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Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids

Author

Listed:
  • Kai Ma

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Chenliang Yuan

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Jie Yang

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Zhixin Liu

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Xinping Guan

    (Key Laboratory of System Control and Information Processing, Ministry of Education, Shanghai Jiao Tong University, Shanghai, 200240, China)

Abstract

This work proposes three switched control strategies for aggregated heating, ventilation, and air conditioning (HVAC) systems in commercial buildings to track the automatic generation control (AGC) signal in smart grid. The existing control strategies include the direct load control strategy and the setpoint regulation strategy. The direct load control strategy cannot track the AGC signal when the state of charge (SOC) of the aggregated thermostatically controlled loads (TCLs) exceeds their regulation capacity, while the setpoint regulation strategy provides flexible regulation capacity, but causes larger tracking errors. To improve the tracking performance, we took the advantages of the two control modes and developed three switched control strategies. The control strategies switch between the direct load control mode and the setpoint regulation mode according to different switching indices. Specifically, we design a discrete-time controller and optimize the controller parameter for the setpoint regulation strategy using the Fibonacci optimization algorithm, enabling us to propose two switched control strategies across multiple time steps. Furthermore, we extend the switched control strategies by introducing a two-stage regulation in a single time step. Simulation results demonstrate that the proposed switched control strategies can reduce the tracking errors for frequency regulation.

Suggested Citation

  • Kai Ma & Chenliang Yuan & Jie Yang & Zhixin Liu & Xinping Guan, 2017. "Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids," Energies, MDPI, vol. 10(7), pages 1-18, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:953-:d:104145
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    References listed on IDEAS

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    1. Patteeuw, Dieter & Henze, Gregor P. & Helsen, Lieve, 2016. "Comparison of load shifting incentives for low-energy buildings with heat pumps to attain grid flexibility benefits," Applied Energy, Elsevier, vol. 167(C), pages 80-92.
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    Citations

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    Cited by:

    1. Bomela, Walter & Zlotnik, Anatoly & Li, Jr-Shin, 2018. "A phase model approach for thermostatically controlled load demand response," Applied Energy, Elsevier, vol. 228(C), pages 667-680.
    2. Nikolaos Kampelis & Nikolaos Sifakis & Dionysia Kolokotsa & Konstantinos Gobakis & Konstantinos Kalaitzakis & Daniela Isidori & Cristina Cristalli, 2019. "HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response," Energies, MDPI, vol. 12(11), pages 1-23, June.
    3. Rahmat Khezri & Arman Oshnoei & Mehrdad Tarafdar Hagh & SM Muyeen, 2018. "Coordination of Heat Pumps, Electric Vehicles and AGC for Efficient LFC in a Smart Hybrid Power System via SCA-Based Optimized FOPID Controllers," Energies, MDPI, vol. 11(2), pages 1-21, February.
    4. Yao Yao & Peichao Zhang & Sijie Chen, 2019. "Aggregating Large-Scale Generalized Energy Storages to Participate in the Energy and Regulation Market," Energies, MDPI, vol. 12(6), pages 1-22, March.
    5. Zhengwei Qu & Chenglin Xu & Kai Ma & Zongxu Jiao, 2019. "Fuzzy Neural Network Control of Thermostatically Controlled Loads for Demand-Side Frequency Regulation," Energies, MDPI, vol. 12(13), pages 1-15, June.

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