IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p4012-d587976.html
   My bibliography  Save this article

Centralized and Decentralized Optimal Control of Variable Speed Heat Pumps

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/4012/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/4012/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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. 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.

    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. Kong, Xiangyu & Kong, Deqian & Yao, Jingtao & Bai, Linquan & Xiao, Jie, 2020. "Online pricing of demand response based on long short-term memory and reinforcement learning," Applied Energy, Elsevier, vol. 271(C).
    2. Song, Yuguang & Xia, Mingchao & Chen, Qifang & Chen, Fangjian, 2023. "A data-model fusion dispatch strategy for the building energy flexibility based on the digital twin," Applied Energy, Elsevier, vol. 332(C).
    3. Song, Yuguang & Chen, Fangjian & Xia, Mingchao & Chen, Qifang, 2022. "The interactive dispatch strategy for thermostatically controlled loads based on the source–load collaborative evolution," Applied Energy, Elsevier, vol. 309(C).
    4. Luciana Marques & Wadaed Uturbey & Miguel Heleno, 2021. "An Integer Non-Cooperative Game Approach for the Transactive Control of Thermal Appliances in Energy Communities," Energies, MDPI, vol. 14(21), pages 1-22, October.
    5. Neupane, Deependra & Kafle, Sagar & Karki, Kaji Ram & Kim, Dae Hyun & Pradhan, Prajal, 2022. "Solar and wind energy potential assessment at provincial level in Nepal: Geospatial and economic analysis," Renewable Energy, Elsevier, vol. 181(C), pages 278-291.
    6. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    7. Omoyele, Olalekan & Hoffmann, Maximilian & Koivisto, Matti & Larrañeta, Miguel & Weinand, Jann Michael & Linßen, Jochen & Stolten, Detlef, 2024. "Increasing the resolution of solar and wind time series for energy system modeling: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    8. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    9. Bracken, Cameron & Voisin, Nathalie & Burleyson, Casey D. & Campbell, Allison M. & Hou, Z. Jason & Broman, Daniel, 2024. "Standardized benchmark of historical compound wind and solar energy droughts across the Continental United States," Renewable Energy, Elsevier, vol. 220(C).
    10. Craig, Michael & Guerra, Omar J. & Brancucci, Carlo & Pambour, Kwabena Addo & Hodge, Bri-Mathias, 2020. "Valuing intra-day coordination of electric power and natural gas system operations," Energy Policy, Elsevier, vol. 141(C).
    11. Zimmerman, Ryan & Panda, Anurag & Bulović, Vladimir, 2020. "Techno-economic assessment and deployment strategies for vertically-mounted photovoltaic panels," Applied Energy, Elsevier, vol. 276(C).
    12. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    13. Sanzana Tabassum & Tanvin Rahman & Ashraf Ul Islam & Sumayya Rahman & Debopriya Roy Dipta & Shidhartho Roy & Naeem Mohammad & Nafiu Nawar & Eklas Hossain, 2021. "Solar Energy in the United States: Development, Challenges and Future Prospects," Energies, MDPI, vol. 14(23), pages 1-65, December.
    14. Amadeh, Ali & Lee, Zachary E. & Zhang, K. Max, 2022. "Quantifying demand flexibility of building energy systems under uncertainty," Energy, Elsevier, vol. 246(C).
    15. Diaz, Cesar & Ruiz, Fredy & Patino, Diego, 2017. "Modeling and control of water booster pressure systems as flexible loads for demand response," Applied Energy, Elsevier, vol. 204(C), pages 106-116.
    16. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    17. Sun, Yinong & Frew, Bethany & Dalvi, Sourabh & Dhulipala, Surya C., 2022. "Insights into methodologies and operational details of resource adequacy assessment: A case study with application to a broader flexibility framework," Applied Energy, Elsevier, vol. 328(C).
    18. Grubbs, E.K. & Gruss, S.M. & Schull, V.Z. & Gosney, M.J. & Mickelbart, M.V. & Brouder, S. & Gitau, M.W. & Bermel, P. & Tuinstra, M.R. & Agrawal, R., 2024. "Optimized agrivoltaic tracking for nearly-full commodity crop and energy production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    19. Musaed Alhussein & Syed Irtaza Haider & Khursheed Aurangzeb, 2019. "Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance," Energies, MDPI, vol. 12(8), pages 1-27, April.
    20. Iria, José & Soares, Filipe & Matos, Manuel, 2019. "Optimal bidding strategy for an aggregator of prosumers in energy and secondary reserve markets," Applied Energy, Elsevier, vol. 238(C), pages 1361-1372.

    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:14:y:2021:i:13:p:4012-:d:587976. 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.