads (TCLs) are one of the most important DR resources and are characterized by a large load and a high degree of control. Due to its distribution characteristic, the aggregation of TCLs and their control are key issues in implementing the load control for the DR. In this study, we focus on air conditioning loads as an example of TCLs and propose a simple and transferable aggregate model by establishing a virtual house model, which accurately captures the aggregate flexibility. The deviation of the aggregate model is analyzed for the model evaluation. An air conditioning DR control scheme is proposed based on the aggregate model; it has the advantage of simple implementation and convenient control for the individual units. Simulations are performed in Gridlab-D to evaluate the accuracy and effectiveness of the proposed model and control method.

Suggested Citation

  • Xiao Zhou & Jing Shi & Yuejin Tang & Yuanyuan Li & Shujian Li & Kang Gong, 2019. "Aggregate Control Strategy for Thermostatically Controlled Loads with Demand Response," Energies, MDPI, vol. 12(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:4:p:683-:d:207662
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/4/683/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/12/4/683/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Torriti, Jacopo & Hassan, Mohamed G. & Leach, Matthew, 2010. "Demand response experience in Europe: Policies, programmes and implementation," Energy, Elsevier, vol. 35(4), pages 1575-1583.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Song, Zhaofang & Shi, Jing & Li, Shujian & Chen, Zexu & Jiao, Fengshun & Yang, Wangwang & Zhang, Zitong, 2022. "Data-driven and physical model-based evaluation method for the achievable demand response potential of residential consumers' air conditioning loads," Applied Energy, Elsevier, vol. 307(C).
    2. Zheng, Zhuang & Pan, Jia & Huang, Gongsheng & Luo, Xiaowei, 2022. "A bottom-up intra-hour proactive scheduling of thermal appliances for household peak avoiding based on model predictive control," Applied Energy, Elsevier, vol. 323(C).
    3. Ricardo Faia & Pedro Faria & Zita Vale & João Spinola, 2019. "Demand Response Optimization Using Particle Swarm Algorithm Considering Optimum Battery Energy Storage Schedule in a Residential House," Energies, MDPI, vol. 12(9), pages 1-18, April.
    4. Fernando Lezama & Ricardo Faia & Pedro Faria & Zita Vale, 2020. "Demand Response of Residential Houses Equipped with PV-Battery Systems: An Application Study Using Evolutionary Algorithms," Energies, MDPI, vol. 13(10), pages 1-18, May.

    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. Elena Vechkinzova & Yelena Petrenko & Yana S. Matkovskaya & Gaukhar Koshebayeva, 2021. "The Dilemma of Long-Term Development of the Electric Power Industry in Kazakhstan," Energies, MDPI, vol. 14(9), pages 1-21, April.
    2. Majdalani, Naim & Aelenei, Daniel & Lopes, Rui Amaral & Silva, Carlos Augusto Santo, 2020. "The potential of energy flexibility of space heating and cooling in Portugal," Utilities Policy, Elsevier, vol. 66(C).
    3. Jean-Luc Gaffard & Mauro Napoletano, 2012. "Agent-based models and economic policy," Sciences Po publications info:hdl:2441/53r60a8s3ku, Sciences Po.
    4. Liu, Yingqi, 2017. "Demand response and energy efficiency in the capacity resource procurement: Case studies of forward capacity markets in ISO New England, PJM and Great Britain," Energy Policy, Elsevier, vol. 100(C), pages 271-282.
    5. Abdul Conteh & Mohammed Elsayed Lotfy & Kiptoo Mark Kipngetich & Tomonobu Senjyu & Paras Mandal & Shantanu Chakraborty, 2019. "An Economic Analysis of Demand Side Management Considering Interruptible Load and Renewable Energy Integration: A Case Study of Freetown Sierra Leone," Sustainability, MDPI, vol. 11(10), pages 1-19, May.
    6. 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.
    7. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    8. Yunusov, Timur & Torriti, Jacopo, 2021. "Distributional effects of Time of Use tariffs based on electricity demand and time use," Energy Policy, Elsevier, vol. 156(C).
    9. Sulaiman A. Almohaimeed & Siddharth Suryanarayanan & Peter O’Neill, 2021. "Simulation Studies to Quantify the Impact of Demand Side Management on Environmental Footprint," Sustainability, MDPI, vol. 13(17), pages 1-24, August.
    10. Zhuang, Chaoqun & Gao, Yafeng & Zhao, Yingru & Levinson, Ronnen & Heiselberg, Per & Wang, Zhiqiang & Guo, Rui, 2021. "Potential benefits and optimization of cool-coated office buildings: A case study in Chongqing, China," Energy, Elsevier, vol. 226(C).
    11. Sila Kiliccote & Daniel Olsen & Michael D. Sohn & Mary Ann Piette, 2016. "Characterization of demand response in the commercial, industrial, and residential sectors in the United States," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(3), pages 288-304, May.
    12. Shen, Bo & Ghatikar, Girish & Lei, Zeng & Li, Jinkai & Wikler, Greg & Martin, Phil, 2014. "The role of regulatory reforms, market changes, and technology development to make demand response a viable resource in meeting energy challenges," Applied Energy, Elsevier, vol. 130(C), pages 814-823.
    13. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    14. Topi Rasku & Juha Kiviluoma, 2018. "A Comparison of Widespread Flexible Residential Electric Heating and Energy Efficiency in a Future Nordic Power System," Energies, MDPI, vol. 12(1), pages 1-27, December.
    15. Jan Stede & Karin Arnold & Christa Dufter & Georg Holtz & Serafin von Roon & Jörn C. Richstein, 2020. "The Role of Aggregators in Facilitating Industrial Demand Response: Evidence from Germany," Discussion Papers of DIW Berlin 1840, DIW Berlin, German Institute for Economic Research.
    16. Eid, Cherrelle & Koliou, Elta & Valles, Mercedes & Reneses, Javier & Hakvoort, Rudi, 2016. "Time-based pricing and electricity demand response: Existing barriers and next steps," Utilities Policy, Elsevier, vol. 40(C), pages 15-25.
    17. Xiao, Jingjie, 2013. "Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming," MPRA Paper 58696, University Library of Munich, Germany.
    18. Pallonetto, Fabiano & De Rosa, Mattia & Milano, Federico & Finn, Donal P., 2019. "Demand response algorithms for smart-grid ready residential buildings using machine learning models," Applied Energy, Elsevier, vol. 239(C), pages 1265-1282.
    19. Derakhshan, Ghasem & Shayanfar, Heidar Ali & Kazemi, Ahad, 2016. "The optimization of demand response programs in smart grids," Energy Policy, Elsevier, vol. 94(C), pages 295-306.
    20. Nikzad, Mehdi & Mozafari, Babak & Bashirvand, Mahdi & Solaymani, Soodabeh & Ranjbar, Ali Mohamad, 2012. "Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index," Energy, Elsevier, vol. 41(1), pages 541-548.

    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:12:y:2019:i:4:p:683-:d:207662. 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.