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10.1016_j.egyr.2023.07.022.pdf (1.51 MB)

Dispatchable capacity optimization strategy for battery swapping and charging station aggregators to participate in grid operations

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submitted on 2024-01-22, 05:05 and posted on 2024-01-22, 08:35 authored by Mingze Zhang, Samson S. Yu, Hanlin Yu, Ping Li, Weidong Li, S.M. Muyeen

Taking the aggregator as a unit, battery swapping and charging stations (BSCSs) for electric vehicles (EVs) can be aggregated and dispatched by grid operators, to realize the demand-side resource regulation. Considering the characteristics of an aggregator’s multilateral services, in this study, BSCSs need to ensure the quality of swapping service for EV users and participate in the demand-side regulation response. Firstly, we analyze the operation mechanism of a BSCS in the aggregation mode and propose a state transition model for EV batteries. On this basis, the EV demand uncertainty is incorporated by a distributed robust optimization (DRO) approach for multi-timescale inventories, and an optimization model to maximize the BSCSs’ income is established, which obtains the optimal load planning and dispatchable capacity scheduling for a BSCS aggregator. Extensive simulations and numerical results show that the BSCS aggregator with demand-side regulation capacity can increase its income by 59.05% and 36.78% on working and non-working days, respectively. Also, the aggregator does not worsen the original power load while meeting the EV swapping demand and can decrease the daily load fluctuations by 0.65% and 12.89%, reduce the peak–valley difference by 5.81% and 7.80%, and increase the load rate by 3.67% and 4.08% in working and non-working day situations through providing the dynamic dispatchable capacity for the grid.

Other Information

Published in: Energy Reports
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.egyr.2023.07.022

Funding

Open Access funding provided by the Qatar National Library.

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2023

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Qatar University
  • College of Engineering - QU

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