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Impacts of synoptic circulation patterns on wind power ramp events in East Japan

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  • Ohba, Masamichi
  • Kadokura, Shinji
  • Nohara, Daisuke

Abstract

This study presents an application of self-organizing maps (SOM) for the climatological/meteorological study of wind power ramp events. SOM constitutes an automatic data-mining clustering technique, which allows for summarizing of a high-dimensional data space in terms of a set of reference vectors. SOM is applied to analyze and establish the relationship between atmospheric synoptic patterns over Japan and wind power generation. SOM is employed on sea level pressure data derived from the JRA-55 reanalysis over the Tohoku region in Japan, whereby a two-dimensional lattice of weather patterns classified during the 1977–2013 period is obtained. Wind-power ramp events (defined as a 30% change in power in less than 6 h) mainly take place during the winter months in East Japan. Our SOM analysis for weather patterns in boreal winter extracts seven typical patterns that are linked to frequent occurrences of wind ramp events. The result of this study suggests that detailed classification of synoptic circulation patterns can be a useful tool for first-order approximations of both the probability of future wind power generation and its variability. Further research relating weather/climate variability and wind power generation is both necessary and valuable in East Asia.

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  • Ohba, Masamichi & Kadokura, Shinji & Nohara, Daisuke, 2016. "Impacts of synoptic circulation patterns on wind power ramp events in East Japan," Renewable Energy, Elsevier, vol. 96(PA), pages 591-602.
    • Handle: RePEc:eee:renene:v:96:y:2016:i:pa:p:591-602
    DOI: 10.1016/j.renene.2016.05.032
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    References listed on IDEAS

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    Cited by:

    1. Kevin Ray Español Lucas & Tomonori Sato & Masamichi Ohba, 2021. "Hourly Variation of Wind Speeds in the Philippines and Its Potential Impact on the Stability of the Power System," Energies, MDPI, vol. 14(8), pages 1-14, April.
    2. Laura Cornejo-Bueno & Lucas Cuadra & Silvia Jiménez-Fernández & Javier Acevedo-Rodríguez & Luis Prieto & Sancho Salcedo-Sanz, 2017. "Wind Power Ramp Events Prediction with Hybrid Machine Learning Regression Techniques and Reanalysis Data," Energies, MDPI, vol. 10(11), pages 1-27, November.
    3. Li, Muyuan & Yao, Jinfeng & Shen, Yanbo & Yuan, Bin & Simmonds, Ian & Liu, Yunyun, 2023. "Impact of synoptic circulation patterns on renewable energy-related variables over China," Renewable Energy, Elsevier, vol. 215(C).
    4. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren & Söder, Lennart, 2018. "Analysis of wind power intermittency based on historical wind power data," Energy, Elsevier, vol. 150(C), pages 482-492.
    5. Ohba, Masamichi & Kanno, Yuki & Nohara, Daisuke, 2022. "Climatology of dark doldrums in Japan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    6. Tian Han & Ying Wang & Xiao Wang & Kang Chen & Huaiwu Peng & Zhenxin Gao & Lanxin Cui & Wentong Sun & Qinke Peng, 2023. "Mixed Multi-Pattern Regression for DNI Prediction in Arid Desert Areas," Sustainability, MDPI, vol. 15(17), pages 1-16, August.
    7. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    8. Ren, Guorui & Liu, Jinfu & Wan, Jie & Guo, Yufeng & Yu, Daren & Liu, Jizhen, 2017. "Measurement and statistical analysis of wind speed intermittency," Energy, Elsevier, vol. 118(C), pages 632-643.
    9. Ohba, Masamichi & Kanno, Yuki & Bando, Shigeru, 2023. "Effects of meteorological and climatological factors on extremely high residual load and possible future changes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
    10. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    11. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    12. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    13. Chiyori T. Urabe & Tetsuo Saitou & Kazuto Kataoka & Takashi Ikegami & Kazuhiko Ogimoto, 2021. "Positive Correlations between Short-Term and Average Long-Term Fluctuations in Wind Power Output," Energies, MDPI, vol. 14(7), pages 1-15, March.
    14. Bedassa R. Cheneka & Simon J. Watson & Sukanta Basu, 2021. "Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps," Energies, MDPI, vol. 14(13), pages 1-14, June.

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