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

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 gi...