考虑互补性的风光功率联合预报研究

岳茜; 任国瑞; 王玮

doi:10.19912/j.0254-0096.tynxb.2025-0614

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (8) : 773-780. DOI: 10.19912/j.0254-0096.tynxb.2025-0614

考虑互补性的风光功率联合预报研究

岳茜, 任国瑞, 王玮

作者信息 +

RESEARCH ON JOINT FORECASTING OF WIND AND SOLAR POWER CONSIDERING COMPLEMENTARITY

Yue Qian, Ren Guorui, Wang Wei

Author information +

文章历史 +

摘要

基于风能与太阳能资源在时空分布上的互补特性,提出一种考虑互补性的风光功率联合预报策略,基于双向长短期记忆神经网络建立基本的预报模型,并进一步提出排序比较寻优算法对预报模型的参数进行优化。在此基础上,对于具有显著互补性的风光场站进行风光功率联合预报。预报实验表明,相比风功率和光伏功率单独预报,所提考虑互补性的风光功率联合预报方法可显著提高预报的准确性,且互补性越显著,风光功率联合预报的精度越高。

Abstract

Based on the complementary characteristics of wind and solar energy resources in time and space distribution, this paper proposes a joint forecasting strategy of wind and solar power considering complementarity. Based on the bidirectional long short-term memory neural network, a basic forecasting model is established, and a sorting comparison optimization algorithm is further proposed to optimize the parameters of the forecasting model. On this basis, the joint forecasting of wind and solar power is carried out for wind and solar stations with significant complementarity. The prediction experiments show that compared with the single prediction of wind power and photovoltaic power, the proposed joint prediction method of wind power and photovoltaic power considering complementarity can significantly improve the accuracy of prediction, and the more significant the complementarity is, the higher the accuracy of joint prediction of wind power and photovoltaic power is.

导出引用

岳茜, 任国瑞, 王玮. 考虑互补性的风光功率联合预报研究[J]. 太阳能学报. 2026, 47(8): 773-780 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0614

Yue Qian, Ren Guorui, Wang Wei. RESEARCH ON JOINT FORECASTING OF WIND AND SOLAR POWER CONSIDERING COMPLEMENTARITY[J]. Acta Energiae Solaris Sinica. 2026, 47(8): 773-780 https://doi.org/10.19912/j.0254-0096.tynxb.2025-0614

