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

Guilherme Vieira Hollweg (), Gajendra Singh Chawda, Shivam Chaturvedi, Bui Van-Hai and Wencong Su ()
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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
Bui Van-Hai: 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

Energies, 2025, vol. 18, issue 6, 1-29

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.

Keywords: applications of control; grid-forming converters; grid-following converters; LMIs; adaptive control; model predictive control; genetic algorithms; particle swarm optimization; AI based (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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