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 CNN has
shown promising results, it does not extract explainable knowledge from the
data. To address this limitation, a physics-inspired CNN model is developed by
incorporating constraints of the ED problem into the CNN training to ensure
that the model follows physical laws while fitting the data. The proposed
method can significantly accelerate real-time economic dispatch of microgrids
without compromising the accuracy of numerical optimization techniques. The
effectiveness of the proposed data-driven approach for optimal allocation of
microgrid resources in real-time is verified through a comprehensive comparison
with conventional numerical optimization approaches.

利用卷积神经网络和深度学习技术，提出了一种基于数据驱动的方法来加速微网的实时经济调度，同时保持数值优化技术的准确性。