Evaluation of Frequency Regulation Capability of Wind Power Considering its Uncertainty

Fan, Kai; Zhang, Kaifeng; Zhao, Dawei; Chen, Mengmeng; Bi, Wenjun; Ding, Zhetong

doi:10.1109/cac.2018.8623639

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…The short-term probabilistic prediction in this study focuses on the cumulative distribution function or probability density function of wind power for the next 24 h. Wang et al (2018) showed that improving the accuracy of wind power forecasting can effectively reduce the impact of power uncertainty. Most of the studies on uncertainty prediction are holistic modeling and error analysis (Teng et al, 2017) for analyzing the potential impact of wind power uncertainty on system dispatch (Fan et al, 2018). In the work of Yang et al (2020b), a wind turbine power curve is proposed to describe the conversion relationship between the output power and the wind turbine speed; the power curve of the wind turbine can be calculated to obtain the results of its power prediction.…”

Section: Introductionmentioning

confidence: 99%

A new framework for short-term wind power probability forecasting considering spatial and temporal dependence of forecast errors

Sun

et al. 2022

Front. Energy Res.

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Since deterministic prediction errors of wind power cannot be avoided, probabilistic prediction can adequately describe the uncertainty of wind power and, thus, provide further guidance to dispatching authorities for decision making. Current probabilistic prediction methods for wind power are still incomplete in mining its physical variation process. Therefore, this study constructs a new framework for short-term wind power probabilistic forecasting considering the spatio-temporal dependence of errors by mining the spatio-temporal characteristics of historical wind power data and numerical weather forecasts at numerical weather prediction (NWP). First, the deterministic prediction results are obtained by an improved deep belief network (DBN); then, a multi-location NWP is introduced to propose a multi-level error scenario partitioning method considering the spatio-temporal dependence property. Finally, a new error sample set is formed by reconstructing the kernel density estimation method to adapt the model, and the short-term wind power probability prediction at different confidence levels is carried out. It is, thus, concluded that the effectiveness of the overall framework under the probabilistic prediction considering spatio-temporal dependence is verified in a wind farm in Jilin, China, and the prediction accuracy is effectively and significantly improved compared with the same confidence level, and the coverage of the evaluation index prediction interval is improved by 1.23, 0.72, and 0.80%, and the average bandwidth of the prediction interval is reduced by 2.14, 1.40, and 0.63%, which confirms the proposed effectiveness and feasibility of the method.

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Section: Introductionmentioning

confidence: 99%