Joint scenario generation method for meteorology－wind－solar power output based on retrieval－augmented probabilistic downscaling

Yihe ZHAO, Jian CHEN, Yiran WANG, Wen ZHANG, Lei DING

Electric Power ›› 2026, Vol. 59 ›› Issue (8) : 78-91.

image
Home Journals Electric Power
Electric Power

Abbreviation (ISO4): Electric Power      Chairperson: Changyu OUYANG

About  /  Aim & scope  /  Editorial board  /  Indexed  /  Contact  / 
Online first  /  Current issue  /  All issues  /  Top access  /  RSS
Electric Power ›› 2026, Vol. 59 ›› Issue (8) : 78-91. DOI: 10.11930/j.issn.1004-9649.202604075
Original article

Joint scenario generation method for meteorology－wind－solar power output based on retrieval－augmented probabilistic downscaling

Author information +
History +

Abstract

The operation and planning of power systems with high penetrations of renewable energy highly depend on high－ temporal－resolution meteorological data and wind－solar power output scenarios.However，most existing climate models only provide daily－scale meteorological information，which is insufficient to directly support the hourly－level operational analysis of power systems.Therefore，this paper proposes a joint scenario generation method for meteorology－wind－ solar power outputs based on retrieval－augmented probabilistic downscaling（RAPD）.First，a similar－day retrieval mechanism based on the rank－sum distance is constructed to extract historical high－resolution meteorological baseline sequences. Second，a Transformer－based conditional variational autoencoder（T－CVAE）learning network is built to realize the probabilistic generation of meteorological fluctuation residuals. Finally，a physical mapping model from meteorological variables to wind－solar power outputs is established to generate continuous hourly wind－solar power output scenarios for a whole year.Validations based on historical meteorological data demonstrate that the mean absolute error（MAE）values for temperature and wind speed are reduced by 30.17％and 28.84% ，respectively，compared with the traditional statistical analog method.Furthermore，taking the daily－scale data from climate models as input，the corresponding hourly－level meteorology－wind－solar power output scenarios are generated through downscaling，and the consistency between the downscaled results and the daily－scale inputs is verified.The results indicate that the proposed method can effectively accomplish temporal downscaling and scenario generation， providing a high－temporal－resolution scenario foundation for renewable energy output analysis under future scenarios.

Key words

meteorological downscaling / wind－solar power output / scenario generation / retrieval augmentation / conditional variational autoencoder

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations
Yihe ZHAO , Jian CHEN , Yiran WANG , et al . Joint scenario generation method for meteorology－wind－solar power output based on retrieval－augmented probabilistic downscaling[J]. Electric Power. 2026, 59(8): 78-91 https://doi.org/10.11930/j.issn.1004-9649.202604075

