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Hoyong Eom

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

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By Seung-Mook Baek, Juseung Choi, Hoyong Eom
| MDPI
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.
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