Hybrid Energy Storage Power Adaptive Optimization Strategy Based on Improved Model Predictive Control and Improved DBO-VMD

Junda Huo and Jianwen Huo ()
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Junda Huo: State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
Jianwen Huo: The Fourth Research and Design Engineering Corporation of CNNC, Shijiazhuang 050021, China

Energies, 2024, vol. 17, issue 13, 1-21

Abstract: In order to optimize the operation of the energy storage system (ESS) and allow it to better smooth renewable energy power fluctuations, an ESS power adaptive optimization strategy is proposed. Firstly, based on the real-time state of charge (SOC) of the ESS, an adaptive weight coefficient is introduced to improve the model predictive control (MPC), and the grid-connected power and the total power of the ESS after smoothing the original photovoltaic output are obtained. Then, the variational mode decomposition (VMD) algorithm optimized by the improved dung beetle optimizer (DBO) algorithm (MSADBO) is proposed to decompose the total power, and the initial distribution of power is completed by combining the ESS characteristics. Finally, considering the charging and discharging times, SOC, and grid-connected volatility of the ESS, and aiming to address the shortcomings of traditional methods, a new ESS power optimization strategy is proposed. According to the simulation results, when compared to the conventional method, the proposed strategy improves the adaptability of ESS operation, reduces the number of ESS charging and discharging conversions, and ensures that the SOC does not exceed the limit when the ESS is operating and the wind power grid-connected fluctuation rate meets the requirements.

Keywords: improved model predictive control; improved DBO algorithm; variational modal decomposition; hybrid energy storage; optimization strategies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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