References

List( Publishing order | Descend order by publishing year | Descend order by cited within ) Chart analysis
[1]
石宇龙, 彭乔, 刘天琪, 等. 基于相似日和双层校正 LSTM 的光伏功率短期预测方法[J]. 电力工程技术, 2026, 45(3):85-94.
SHI Yulong, PENG Qiao, LIU Tianqi, et al. Photovoltaic power short-term forecasting method based on similar days and bi-layer correction LSTM model[J]. Electric Power Engineering Technology, 2026, 45(3):85-94.
Cited in this article [1]
[2]
皮晟源, 刘牧阳, 陈雨田, 等. 基于 IPOA-VMD 的风储联合发电系统混合储能容量配置[J]. 电力系统及其自动化学报, 2025, 37(8): 87-96.
PI Shengyuan, LIU Muyang, CHEN Yutian, et al. Hybrid energy storage capacity configuration for wind-storage co-generation system based on IPOA-VMD[J]. Proceedings of the CSU-EPSA, 2025, 37(8):87-96.
Cited in this article [1]
[3]
李夫刚, 李夫海, 琚承乾, 等. 新能源不确定性表征方法及关键技术问题研究现状、挑战及展望[J]. 电力系统保护与控制, 2025, 53(15):172-187.
LI Fugang, LI Fuhai, JU Chengqian, et al. Methods for characterizing renewable energy uncertainty and key technical issues:research status,challenges,and prospects[J]. Power System Protection and Control, 2025, 53(15):172-187.
Cited in this article [1]
[4]
李文武, 万梓幸, 何睦, 等. 基于概率调控的风光联合出力极端场景生成方法[J]. 电力系统自动化, 2026, 50(5):197-208.
LI Wenwu, WAN Zixing, HE Mu, et al. Extreme scenario generation method for joint wind-solar power output based on probability regulation[J]. Automation of Electric Power Systems, 2026, 50(5): 197-208.
Cited in this article [1]
[5]
黄学勤, 杨鹏举, 赵耀, 等. 考虑多维气象数据与时间影响的风电功率区间预测[J]. 浙江电力, 2026, 45(1):66-77.
HUANG Xueqin, YANG Pengju, ZHAO Yao, et al. Wind power interval forecasting based on multidimensional meteorological data and temporal effects[J]. Zhejiang Electric Power, 2026, 45(1):66-77.
Cited in this article [1]
[6]
姜通海, 王峰, 刘子琪, 等. 基于改进生成对抗网络的风光气象资源联合场景生成方法[J]. 中国电力, 2025,58(3): 183-192. JIANG Tonghai,WANG Feng,LIU Ziqi,et al. A joint scenario generation method for wind-solar meteorological resources based on improved generative adversarial network[J]. Electric Power,2025, 58(3):183-192.
Cited in this article [1]
[7]
王成磊, 匡熠, 濮永现, 等. 基于改进生成对抗网络场景生成的长-短期储能优化配置[J]. 高电压技术, 2024, 50(3):1131-1144, I0016-I0020.
WANG Chenglei, KUANG Yi, PU Yongxian, et al. Optimal configuration of long-term and short-term energy storage based on improved generative adversarial network scenarios[J]. High Voltage Engineering, 2024, 50(3):1131-1144,I0016-I0020.
Cited in this article [1]
[8]
WANG S, WU S W, TANG B, et al. Generation method of wind power and photovoltaic output scenarios based on LHS-GRU[J]. Sustainable Energy,Grids and Networks, 2025, 41: 101602.
Cited in this article [1]
[9]
徐聪, 徐静静, 江婷, 等. 数据驱动的综合能源系统运行优化研究[J]. 综合智慧能源, 2026, 48(1):34-42.
XU Cong, XU Jingjing, JIANG Ting, et al. Research on data-driven operation optimization of integrated energy systems[J]. Integrated Intelligent Energy, 2026, 48(1):34-42.
Cited in this article [1]
[10]
袁家海, 牟琪林, 张浩楠, 等. 考虑气候风险耦合影响的中长期电力资源规划[J]. 气候变化研究进展, 2024, 20(5):624-635.
YUAN Jiahai, MOU Qilin, ZHANG Haonan, et al. Medium and long term power resource planning considering the coupled impact of climate risks[J]. Climate Change Research, 2024, 20(5):624-635.
Cited in this article [1]
[11]
郭红霞, 陈凌轩, 张启, 等. 电力电量平衡视角下新型电力系统极端场景研究及应对综述[J]. 电网技术, 2024, 48(10):3975-3991, I0002,I0004,I0005.
GUO Hongxia, CHEN Lingxuan, ZHANG Qi, et al. Research and response to extreme scenarios in new power system:a review from perspective of electricity and power balance[J]. Power System Technology, 2024, 48(10):3975-3991,I0002,I0004,I0005.
Cited in this article [1]
[12]
ZENG Z Y, KIM J H, TAN H C, et al. A review of future weather data for assessing climate change impacts on buildings and energy systems[J]. Renewable and Sustainable Energy Reviews, 2025, 212: 115213.
Cited in this article [1]
[13]
ZABEL F, POSCHLOD B. The Teddy tool v1.1:temporal disaggregation of daily climate model data for climate impact analysis[J]. Geoscientific Model Development, 2023, 16(18): 5383-5399.
Cited in this article [2]
[14]
RODRIGUES E, FERNANDES M S, CARVALHO D. Future weather generator for building performance research:an open-source morphing tool and an application[J]. Building and Environment, 2023, 233: 110104.
