Physics-informed Convolutional Neural Network for Microgrid Economic Dispatch

TL;DR

This paper introduces a physics-informed CNN model that enhances real-time microgrid economic dispatch by combining deep learning efficiency with physical law constraints, outperforming traditional optimization methods.

Contribution

A novel physics-inspired CNN approach that integrates physical constraints into deep learning for faster, reliable microgrid economic dispatch.

Findings

Significantly faster dispatch computation compared to traditional methods

Maintains high accuracy in resource allocation

Ensures physical law compliance in predictions

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 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.