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A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids

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  • Wang, Huilong
  • Wang, Shengwei

Abstract

Ensuring the power balance and reliability of power grids is an increasing challenge due to the increasing involvement of intermittent renewable power generations. The use of existing heating, ventilation and air-conditioning (HVAC) systems in buildings has attracted increasing attention to implement continuous demand response in providing frequency regulation service, which can enhance instantaneous power balance and reliability of power grids without extra huge investment. However, the energy flexibility of buildings is not consistent, and the capacity available for frequency regulation service changes over time due to the changes of working conditions. In this study, a hierarchical optimal control strategy, consisting of a regulation bidding controller and a power use following controller, is proposed. It optimizes the power use baseline and regulation capacity, and controls HVAC systems to provide qualified frequency regulation service, considering the tradeoff between financial reward (regulation capacity) and thermal comfort while satisfying the operating constraints of HVAC systems. The proposed control strategy is validated on a simulation test platform. Results show that the strategy can maximize the use of regulation capacity provided by HVAC systems while ensuring the indoor environment control quality under a given guarantee rate.

Suggested Citation

  • Wang, Huilong & Wang, Shengwei, 2021. "A hierarchical optimal control strategy for continuous demand response of building HVAC systems to provide frequency regulation service to smart power grids," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221009890
    DOI: 10.1016/j.energy.2021.120741
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    References listed on IDEAS

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    Cited by:

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    5. Liu, Xin & Li, Yang & Lin, Xueshan & Guo, Jiqun & Shi, Yunpeng & Shen, Yunwei, 2022. "Dynamic bidding strategy for a demand response aggregator in the frequency regulation market," Applied Energy, Elsevier, vol. 314(C).
    6. Tang, Hong & Wang, Shengwei, 2023. "Game-theoretic optimization of demand-side flexibility engagement considering the perspectives of different stakeholders and multiple flexibility services," Applied Energy, Elsevier, vol. 332(C).
    7. Leonidas Zouloumis & Angelos Karanasos & Nikolaos Ploskas & Giorgos Panaras, 2023. "Multicriteria Design and Operation Optimization of a Solar-Assisted Geothermal Heat Pump System," Energies, MDPI, vol. 16(3), pages 1-16, January.
    8. 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).
    9. Rehman, Obaid Ur & Khan, Shahid A. & Javaid, Nadeem, 2021. "Decoupled building-to-transmission-network for frequency support in PV systems dominated grid," Renewable Energy, Elsevier, vol. 178(C), pages 930-945.

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