Cited in this article [2]
[15]
HADJIPETROU S. A review of statistical methods for climate downscaling:the underexplored potential of geostatisticalsimulation[J]. Theoretical and Applied Climatology, 2026, 157(4): 216.
Cited in this article [1]
[16]
FALLAH B, ROSTAMI M, RUSSO E, et al. Climate model downscaling in central Asia:a dynamical and a neural network approach[J]. Geoscientific Model Development, 2025, 18(1): 161-180.
Cited in this article [1]
[17]
李丹, 梁云嫣, 缪书唯, 等. 基于高斯混合聚类和改进条件变分自编码的多风电场功率日场景生成方法[J]. 中国电力, 2024, 57(12): 17-29.
LI Dan, LIANG Yunyan, MIAO Shuwei, et al. Daily power scenario generation method for multiple wind farms based on Gaussian mixture clustering and improved conditional variational autoencoder[J]. Electric Power, 2024, 57(12):17-29.
Cited in this article [1]
[18]
LI Z L, PENG X G, CUI W B, et al. A novel scenario generation method of renewable energy using improved VAEGAN with controllable interpretable features[J]. Applied Energy, 2024, 363: 122905.
Cited in this article [1]
[19]
LIU J R, FU Y C. Renewable energy forecasting:a self-supervised learning-based transformer variant[J]. Energy, 2023, 284: 128730.
Cited in this article [1]
[20]
LOPEZ-GOMEZ I, WAN Z Y, ZEPEDA-NÚNEZ L, et al. Dynamical-generative downscaling of climate model ensembles[J]. Proceedings of the National Academy of Sciences of the United States of America, 2025, 122(17):e2420288122.
Cited in this article [1]
[21]
OYAMA N, ISHIZAKI N N, KOIDE S, et al. Deep generative model super-resolves spatially correlated multiregional climate data[J]. Scientific Reports, 2023, 13: 5992.
Cited in this article [1]
[22]
JARGALSAIKHAN N, UEDA S, MASAHIRO F, et al. Exploring influence of air density deviation on power production of wind energy conversion system:Study on correction method[J]. Renewable Energy, 2024, 220: 119636.
Cited in this article [1]
[23]
石铖, 高红均, 罗龙波, 等. 极端高温场景下计及需求响应的配电网动态重构[J]. 电网技术, 2025, 49(1):136-145,I0055-I0058.
SHI Cheng, GAO Hongjun, LUO Longbo, et al. Distribution network reconfiguration considering demand response in extreme heat scenarios[J]. Power System Technology, 2025, 49(1):136-145, I0055-I0058.
Cited in this article [1]
[24]
BAYRAK F. Prediction of photovoltaic panel cell temperatures: Application of empirical and machine learning models[J]. Energy, 2025, 323: 135764.
Cited in this article [1]
[25]
高红均, 郭明浩, 刘俊勇, 等. 从四川高温干旱限电事件看新型电力系统保供挑战与应对展望[J]. 中国电机工程学报, 2023, 43(12):4517-4537. GAO Hongjun,GUO Minghao,LIU Junyong,et al. Power supply
challenges and prospects in new power system from Sichuan electricity curtailment events caused by high-temperature drought weather[J]. Proceedings of the CSEE, 2023, 43(12):4517-4537.
Cited in this article [1]
[26]
吴晓刚, 阎洁, 葛畅, 等. 基于改进 GRU-CNN 的风光水一体化超短期功率预测方法[J]. 中国电力, 2023,56(9):178-186, 205. WU Xiaogang, YAN Jie,GE Chang,et al. Ultra-short-term power forecasting method for wind-solar-hydro integration based on improved GRU-CNN[J]. Electric Power,2023, 56(9):178-186, 205.
Cited in this article [1]
[27]
KIM G H, JEONG H S, KIM H S, et al. Anomaly detection in KOMAC high-power systems using transformer-based conditional variational autoencoder[J]. Journal of the Korean Physical Society, 2025, 87(7):883-891.
Cited in this article [2]
[28]
XU G P, WANG X X, WU X L, et al. Development of residual learning in deep neural networks for computer vision:a survey[J]. Engineering Applications of Artificial Intelligence, 2025, 142: 109890.
Cited in this article [1]
[29]
FAIMAN D. Assessing the outdoor operating temperature of photovoltaic modules[J]. Progress in Photovoltaics:Research and Applications, 2008, 16(4):307-315.
Cited in this article [2]
[30]
PRETORIUS J, NIELSEN S. Understanding heat dissipation factors for fixed-tilt and single-axis tracked open-rack photovoltaic modules: experimental insights[J]. Progress in Photovoltaics:Research and Applications,2025,33( 2):326-343.
Cited in this article [1]

Accesses

Citation

Detail
Sections
Recommended

/

〈 〉