| Year of publication |
2022 |
| Title of paper |
Probabilistic Optimal Power Flow-based Spectral Clustering Method considering Variable Renewable Energy Sources |
| Author |
Juhwan Kim, Jaehyeong Lee, Sungwoo Kang, Sungchul Hwang, Minhan Yoon, Gilsoo Jang |
| Journal |
Frontiers in Energy Research |
| Keyword |
Hierarchical spectral clustering, Electric power system, Photovoltaics, Power system analysis, Expansion |
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. |
