AUTHOR=Kim Juhwan , Lee Jaehyeong , Kang Sungwoo , Hwang Sungchul , Yoon Minhan , Jang Gilsoo 

TITLE=Probabilistic Optimal Power Flow-Based Spectral Clustering Method Considering Variable Renewable Energy Sources

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2022.909611

DOI=10.3389/fenrg.2022.909611

ISSN=2296-598X

ABSTRACT=Power system clustering is an effective method for providing voltage control and preventing failure propagation. There are various methods for power system clustering. A graph theory-based spectral clustering method is widely used, as it is a simple approach with a short calculation time. However, a spectral clustering method can only be applied in a system environment where the power generation amount and load are known. Moreover, it is often impossible to sufficiently reflect the influences of volatile power sources (such as renewable energy sources) in the clustering. To this end, this study proposes a probabilistic spectral clustering algorithm applicable to a power system including a photovoltaic (PV) model for volatile energy sources and classification method for neutral buses. The algorithm is a clustering method for reflecting the random outputs of PV sources, and the neutral buses can be reclassified as a result of the clustering to obtain the optimal clustering results. The algorithm is verified through an IEEE 118-bus test system including PV sources.