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Flexible Power Point Tracking Using a Neural Network for Power Reserve Control in a Grid-Connected PV System

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  • Jishu Mary Gomez

    (School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India)

  • Prabhakar Karthikeyan Shanmugam

    (School of Electrical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India)

Abstract

Renewable energy penetration in the global energy sector is in a state of steady growth. A major criterion imposed by the regulatory boards in the wake of electronic-driven power systems is frequency regulation capability. As more rooftop PV systems are under installation, the inertia response of the power utility system is descending. The PV systems are not equipped inherently with inertial or governor control for unseen frequency deviation scenarios. In the proposed method, inertial and droop frequency control is implemented by creating the necessary power reserve by the derated operation of the PV system. While, traditionally, PV systems operate in normal MPPT mode, a derated PV system follows a flexible power point tracking (FPPT) algorithm for creating virtual energy storage. The point of operation for the FPPT of the PV is determined by using a neural network block set available in MATLAB. For the verification of the controller, it is applied to a PV array in a modified IEEE-13 bus system modeled in the MATLAB/Simulink platform. The simulation results prove that when the proposed control is applied to the test network with renewable energy penetration, there is an improved system inertia response.

Suggested Citation

  • Jishu Mary Gomez & Prabhakar Karthikeyan Shanmugam, 2022. "Flexible Power Point Tracking Using a Neural Network for Power Reserve Control in a Grid-Connected PV System," Energies, MDPI, vol. 15(21), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8234-:d:963155
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    References listed on IDEAS

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    1. Rajan, Rijo & Fernandez, Francis M. & Yang, Yongheng, 2021. "Primary frequency control techniques for large-scale PV-integrated power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    2. Mehdi Tavakkoli & Jafar Adabi & Sasan Zabihi & Radu Godina & Edris Pouresmaeil, 2018. "Reserve Allocation of Photovoltaic Systems to Improve Frequency Stability in Hybrid Power Systems," Energies, MDPI, vol. 11(10), pages 1-19, September.
    3. Claudia Rahmann & Alfredo Castillo, 2014. "Fast Frequency Response Capability of Photovoltaic Power Plants: The Necessity of New Grid Requirements and Definitions," Energies, MDPI, vol. 7(10), pages 1-17, September.
    4. Guangqing Bao & Hongtao Tan & Kun Ding & Ming Ma & Ningbo Wang, 2019. "A Novel Photovoltaic Virtual Synchronous Generator Control Technology Without Energy Storage Systems," Energies, MDPI, vol. 12(12), pages 1-14, June.
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    Cited by:

    1. Fatemeh Jamshidi & Mohammad Reza Salehizadeh & Reza Yazdani & Brian Azzopardi & Vibhu Jately, 2023. "An Improved Sliding Mode Controller for MPP Tracking of Photovoltaics," Energies, MDPI, vol. 16(5), pages 1-20, March.
    2. Loredana Cristaldi & Marco Faifer & Christian Laurano & Emil Petkovski & Ferdinanda Ponci & Igor Sowa & Sergio Toscani, 2024. "Model-Based Algorithm for Flexible Power Point Tracking for Photovoltaic Participation in Primary Frequency Regulation," Energies, MDPI, vol. 17(9), pages 1-14, April.

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