• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 58 Issue 6
Dec.  2023
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Article Contents
GUO Ai, YE Hanchang, DAI Chaohua, WANG Yongqiang, YE Shengyong, CHEN Weirong. Capacity Optimization Configuration of Electric Vehicle Swapping-Storage Integrated Station Considering Support Ability to Grid[J]. Journal of Southwest Jiaotong University, 2023, 58(6): 1257-1266. doi: 10.3969/j.issn.0258-2724.20220431
Citation: GUO Ai, YE Hanchang, DAI Chaohua, WANG Yongqiang, YE Shengyong, CHEN Weirong. Capacity Optimization Configuration of Electric Vehicle Swapping-Storage Integrated Station Considering Support Ability to Grid[J]. Journal of Southwest Jiaotong University, 2023, 58(6): 1257-1266. doi: 10.3969/j.issn.0258-2724.20220431

Capacity Optimization Configuration of Electric Vehicle Swapping-Storage Integrated Station Considering Support Ability to Grid

doi: 10.3969/j.issn.0258-2724.20220431
  • Received Date: 16 Jun 2022
  • Rev Recd Date: 13 Oct 2022
  • Available Online: 02 Sep 2023
  • Publish Date: 13 Oct 2022
  • Electric vehicle (EV) swapping stations can achieve economic benefits while also supporting the power grid by serving as energy storage stations. However, there is currently a lack of research on the capacity configuration of such EV storage and swapping integrated stations (EVSS-IS). To this end, the working mode and tariff period of EVSS-IS were firstly analyzed to build an operational model. Then, a predictive model for EV swapping demand was developed based on user travel simulations. Next, a bi-level capacity programming model of the EVSS-IS was established, which considered life cycle benefits and grid support capacity. Specifically, the outer planning aimed at the total revenue during the whole life cycle to optimize the capacity of the EVSS-IS; the inner planning aimed at supporting the power grid and optimizing the charging and discharging behaviors; the optimal charging and discharging power from the inner layer was returned to the outer layer to realize the optimal capacity programming of the EVSS-IS. Finally, the effectiveness of the planning model was verified on the IEEE33 node system, which provided theoretical support for the construction of the EVSS-IS. The results show that the return on investment of the EVSS-IS is 1.51%–2.26% higher compared with other models; the capacity optimization allocation method based on bi-level planning can support the grid voltage while ensuring the economy of the station, resulting in a 20% reduction in the daily variance of the voltage; as the number of EVs swapping batteries increases, the economics of the EVSS-IS is further improved.

     

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