中图分类号： TK513.5

参考文献

[1] 崔立勇. 2023年电力建设行业向“绿色”和“创新”加速发展[N]. 中国改革报, 2024-08-23(3).
Cui L Y. Accelerated green and innovative development of power construction industry in2023[N]. China Reform Daily, 2024-08-23(3).
[2] Zhou Q, Zhang J M, Zhang R X, et al.Echos, suggestions, and directions on new energy development in China under the grid parity[C]//2023 7th International Conference on Smart Grid and Smart Cities (ICSGSC). Lanzhou, China, 2023: 250-255.
[3] 袁润娇. 新能源高渗透区域风光储参与电网紧急控制策略研究[D]. 北京: 北方工业大学, 2023.
Yuan R J.Research on emergency control strategy for participation of wind photovoltaic and storage in power grid in new energy high penetration areas[D]. Beijing: North China University of Technology, 2023.
[4] 张尚. 双碳背景下冲击性负荷对“双高”电网电能质量的影响研究[J]. 自动化应用, 2025(1): 137-139, 143.
Zhang S.Research on impact of impulsive load on power quality of “dual high” grid under dual carbon background[J]. Automation Application, 2025(1): 137-139, 143.
[5] 王玲芝, 李晨阳, 刘婧, 等. 基于GRO-SSA-LSTM的短期光伏发电功率预测[J]. 太阳能学报, 2025, 46(2): 401-409.
Wang L Z, Li C Y, Liu J, et al.Short term photovoltaic power prediction based on GRO-SSA-LSTM[J]. Acta Energiae Solaris Sinica, 2025, 46(2): 401-409.
[6] 彭曙蓉, 陈慧霞, 孙万通, 等. 基于改进LSTM的光伏发电功率预测方法研究[J]. 太阳能学报, 2024, 45(11): 296-302.
Peng S R, Chen H X, Sun W T, et al.Research on photovoitaic power prediction method based on improved LSTM[J]. Acta Energiae Solaris Sinica, 2024, 45(11): 296-302.
[7] 臧海祥, 赵勇凯, 张越, 等. 基于低风速功率修正和损失函数改进的超短期风电功率预测[J]. 电力系统自动化, 2024, 48(7): 248-257.
Zang H X, Zhao Y K, Zhang Y, et al.Ultra-short-term wind power prediction based on power correction under low wind speed and improved loss function[J]. Automation of Electric Power Systems, 2024, 48(7): 248-257.
[8] Wang R X, Ma R, Zeng L J, et al.Improved bidirectional long short-term memory network-based short-term forecasting of photovoltaic power for different seasonal types and weather factors[J]. Computers and Electrical Engineering, 2025, 123: 110219.
[9] Deng W S, Dai Z F, Liu X Z, et al.Short-term wind power prediction based on wind speed interval division and TimeGAN for gale weather[C]//2023 International Conference on Power Energy Systems and Applications (ICoPESA). Nanjing, China, 2023: 352-357.
[10] 和萍, 姚依林, 耿斯涵, 等. 风光互补发电系统对电网低频振荡特性的影响之研究现状述评[J]. 轻工学报, 2018, 33(2): 76-86.
He P, Yao Y L, Geng S H, et al.Review of research status on impacts of wind and solar hybrid power generation system on low frequency oscillation characteristics of power grid[J]. Journal of Light Industry, 2018, 33(2): 76-86.
[11] 张明锐, 林承鑫, 王少波, 等. 一种并网型风光互补发电系统的建模与仿真[J]. 电网与清洁能源, 2014, 30(1): 68-74, 80.
Zhang M R, Lin C X, Wang S B, et al.Modeling and simulation of grid-connected wind/PV hybrid generation system[J]. Power System and Clean Energy, 2014, 30(1): 68-74, 80.
[12] 张永蕊. 基于注意力机制的风光发电功率联合预测方法研究[D]. 北京: 华北电力大学, 2022.
Zhang Y R.Research on the integrated forecasting method of wind and solar power based on attention mechanism[D]. Beijing: North China Electric Power University, 2022.
[13] 黄慧. 风光场站短期出力预测及多能互补优化运行研究[D]. 西安: 西安理工大学, 2022.
Huang H.Research on short-term forecast of wind/PV station output and optimal operation for multi-energy complementary system[D]. Xi’an: Xi’an University of Technology, 2022.
[14] 孔波利, 崔丽艳, 丁钊, 等. 基于风光混合模型的短期功率预测方法研究[J]. 电力系统保护与控制, 2015, 43(18): 62-66.
Kong B L, Cui L Y, Ding Z, et al.Short term power prediction based on hybrid wind-PV forecasting model[J]. Power System Protection and Control, 2015, 43(18): 62-66.
[15] 何玉灵, 焦凌钰, 孙凯, 等. 基于变权重组合的短期风光发电功率混合预测[J]. 华北电力大学学报(自然科学版), 2025, 52(6): 49-59.
He Y L, Jiao L Y, Sun K, et al.Short-term wind and photovoltaic power hybrid prediction based on variable weight combination prediction model[J]. Journal of North China Electric Power University, 2025, 52(6): 49-59.
[16] Li L, Qiang G Z, Xu Y Q, et al.Large-scale wind and solar grid-connected power prediction based on causal delay spectrum and LSTM[C]//2021 China International Conference on Electricity Distribution (CICED). Shanghai, China, 2021: 76-80.
[17] 陈庆明, 廖鸿飞, 孙颖楷, 等. 基于GWO-GRU的光伏发电功率预测[J]. 太阳能学报, 2024, 45(7): 438-444.
Chen Q M, Liao H F, Sun Y K, et al.Photovoltaic power prediction model based on GWO-GRU[J]. Acta Energiae Solaris Sinica, 2024, 45(7): 438-444.
[18] 叶林, 陈政, 赵永宁, 等. 基于遗传算法-模糊径向基神经网络的光伏发电功率预测模型[J]. 电力系统自动化, 2015, 39(16): 16-22.
Ye L, Chen Z, Zhao Y N, et al.Photovoltaic power forecasting model based on genetic algorithm and fuzzy radial basis function neural network[J]. Automation of Electric Power Systems, 2015, 39(16): 16-22.