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Flexibility Mining and Optimal Scheduling for Electric Vehicle Clusters Considering Dynamic Coordination of Power-Transportation Coupling Network
Author:
Affiliation:

School of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China

Abstract:

As a cross-domain subject with both transportation and energy attributes, electric vehicles (EVs) can exert their spatio-temporal flexibility to help the coordinated operation of the power-transportation coupling network. Therefore, a scheduling strategy for EV clusters considering the comprehensive benefits of power-transportation coupling network is proposed. First, a dynamic transportation network loading model is constructed based on arc impedance function. Then, considering the difference and coupling of the characteristic parameters of EV users, the flexible operation domain model of individual EV is constructed. Based on the Minkowski sum algorithm under the linear approximation of zonotope, the time-varying flexible operation domain of EV clusters is obtained. On this basis, a two-layer model for flexibility scheduling of EV clusters under optimal assignment of dynamic traffic flow is proposed, and the instantaneous travel cost of unit flow composed of the coupling variables of the upper and lower layers is obtained through iterative solution, which can guide the traveling and charging/discharging behaviors of EVs. Finally, the validity of the proposed scheduling strategy for EV clusters is verified by comparing it with the shortest path guidance strategy.

Foundation:

This work is supported by Yunnan Fundamental Research Projects (No. 202301AS070055) and National Key R&D Program of China (No. 2022YFB2703500).

Get Citation
[1]LIU Zhijian, DAI Jing, YANG Lingrui. Flexibility Mining and Optimal Scheduling for Electric Vehicle Clusters Considering Dynamic Coordination of Power-Transportation Coupling Network[J]. Automation of Electric Power Systems,2024,48(7):127-137. DOI:10.7500/AEPS20230728004
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History
  • Received:July 28,2023
  • Revised:November 09,2023
  • Adopted:November 10,2023
  • Online: April 01,2024