IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i13p4851-d1176354.html
   My bibliography  Save this article

Microgrids with Model Predictive Control: A Critical Review

Author

Listed:
  • Karan Singh Joshal

    (National Institute of Technology, Srinagar 190006, India
    These authors contributed equally to this work.)

  • Neeraj Gupta

    (National Institute of Technology, Srinagar 190006, India
    These authors contributed equally to this work.)

Abstract

Microgrids face significant challenges due to the unpredictability of distributed generation (DG) technologies and fluctuating load demands. These challenges result in complex power management systems characterised by voltage/frequency variations and intricate interactions with the utility grid. Model predictive control (MPC) has emerged as a powerful technique to effectively address these challenges. By applying a receding horizon control strategy, MPC offers promising solutions for optimising constraints and enhancing microgrid operations. The purpose of this review paper is to comprehensively analyse the application of MPC in microgrids, covering various levels of the hierarchical control structure. Furthermore, this paper explores the emerging trend of employing MPC across microgrid applications, ranging from converter control levels for power quality to overarching energy management systems. It also investigates the future research perspectives by considering the challenges associated with establishing MPC-based microgrid control. The key conclusion derived from this review paper is that the implementation of MPC techniques in microgrid operations can greatly improve their overall performance, efficiency, and resilience. This paper thoroughly examines the various challenges faced in MPC-based microgrid operations, underscoring the significance of conducting research in advanced artificial intelligence (AI)-based MPC methods. It highlights how these cutting-edge AI techniques can bring about economic benefits in microgrid operations, addressing the complex demands of efficient energy management in a rapidly evolving landscape. The presented insights strive to enhance the comprehension and adoption of MPC techniques in microgrid settings, actively contributing to the ongoing improvement of their operational processes. By shedding light on key aspects and offering valuable guidance, this work aims to propel the advancement and effective utilisation of MPC methodologies in microgrids, ultimately leading to optimised performance and enhanced overall operations.

