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Efficient Model Predictive Control Strategies for Resource Management in an Islanded Microgrid

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  • Seaseung Oh

    (Energy Efficiency Research Division, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Suyong Chae

    (Energy Efficiency Research Division, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Jason Neely

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

  • Jongbok Baek

    (Energy Efficiency Research Division, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Marvin Cook

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

Abstract

The energy research community is continuously pursuing improvements in power system resiliency and reliability. Microgrids offer a unique opportunity for enhanced reliability and resiliency by utilizing localized generation and energy storage when grid power is unavailable or too expensive. Energy management is a critical aspect of these systems to ensure proper balancing of sources and ensuring power supply to critical loads with minimum cost, especially in an islanded microgrid. This paper presents a hierarchical real-time optimization with mathematical formulations to achieve optimal operation for an islanded microgrid. The optimization is implemented using simple numerically tractable model predictive control strategies and enables appropriate decisions in response to constantly changing conditions. The optimization method is extended for experimentation within the real-time simulation. Simulation results show that the proposed resource management algorithm shows near-optimal performance while effectively dealing with uncertainties in forecasting.

Suggested Citation

  • Seaseung Oh & Suyong Chae & Jason Neely & Jongbok Baek & Marvin Cook, 2017. "Efficient Model Predictive Control Strategies for Resource Management in an Islanded Microgrid," Energies, MDPI, vol. 10(7), pages 1-16, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1008-:d:104863
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    References listed on IDEAS

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    1. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
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    Cited by:

    1. Xiaogang Guo & Zhejing Bao & Zhijie Li & Wenjun Yan, 2018. "Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid," Energies, MDPI, vol. 11(1), pages 1-17, January.
    2. Mohamed Derbeli & Cristian Napole & Oscar Barambones & Jesus Sanchez & Isidro Calvo & Pablo Fernández-Bustamante, 2021. "Maximum Power Point Tracking Techniques for Photovoltaic Panel: A Review and Experimental Applications," Energies, MDPI, vol. 14(22), pages 1-31, November.
    3. Raya-Armenta, Jose Maurilio & Bazmohammadi, Najmeh & Avina-Cervantes, Juan Gabriel & Sáez, Doris & Vasquez, Juan C. & Guerrero, Josep M., 2021. "Energy management system optimization in islanded microgrids: An overview and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    4. Saif Jamal & Nadia M. L. Tan & Jagadeesh Pasupuleti, 2021. "A Review of Energy Management and Power Management Systems for Microgrid and Nanogrid Applications," Sustainability, MDPI, vol. 13(18), pages 1-31, September.
    5. Feras Alasali & Stephen Haben & Husam Foudeh & William Holderbaum, 2020. "A Comparative Study of Optimal Energy Management Strategies for Energy Storage with Stochastic Loads," Energies, MDPI, vol. 13(10), pages 1-19, May.
    6. Carli, Raffaele & Dotoli, Mariagrazia & Jantzen, Jan & Kristensen, Michael & Ben Othman, Sarah, 2020. "Energy scheduling of a smart microgrid with shared photovoltaic panels and storage: The case of the Ballen marina in Samsø," Energy, Elsevier, vol. 198(C).
    7. Feras Alasali & Stephen Haben & Victor Becerra & William Holderbaum, 2017. "Optimal Energy Management and MPC Strategies for Electrified RTG Cranes with Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-18, October.

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