Active Constraint Identification Assisted DC Optimal Power Flow

Huayi Wu1; Minghao Wang1; Zhao Xu1; Youwei Jia2

10.1109/ICPSAsia55496.2022.9949655

2022

978-1-6654-5067-6

2022 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia)

185-189

8-11 July 2022

Shanghai, China

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.

Optimal power flow deep convolutional neural network renewables active constraint

其他

IEEE

1.Electrical Engineering Department, The Hong Kong Polytechnic University, Hong Kong, China
2.Electrical and Electronic Engineering Department, Southern University of Science and Technology, Shenzhen, China

Huayi Wu,Minghao Wang,Zhao Xu,et al. Active Constraint Identification Assisted DC Optimal Power Flow[C],2022:185-189.