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Optimal control of grid-connected microgrid PV-based source under partially shaded conditions

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  • Guichi, A.
  • Mekhilef, S.
  • Berkouk, E.M.
  • Talha, A.

Abstract

Microgrids are gaining increasing attention globally and becoming increasingly powered by photovoltaic (PV) systems, thereby requiring high-efficiency control to function as a microgrid distributed generation unit. Accordingly, this study presents an optimal control of a grid-connected Microgrid PV Source (MPVS) under partially shaded conditions. The objective is to ensure the MPVSs ability to rapidly and precisely deliver the amount of power assigned by the supervisory controller. Thus, MPVS must shift rapidly and smoothly between the maximum and intermediate power point modes. The proposed system is composed of PV array, grid emulators, and two converters coupled to a common DC bus. The control strategy of the boost converter is based on the combination of two algorithms: particle swarm optimization algorithm and the proposed intermediate power point tracker algorithm. The voltage source inverter is controlled to keep the DC bus voltage constant and inject the power to the grid, in which the voltage-oriented control technique is applied and combined with the phase-locked loop algorithm for voltage synchronization. Lastly, all control algorithms are implemented in a DSpace 1104 environment and largely tested under various partially shaded patterns.

Suggested Citation

  • Guichi, A. & Mekhilef, S. & Berkouk, E.M. & Talha, A., 2021. "Optimal control of grid-connected microgrid PV-based source under partially shaded conditions," Energy, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:energy:v:230:y:2021:i:c:s0360544221008987
    DOI: 10.1016/j.energy.2021.120649
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    6. Ahmad, Riaz & Murtaza, Ali F. & Sher, Hadeed Ahmed, 2019. "Power tracking techniques for efficient operation of photovoltaic array in solar applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 82-102.
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    Cited by:

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    2. Gao, Fang & Hu, Rongzhao & Yin, Linfei, 2023. "Variable boundary reinforcement learning for maximum power point tracking of photovoltaic grid-connected systems," Energy, Elsevier, vol. 264(C).
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    4. Hartani, Mohamed Amine & Rezk, Hegazy & Benhammou, Aissa & Hamouda, Messaoud & Abdelkhalek, Othmane & Mekhilef, Saad & Olabi, A.G., 2023. "Proposed frequency decoupling-based fuzzy logic control for power allocation and state-of-charge recovery of hybrid energy storage systems adopting multi-level energy management for multi-DC-microgrid," Energy, Elsevier, vol. 278(C).

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