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A review on state of art development of model predictive control for renewable energy applications

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  • Sultana, W. Razia
  • Sahoo, Sarat Kumar
  • Sukchai, Sukruedee
  • Yamuna, S.
  • Venkatesh, D.

Abstract

Renewable energy sector is undergoing rapid expansion as the global focus is shifting towards cleaner, reliable and sustainable resources. As the new installation of these resources are well underway, there is tremendous potential for exploring these to more advanced control algorithms. Model predictive control is gaining immense popularity because of its flexible controllability, its ability to be used in any of application irrespective of its field as well as the availability of fast processors. This paper presents a systematic review on Photo-voltaic (PV) and wind energy systems controlled by Model predictive control approach. The work presented here will help the researchers to further explore the flexibility of this controller for design, analysis and implementation in renewable energy systems.

Suggested Citation

  • Sultana, W. Razia & Sahoo, Sarat Kumar & Sukchai, Sukruedee & Yamuna, S. & Venkatesh, D., 2017. "A review on state of art development of model predictive control for renewable energy applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 391-406.
    • Handle: RePEc:eee:rensus:v:76:y:2017:i:c:p:391-406
    DOI: 10.1016/j.rser.2017.03.058
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    2. 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.
    3. Edison Banguero & Antonio Correcher & Ángel Pérez-Navarro & Francisco Morant & Andrés Aristizabal, 2018. "A Review on Battery Charging and Discharging Control Strategies: Application to Renewable Energy Systems," Energies, MDPI, vol. 11(4), pages 1-15, April.
    4. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Juan Moreno-Castro & Victor Samuel Ocaña Guevara & Lesyani Teresa León Viltre & Yandi Gallego Landera & Oscar Cuaresma Zevallos & Miguel Aybar-Mejía, 2023. "Microgrid Management Strategies for Economic Dispatch of Electricity Using Model Predictive Control Techniques: A Review," Energies, MDPI, vol. 16(16), pages 1-24, August.
    6. Kneiske, T.M. & Braun, M. & Hidalgo-Rodriguez, D.I., 2018. "A new combined control algorithm for PV-CHP hybrid systems," Applied Energy, Elsevier, vol. 210(C), pages 964-973.
    7. Wu, Jinhui & Yang, Fuwen, 2023. "A dual-driven predictive control for photovoltaic-diesel microgrid secondary frequency regulation," Applied Energy, Elsevier, vol. 334(C).
    8. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & de Gracia, Alvaro & Cabeza, Luisa F., 2021. "Systematic review on model predictive control strategies applied to active thermal energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Sulaiman, N. & Hannan, M.A. & Mohamed, A. & Ker, P.J. & Majlan, E.H. & Wan Daud, W.R., 2018. "Optimization of energy management system for fuel-cell hybrid electric vehicles: Issues and recommendations," Applied Energy, Elsevier, vol. 228(C), pages 2061-2079.
    10. Hu, Maomao & Xiao, Fu & Jørgensen, John Bagterp & Wang, Shengwei, 2019. "Frequency control of air conditioners in response to real-time dynamic electricity prices in smart grids," Applied Energy, Elsevier, vol. 242(C), pages 92-106.
    11. Quan, Shengwei & Wang, Ya-Xiong & Xiao, Xuelian & He, Hongwen & Sun, Fengchun, 2021. "Feedback linearization-based MIMO model predictive control with defined pseudo-reference for hydrogen regulation of automotive fuel cells," Applied Energy, Elsevier, vol. 293(C).

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