Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch

arXiv:2404.18362v1 Announce Type: cross
Abstract: The variability of renewable energy generation and the unpredictability of electricity demand create a need for real-time economic dispatch (ED) of assets in microgrids. However, solving numerical optimization problems in real-time can be incredibly challenging. This study proposes using a convolutional neural network (CNN) based on deep learning to address these challenges. Compared to traditional methods, CNN is more efficient, delivers more dependable results, and has a shorter response time when dealing with uncertainties. While …

abstract arxiv cnn convolutional convolutional neural network create cs.lg cs.sy demand economic eess.sy electricity energy however network neural network numerical optimization physics physics-informed real-time renewable study type