State-of-Charge Droop Control for Efficient Power Sharing and Transient Response in Microgrids

Abstract

Microgrids (MGs) are essential in transitioning towards a low carbon future in energy systems, as they offer a highly efficient network architecture. This architecture facilitates the flexible integration of various DC/AC loads, distributed renewable energy sources, and energy storage systems, contributing to more resilient and economical energy management. However, MGs encounter challenges as newcomers to the utility grid, stemming from economic deregulation, restructuring of generation, and market-based operation. This paper presents a comprehensive overview of published research on MGs and associated energy management modeling and solution techniques. It highlights the organization of MGs and energy storage systems into multiple branches and typical combinations, which form the foundation of MG energy management. The paper also examines energy management models that address exogenous and endogenous uncertainties, extending them to transactive energy management. Furthermore, it investigates various solution methods, including mathematical programming, adaptive dynamic programming, and deep reinforcement learning-based approaches. Lastly, the implementation schemes of these solution methods are explored to provide a thorough understanding of their practical applications.