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Multi-scale analysis of wind power and load time series data

Author

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  • Coughlin, Katie
  • Murthi, Aditya
  • Eto, Joseph

Abstract

This paper presents novel analyses of high-resolution wind power and electric system load time series data. We use a discrete wavelet transform to resolve the data into independent time series of step changes (deltas) at different time scales, and present a variety of statistical metrics as a function of the time scale. We show that the probability distribution for wind power deltas is not Gaussian, has an exponential shape near the center and is well fit by a power-law in the tails. We provide a physical interpretation for the observed power-law behavior, and discuss the potential significance for modeling studies, prediction of extreme events, and the extrapolation of statistical characteristics to higher wind penetration levels. Several metrics are presented to quantify the degree of auto-correlation in the wind data, and of the correlations between wind and load. We show that the shape of the autocorrelation function is the same at different time scales, a property of self-similar statistical processes that is consistent with the observed power-law behavior.

Suggested Citation

  • Coughlin, Katie & Murthi, Aditya & Eto, Joseph, 2014. "Multi-scale analysis of wind power and load time series data," Renewable Energy, Elsevier, vol. 68(C), pages 494-504.
    • Handle: RePEc:eee:renene:v:68:y:2014:i:c:p:494-504
    DOI: 10.1016/j.renene.2014.02.011
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    Citations

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    Cited by:

    1. Arjmand, Reza & Rahimiyan, Morteza, 2016. "Statistical analysis of a competitive day-ahead market coupled with correlated wind production and electric load," Applied Energy, Elsevier, vol. 161(C), pages 153-167.
    2. Dmitriy O. Afanasyev & Elena A. Fedorova & Evgeniy V. Gilenko, 2021. "The fundamental drivers of electricity price: a multi-scale adaptive regression analysis," Empirical Economics, Springer, vol. 60(4), pages 1913-1938, April.
    3. Liang, Zhengtang & Liang, Jun & Zhang, Li & Wang, Chengfu & Yun, Zhihao & Zhang, Xu, 2015. "Analysis of multi-scale chaotic characteristics of wind power based on Hilbert–Huang transform and Hurst analysis," Applied Energy, Elsevier, vol. 159(C), pages 51-61.
    4. Ian M. Trotter & Torjus F. Bolkesj{o} & Eirik O. J{aa}stad & Jon Gustav Kirkerud, 2021. "Increased Electrification of Heating and Weather Risk in the Nordic Power System," Papers 2112.02893, arXiv.org.
    5. Han, Xiaojuan & Liu, Dahe & Liu, Jian & Kong, Lingda, 2017. "Sensitivity analysis of acquisition granularity of photovoltaic output power to capacity configuration of energy storage systems," Applied Energy, Elsevier, vol. 203(C), pages 794-807.
    6. Yang, Yulong & Wu, Kai & Long, Hongyu & Gao, Jianchao & Yan, Xu & Kato, Takeyoshi & Suzuoki, Yasuo, 2014. "Integrated electricity and heating demand-side management for wind power integration in China," Energy, Elsevier, vol. 78(C), pages 235-246.
    7. Dao, Fang & Zeng, Yun & Qian, Jing, 2024. "Fault diagnosis of hydro-turbine via the incorporation of bayesian algorithm optimized CNN-LSTM neural network," Energy, Elsevier, vol. 290(C).

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