Primary control in AC microgrid

Seema Diwan; Rajin Linus

doi:10.31893/multirev.2026156

Keywords:

microgrid

AC microgrid

hierarchical control

traditional droop control

robust and adaptive control

artificial intelligence-based control

Abstract

Microgrids (MGs) are a transformative development in modern power systems, enabling the integration of Distributed Generation (DG) units and Renewable Energy Sources (RESs) into localized energy networks. DG unit’s interface with the main grid through power electronics-based converters, which pose significant challenges in both steady-state and dynamic operation. This paper highlights the importance of primary control in MGs, which ensures key functions such as real-time power sharing, voltage and frequency regulation, and stability. In this study, traditional primary control methods are categorized into communication-dependent, communication-independent, and enhanced droop control techniques. While these methods are effective under steady-state conditions, they often rely on linear models and lack the responsiveness needed for fast-changing dynamics, leading to degraded performance during transient conditions. To address this, adaptive droop controllers that modify parameters in real time are explored for improved dynamic response and system stability. Further, traditional controllers struggle with handling nonlinearities and system uncertainties. To overcome this, robust nonlinear control strategies such as Sliding Mode Control (SMC), H∞ control, and backstepping are analyzed. These methods improve performance but depend heavily on accurate system modeling and predefined uncertainty bounds, limiting their practical deployment. To meet the growing complexity of MGs, this study explores Artificial Intelligence (AI)-based control strategies, including fuzzy logic (FL), machine learning (ML), and optimization techniques. These intelligent controllers offer self-adaptive capabilities, allowing real-time learning and adjustment to changing conditions, thereby enhancing system resilience, fault handling, and performance. This review presents a structured comparison of conventional, robust, and AI-based primary control methods, outlining their respective advantages, limitations, and the potential for hybrid solutions. It provides a comprehensive overview that supports the development of more intelligent and responsive MG control systems.
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