Renewable Energy Global Innovations features: Reproducing Statistical Property of Short-term Fluctuation in Wind Power Profiles

Significance Statement

Wind power generation which serves as a source of renewable energy faces certain challenges due to short-term fluctuations in power output. This led to the addition of a battery system in order to reduce these pitfalls and as a result, the effect of the short-term fluctuations in relation to the battery system needs to be evaluated. One means of evaluating this effect is the use of a power flow simulation.

A group of researchers from , Waseda University in Japan, proposed an innovative method whereby synthetic wind power profiles with high temporal resolutions for power flow simulation can be generated by reproducing plausible statistical behavior of a realistic short-term fluctuation. The work is now published in journal, Energy Procedia.

In order to achieve a realistic short-term fluctuation which occurs in wind power generation, the power flow simulation observes the time-series statistical behaviors. The methods used in achieving the realistic short-term fluctuation include; the previously used autoregressive mean average approach and the block bootstrap approach.

The authors further compared the statistical property of the short-term fluctuations generated from three different approaches; naive bootstrap, autoregressive mean average bootstrap approach and the block bootstrap coupled with evaluations made by finding the autocorrelation functions of the detrended sequence which stands for the typical short-term fluctuation in wind power generation.

Following the generation of ten plausible short-term fluctuations for each approach from a dataset of a case study in Japan, the lowest root mean square error of the autocorrelation functions was observed in the block bootstrap approach. This shows that the block bootstrap approach gave the highest accuracy amongst the three. It improved 26.5% from the autoregressive mean average approach.

The lowest root mean square error for variance sequences was also observed with the block bootstrap approach, which indicates that the generated short-term fluctuations possess realistic volatility.

The block bootstrap approach which exhibited plausible volatility and accuracy of the detrended sequence indicated an imaginative time-series statistical property of the real-world fluctuation in wind power generation which would be of relevance in determining the effects of the short-term fluctuations on battery systems of future wind energy technologies.

About The Author

Seigo Furuya received his B.Eng and M.Eng degree in electrical engineering and bioscience from Waseda University, Japan, in 2014 and 2016, respectively. His research interests are generating synthetic wind power generation profiles by statistical approach.

About The Author

Yu Fujimoto received his Ph.D. in engineering from Waseda University, Tokyo, Japan, in 2007. He is an Associate Professor at the Advanced Collaborative Research Organization for Smart Society (ACROSS), Waseda University.

His primary areas of interest are machine learning and statistical data analysis. His current research interests include data mining in energy domains especially for operating and controlling devices in smart grids, and statistical prediction of the power fluctuation under the large introduction of renewable energy sources. He is a Member of Information Processing Society of Japan.

About The Author

Noboru Murata received the B. Eng, M. Eng, and Dr. Eng degrees in mathematical engineering and
information physics from the University of Tokyo in 1987, 1989, and 1992, respectively. After working at the University of Tokyo, GMD FIRST in Germany, and RIKEN in Japan, since April 2000, he joined Waseda University in Japan where he is currently a professor.

His research interest includes the theoretical aspects of learning machines such as neural networks, focusing on the dynamics and statistical properties of learning.

About The Author

Yasuhiro Hayashi received his B. Eng., M. Eng., and D. Eng. degrees from Waseda University, Japan, in 1989, 1991, and 1994, respectively. In 1994, he became a Research Associate with Ibaraki University, Mito, Japan. In 2000, he became an Associate Professor with the Department of Electrical and Electronics Engineering, Fukui University, Fukui, Japan. He has been with Waseda University as a Professor of the Department of Electrical Engineering and Bioscience since 2009; and as a Director of the Research Institute of Advanced Network Technology since 2010. Since 2014, he has been a Dean of the Advanced Collaborative Research Organization for Smart Society at Waseda University.

His current research interests include optimization of distribution system operation and forecasting, operation, planning, and control concerned with renewable energy sources and demand response. Prof. Hayashi is a Member of the Institute of Electrical Engineers of Japan and the International Council on Large Electric Systems.

Reference

Furuya, S., Fujimoto, Y., Murata, N., Hayashi, Y. Reproducing Statistical Property of Short-term Fluctuation in Wind Power Profiles, Energy Procedia 99 ( 2016 ) 130 – 136.

Waseda University, 3-4-1 Okubo, Shinjuku, Tokyo 169-8555, Japan.

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