Abstract
Power system clustering is an effective method for realizing voltage control and preventing failure propagation. Various approaches are used for power system clustering. Graph-theory-based spectral clustering methods are widely used because they follow a simple approach with a short calculation time. However, spectral clustering methods can only be applied in system environments for which the power generation amount and load are known. Moreover, it is often impossible to sufficiently reflect the influence of volatile power sources (e.g., 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 a classification method (for neutral buses). The algorithm applies a clustering method that reflects the random outputs of PV sources, and the neutral buses can be reclassified via clustering to obtain optimal clustering results. The algorithm is verified through an IEEE 118-bus test system, including PV sources.
| Original language | English |
|---|---|
| Article number | 909611 |
| Journal | Frontiers in Energy Research |
| Volume | 10 |
| DOIs | |
| Publication status | Published - 2022 Jul 14 |
Bibliographical note
Funding Information:This research was supported by the Basic Research Program through the National Research Foundation of Korea (NRF), funded by the MSIT (No. 2020R1A4A1019405), as well as a Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korean Government (MOTIE) (No. 20191210301890).
Publisher Copyright:
Copyright © 2022 Kim, Lee, Kang, Hwang, Yoon and Jang.
Keywords
- electric power system
- expansion
- hierarchical spectral clustering
- photovolataics
- power system analysis
ASJC Scopus subject areas
- Renewable Energy, Sustainability and the Environment
- Fuel Technology
- Energy Engineering and Power Technology
- Economics and Econometrics
