DC Microgrid Application

DC microgrid applications are being actively researched to improve the efficiency, reliability, and resilience of decentralized power systems. Current research focuses on developing advanced control strategies, often employing reinforcement learning (RL), model predictive control (MPC), and various neural network architectures (e.g., convolutional neural networks, recurrent neural networks, spiking neural networks) to optimize energy management, predict microgrid behavior, and enhance cybersecurity. These efforts aim to address challenges posed by the integration of renewable energy sources and the need for efficient resource allocation, ultimately contributing to a more sustainable and robust electricity grid.