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An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings

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  • Dong, Lianxin
  • Wu, Qing
  • Hong, Juhua
  • Wang, Zhihua
  • Fan, Shuai
  • He, Guangyu

Abstract

To deal with the power fluctuation caused by renewable energy integration and load growth, it has been a general recognition to provide regulation services for local power system or main grid by flexible loads. There is a certain range of adaption to temperature for users and thermal inertias for building components, making it possible for air conditioning (AC) loads to participate in demand response (DR). Large cluster of ACs offers a resource akin to that of a distributed energy storage system, which is also an important regulation module of virtual power plant (VPP). Inverter air conditioning (IAC) is gradually occupying the market proportion owing to comfort and power savings. It has become a key technology in the new power system on how to coordinate massive IAC resources with heterogeneous parameters and status to output reliable load regulation services. To this end, this paper proposes a consensus control strategy for the IACs cluster based on lightweight decentralized architecture. The task is distributed fairly while ensuring the occupants’ comfort. The target load curve can be accurately tracked with high adaptability and robustness. The interactive parameters are generated from the bottom up based on individuals’ response ability, which facilitates rapid convergence. Together with the intrinsic adaptability and robustness, the hosting mechanism for the coordinator is designed to guarantee high fault-tolerance, and therefore plug-and-play can be satisfied. Besides, dimensionality reduction and locality for the interaction data ensure a higher level of privacy protection. The advantages are illustrated in case studies and compared with other approaches. To the best of our knowledge, the proposed method has the overwhelming convergence performance over the present literatures. These characteristics indicate a sufficient basis for its application in the variable conditions with massive users and can be extended to more scenarios using consensus control.

Suggested Citation

  • Dong, Lianxin & Wu, Qing & Hong, Juhua & Wang, Zhihua & Fan, Shuai & He, Guangyu, 2023. "An adaptive decentralized regulation strategy for the cluster with massive inverter air conditionings," Applied Energy, Elsevier, vol. 330(PA).
    • Handle: RePEc:eee:appene:v:330:y:2023:i:pa:s0306261922015616
    DOI: 10.1016/j.apenergy.2022.120304
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    References listed on IDEAS

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    1. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    2. Li, Wenzhuo & Wang, Shengwei & Koo, Choongwan, 2021. "A real-time optimal control strategy for multi-zone VAV air-conditioning systems adopting a multi-agent based distributed optimization method," Applied Energy, Elsevier, vol. 287(C).
    3. Zhou, Huan & Fan, Shuai & Wu, Qing & Dong, Lianxin & Li, Zuyi & He, Guangyu, 2021. "Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant," Applied Energy, Elsevier, vol. 285(C).
    4. Utama, Christian & Troitzsch, Sebastian & Thakur, Jagruti, 2021. "Demand-side flexibility and demand-side bidding for flexible loads in air-conditioned buildings," Applied Energy, Elsevier, vol. 285(C).
    5. Xie, Kang & Hui, Hongxun & Ding, Yi & Song, Yonghua & Ye, Chengjin & Zheng, Wandong & Ye, Shuiquan, 2022. "Modeling and control of central air conditionings for providing regulation services for power systems," Applied Energy, Elsevier, vol. 315(C).
    6. Kong, Meng & Dong, Bing & Zhang, Rongpeng & O'Neill, Zheng, 2022. "HVAC energy savings, thermal comfort and air quality for occupant-centric control through a side-by-side experimental study," Applied Energy, Elsevier, vol. 306(PA).
    7. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    8. Fan, Shuai & Liu, Jiang & Wu, Qing & Cui, Mingjian & Zhou, Huan & He, Guangyu, 2020. "Optimal coordination of virtual power plant with photovoltaics and electric vehicles: A temporally coupled distributed online algorithm," Applied Energy, Elsevier, vol. 277(C).
    9. Wang, D. & Parkinson, S. & Miao, W. & Jia, H. & Crawford, C. & Djilali, N., 2012. "Online voltage security assessment considering comfort-constrained demand response control of distributed heat pump systems," Applied Energy, Elsevier, vol. 96(C), pages 104-114.
    10. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
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    Cited by:

    1. Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).
    2. Etedadi, Farshad & Kelouwani, Sousso & Agbossou, Kodjo & Henao, Nilson & Laurencelle, François, 2023. "Consensus and sharing based distributed coordination of home energy management systems with demand response enabled baseboard heaters," Applied Energy, Elsevier, vol. 336(C).
    3. Zhou, Te & Chen, Honghu & Zhang, Ning & Han, Yang & Zhou, Siyu & Li, Zhi & Zhou, Meng, 2024. "An analogue on/off state-switching control method suitable for inverter-based air conditioner load cluster participating in demand response," Applied Energy, Elsevier, vol. 363(C).
    4. Zhang, Jiarui & Mu, Yunfei & Wu, Zhijun & Jia, Hongjie & Jin, Xiaolong & Qi, Yan, 2024. "Two-stage affine assessment method for flexible ramping capacity: An inverter heat pump virtual power plant case," Applied Energy, Elsevier, vol. 365(C).

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