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Fast DC Optimal Power Flow Based on Deep Convolutional Neural Network
ID：82 View Protection：ATTENDEE Updated Time：2022-05-16 09:41:51 Hits：373 Poster Presentation

Start Time：Pending（Asia/Shanghai）

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Abstract
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.
Keywords
Optimal power flow, deep convolutional neural network, renewable energy, uncertain
Speaker
WUHuayi
PolyU

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Important Date

  • Conference Date

    May 27

    2022

    to

    May 29

    2022

  • Feb 28 2022

    Draft paper submission deadline

  • May 29 2022

    Registration deadline

  • Jun 22 2022

    Contribution Submission Deadline

Sponsored By
IEEE Beijing Section
China Electrotechnical Society
Southeast University
Supported By
IEEE Industry Applications Society
IEEE Nanjing Section
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