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Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid

Author

Listed:
  • Mohammed M. Olama

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

  • Teja Kuruganti

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

  • James Nutaro

    (Computational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

  • Jin Dong

    (Energy and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
    These authors contributed equally to this work.)

Abstract

Buildings consume 73% of electricity produced in the United States and, currently, they are largely passive participants in the electric grid. However, the flexibility in building loads can be exploited to provide ancillary services to enhance the grid reliability. In this paper, we investigate two control strategies that allow Heating, Ventilation and Air-Conditioning (HVAC) systems in commercial and residential buildings to provide frequency regulation services to the grid while maintaining occupants comfort. The first optimal control strategy is based on model predictive control acting on a variable air volume HVAC system (continuously variable HVAC load), which is available in large commercial buildings. The second strategy is rule-based control acting on an aggregate of on/off HVAC systems, which are available in residential buildings in addition to many small to medium size commercial buildings. Hardware constraints that include limiting the switching between the different states for on/off HVAC units to maintain their lifetimes are considered. Simulations illustrate that the proposed control strategies provide frequency regulation to the grid, without affecting the indoor climate significantly.

Suggested Citation

  • Mohammed M. Olama & Teja Kuruganti & James Nutaro & Jin Dong, 2018. "Coordination and Control of Building HVAC Systems to Provide Frequency Regulation to the Electric Grid," Energies, MDPI, vol. 11(7), pages 1-15, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1852-:d:158138
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    References listed on IDEAS

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    1. Strbac, Goran, 2008. "Demand side management: Benefits and challenges," Energy Policy, Elsevier, vol. 36(12), pages 4419-4426, December.
    2. Woo, C.K. & Kollman, E. & Orans, R. & Price, S. & Horii, B., 2008. "Now that California has AMI, what can the state do with it?," Energy Policy, Elsevier, vol. 36(4), pages 1366-1374, April.
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    Cited by:

    1. O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
    2. Saeid Bashash & Kai Lun Lee, 2019. "Automatic Coordination of Internet-Connected Thermostats for Power Balancing and Frequency Control in Smart Microgrids," Energies, MDPI, vol. 12(10), pages 1-23, May.
    3. Mohammed Olama & Jin Dong & Isha Sharma & Yaosuo Xue & Teja Kuruganti, 2020. "Frequency Analysis of Solar PV Power to Enable Optimal Building Load Control," Energies, MDPI, vol. 13(18), pages 1-18, September.
    4. Andrew Ly & Saeid Bashash, 2020. "Fast Transactive Control for Frequency Regulation in Smart Grids with Demand Response and Energy Storage," Energies, MDPI, vol. 13(18), pages 1-23, September.
    5. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
    6. Jin Dong & Christopher Winstead & James Nutaro & Teja Kuruganti, 2018. "Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings," Energies, MDPI, vol. 11(9), pages 1-20, September.
    7. Huang, Sen & Ye, Yunyang & Wu, Di & Zuo, Wangda, 2021. "An assessment of power flexibility from commercial building cooling systems in the United States," Energy, Elsevier, vol. 221(C).
    8. Liu, Xiangfei & Ren, Mifeng & Yang, Zhile & Yan, Gaowei & Guo, Yuanjun & Cheng, Lan & Wu, Chengke, 2022. "A multi-step predictive deep reinforcement learning algorithm for HVAC control systems in smart buildings," Energy, Elsevier, vol. 259(C).
    9. Anastasios Dounis, 2019. "Special Issue “Intelligent Control in Energy Systems”," Energies, MDPI, vol. 12(15), pages 1-9, August.
    10. Aragón, Gustavo & Pandian, Vinoth & Krauß, Veronika & Werner-Kytölä, Otilia & Thybo, Gitte & Pautasso, Elisa, 2022. "Feasibility and economical analysis of energy storage systems as enabler of higher renewable energy sources penetration in an existing grid," Energy, Elsevier, vol. 251(C).

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