人工神经网络
数学优化
计算机科学
一般化
网格
过程(计算)
功率(物理)
分数(化学)
钥匙(锁)
集合(抽象数据类型)
数学
机器学习
程序设计语言
几何学
化学
有机化学
数学分析
物理
操作系统
量子力学
计算机安全
作者
Xiaojun Pan,Minghua Chen,Tianyu Zhao,Steven H. Low
出处
期刊:IEEE Systems Journal
[Institute of Electrical and Electronics Engineers]
日期:2023-03-01
卷期号:17 (1): 673-683
被引量:13
标识
DOI:10.1109/jsyst.2022.3201041
摘要
To cope with increasing uncertainty from renewable generation and flexible load, grid operators need to solve alternative current optimal power flow (AC-OPF) problems more frequently for efficient and reliable operation. In this article, we develop a deep neural network (DNN) approach, called DeepOPF, for solving AC-OPF problems in a fraction of the time used by conventional iterative solvers. A key difficulty for applying machine learning techniques for solving AC-OPF problems lies in ensuring that the obtained solutions respect the equality and inequality physical and operational constraints. Generalized a prediction-and-reconstruction procedure in our previous studies, DeepOPF first trains a DNN model to predict a set of independent operating variables and then directly compute the remaining ones by solving the power flow equations. Such an approach not only preserves the power-flow balance equality constraints but also reduces the number of variables to be predicted by the DNN, cutting down the number of neurons and training data needed. DeepOPF then employs a penalty approach with a zero-order gradient estimation technique in the training process toward guaranteeing the inequality constraints. We also drive a condition for tuning the DNN size according to the desired approximation accuracy, which measures its generalization capability. It provides theoretical justification for using DNN to solve AC-OPF problems. Simulation results for IEEE 30/118/300-bus and a synthetic 2000-bus test cases demonstrate the effectiveness of the penalty approach. They also show that DeepOPF speeds up the computing time by up to two orders of magnitude as compared to a state-of-the-art iterative solver, at the expense of $< $ 0.2% cost difference.
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