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Adaptable Source-Grid Planning for High Penetration of Renewable Energy Integrated System

Author

Listed:
  • Ming Tang

    (College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Jian Wang

    (Tsinghua Sichuan Energy Internet Research Institute, Chengdu 610213, China)

  • Xiaohua Wang

    (Tsinghua Sichuan Energy Internet Research Institute, Chengdu 610213, China)

Abstract

To adapt to the growing scale of renewable energy and improve the consume ability of the power system, it is necessary to design a highly adaptable planning scheme for high penetration of the renewable energy integrated system. Thus, this paper firstly gives the conception of system adaptability and designs an adaptability index system, which considers the supply and demand balance, operation state, and network structure of the high penetrated renewable energy integrated system. It can help to comprehensively evaluate the system ability towards uncertain shocks. Then, a two-stage source-grid coordinative expansion planning model is presented. The adaptability indexes of supply and demand balance are used as objection of the source planning stage, the adaptability indexed of the operation state and network structure are used to guide the grid planning stage. The model is further solved based on the coordination between the source and grid planning stage. Finally, the case study verifies that the obtained optimal plan has good adaptability to the impact of renewable energy on the power supply capacity and security operation.

Suggested Citation

  • Ming Tang & Jian Wang & Xiaohua Wang, 2020. "Adaptable Source-Grid Planning for High Penetration of Renewable Energy Integrated System," Energies, MDPI, vol. 13(13), pages 1-26, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:13:p:3304-:d:377374
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    References listed on IDEAS

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    Cited by:

    1. Yuhong Wang & Xu Zhou & Yunxiang Shi & Zongsheng Zheng & Qi Zeng & Lei Chen & Bo Xiang & Rui Huang, 2021. "Transmission Network Expansion Planning Considering Wind Power and Load Uncertainties Based on Multi-Agent DDQN," Energies, MDPI, vol. 14(19), pages 1-28, September.

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