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A machine learning-based control strategy for improved performance of HVAC systems in providing large capacity of frequency regulation service

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  • Wang, Huilong
  • Ding, Zhikun
  • Tang, Rui
  • Chen, Yongbao
  • Fan, Cheng
  • Wang, Jiayuan

Abstract

Heating, ventilation and air-conditioning systems (HVAC), at demand side, have been regarded increasingly as promising candidates to provide frequency regulation service to smart power grids. In many control systems, chilled water outlet temperature setpoint is reset to change the power use of HVAC systems after the regulation capacity is determined. However, the conflict between changed power use and unchanged cooling/heating demand could become a prominent problem when a large regulation capacity is provided. This problem can deteriorate the performance of frequency regulation service provided by HVAC systems. In this study, a machine learning-based control strategy is proposed to solve this problem for improved performance of HVAC systems in providing large capacity of frequency regulation service. It adjusts the power use of HVAC systems by simultaneously resetting chilled water outlet temperature setpoint and indoor temperature setpoint. The proposed control strategy is validated on a simulation platform. Results show that the strategy can significantly increase the performance of service when an HVAC system provides different regulation capacities. Moreover, the robustness of the strategy is studied. The results show that the strategy can still work effectively even the machine learning algorithms has a relatively low prediction performance in real application due to practical difficulties.

Suggested Citation

  • Wang, Huilong & Ding, Zhikun & Tang, Rui & Chen, Yongbao & Fan, Cheng & Wang, Jiayuan, 2022. "A machine learning-based control strategy for improved performance of HVAC systems in providing large capacity of frequency regulation service," Applied Energy, Elsevier, vol. 326(C).
    • Handle: RePEc:eee:appene:v:326:y:2022:i:c:s0306261922012193
    DOI: 10.1016/j.apenergy.2022.119962
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    References listed on IDEAS

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    Cited by:

    1. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    2. Sun, Xiaotian & Xie, Haipeng & Qiu, Dawei & Xiao, Yunpeng & Bie, Zhaohong & Strbac, Goran, 2023. "Decentralized frequency regulation service provision for virtual power plants: A best response potential game approach," Applied Energy, Elsevier, vol. 352(C).
    3. Bo Gao & Ji Ni & Zhongyuan Yuan & Nanyang Yu, 2023. "Pump-Valve Combined Control of a HVAC Chilled Water System Using an Artificial Neural Network Model," Energies, MDPI, vol. 16(5), pages 1-16, March.
    4. Lichen Su & Jinlong Ouyang & Li Yang, 2023. "Mixed-Mode Ventilation Based on Adjustable Air Velocity for Energy Benefits in Residential Buildings," Energies, MDPI, vol. 16(6), pages 1-17, March.

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