IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i6p1365-d1355723.html
   My bibliography  Save this article

Growing Importance of Micro-Meteorology in the New Power System: Review, Analysis and Case Study

Author

Listed:
  • Huijun Zhang

    (China Huaneng Clean Energy Research Institute, Beijing 102209, China
    These authors contributed equally to this work.)

  • Mingjie Zhang

    (China Huaneng Jilin Branch, Jilin 130012, China
    These authors contributed equally to this work.)

  • Ran Yi

    (China Huaneng Clean Energy Research Institute, Beijing 102209, China)

  • Yaxin Liu

    (China Huaneng Clean Energy Research Institute, Beijing 102209, China)

  • Qiuzi Han Wen

    (China Huaneng Clean Energy Research Institute, Beijing 102209, China)

  • Xin Meng

    (China Huaneng Jilin Branch, Jilin 130012, China)

Abstract

With the increasing penetration of renewable energy resources, their variable, intermittent and unpredictable characteristics bring new challenges to the power system. These challenges require micro-meteorological data and techniques to provide more support for the power systems, including planning, dispatching, operation, and so on. This paper aims to provide readers with insights into the effects of micro-meteorology on power systems, as well as the actual improvement brought by micro-meteorology in some power system scenarios. This paper provides a review including the relevant micro-meteorological techniques such as observation, assimilation and numerical techniques, as well as artificial intelligence, presenting a relatively complete overview of the most recent and relevant micro-meteorology-related literature associated with power systems. The impact of micro-meteorology on power systems is analyzed in six different forms of power generation and three typical scenarios of different stages in the power system, as well as integrated energy systems and disaster prevention and reduction. Finally, a case study in China is provided. This case takes wind power prediction as an example in a power system to compare the performance when applying micro-meteorological data or not. The experimental results demonstrated that using the micro-meteorological reanalysis dataset with high spatial--temporal resolution for wind power prediction performed better, verifying the improvement of micro-meteorology to the power system to some extent.

