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Distributed state-of-charge and power balance estimation for aggregated battery energy storage systems with EV aggregators

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  • Zhao, Jia-Wei
  • Zhang, Hong-Li
  • Wang, Cong

Abstract

Aggregated battery energy storage systems (ABESSs) play an important role in smart grids. This study considers distributed ABESSs containing electric vehicle (EV) aggregators and battery energy storage systems (BESSs). Due to randomness and uncertainty caused by EV aggregators, the distributed ABESSs face heterogeneous random communication failures between the EV aggregators and the BESSs. Aiming to solve this problem, this study proposes a distributed state-of-charge (SoC) and power balance estimation strategy for ABESSs with an event-triggered mechanism. Different from the traditional SoC balance problem definition in the BESSs, this study introduces an innovative balance convergence condition to solve the problem of power imbalance between the EV aggregators and the BESS units. In addition, an adaptively robust estimation algorithm is designed to estimate the precise value of SoC and power under heterogeneous random failures caused by EV aggregators. Further, a distributed periodic dynamic event-triggered mechanism is developed considering redundant messages in the ABESS communication channels. This mechanism can both handle the Zeno phenomenon and obtain a larger minimum time interval with fewer sent measurements. The simulation results demonstrate the effectiveness of the proposed distributed approach in both the discharging and charging modes of ABESSs.

Suggested Citation

  • Zhao, Jia-Wei & Zhang, Hong-Li & Wang, Cong, 2024. "Distributed state-of-charge and power balance estimation for aggregated battery energy storage systems with EV aggregators," Energy, Elsevier, vol. 305(C).
    • Handle: RePEc:eee:energy:v:305:y:2024:i:c:s0360544224019674
    DOI: 10.1016/j.energy.2024.132193
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    References listed on IDEAS

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    1. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2023. "Particle swarm optimization of Elman neural network applied to battery state of charge and state of health estimation," Energy, Elsevier, vol. 285(C).
    2. He, Lin & Hu, Xingwen & Yin, Guangwei & Wang, Guoqiang & Shao, Xingguo & Liu, Jichao, 2024. "A current dynamics model and proportional–integral observer for state-of-charge estimation of lithium-ion battery," Energy, Elsevier, vol. 288(C).
    3. Zhao, Chunyang & Andersen, Peter Bach & Træholt, Chresten & Hashemi, Seyedmostafa, 2023. "Grid-connected battery energy storage system: a review on application and integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    4. Xiao, Feng & Yang, Zhengguang & Wei, Bo, 2024. "Distributed fixed-time cooperative control for flywheel energy storage systems with state-of-energy constraints," Energy, Elsevier, vol. 293(C).
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