Spatial prediction of renewable energy resources for reinforcing and expanding power grids

Beom Jun Park, Jin Hur

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

Abstract

Due to intermittency of wind and solar generating resources, it is very hard to manage renewable energy resources in system operation and planning. In order to incorporate higher wind and solar power penetrations into power systems maintaining a secure and economic power system operation, the accurate estimation of wind and solar power outputs is needed. As wind and solar farm outputs depend on natural resources that vary over space and time, spatial analysis is also needed. Predictions about suitability for locating new wind and solar generating resources can be performed by optimal spatial modelling. In this paper, we propose a new spatial prediction of renewable energy resources for reinforcing and expanding power grids. Potential capacity factors of renewable energy resources for long-term power grid planning are estimated by optimal spatial modelling based on Kriging techniques. The proposed method is verified by empirical data from industrial wind and solar farms in South Korea.

Original languageEnglish
Pages (from-to)757-772
Number of pages16
JournalEnergy
Volume164
DOIs
StatePublished - 1 Dec 2018

Keywords

  • Grid integration analysis
  • Kriging techniques
  • Potential capacity factor
  • Slope estimation
  • Spatial modelling
  • Spatial prediction

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