Application of energy storage allocation model in the context of mitigating new energy source power fluctuation

To address the impact of new energy source power fluctuations on the power grid, research has been conducted on energy storage allocation applied to mitigate the power fluctuations of new energy source. Firstly, based on the first-order low-pass filtering algorithm and discrete Fourier transform algorithm, the original power data of new energy sources were preprocessed to achieve the reconstruction of power signal. Subsequently, a more secure and reliable energy storage allocation model is constructed by taking into account the boundary conditions of energy storage charging and discharging efficiency, energy balance, state of charge, and target power output fluctuation. Then, a comprehensive Life-Cycle-Cost model for energy storage systems was developed and applied to economic evaluation of energy storage under two algorithms. Finally, the calculation case study analysis shows that the energy storage allocation model effectively improves the power fluctuations of new energy sources, represented by wind power, and ensure the safe and stable operation of energy storage system throughout the entire cycle, thus verifying the effectiveness and feasibility of the energy storage configuration model. Under fluctuation rate constraints of 10%, 15%, and 20%, the rated power required for the first-order low-pass filtering algorithm is 8.795 MW, 6.485 MW, and 4.022 MW, respectively, representing 18.32%, 13.51%, and 8.38% of the new energy source rated power(48 MW). The corresponding rated capacity required is 7.763 MWh, 3.675 MWh, and 1.123 MWh. In contrast, for the discrete fourier transform algorithm, the rated power required is 9.159 MW, 8.84 MW, and 5.78 MW, respectively, representing 19.08%, 18.42%, and 12.04% of the new energy source rated power. The corresponding rated capacity required is 2.01 MWh, 0.97 MWh, and 0.5 MWh. Form an economic feasibility perspective, the economic efficiency of energy storage allocation by discrete fourier transform algorithm is significantly better than allocated by the first-order low-pass filtering algorithm. For instance, when U10minup= 10%, the cost difference reaches a maximum of 1256 million ¥