DAY-AHEAD REPORTING STRATEGY OF WIND STORAGE SYSTEM BASED ON NWP ASSISTED COMPOSITE NEURAL NETWORK PREDICTION ERROR CORRECTION

Li Cuiping; Zhang Bing; Li Junhui; Zhu Hui; Zhu Xingxu; He Li

doi:10.19912/j.0254-0096.tynxb.2023-0777

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Acta Energiae Solaris Sinica ›› 2024, Vol. 45 ›› Issue (10) : 86-96. DOI: 10.19912/j.0254-0096.tynxb.2023-0777

DAY-AHEAD REPORTING STRATEGY OF WIND STORAGE SYSTEM BASED ON NWP ASSISTED COMPOSITE NEURAL NETWORK PREDICTION ERROR CORRECTION

Li Cuiping¹, Zhang Bing¹, Li Junhui¹, Zhu Hui¹, Zhu Xingxu¹, He Li²

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Abstract

The output of new energy power stations has strong fluctuations that lead to huge deviation assessment expenditures. Therefore, based on numerical weather prediction （NWP） and composite deep learning algorithms, a day-ahead reporting strategy for wind-storage combined system that takes into account error prediction corrections is proposed. Firstly, the improved combined data preprocessing algorithm is used to clean the data to reduce the difficulty of subsequent predictions, and a long short-term memory network prediction network （LSTM） based on piecewise convergent particle swarm optimization （PCPSO） parameter optimization is established to predict the components respectively. Reconstruct the forecast results to obtain the original forecast curve. Secondly, the prediction error curve is obtained by considering the prediction error and the NWP information import multi-input backpropagation neural network （MIBP）. After revising the prediction error curve using the non-parametric kernel density function, the optimal energy storage action curve is obtained by simulating the energy storage operation for the purpose of minimizing the energy storage tracking error and maximizing the global control ability of energy storage. Superimpose the original prediction curve and the optimal energy storage action curve to obtain the final day-ahead reporting curve. Finally, the correctness and feasibility of the reporting strategy are verified by simulation analysis.

Key words

wind power / deep neural network / particle swarm optimization / energy storage / day-ahead reporting strategy / prediction error characteristics / NWP information

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Li Cuiping, Zhang Bing, Li Junhui, Zhu Hui, Zhu Xingxu, He Li. DAY-AHEAD REPORTING STRATEGY OF WIND STORAGE SYSTEM BASED ON NWP ASSISTED COMPOSITE NEURAL NETWORK PREDICTION ERROR CORRECTION[J]. Acta Energiae Solaris Sinica. 2024, 45(10): 86-96 https://doi.org/10.19912/j.0254-0096.tynxb.2023-0777

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