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

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

Research output: Contribution to journalArticlepeer-review

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 languageEnglish
Article number909611
JournalFrontiers in Energy Research
Volume10
DOIs
Publication statusPublished - 2022 Jul 14

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

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