Electric Power ›› 2024, Vol. 57 ›› Issue (10): 1-11.DOI: 10.11930/j.issn.1004-9649.202405058

• Special Contribution • Previous Articles     Next Articles

Study on the Influence of Electric Vehicle Development and the Vehicle-Grid Interaction on New Energy Storage Configuration in China

Yuanbing ZHOU1,2(), Naiwei GONG1,2(), Haojie WANG1,2, Jinyu XIAO1,2, Yun ZHANG1,2   

  1. 1. Global Energy Interconnection Co., Ltd., Beijing 100031, China
    2. Global Energy Interconnection Development and Cooperation Organization, Beijing 100031, China
  • Received:2024-05-13 Accepted:2024-08-11 Online:2024-10-23 Published:2024-10-28
  • Supported by:
    This work is supported by Science and Technology Project of Global Energy Interconnection Development and Cooperation Organization (No.SGGE0000JYJS2400035).

Abstract:

With the continuous and rapid development of electric vehicles, the positive interaction between electric vehicles and the power grid in the future will affect the new energy storage configuration of the power system. In view of the insufficient overall planning of the current new energy storage, resulting in high development expectations, this paper proposes that new energy storage and the development of electric vehicles must be considered as a whole, and pay attention to the replacement of vehicle-grid interaction for short-term new energy storage. The key factors affecting the development of electric vehicles and the adjustment ability of the vehicle-grid interaction are analyzed. Based on the development of electric vehicles in China, combined with the energy consumption habits of electric vehicles and the system adjustment demand, the interaction model of the electric vehicles and the system in China is constructed. The flexible adjustment ability of electric vehicles is evaluated, and the impact on the new energy storage configuration in China and its seven regions is analyzed.

Key words: new energy storage, electric vehicles, the vehicle-grid interaction, source-network-load integration planning, stochastic dynamic model