TY - GEN
T1 - Active Constraint Identification Assisted DC Optimal Power Flow
AU - Wu, Huayi
AU - Wang, Minghao
AU - Xu, Zhao
AU - Jia, Youwei
N1 - Funding Information:
The work is supported by the National Natural Science Foundation of China Under Grant No. 62101473 and PolyU Research Institute for Smart Energy (RISE) Strategic Supporting Scheme P0039642.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/7
Y1 - 2022/7
N2 - 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.
AB - 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.
KW - active constraint
KW - deep convolutional neural network
KW - Optimal power flow
KW - renewables
UR - http://www.scopus.com/inward/record.url?scp=85143440461&partnerID=8YFLogxK
U2 - 10.1109/ICPSAsia55496.2022.9949655
DO - 10.1109/ICPSAsia55496.2022.9949655
M3 - Conference article published in proceeding or book
AN - SCOPUS:85143440461
T3 - I and CPS Asia 2022 - 2022 IEEE IAS Industrial and Commercial Power System Asia
SP - 185
EP - 189
BT - I and CPS Asia 2022 - 2022 IEEE IAS Industrial and Commercial Power System Asia
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE IAS Industrial and Commercial Power System Asia, I and CPS Asia 2022
Y2 - 8 July 2022 through 11 July 2022
ER -