939 / 2019-04-30 18:58:22
Deep Learning based VPP Active Power Dispatching Equivalent Modelling for Global Dispatching Optimization
Virtual power plant, deep learning, dispatching equivalent model, global dispatching optimization, optimal power generation plan
Draft Accepted
Xinhe Chen / Institute of Electrical Engineering, Chinese Academy of Sciences
Wei Pei / Institute of Electrical Engineering, Chinese Academy of Sciences
Wei Deng / Institute of Electrical Engineering, Chinese Academy of Sciences
Qian Sun / State Grid Henan Electric Power Company
Hongjian Sun / Department of Engineering, Durham University
The active power dispatching equivalent model of the virtual power plant (VPP) and the global dispatching optimization issue of the whole system integrated with the VPP equivalent model are studied in this paper. The active power dispatching equivalent model of the VPP is built as a deep learning model and trained by data sets of power output curve and the corresponding generation cost of the VPP. Global dispatching issue is formulated as a multi-objective optimization problem and solved by NSGAII algorithm. The deep learning model is verified by case study, and results show that the model is beneficial to both the VPP and the whole system. The proposed VPP equivalent model makes it possible to schedule VPP’s optimal power generation plan for the power dispatching center so as to maximize the generation revenue of the VPP and minimize the total generation cost of the whole system.
Important Date
  • Conference Date

    Oct 21

    2019

    to

    Oct 24

    2019

  • Oct 13 2019

    Abstract Notification of Acceptance

  • Oct 13 2019

    Draft paper submission deadline

  • Oct 14 2019

    Draft Paper Acceptance Notification

  • Oct 24 2019

    Registration deadline

  • Oct 29 2019

    Final Paper Deadline

Organized By
Xi'an Jiaotong University
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