A Wind Power Probabilistic Model Using the Reflection Method and Multi-Kernel Function Kernel Density Estimation

Juseung Choi, Hoyong Eom and Seung-Mook Baek ()
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Juseung Choi: Department of Electrical, Electronic, and Control Engineering, Institute of IT Convergence Technology, Kongju National University, Cheonan 31080, Republic of Korea
Hoyong Eom: Department of Electrical, Electronic, and Control Engineering, Institute of IT Convergence Technology, Kongju National University, Cheonan 31080, Republic of Korea
Seung-Mook Baek: Department of Electrical, Electronic, and Control Engineering, Institute of IT Convergence Technology, Kongju National University, Cheonan 31080, Republic of Korea

Energies, 2022, vol. 15, issue 24, 1-17

Abstract: This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabilistic approaches have been introduced to assess the impact of RESs on the power system. A probabilistic approach requires a wind power scenario (WPS), and the WPS is generated from the WPPM. Previously, WPPM was generated using a parametric density estimation, and it had limitations in reflecting the characteristics of wind power data (WPD) due to a boundary bias problem. The paper proposes a WPPM generated using the KDE, which is a non-parametric method. Additionally, the paper proposes a reflection method correcting for the boundary bias problem caused by the double-bounded characteristic of the WPD and the multi-kernel function KDE minimizing the effect of tied values. Six bandwidth selectors are used to calculate the bandwidth for the KDE, and one is selected by analyzing the correlation between the normalized WPD and the calculated bandwidth. The results were validated by generating WPPMs with WPDs in six regions of the Republic of Korea, and it was confirmed that the accuracy and goodness-of-fit are improved when the proposed method is used.

Keywords: bandwidth selection; kernel density estimation; probabilistic model; sampling-based method; scenario generation; wind power output (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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