Sparrow Search Algorithm Optimized BP Neural Network for Wind Power Forecasting with Energy Storage Control and Grid Stability Analysis

DOI: https://doi.org/10.62517/jbdc.202601311

Author(s)

Haoyang Zhang

Affiliation(s)

School of Engineering, University of Glasgow, Electronic and Electric Engineering, Glasgow, G128QQ, United Kingdom

Abstract

In response to the urgent demand for stable and reliable renewable energy integration, this study presents a hybrid forecasting and decision framework that combines the Sparrow Search Algorithm (SSA) with a Backpropagation Neural Network (BPNN) for enhanced short-term wind power prediction, and further extends its application to an energy storage dispatch strategy aimed at grid stability. By addressing the inherent intermittency and stochastic nature of wind power, the proposed SSA-BPNN model effectively optimizes initial weights and thresholds, improving convergence speed and predictive accuracy compared to conventional BP models. Extensive MATLAB/Simulink-based simulations, using authentic wind farm data, demonstrate that the improved model significantly reduces forecasting errors and successfully mitigates grid fluctuations through a model-predictive control mechanism for battery energy storage. This dual-stage system not only advances the theoretical framework of intelligent forecasting algorithms but also validates their practical feasibility for real-world grid integration, ensuring more efficient renewable resource utilization. The findings underscore the potential of bio-inspired optimization techniques in bridging the gap between accurate renewable energy forecasting and adaptive grid operations, paving the way for resilient, low-carbon power systems.

Keywords

Wind Power Forecasting; Sparrow Search Algorithm; Backpropagation Neural Network; Energy Storage Dispatch; Grid Stability; Model Predictive Control; Renewable Energy Integration

References

[1] A. M. Foley, P. G. Leahy, A. Marvuglia, and E. J. McKeogh, “Current methods and advances in forecasting of wind power generation,” Renewable Energy, vol. 37, no. 1, pp. 1-8, Jan. 2012. [2] G. Giebel and G. Kariniotakis, “Wind power forecasting-A review of the state of the art,” in Renewable Energy Forecasting, Amsterdam, Netherlands: Elsevier, 2017, pp. 59-109. [3] L. Zhang, F. Wang, T. Sun, and B. Xu, “A constrained optimization method based on BP neural network,” Neural Comput. Appl., vol. 29, pp. 413-421, Jan. 2018. [4] N. Li, Y. Wang, W. Ma, Z. Xiao, and Z. An, “A wind power prediction method based on DE-BP neural network,” Frontiers in Energy Research, vol. 10, p. 845499, Mar. 2022. [5] Z. Wu and B. Wang, “An ensemble neural network based on variational mode decomposition and an improved sparrow search algorithm for wind and solar power forecasting,” IEEE Access, vol. 9, pp. 166709-166719, Dec. 2021. [6] J. Xue and B. Shen, “A novel swarm intelligence optimization approach: Sparrow search algorithm,” Syst. Sci. Control Eng., vol. 8, no. 1, pp. 22-34, Jan. 2020. [7] L. Zhang, S. He, J. Cheng, Z. Yuan, and X. Yan, “Research on neural network wind speed prediction model based on improved sparrow algorithm optimization,” Energy Reports, vol. 8, pp. 739-747, Nov. 2022. [8] S. Hanifi, X. Liu, Z. Lin, and S. Lotfian, “A critical review of wind power forecasting methods-Past, present and future,” Energies, vol. 13, no. 15, p. 3764, Jul. 2020. [9] Y. Yu et al., “Prediction model for safe operation of pumping stations optimized by the sparrow search algorithm and BP neural network,” Advances in Civil Engineering, vol. 2024, Article ID 6653172, Jan. 2024. [10] Z. Gharehchopogh, M. Namazi, L. Ebrahimi, and B. Abdollahzadeh, “Advances in sparrow search algorithm: A comprehensive survey,” Arch. Comput. Methods Eng., vol. 30, pp. 427-455, Jan. 2023. [11] C. Ji and H. Ding, “Optimizing back-propagation neural network to retrieve sea surface temperature based on improved sparrow search algorithm,” Remote Sensing, vol. 15, no. 24, p. 5722, Dec. 2023. [12] N. Guo and Z. Wang, “A combined model based on sparrow search optimized BP neural network and Markov chain for precipitation prediction in Zhengzhou City, China,” AQUA- Water Infrastructure, Ecosystems and Society, vol. 71, no. 6, pp. 782-800, 2022. [13] X. Cui and Y. Sun, “Fan power forecast based on sparrow search algorithm optimized back propagation neural network,” in Proc. 6th Int. Conf. Info. Sci., Comput. Tech. Transp. (ISCTT), VDE, 2021, pp. 1-4. [14] C. Moler and J. Little, “A history of MATLAB,” Proc. ACM Program. Lang., vol. 4, no. HOPL, pp. 1-67, Jun. 2020. [15] S. Nagar, Introduction to MATLAB for Engineers and Scientists: Solutions for Numerical Computation and Modeling. Berkeley, CA, USA: Apress, 2017. [16] M. Zeraati, M. E. H. Golshan, and J. M. Guerrero, “Distributed control of battery energy storage systems for voltage regulation in distribution networks with high photovoltaic penetration,” IEEE Trans. Smart Grid, vol. 9, no. 4, pp. 3582-3593, Jul. 2018.