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Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control

Author

Listed:
  • Eros D. Escobar

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Antioquia, Colombia)

  • Tatiana Manrique

    (Mechatronics Engineering Program, Universidad EIA, km 2 + 200 Vía al Aeropuerto JMC, Envigado 055428, Antioquia, Colombia)

  • Idi A. Isaac

    (Research Group on Transmission and Distribution of Electric Power, Universidad Pontificia Bolivariana, Medellín 050031, Antioquia, Colombia)

Abstract

Microgrids deal with challenges presented by intermittent distributed generation, electrical faults and mode transition. To address these issues, to understand their static and dynamic behavior, and to develop control systems, it is necessary to reproduce their stable operation and transient response through mathematical models. This paper presents a data-driven low-order model identification methodology applied to voltage characterization in a photovoltaic system of a real campus microgrid for secondary voltage regulation. First, a literature review is presented focusing on secondary voltage modeling strategies and control. Then, experimental data is used to estimate and validate a low-order MIMO (multiple input–multiple output) model of the microgrid, considering reactive power, solar irradiance, and power demand inputs and the voltage output of the grid node. The obtained model reproduced the real system response with an accuracy of 88.4%. This model is used for dynamical analysis of the microgrid and the development of a secondary voltage control system based on model predictive control (MPC). The MPC strategy uses polytopic invariant sets as terminal sets to guarantee stability. Simulations are carried out to evaluate the controller performance using experimental data from solar irradiance and power demand as the system disturbances. Successful regulation of the secondary voltage output is obtained with a fast response despite the wide range of disturbance values.

Suggested Citation

  • Eros D. Escobar & Tatiana Manrique & Idi A. Isaac, 2022. "Campus Microgrid Data-Driven Model Identification and Secondary Voltage Control," Energies, MDPI, vol. 15(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:7846-:d:950872
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    References listed on IDEAS

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    1. Elkazaz, Mahmoud & Sumner, Mark & Naghiyev, Eldar & Pholboon, Seksak & Davies, Richard & Thomas, David, 2020. "A hierarchical two-stage energy management for a home microgrid using model predictive and real-time controllers," Applied Energy, Elsevier, vol. 269(C).
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    7. Ariel Villalón & Marco Rivera & Yamisleydi Salgueiro & Javier Muñoz & Tomislav Dragičević & Frede Blaabjerg, 2020. "Predictive Control for Microgrid Applications: A Review Study," Energies, MDPI, vol. 13(10), pages 1-32, May.
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    Cited by:

    1. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    2. Escobar, Eros D. & Betancur, Daniel & Manrique, Tatiana & Isaac, Idi A., 2023. "Model predictive real-time architecture for secondary voltage control of microgrids," Applied Energy, Elsevier, vol. 345(C).

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