Issue |
E3S Web Conf.
Volume 260, 2021
2021 International Conference on Advanced Energy, Power and Electrical Engineering (AEPEE2021)
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|
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Article Number | 02012 | |
Number of page(s) | 7 | |
Section | Power Electronics Technology and Application | |
DOI | https://doi.org/10.1051/e3sconf/202126002012 | |
Published online | 19 May 2021 |
Wind power short-term prediction over mountain area using a high-resolution WRF model
1 State Key Laboratory of Disaster Prevention and Reduction for Power Grid Transmission and Distribution Equipment, 410129, Changsha, China
2 State Grid Hunan Electric Power Corporation Limited Disaster Prevention & Reduction Center, 410129, Changsha, China
* Corresponding author: wanglei@lasg.iap.ac.cn
Accurate wind power prediction are crucial for power-grid integration and load balancing, as well as the safe and stable operation of the power grid. In this study, the relationship between the wind speed and wind power over mountain area is firstly investigated using the observations in Hunan Baiguoshan Mountain, and the fitting equation is proposed to predict the wind power with wind speed. Using the simulation of the WRF model with a 3-kilometer horizontal resolution, its prediction performance for short-term wind power is further analyzed. The results show that a sixth power relationship exists between wind speeds and wind powers over the mountain area. Also, when the wind speed reaches up to about 9.5 m/s (half of the cut-out wind speeds), the wind power is almost up to its rated power (2200 KW). The evolution characteristics of the wind powers predicted by the WRF model resemble that in observations, but the predicted wind powers are larger than that as observations in most time, which results from the overestimated predicted wind speeds like that in observations.
Key words: Wind power prediction / Wind speed / Mountain area / High resolution / WRF model
© The Authors, published by EDP Sciences, 2021
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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