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Predictive Control of PV/Battery System under Load and Environmental Uncertainty

Author

Listed:
  • Salem Batiyah

    (Department of Electrical and Electronics Engineering Technology, Yanbu Industrial College, Yanbu Industrial, Almadina 46452, Saudi Arabia
    Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
    These authors contributed equally to this work.)

  • Roshan Sharma

    (Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
    Smart Grid and Emerging Technology, Commonwealth Edison Company (ComEd), Chicago, IL 60181, USA
    These authors contributed equally to this work.)

  • Sherif Abdelwahed

    (Department of Electrical and Computer Engineering, Virginia Commonwealth University, Richmond, VA 23284, USA)

  • Waleed Alhosaini

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Obaid Aldosari

    (Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Wadi Addawaser, Najd 11991, Saudi Arabia)

Abstract

The standalone microgrids with renewable energy resources (RERs) such as a photovoltaic (PV) system and fast changing loads face major challenges in terms of reliability and power management due to a lack of inherent inertial support from RERs and their intermittent nature. Thus, energy storage technologies such as battery energy storage (BES) are typically used to mitigate the power fluctuations and maintain a power balance in the system. This paper presents a model predictive control (MPC) based power management strategy (PMS) for such standalone PV/battery systems. The proposed method is equipped with an autoregressive integrated moving average (ARIMA) prediction method to forecast the load and environmental parameters. The proposed controller has the capabilities of (1) effective power management, (2) minimization of transients during disturbances, and (3) automatic switching of the operation of the PV between the maximum power point tracking (MPPT) mode and power-curtailed mode that prevents the overcharging of the battery and at the same time maximize the PV utilization. The effectiveness of the proposed method has been verified through a comprehensive simulation-based analysis.

Suggested Citation

  • Salem Batiyah & Roshan Sharma & Sherif Abdelwahed & Waleed Alhosaini & Obaid Aldosari, 2022. "Predictive Control of PV/Battery System under Load and Environmental Uncertainty," Energies, MDPI, vol. 15(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4100-:d:830538
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    References listed on IDEAS

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    1. Roshan Sharma & Masoud Karimi-Ghartemani, 2020. "Addressing Abrupt PV Disturbances, and Mitigating Net Load Profile’s Ramp and Peak Demands, Using Distributed Storage Devices," Energies, MDPI, vol. 13(5), pages 1-21, February.
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    Cited by:

    1. Horrillo-Quintero, Pablo & García-Triviño, Pablo & Sarrias-Mena, Raúl & García-Vázquez, Carlos A. & Fernández-Ramírez, Luis M., 2023. "Model predictive control of a microgrid with energy-stored quasi-Z-source cascaded H-bridge multilevel inverter and PV systems," Applied Energy, Elsevier, vol. 346(C).

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