Advanced Control Strategies for Power Electronics in Microgrid Applications

Ananya Sharma, Ravi Kumar, Priya Yadav

Under the Guidance of Dr. Amit Kumar Singh

Department of Electrical Engineering, Dewan VS Institute of Engineering and Technology, Meerut,

Uttar Pradesh, India

Abstract

Microgrids (MGs) have emerged as a cornerstone of modern energy systems, integrating distributed energy resources (DERs) to enhance reliability, sustainability, and efficiency in power distribution. The integration of power electronics in microgrids enables precise control of voltage, frequency, and power flow, addressing challenges posed by the intermittent nature of renewable energy sources (RESs) and dynamic loads. This article provides a comprehensive review of advanced control strategies for power electronics in microgrid applications, focusing on hierarchical control, droop control, model predictive control (MPC), adaptive control, and artificial intelligence (AI)-based techniques. The study synthesizes recent research, evaluates the effectiveness of these strategies, and identifies gaps for future exploration. Key findings highlight the superiority of adaptive and AI-driven controls in handling non-linear and complex microgrid dynamics, though challenges like computational complexity and cybersecurity remain. Recommendations for future research include hybrid control frameworks and enhanced real-time monitoring systems.Microgrids (MGs) have revolutionized energy distribution by seamlessly integrating distributed energy resources (DERs) and advanced power electronics. This integration allows for precise management of voltage, frequency, and power flow, effectively addressing the challenges associated with the variable nature of renewable energy sources (RESs) and fluctuating load demands. The implementation of sophisticated control strategies, including hierarchical control, droop control, model predictive control (MPC), adaptive control, and artificial intelligence (AI)-based techniques, has significantly enhanced the operational efficiency and reliability of microgrids. These advanced control methodologies enable microgrids to maintain stability, optimize resource allocation, and respond swiftly to changes in energy production and consumption patterns.

The evolution of microgrid control strategies has led to notable improvements in system performance and resilience. Adaptive and AI-driven controls have demonstrated superior capabilities in managing the complex, non-linear dynamics inherent in microgrid operations. These advanced techniques allow for real-time optimization of power flow, predictive maintenance, and efficient integration of diverse energy sources. However, the implementation of such sophisticated control systems is not without challenges. Issues such as computational complexity and cybersecurity vulnerabilities remain significant concerns that require ongoing research and development. Future advancements in microgrid control are expected to focus on developing hybrid control frameworks that combine the strengths of multiple strategies and enhancing real-time monitoring systems to improve overall grid intelligence and responsiveness.

Keywords

Microgrids, Power Electronics, Control Strategies, Hierarchical Control, Droop Control, Model Predictive Control, Adaptive Control, Artificial Intelligence, Renewable Energy Sources, Energy Management Systems

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