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

Beom Jun Park, Jin Hur

Research output: Contribution to journalArticlepeer-review

31 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

Bibliographical note

Funding Information:
This research was supported by the MSIP (Ministry of Science, ICT and Future Planning), Korea , under the ITRC (Information Technology Research Center) support program ( 2015-0-00445 ) supervised by the IITP (Institute for Information & communications Technology Promotion). This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP ) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20161210200560 ).

Publisher Copyright:
© 2018 Elsevier Ltd

Keywords

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

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