Optimal Allocation of Hybrid Energy Storage System Based on Smoothing Wind Power Fluctuation and Improved Scenario Clustering Algorithm

Abstract

Against the backdrop of the global energy transition, wind power generation has seen rapid development. However, the intermittent and fluctuating nature of wind power poses a challenge to the stability of grid operation. To solve this problem, a solution based on a hybrid energy storage system is proposed. The hybrid energy storage system is characterized by fast and precise control and bidirectional energy throughput, which can improve the impact of wind power fluctuations on grid stability. An ensemble empirical modal decomposition method was used to assign the raw wind power data to the grid-connected power and energy storage power commands with two reasonable corrections to meet the power allocation of the hybrid energy storage characteristics. In addition, a hybrid energy storage system model considering the whole life cycle cost was developed, and the optimal energy storage power cutoff was determined by exhaustively enumerating the high- and low-frequency power cutoffs. Finally, a comparison with a single storage capacity optimization model was carried out to verify the technical and economic advantages of hybrid energy storage in smoothing wind power fluctuations. To address the shortcomings of the traditional fuzzy c-means clustering algorithm, such as the need to specify the number of clusters in advance and sensitivity to the selection of the initial clustering centers, a combination of the cloud modeling theory and fuzzy c-means was used to make the process more automated and efficient. The improved clustering method algorithmic scheme had capacity error, power error, and cost error of around 3%, and the computational time was also significantly reduced and was computationally efficient compared to the full-year time series simulation. Through MATLAB (2020b) experimental simulation, it was found that the algorithm had a better balance of computational accuracy and efficiency.