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A Model Predictive Control to Improve Grid Resilience

Author

Listed:
  • Joseph Young

    (OptimoJoe, Houston, TX 77254, USA)

  • David G. Wilson

    (Electrical Sciences, Sandia National Laboratories, Albuquerque, NM 87185, USA)

  • Wayne Weaver

    (Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI 49931, USA)

  • Rush D. Robinett

    (Mechanical and Aerospace Engineering, Michigan Technological University, Houghton, MI 49931, USA)

Abstract

The following article details a model predictive control (MPC) to improve grid resilience when faced with variable generation resources. This topic is of significant interest to utility power systems where distributed intermittent energy sources will increase significantly and be relied on for electric grid ancillary services. Previous work on MPCs has focused on narrowly targeted control applications such as improving electric vehicle (EV) charging infrastructure or reducing the cost of integrating Energy Storage Systems (ESSs) into the grid. In contrast, this article develops a comprehensive treatment of the construction of an MPC tailored to electric grids and then applies it integration of intermittent energy resources. To accomplish this, the following article includes a description of a reduced order model (ROM) of an electric power grid based on a circuit model, an optimization formulation that describes the MPC, a collocation method for solving linear time-dependent differential algebraic equations (DAEs) that result from the ROM, and an overall strategy for iteratively refining the behavior of the MPC. Next, the algorithm is validated using two separate numerical experiments. First, the algorithm is compared to an existing MPC code and the results are verified by a numerically precise simulation. It is shown that this algorithm produces a control comparable to existing algorithms and the behavior of the control carefully respects the bounds specified. Second, the MPC is applied to a small nine bus system that contains a mix of turbine-spinning-machine-based and intermittent generation in order to demonstrate the algorithm’s utility for resource planning and control of intermittent resources. This study demonstrates how the MPC can be tuned to change the behavior of the control, which can then assist with the integration of intermittent resources into the grid. The emphasis throughout the paper is to provide systematic treatment of the topic and produce a novel nonlinear control compatible design framework applicable to electric grids and the control of variable resources. This differs from the more targeted application-based focus in most presentations.

Suggested Citation

  • Joseph Young & David G. Wilson & Wayne Weaver & Rush D. Robinett, 2025. "A Model Predictive Control to Improve Grid Resilience," Energies, MDPI, vol. 18(7), pages 1-33, April.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:7:p:1865-:d:1629652
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    References listed on IDEAS

    as
    1. 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.
    2. Nor Liza Tumeran & Siti Hajar Yusoff & Teddy Surya Gunawan & Mohd Shahrin Abu Hanifah & Suriza Ahmad Zabidi & Bernardi Pranggono & Muhammad Sharir Fathullah Mohd Yunus & Siti Nadiah Mohd Sapihie & Asm, 2023. "Model Predictive Control Based Energy Management System Literature Assessment for RES Integration," Energies, MDPI, vol. 16(8), pages 1-27, April.
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