JOINT SHORT-TERM POWER PREDICTION MODEL FOR WIND AND PHOTOVOLTAIC IN NEW ENERGY POWER SYSTEM CONSIDERING CORRELATION

Shen Fu; Liu Sirui; Cai Zilong; Wang Zhe; Yang Guangbing; Zhai Suwei

doi:10.19912/j.0254-0096.tynxb.2024-0163

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (5) : 203-212. DOI: 10.19912/j.0254-0096.tynxb.2024-0163

JOINT SHORT-TERM POWER PREDICTION MODEL FOR WIND AND PHOTOVOLTAIC IN NEW ENERGY POWER SYSTEM CONSIDERING CORRELATION

Shen Fu¹, Liu Sirui¹, Cai Zilong¹, Wang Zhe¹, Yang Guangbing¹, Zhai Suwei²

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Abstract

To reduce the impact of new energy grid integration on grid stability, by improving the accuracy of power prediction of wind and photovoltaic （PV） power generation in new energy power system （NEPS）, this paper proposes a joint short-term power prediction model for wind and PV in NEPS considering correlation between the influencing factors of wind and PV power generation, the specificity of NEPS distributed power, and the adaptive optimization ability of the model according to its own prediction error. The raw data are first processed by complete ensemble empirical mode decomposition with adaptive noise （CEEMDAN）, and then the wind and PV power are initially predicted using the bidirectional long short term memory （BILSTM） model optimized based on the chaotic whale optimization algorithm （CWOA） to obtain the prediction errors. Then the prediction errors of wind and PV power after CEEMDAN decomposition are merged together into the input features for the joint prediction of PV and wind power crossover. The experimental results of a NEPS station in eastern China show that the accuracy of both wind and PV power prediction is improved by the proposed model in this paper compared with the traditional prediction model.

Key words

wind power generation / PV power generation / power prediction / bidirectional long short term memory（BILSTM） / new energy power system（NEPS） / complete ensemble empirical mode decomposition with adaptive noise（CEEMDAN） / chaotic whale optimization algorithm（CWOA）

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Shen Fu, Liu Sirui, Cai Zilong, Wang Zhe, Yang Guangbing, Zhai Suwei. JOINT SHORT-TERM POWER PREDICTION MODEL FOR WIND AND PHOTOVOLTAIC IN NEW ENERGY POWER SYSTEM CONSIDERING CORRELATION[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 203-212 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0163

