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 crucial element in modern energy systems by integrating distributed energy resources (DERs) to enhance the reliability, sustainability, and efficiency of power distribution. The incorporation of power electronics in microgrids enables precise control over voltage, frequency, and power flow, effectively addressing the 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 consolidates recent research, evaluates the effectiveness of these strategies, and identifies areas for future exploration. Key findings highlight the benefits of adaptive and AI-driven controls in managing the non-linear and complex dynamics of microgrids, although challenges such as computational complexity and cybersecurity remain. Recommendations for future research include developing hybrid control frameworks and enhancing 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 control over 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, such as hierarchical control, droop control, model predictive control (MPC), adaptive control, and AI-based techniques, has significantly improved the operational efficiency and reliability of microgrids. These advanced control methodologies enable microgrids to maintain stability, optimize resource allocation, and quickly adapt to changes in energy production and consumption patterns. The development of control strategies for microgrids has significantly enhanced system performance and resilience. Adaptive controls and those driven by AI have demonstrated exceptional capabilities in handling the intricate, non-linear dynamics typical of microgrid operations. These cutting-edge methods enable real-time power flow optimization, predictive maintenance, and the efficient integration of various energy sources. Nonetheless, deploying such advanced control systems presents challenges, including computational complexity and cybersecurity risks, which continue to be major areas of concern requiring further research and development. Future advancements in microgrid control are expected to focus on creating hybrid control frameworks that leverage the strengths of multiple strategies and improving real-time monitoring systems to enhance overall grid intelligence and responsiveness.

Keywords

Microgrids, Power Electronics, Control Techniques, Layered Control, Droop Method, Predictive Model Control, Adaptive Regulation, Artificial Intelligence, Sustainable Energy Sources, Energy Management Systems

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