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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
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    References listed on IDEAS

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    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.
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    Cited by:

    1. 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).
    2. 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).
    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.

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