| Title |
Analysis of Solar Power Generation Variability by Regional Proportion of Solar Power Plants Penetration |
| Authors |
이동준(Dong jun Lee) ; 주성관(Sung-Kwan Joo) |
| DOI |
https://doi.org/10.5370/KIEE.2021.70.8.1102 |
| Keywords |
Solar Power Generation; Variable Renewable Energy; Variability; Weather Data; Machine Learning |
| Abstract |
As the share of variable renewable energy such as solar power in a power system increases, the variability of the net load also increases. Flexible resources, which can quickly increase or decrease generation, such as energy storage systems or gas turbines can respond to the variability of a power system. However, flexible resources are more expensive than conventional generators. It is necessary to mitigate the variability of renewable energy. This paper presents a machine learning-based method for estimating the variability of solar power generation using weather data considering new solar power plants by region. In the case study, the variability of solar power generation is estimated using the proposed method in this paper. In addition, the mitigation effect of solar power generation variability by regional proportion of new solar power plants penetration is studied. |