A Deep Neural Network-Based Optimal Power Flow Approach for Identifying Network Congestion and Renewable Energy Generation Curtailment

Abstract

Herein, we propose a novel method for identifying power congestion and renewable-energy source (RES) power curtailment in power grids by using deep neural network (DNN)-based optimal power flow (OPF) analysis. Synthetic data for load demand and RES power generation are used to obtain the OPF solutions by using an OPF solver. RES locations are selected based on an analysis of the congestion cases, and the DNN is trained by using the load demand and RES power as inputs and the OPF solution as the output. Thereafter, post-processing is performed on the DNN-OPF output by using network information. The final results show the accuracies of identified values on RES curtailment, line loading, generation dispatch schedule, and total generation operating cost. The IEEE 39-bus system was adopted as a case study to validate the proposed model. The results demonstrate that the proposed scheme is much faster and more suitable for transmission systems than conventional OPF methods. Furthermore, the normalized root-mean-squared error was less than 1%, and the computational time was more than 30 times faster than conventional OPF analysis.