Active Constraint Identification Assisted DC Optimal Power Flow

被引:0
|
作者
Wu, Huayi [1 ]
Wang, Minghao [1 ]
Xu, Zhao [1 ]
Jia, Youwei [2 ]
机构
[1] Hong Kong Polytech Univ, Elect Engn Dept, Hong Kong, Peoples R China
[2] Southern Univ Sci & Technol, Elect & Elect Engn Dept, Shenzhen, Peoples R China
来源
2022 IEEE/IAS INDUSTRIAL AND COMMERCIAL POWER SYSTEM ASIA (I&CPS ASIA 2022) | 2022年
基金
中国国家自然科学基金;
关键词
Optimal power flow; deep convolutional; neural network; renewables; active constraint;
D O I
10.1109/ICPSAsia5496.2022.9949655
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
The optimal power flow (OPF) is important for the reliable operation and management of power systems. Due to the uncertainties introduced by the increasing penetration of renewable energy resources (RES), more frequent OPF calculations are compulsorily required, posing significant computational burdens to the timely derivation of optimal dispatching solutions. In this paper, an active constraint identification (ACI) approach is proposed to identify the active constraints under different generation and demand conditions so that the OPF computational time can be reduced. The ACI is based on deep convolutional neural networks. Simulation studies are performed on the IEEE 14/118/300 bus systems, and the optimal power flow is solved by using Gurobi/Python. Simulation results of the proposed methods are compared with those of the state-of-the-art to demonstrate the calculation speed improvement of the proposed method.
引用
收藏
页码:185 / 189
页数:5
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