Suggested Citation

  • Huijun Zhang & Mingjie Zhang & Ran Yi & Yaxin Liu & Qiuzi Han Wen & Xin Meng, 2024. "Growing Importance of Micro-Meteorology in the New Power System: Review, Analysis and Case Study," Energies, MDPI, vol. 17(6), pages 1-33, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1365-:d:1355723
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/6/1365/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/6/1365/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sayed, Enas Taha & Abdelkareem, Mohammad Ali & Bahaa, Ahmed & Eisa, Tasnim & Alawadhi, Hussain & Al-Asheh, Sameer & Chae, Kyu-Jung & Olabi, A.G., 2021. "Synthesis and performance evaluation of various metal chalcogenides as active anodes for direct urea fuel cells," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    2. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    3. Carvalho, D. & Rocha, A. & Gómez-Gesteira, M. & Silva Santos, C., 2014. "WRF wind simulation and wind energy production estimates forced by different reanalyses: Comparison with observed data for Portugal," Applied Energy, Elsevier, vol. 117(C), pages 116-126.
    4. Gordon McBean, 2004. "Climate Change and Extreme Weather: A Basis for Action," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 31(1), pages 177-190, January.
    5. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Author Correction: Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 621(7980), pages 45-45, September.
    6. Kaifeng Bi & Lingxi Xie & Hengheng Zhang & Xin Chen & Xiaotao Gu & Qi Tian, 2023. "Accurate medium-range global weather forecasting with 3D neural networks," Nature, Nature, vol. 619(7970), pages 533-538, July.
    7. Zhao, Jing & Guo, Yanling & Xiao, Xia & Wang, Jianzhou & Chi, Dezhong & Guo, Zhenhai, 2017. "Multi-step wind speed and power forecasts based on a WRF simulation and an optimized association method," Applied Energy, Elsevier, vol. 197(C), pages 183-202.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. He, Jinhua & Hu, Zechun & Wang, Songpo & Mujeeb, Asad & Yang, Pengwei, 2024. "Windformer: A novel 4D high-resolution system for multi-step wind speed vector forecasting based on temporal shifted window multi-head self-attention," Energy, Elsevier, vol. 310(C).
    2. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegui, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2017. "Using 3DVAR data assimilation to measure offshore wind energy potential at different turbine heights in the West Mediterranean," Applied Energy, Elsevier, vol. 208(C), pages 1232-1245.
    3. Salvação, N. & Guedes Soares, C., 2018. "Wind resource assessment offshore the Atlantic Iberian coast with the WRF model," Energy, Elsevier, vol. 145(C), pages 276-287.
    4. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    5. Bai, Huimin & Gong, Zhiqiang & Li, Li & Ma, Junjie & Dogar, Muhammad Mubashar, 2025. "Vegetation coverage variability and its driving factors in the semi-arid to semi-humid transition zone of North China," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    6. Fabian Dvorak & Regina Stumpf & Sebastian Fehrler & Urs Fischbacher, 2024. "Generative AI Triggers Welfare-Reducing Decisions in Humans," Papers 2401.12773, arXiv.org.
    7. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    8. Song Chen & Jiaxu Liu & Pengkai Wang & Chao Xu & Shengze Cai & Jian Chu, 2024. "Accelerated optimization in deep learning with a proportional-integral-derivative controller," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    9. Chen, Xue-Jun & Zhao, Jing & Jia, Xiao-Zhong & Li, Zhong-Long, 2021. "Multi-step wind speed forecast based on sample clustering and an optimized hybrid system," Renewable Energy, Elsevier, vol. 165(P1), pages 595-611.
    10. Yuchen Cai & Jia Yang & Yutang Hou & Feng Wang & Lei Yin & Shuhui Li & Yanrong Wang & Tao Yan & Shan Yan & Xueying Zhan & Jun He & Zhenxing Wang, 2025. "8-bit states in 2D floating-gate memories using gate-injection mode for large-scale convolutional neural networks," Nature Communications, Nature, vol. 16(1), pages 1-10, December.
    11. Kyle Lesinger & Di Tian, 2025. "Skillful subseasonal soil moisture drought forecasts with deep learning-dynamic models," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    12. Huaisheng Tu & Haotian Liu & Tuqiang Pan & Wuping Xie & Zihao Ma & Fan Zhang & Pengbai Xu & Leiming Wu & Ou Xu & Yi Xu & Yuwen Qin, 2025. "Deep empirical neural network for optical phase retrieval over a scattering medium," Nature Communications, Nature, vol. 16(1), pages 1-9, December.
    13. Li Hu Wang & Xue Mei Liu & Yang Liu & Hai Rui Li & Jia QI Liu & Li Bo Yang, 2023. "Emergency entity relationship extraction for water diversion project based on pre-trained model and multi-featured graph convolutional network," PLOS ONE, Public Library of Science, vol. 18(10), pages 1-18, October.
    14. Lei Chen & Xiaohui Zhong & Hao Li & Jie Wu & Bo Lu & Deliang Chen & Shang-Ping Xie & Libo Wu & Qingchen Chao & Chensen Lin & Zixin Hu & Yuan Qi, 2024. "A machine learning model that outperforms conventional global subseasonal forecast models," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    15. Hu, Rong & Zhou, Kaile & Lu, Xinhui, 2025. "Integrated loads forecasting with absence of crucial factors," Energy, Elsevier, vol. 322(C).
    16. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.
    17. Zhao, Jing & Guo, Zhenhai & Guo, Yanling & Lin, Wantao & Zhu, Wenjin, 2021. "A self-organizing forecast of day-ahead wind speed: Selective ensemble strategy based on numerical weather predictions," Energy, Elsevier, vol. 218(C).
    18. Yassin Boussafa & Lynn Sader & Van Thuy Hoang & Bruno P. Chaves & Alexis Bougaud & Marc Fabert & Alessandro Tonello & John M. Dudley & Michael Kues & Benjamin Wetzel, 2025. "Deep learning prediction of noise-driven nonlinear instabilities in fibre optics," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    19. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    20. Yanli Zhang & Haofan Ran & Alex Guenther & Qiang Zhang & Christian George & Wahid Mellouki & Guoying Sheng & Ping’an Peng & Xinming Wang, 2025. "Improved modelling of biogenic emissions in human-disturbed forest edges and urban areas," Nature Communications, Nature, vol. 16(1), pages 1-10, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:17:y:2024:i:6:p:1365-:d:1355723. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.