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Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review

Author

Listed:
  • Guilherme Vieira Hollweg

    (Department of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Gajendra Singh Chawda

    (Department of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Shivam Chaturvedi

    (Department of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Van-Hai Bui

    (Department of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering (ECE), University of Michigan-Dearborn, Dearborn, MI 48128, USA)

Abstract

The optimization of low-level control for DC–AC power converters is crucial for enhancing efficiency, stability, and adaptability in modern power systems. With the increasing penetration of renewable energy sources and the shift toward decentralized grid architectures, advanced control strategies are needed to address challenges such as reduced system inertia and dynamic operating conditions. This paper provides a concise review of key optimization techniques for low-level control, highlighting their advantages, limitations, and applicability. Additionally, emerging trends, such as artificial intelligence (AI)-based real-time control algorithms and hybrid optimization approaches, are explored as potential enablers for the next generation of power conversion systems. Notably, no single optimized control technique universally outperforms others, as each involves trade-offs in mathematical complexity, robustness, computational burden, and implementation feasibility. Therefore, selecting the most appropriate control strategy requires a thorough understanding of the specific application and system constraints.

Suggested Citation

  • Guilherme Vieira Hollweg & Gajendra Singh Chawda & Shivam Chaturvedi & Van-Hai Bui & Wencong Su, 2025. "Optimization Techniques for Low-Level Control of DC–AC Converters in Renewable-Integrated Microgrids: A Brief Review," Energies, MDPI, vol. 18(6), pages 1-29, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:6:p:1429-:d:1611567
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    References listed on IDEAS

    as
    1. Guilherme V. Hollweg & Shahid A. Khan & Shivam Chaturvedi & Yaoyu Fan & Mengqi Wang & Wencong Su, 2023. "Grid-Connected Converters: A Brief Survey of Topologies, Output Filters, Current Control, and Weak Grids Operation," Energies, MDPI, vol. 16(9), pages 1-31, April.
    2. Gustavo G. Koch & Caio R. D. Osório & Ricardo C. L. F. Oliveira & Vinícius F. Montagner, 2023. "Robust Control Based on Observed States Designed by Means of Linear Matrix Inequalities for Grid-Connected Converters," Energies, MDPI, vol. 16(4), pages 1-24, February.
    3. 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).
    4. Mbungu, Nsilulu T. & Siti, Mukwanga W. & Bansal, Ramesh C. & Naidoo, Raj M. & Elnady, A. & Ismail, Ali A. Adam & Abokhali, Ahmed G. & Hamid, Abdul-Kadir, 2025. "A dynamic coordination of microgrids," Applied Energy, Elsevier, vol. 377(PD).
    5. Neto, Pedro Bezerra Leite & Saavedra, Osvaldo R. & Oliveira, Denisson Q., 2020. "The effect of complementarity between solar, wind and tidal energy in isolated hybrid microgrids," Renewable Energy, Elsevier, vol. 147(P1), pages 339-355.
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