References

[1] 张天策, 王剑晓, 李庚银, 等. 面向高比例新能源接入的配电网电压时空分布感知方法[J]. 电力系统自动化, 2021, 45(2): 37-45.
ZHANG T C, WANG J X, LI G Y, et al.Perception method of voltage spatial-temporal distribution of distribution network with high penetration of renewable energy[J]. Automation of electric power systems, 2021, 45(2): 37-45.
[2] RAHBAR K, CHAI C C, ZHANG R.Energy cooperation optimization in microgrids with renewable energy integration[J]. IEEE transactions on smart grid, 2018, 9(2): 1482-1493.
[3] 朱琼锋, 李家腾, 乔骥, 等. 人工智能技术在新能源功率预测的应用及展望[J]. 中国电机工程学报, 2023, 43(8): 3027-3048.
ZHU Q F, LI J T, QIAO J, et al.Application and prospect of artificial intelligence technology in renewable energy forecasting[J]. Proceedings of the CSEE, 2023, 43(8): 3027-3048.
[4] EP, CE. Directive2009/28/EC of the European parliament and of the council of 23 april 2009 on the promotion of the use of energy from renewable sources and amending and subsequently repealing directives 2001/77/EC and 2003/30/EC[EB/OL]. https://www.eea.europa.eu/policy-documents/2009-28-ec.
[5] NREL. Western wind and solar integration study[EB/OL].https://www.nrel.gov/docs/fy10osti/47781.pdf.
[6] 中华人民共和国国家发展和改革委员会. “十四五”可再生能源发展规划[EB/OL].https://www.ndrc.gov.cn/xwdt/tzgg/202206/P020220602315650388122.pdf.
NDRC. 14th Five Year Plan for renewable energy development[EB/OL].https://www.ndrc.gov.cn/xwdt/tzgg/202206/P020220602315650388122.pdf.
[7] 张岚, 张艳霞, 郭嫦敏, 等. 基于神经网络的光伏系统发电功率预测[J]. 中国电力, 2010, 43(9): 75-78.
ZHANG L, ZHANG Y X, GUO C M, et al.Photovoltaic system power forecasting based on neutral networks[J]. Electric power, 2010, 43(9): 75-78.
[8] 朱乔木, 李弘毅, 王子琪, 等. 基于长短期记忆网络的风电场发电功率超短期预测[J]. 电网技术, 2017, 41(12): 3797-3802.
ZHU Q M, LI H Y, WANG Z Q, et al.Short-term wind power forecasting based on LSTM[J]. Power system technology, 2017, 41(12): 3797-3802.
[9] LI Y Z, ZHANG P G, WANG S Y.Forecast of power generation for grid-connected photovoltaic system based on inclusion degree theory[C]//The 27th Chinese Control and Decision Conference (2015 CCDC). Qingdao, China, 2015: 5070-5074.
[10] 景惠甜, 韩丽, 高志宇. 基于卷积神经网络特征提取的风电功率爬坡预测[J]. 电力系统自动化, 2021, 45(4): 98-105.
JING H T, HAN L, GAO Z Y.Wind power ramp forecast based on feature extraction using convolutional neural network[J]. Automation of electric power systems, 2021, 45(4): 98-105.
[11] WANG C, WANG Y, DING Z T, et al.A transformer-based method of multienergy load forecasting in integrated energy system[J]. IEEE transactions on smart grid, 2022, 13(4): 2703-2714.
[12] 杨维熙, 刘勇, 舒勤. 基于补充集合经验模态分解的短期负荷预测模型[J]. 电网技术, 2022, 46(9): 3615-3623.
YANG W X, LIU Y, SHU Q.A short-term load forecasting model based on CEEMD[J]. Power system technology, 2022, 46(9): 3615-3623.
[13] 陈臣鹏, 赵鑫, 毕贵红, 等. 基于多模式分解和麻雀优化残差网络的短期风速预测模型[J]. 电网技术, 2022, 46(8): 2975-2985.
CHEN C P, ZHAO X, BI G H, et al.SSA-res-GRU short-term wind speed prediction model based on multi-model decomposition[J]. Power system technology, 2022, 46(8): 2975-2985.
[14] 王佶宣, 邓斌, 王江. 基于经验模态分解与RBF神经网络的短期风功率预测[J]. 电力系统及其自动化学报, 2020, 32(11): 109-115.
WANG J X, DENG B, WANG J.Short-term wind power prediction based on empirical mode decomposition and RBF neural network[J]. Proceedings of the CSU-EPSA, 2020, 32(11): 109-115.
[15] 王振浩, 王翀, 成龙, 等. 基于集合经验模态分解和深度学习的光伏功率组合预测[J]. 高电压技术, 2022, 48(10): 4133-4142.
WANG Z H, WANG C, CHENG L, et al.Photovoltaic power combined prediction based on ensemble empirical mode decomposition and deep learning[J]. High voltage engineering, 2022, 48(10): 4133-4142.
[16] 叶剑华, 曹旌, 杨理, 等. 基于变分模态分解和多模型融合的用户级综合能源系统超短期负荷预测[J]. 电网技术, 2022, 46(7): 2610-2622.
YE J H, CAO J, YANG L, et al.Ultra short-term load forecasting of user level integrated energy system based on variational mode decomposition and multi-model fusion[J]. Power system technology, 2022, 46(7): 2610-2622.
[17] 李大中, 李颖宇. 基于深度学习与误差修正的超短期风电功率预测[J]. 太阳能学报, 2021, 42(12): 200-205.
LI D Z, LI Y Y.Ultra-short term wind power prediction based on deep learning and error correction[J]. Acta energiae solaris sinica, 2021, 42(12): 200-205.
[18] 王斌, 魏成伟, 谢丽蓉, 等. 基于风速误差校正和ALO-LSSVM的风电功率预测[J]. 太阳能学报, 2022, 43(1): 58-63.
WANG B, WEI C W, XIE L R, et al.Wind power forecasting based on wind speed error corretion and ALO-LSSVM[J]. Acta energiae solaris sinica, 2022, 43(1): 58-63.
[19] 王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103.
WANG F Z, WANG S F, ZHANG L.Ultra short term power prediction of photovoltaic power generation based on VMD-LSTM and error compensation[J]. Acta energiae solaris sinica, 2022, 43(8): 96-103.
[20] TORRES M E, COLOMINAS M A, SCHLOTTHAUER G, et al.A complete ensemble empirical mode decomposition with adaptive noise[C]//2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Prague, Czech Republic, 2011: 4144-4147.
[21] ZHANG S Q, LIU H T, HU M F, et al.An adaptive CEEMDAN thresholding denoising method optimized by nonlocal means algorithm[J]. IEEE transactions on instrumentation and measurement, 2020, 69(9): 6891-6903.
[22] PINCUS S M.Approximate entropy as a measure of system complexity[J]. Proceedings of the National Academy of Sciences of the United States of America, 1991, 88(6): 2297-2301.
[23] 王坚浩, 张亮, 史超, 等. 基于混沌搜索策略的鲸鱼优化算法[J]. 控制与决策, 2019, 34(9): 1893-1900.
WANG J H, ZHANG L, SHI C, et al.Whale optimization algorithm based on chaotic search strategy[J]. Control and decision, 2019, 34(9): 1893-1900.
[24] GB/T 40607—2021, 调度侧风电或光伏功率预测系统技术要求[S].
GB/T 40607—2021, Technical requirements for dispatching side forecasting system of wind or photovoltaic power[S].
[25] 王函. 风光发电功率与用电负荷联合预测方法研究[D]. 北京: 华北电力大学, 2021.
WANG H.Study on joint forecasting method of wind and solar power generation power and power load[D]. Beijing: North China Electric Power University, 2021.
[26] 张大海, 孙锴, 和敬涵. 基于相似日与多模型融合的短期负荷预测[J]. 电网技术, 2023, 47(5): 1961-1970.
ZHANG D H, SUN K, HE J H.Short-term load forecasting based on similar day and multi-model fusion[J]. Power system technology, 2023, 47(5): 1961-1970.