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Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures

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  • Zhang, Mingze
  • Li, Weidong
  • Yu, Samson Shenglong
  • Wen, Kerui
  • Muyeen, S.M.

Abstract

A large-scale battery energy storage station (LS-BESS) directly dispatched by grid operators has operational advantages of power-type and energy-type storages. It can help address the power and electricity energy imbalance problems caused by high-proportion wind power in the grid and ensure the secure, reliable, and economic operations of power systems together with conventional power generation units. To enable power systems to resist any power disturbance in the prediction failure set and cope with wind power and load fluctuations while meeting the load demand, a day-ahead dispatch optimization model to minimize operation costs on the dispatch day is established, which utilizes the regulation advantages of conventional units and a LS-BESS to participate in regulation services of diverse timescales and effectively achieve the coordination of various service demands. To account for wind power variations on the dispatch day, a robust optimization (RO) approach based on the budget uncertainty set is proposed, which improves the robustness and economy of grid operations against realistic uncertainties. The effectiveness of the day-ahead dispatch strategy is verified through extensive simulations and comparisons, which can better serve modern power systems with high penetration of wind power.

Suggested Citation

  • Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wen, Kerui & Muyeen, S.M., 2023. "Day-ahead optimization dispatch strategy for large-scale battery energy storage considering multiple regulation and prediction failures," Energy, Elsevier, vol. 270(C).
    • Handle: RePEc:eee:energy:v:270:y:2023:i:c:s0360544223003390
    DOI: 10.1016/j.energy.2023.126945
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    Cited by:

    1. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wang, Haixia & Ba, Yu, 2024. "Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Li, Zifeng & Guo, Litao & Yu, Samson S. & Zhang, Mingli & Ren, Yupeng & Zhang, Na & Li, Weidong, 2023. "An efficient full-response analytical model for probabilistic production simulation in fast frequency response reserve planning," Energy, Elsevier, vol. 273(C).
    3. Jiaqi Liu & Hongji Hu & Samson S. Yu & Hieu Trinh, 2023. "Virtual Power Plant with Renewable Energy Sources and Energy Storage Systems for Sustainable Power Grid-Formation, Control Techniques and Demand Response," Energies, MDPI, vol. 16(9), pages 1-28, April.
    4. Ye, Lin & Jin, Yifei & Wang, Kaifeng & Chen, Wei & Wang, Fei & Dai, Binhua, 2023. "A multi-area intra-day dispatch strategy for power systems under high share of renewable energy with power support capacity assessment," Applied Energy, Elsevier, vol. 351(C).

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