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Distributed model predictive control for coordinated, grid-interactive buildings

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  • Bay, Christopher J.
  • Chintala, Rohit
  • Chinde, Venkatesh
  • King, Jennifer

Abstract

Continued focus on reducing carbon emissions and improving energy efficiency requires buildings to become grid-interactive and not just behave as static consumers. A distributed model predictive control (DMPC) algorithm known as Limited-Communication (LC) DMPC is modified to enable grid-interactive buildings. A grid-aggregator subsystem is added that allows for a bulk grid power reference signal to be followed while the individual building subsystems also achieve their local comfort objectives. The LC-DMPC algorithm is applied for the first time to systems with multiple buildings. Adequate power tracking is shown for different simulation scenarios involving heterogeneous buildings, and next steps are discussed.

Suggested Citation

  • Bay, Christopher J. & Chintala, Rohit & Chinde, Venkatesh & King, Jennifer, 2022. "Distributed model predictive control for coordinated, grid-interactive buildings," Applied Energy, Elsevier, vol. 312(C).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922000861
    DOI: 10.1016/j.apenergy.2022.118612
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    References listed on IDEAS

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    1. Wang, Huilong & Wang, Shengwei & Tang, Rui, 2019. "Development of grid-responsive buildings: Opportunities, challenges, capabilities and applications of HVAC systems in non-residential buildings in providing ancillary services by fast demand responses," Applied Energy, Elsevier, vol. 250(C), pages 697-712.
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    3. Wei, Congying & Wu, Qiuwei & Xu, Jian & Sun, Yuanzhang & Jin, Xiaolong & Liao, Siyang & Yuan, Zhiyong & Yu, Li, 2020. "Distributed scheduling of smart buildings to smooth power fluctuations considering load rebound," Applied Energy, Elsevier, vol. 276(C).
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    Cited by:

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    5. Anujin Bayasgalan & Yoo Shin Park & Seak Bai Koh & Sung-Yong Son, 2024. "Comprehensive Review of Building Energy Management Models: Grid-Interactive Efficient Building Perspective," Energies, MDPI, vol. 17(19), pages 1-25, September.
    6. Wenya Xu & Yanxue Li & Guanjie He & Yang Xu & Weijun Gao, 2023. "Performance Assessment and Comparative Analysis of Photovoltaic-Battery System Scheduling in an Existing Zero-Energy House Based on Reinforcement Learning Control," Energies, MDPI, vol. 16(13), pages 1-19, June.
    7. Song, Yuguang & Xia, Mingchao & Chen, Qifang, 2023. "The robust synchronization control scheme for flexible resources considering the stochastic and delay response process," Applied Energy, Elsevier, vol. 343(C).
    8. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2022. "Economic model predictive control of integrated energy systems: A multi-time-scale framework," Applied Energy, Elsevier, vol. 328(C).
    9. Wald, Dylan & King, Jennifer & Bay, Christopher J. & Chintala, Rohit & Johnson, Kathryn, 2022. "Integration of distributed controllers: Power reference tracking through charging station and building coordination," Applied Energy, Elsevier, vol. 314(C).
    10. Deng, Xiangtian & Zhang, Yi & Jiang, Yi & Zhang, Yi & Qi, He, 2024. "A novel operation method for renewable building by combining distributed DC energy system and deep reinforcement learning," Applied Energy, Elsevier, vol. 353(PB).
    11. Amal Azzi & Mohamed Tabaa & Badr Chegari & Hanaa Hachimi, 2024. "Balancing Sustainability and Comfort: A Holistic Study of Building Control Strategies That Meet the Global Standards for Efficiency and Thermal Comfort," Sustainability, MDPI, vol. 16(5), pages 1-36, March.

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