Suggested Citation

  • Karan Singh Joshal & Neeraj Gupta, 2023. "Microgrids with Model Predictive Control: A Critical Review," Energies, MDPI, vol. 16(13), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4851-:d:1176354
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/13/4851/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/13/4851/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ipsakis, Dimitris & Voutetakis, Spyros & Seferlis, Panos & Stergiopoulos, Fotis & Papadopoulou, Simira & Elmasides, Costas, 2008. "The effect of the hysteresis band on power management strategies in a stand-alone power system," Energy, Elsevier, vol. 33(10), pages 1537-1550.
    2. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    3. Giaouris, Damian & Papadopoulos, Athanasios I. & Ziogou, Chrysovalantou & Ipsakis, Dimitris & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos & Stergiopoulos, Fotis & Elmasides, Costas, 2013. "Performance investigation of a hybrid renewable power generation and storage system using systemic power management models," Energy, Elsevier, vol. 61(C), pages 621-635.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Anton Petrochenkov & Nikolai Pavlov & Nikolai Bachev & Alexander Romodin & Iurii Butorin & Nikolai Kolesnikov, 2023. "Ensuring Power Balance in the Electrical Grid of an Oil-and-Gas-Producing Enterprise with Distributed Generation Using Associated Petroleum Gas," Sustainability, MDPI, vol. 15(19), pages 1-15, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bizon, Nicu & Radut, Marin & Oproescu, Mihai, 2015. "Energy control strategies for the Fuel Cell Hybrid Power Source under unknown load profile," Energy, Elsevier, vol. 86(C), pages 31-41.
    2. Bruni, G. & Cordiner, S. & Mulone, V., 2014. "Domestic distributed power generation: Effect of sizing and energy management strategy on the environmental efficiency of a photovoltaic-battery-fuel cell system," Energy, Elsevier, vol. 77(C), pages 133-143.
    3. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Rocco, Vittorio & Rossi, Joao Luis, 2018. "Hybrid renewable energy systems for renewable integration in microgrids: Influence of sizing on performance," Energy, Elsevier, vol. 152(C), pages 744-758.
    4. Giaouris, Damian & Papadopoulos, Athanasios I. & Seferlis, Panos & Voutetakis, Spyros & Papadopoulou, Simira, 2016. "Power grand composite curves shaping for adaptive energy management of hybrid microgrids," Renewable Energy, Elsevier, vol. 95(C), pages 433-448.
    5. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    6. Esteban, Miguel & Portugal-Pereira, Joana, 2014. "Post-disaster resilience of a 100% renewable energy system in Japan," Energy, Elsevier, vol. 68(C), pages 756-764.
    7. Ming Li & Jin Ye, 2022. "Design and Implementation of Demand Side Response Based on Binomial Distribution," Energies, MDPI, vol. 15(22), pages 1-15, November.
    8. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    9. Maria C. Fotopoulou & Panagiotis Drosatos & Stefanos Petridis & Dimitrios Rakopoulos & Fotis Stergiopoulos & Nikolaos Nikolopoulos, 2021. "Model Predictive Control for the Energy Management in a District of Buildings Equipped with Building Integrated Photovoltaic Systems and Batteries," Energies, MDPI, vol. 14(12), pages 1-21, June.
    10. Cheng, Yu-Shan & Chuang, Man-Tsai & Liu, Yi-Hua & Wang, Shun-Chung & Yang, Zong-Zhen, 2016. "A particle swarm optimization based power dispatch algorithm with roulette wheel re-distribution mechanism for equality constraint," Renewable Energy, Elsevier, vol. 88(C), pages 58-72.
    11. Nicu Bizon & Phatiphat Thounthong, 2020. "Energy Efficiency and Fuel Economy of a Fuel Cell/Renewable Energy Sources Hybrid Power System with the Load-Following Control of the Fueling Regulators," Mathematics, MDPI, vol. 8(2), pages 1-22, January.
    12. Zhang, Xiuqin & Guo, Juncheng & Chen, Jincan, 2010. "The parametric optimum analysis of a proton exchange membrane (PEM) fuel cell and its load matching," Energy, Elsevier, vol. 35(12), pages 5294-5299.
    13. Esteban, Miguel & Portugal-Pereira, Joana & Mclellan, Benjamin C. & Bricker, Jeremy & Farzaneh, Hooman & Djalilova, Nigora & Ishihara, Keiichi N. & Takagi, Hiroshi & Roeber, Volker, 2018. "100% renewable energy system in Japan: Smoothening and ancillary services," Applied Energy, Elsevier, vol. 224(C), pages 698-707.
    14. Chatterjee, Arunava & Roy, Krishna & Chatterjee, Debashis, 2014. "A Gravitational Search Algorithm (GSA) based Photo-Voltaic (PV) excitation control strategy for single phase operation of three phase wind-turbine coupled induction generator," Energy, Elsevier, vol. 74(C), pages 707-718.
    15. Francisco J. Vivas Fernández & Francisca Segura Manzano & José Manuel Andújar Márquez & Antonio J. Calderón Godoy, 2020. "Extended Model Predictive Controller to Develop Energy Management Systems in Renewable Source-Based Smart Microgrids with Hydrogen as Backup. Theoretical Foundation and Case Study," Sustainability, MDPI, vol. 12(21), pages 1-28, October.
    16. Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    17. Ouzounidou, Martha & Ipsakis, Dimitris & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2009. "A combined methanol autothermal steam reforming and PEM fuel cell pilot plant unit: Experimental and simulation studies," Energy, Elsevier, vol. 34(10), pages 1733-1743.
    18. Muhammad Nauman & Wajiha Shireen & Amir Hussain, 2022. "Model-Free Predictive Control and Its Applications," Energies, MDPI, vol. 15(14), pages 1-24, July.
    19. Zaibi, Malek & Champenois, Gérard & Roboam, Xavier & Belhadj, Jamel & Sareni, Bruno, 2018. "Smart power management of a hybrid photovoltaic/wind stand-alone system coupling battery storage and hydraulic network," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 146(C), pages 210-228.
    20. Amrutha Raju Battula & Sandeep Vuddanti & Surender Reddy Salkuti, 2021. "Review of Energy Management System Approaches in Microgrids," Energies, MDPI, vol. 14(17), pages 1-32, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:13:p:4851-:d:1176354. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.