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Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps

Author

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  • Ryan S. Montrose

    (Department of Mechanical & Biomedical Engineering, Boise State University, Boise, ID 83706, USA)

  • John F. Gardner

    (Department of Mechanical & Biomedical Engineering, Boise State University, Boise, ID 83706, USA)

  • Aykut C. Satici

    (Department of Mechanical & Biomedical Engineering, Boise State University, Boise, ID 83706, USA)

Abstract

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, as a result of naturally occurring and demand-response events, has the potential to damage power distribution equipment. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this paper is the development of an optimal control algorithm for commonly used variable speed heat pumps. By means of selective peer-to-peer communication, our control architecture allows for the regulation of home temperatures while simultaneously minimizing aggregate power consumption, and aggregate load volatility. An optimal centralized controller is also explored and compared against its decentralized counterpart.

Suggested Citation

  • Ryan S. Montrose & John F. Gardner & Aykut C. Satici, 2021. "Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps," Energies, MDPI, vol. 14(13), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:4012-:d:587976
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    References listed on IDEAS

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    1. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    2. Kimani, Kenneth & Oduol, Vitalice & Langat, Kibet, 2019. "Cyber security challenges for IoT-based smart grid networks," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 36-49.
    3. Zhou, Yue & Wang, Chengshan & Wu, Jianzhong & Wang, Jidong & Cheng, Meng & Li, Gen, 2017. "Optimal scheduling of aggregated thermostatically controlled loads with renewable generation in the intraday electricity market," Applied Energy, Elsevier, vol. 188(C), pages 456-465.
    4. Kazmi, Hussain & Suykens, Johan & Balint, Attila & Driesen, Johan, 2019. "Multi-agent reinforcement learning for modeling and control of thermostatically controlled loads," Applied Energy, Elsevier, vol. 238(C), pages 1022-1035.
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    Cited by:

    1. Dhirendran Munith Kumar & Pietro Catrini & Antonio Piacentino & Maurizio Cirrincione, 2023. "Integrated Thermodynamic and Control Modeling of an Air-to-Water Heat Pump for Estimating Energy-Saving Potential and Flexibility in the Building Sector," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    2. Siyue Lu & Teng Li & Xuefeng Yan & Shaobing Yang, 2022. "Evaluation of Photovoltaic Consumption Potential of Residential Temperature-Control Load Based on ANP-Fuzzy and Research on Optimal Incentive Strategy," Energies, MDPI, vol. 15(22), pages 1-21, November.

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