TY - GEN
T1 - Fast DC Optimal Power Flow Based on Deep Convolutional Neural Network
AU - Wu, Huayi
AU - Xu, Zhao
N1 - Funding Information:
The work is supported by the Teaching Postgraduate Studentship (TPS) Scheme of Hong Kong Polytechnic University.
Publisher Copyright:
© 2022 IEEE.
PY - 2022/5
Y1 - 2022/5
N2 - The optimal power flow is the cornerstone of the operation and management of electric power systems. However, the stochastic and intermittent uncertainty due to the proliferation of renewable energy resources (RES) poses a non-trivial challenge to timely obtain the optimal operation point of the power system. To address the computational burden issue, a deep convolutional neural network (DCNN) model is proposed to learn the mapping from the injections to the optimal objective. The DCNN reduces the training parameters as well as improves the approximation accuracy. IEEE 14/118/300 bus power systems are conducted, and the optimal power flow model is solved by Gurobi/Python. Simulation results show that DCNN speeds up the calculation time by up to 100 times in comparison to the state-of-the-art solver and simultaneously maintains the required accuracy.
AB - The optimal power flow is the cornerstone of the operation and management of electric power systems. However, the stochastic and intermittent uncertainty due to the proliferation of renewable energy resources (RES) poses a non-trivial challenge to timely obtain the optimal operation point of the power system. To address the computational burden issue, a deep convolutional neural network (DCNN) model is proposed to learn the mapping from the injections to the optimal objective. The DCNN reduces the training parameters as well as improves the approximation accuracy. IEEE 14/118/300 bus power systems are conducted, and the optimal power flow model is solved by Gurobi/Python. Simulation results show that DCNN speeds up the calculation time by up to 100 times in comparison to the state-of-the-art solver and simultaneously maintains the required accuracy.
KW - deep convolutional neural network
KW - Optimal power flow
KW - renewable energy
KW - uncertain
UR - http://www.scopus.com/inward/record.url?scp=85137337311&partnerID=8YFLogxK
U2 - 10.1109/CIEEC54735.2022.9846143
DO - 10.1109/CIEEC54735.2022.9846143
M3 - Conference article published in proceeding or book
AN - SCOPUS:85137337311
T3 - Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022
SP - 2508
EP - 2512
BT - Proceedings of 2022 IEEE 5th International Electrical and Energy Conference, CIEEC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Electrical and Energy Conference, CIEEC 2022
Y2 - 27 May 2022 through 29 May 2022
ER -