Fast Economic Dispatch in Smart Grids Using Deep Learning: An Active Constraint Screening Approach

Abstract

In smart grids, the power supply and demand are balanced through the electricity market to promote the maximization of social welfare. An important procedure in electricity market clearing is to sequentially solve the security-constrained economic dispatch (SCED) problem. However, the scale of the SCED problem with all ${N}-1$ constraints is huge. Directly optimizing such a problem is inefficient and not robust. With the development of smart grids, the frequency of market clearing is increasing, which presents new requirements for fast calculation of SCED. To solve this problem, we propose an intelligent prescreening method to identify the active constraints of SCED based on deep learning. We utilize stacked denoising autoencoders (SDAEs) to extract the nonlinear relationship between the system operating condition and the active constraint set of SCED. Especially, the input/output feature vectors and learning strategy are designed to improve the training efficiency and guarantee the learning accuracy of the deep neural network (DNN). Besides, a fast tuning strategy of neural network parameters based on transfer learning is proposed to handle new scenarios such as topology change. The computational efficiency of the SCED problem is significantly improved while the accuracy is not influenced. The IEEE 30-bus, IEEE 118-bus, and practical utility 661-bus systems are used to demonstrate the effectiveness of the proposed method.