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Integrating Demand Response for Enhanced Load Frequency Control in Micro-Grids with Heating, Ventilation and Air-Conditioning Systems

Author

Listed:
  • Tanima Bal

    (Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, India)

  • Saheli Ray

    (Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, India)

  • Nidul Sinha

    (Department of Electrical Engineering, National Institute of Technology Silchar, Silchar 788010, India)

  • Ramesh Devarapalli

    (Department of Electrical/Electronics and Instrumentation Engineering, Institute of Chemical Technology, Indianoil Odisha Campus, Bhubaneswar 751013, India)

  • Łukasz Knypiński

    (Faculty of Automatic Control, Robotic and Electrical Engineering, Poznan University of Technology, 60-965 Poznan, Poland)

Abstract

Heating, ventilation and air-conditioning (HVAC) systems constitute the majority of the demands in modern power systems for aggregated buildings. However, HVAC integrated with renewable energy sources (RES) face notable issues, such as uneven demand–supply balance, frequency oscillation and significant drop in system inertia owing to sudden disturbances in nearby generation for a longer period. To overcome these challenges, load frequency control (LFC) is implemented to regulate the frequency, maintain zero steady-state error between the generation and demand, reduce frequency deviations and balance the active power flow with neighboring control areas at a specified value. In view of this, the present paper investigates LFC with a proposed centralized single control strategy for a micro-grid (µG) system consisting of RESs and critical load of a HVAC system. The proposed control strategy includes a newly developed cascaded two-degree-of-freedom (2-DOF) proportional integral (PI) and proportional derivative filter (PDF) controller optimized with a very recent meta-heuristic algorithm—a modified crow search algorithm (mCSA)—after experimenting with the number of performance indices (PICs). The superiority of both the proposed optimization algorithm and the proposed controller is arrived at after comparison with similar other algorithms and similar controllers, respectively. Compared to conventional control schemes, the proposed scheme significantly reduces the frequency deviations, improving by 27.22% from the initial value and reducing the performance index criteria (ƞ ISE ) control error to 0.000057. Furthermore, the demand response (DR) is implemented by an energy storage device (ESD), which validates the suitability of the proposed control strategy for the µG system and helps overcome the challenges associated with variable RESs inputs and load demand. Additionally, the improved robustness of the proposed controller for this application is demonstrated through sensitivity analysis with ±20% μG coefficient variation.

Suggested Citation

  • Tanima Bal & Saheli Ray & Nidul Sinha & Ramesh Devarapalli & Łukasz Knypiński, 2023. "Integrating Demand Response for Enhanced Load Frequency Control in Micro-Grids with Heating, Ventilation and Air-Conditioning Systems," Energies, MDPI, vol. 16(15), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:15:p:5767-:d:1209112
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    References listed on IDEAS

    as
    1. Malik, Anam & Ravishankar, Jayashri, 2018. "A hybrid control approach for regulating frequency through demand response," Applied Energy, Elsevier, vol. 210(C), pages 1347-1362.
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    3. Lei Liu & Hidehito Matayoshi & Mohammed Elsayed Lotfy & Manoj Datta & Tomonobu Senjyu, 2018. "Load Frequency Control Using Demand Response and Storage Battery by Considering Renewable Energy Sources," Energies, MDPI, vol. 11(12), pages 1-40, December.
    4. Latif, Abdul & Hussain, S.M. Suhail & Das, Dulal Chandra & Ustun, Taha Selim, 2020. "State-of-the-art of controllers and soft computing techniques for regulated load frequency management of single/multi-area traditional and renewable energy based power systems," Applied Energy, Elsevier, vol. 266(C).
    5. Xiaoqing Hu & Beibei Wang & Shengchun Yang & Taylor Short & Lei Zhou, 2015. "A Closed-Loop Control Strategy for Air Conditioning Loads to Participate in Demand Response," Energies, MDPI, vol. 8(8), pages 1-32, August.
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