DeepOPF: A Deep Neural Network Approach for Security-Constrained DC Optimal Power Flow

• Published in: IEEE transactions on power systems Vol. 36; no. 3; pp. 1725 - 1735
• Main Authors: Pan, Xiang, Zhao, Tianyu, Chen, Minghua, Zhang, Shengyu
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Biological neural networks; Complexity theory; Deep learning; deep neural network; Direct current; Feasibility; Flow equations; Forecasting; Generators; Machine learning; Mapping; Neural networks; optimal power flow; Optimization; Post-processing; Power flow; Power system stability; Security
• DOI: 10.1109/TPWRS.2020.3026379
• ISSN: 0885-8950

We develop DeepOPF as a Deep Neural Network (DNN) approach for solving security-constrained direct current optimal power flow (SC-DCOPF) problems, which are critical for reliable and cost-effective power system operation. DeepOPF is inspired by the observation that solving SC-DCOPF problems for a given power network is equivalent to depicting a high-dimensional mapping from the load inputs to the generation and phase angle outputs. We first train a DNN to learn the mapping and predict the generations from the load inputs. We then directly reconstruct the phase angles from the generations and loads by using the power flow equations. Such a predict-and-reconstruct approach reduces the dimension of the mapping to learn, subsequently cutting down the size of the DNN and the amount of training data needed. We further derive a condition for tuning the size of the DNN according to the desired approximation accuracy of the load-generation mapping. We develop a post-processing procedure based on <inline-formula><tex-math notation="LaTeX">\ell _1</tex-math></inline-formula>-projection to ensure the feasibility of the obtained solution, which can be of independent interest. Simulation results for IEEE test cases show that DeepOPF generates feasible solutions with less than 0.2% optimality loss, while speeding up the computation time by up to two orders of magnitude as compared to a state-of-the-art solver.