Microgrids (MGs) technologies, with their advanced control techniques and real-time monitoring systems, provide users with attractive benefits including enhanced power quality, stability,
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The concept of microgrids (MGs) as compact power systems, incorporating distributed energy resources, generating units, storage systems, and loads, is widely acknowledged in the
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The most relevant control methods identified for microgrid applications are the intelligent, robust, predictive, adaptive, linear, and non-linear control methods.
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The intrinsic control performance of an intelligent microgrid comprises four interdependent systems: control techniques, control layers, control structures, and control strategies.
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Microgrids can include distributed energy resources such as generators, storage devices, and controllable loads. Microgrids generally must also include a control strategy to maintain, on an
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MG control methods can be categorized as centralized, decentralized, or distributed, as shown in Fig. 1.2. A short explanation of these control structures is given below. A central controller
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Advanced control strategies are essential to ensure stability, power quality, and optimal energy management in microgrids. These strategies leverage power electronics to regulate voltage,
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Effective control systems are essential for ensuring smooth integration, managing energy storage systems, and maintaining microgrid safety. In this study, a review of recent control methods
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Microgrids (MGs) provide a promising solution by enabling localized control over energy generation, storage, and distribution. This paper presents a novel reinforcement learning (RL)-based
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