Optimal Energy Storage System Operation for Peak Reduction in a Distribution Network Using a Prediction Interval

Daisuke Kodaira, Wonwook Jung, Sekyung Han

Research output: Contribution to journalArticlepeer-review

41 Scopus citations

Abstract

This study is aimed at determining the optimal energy storage system (ESS) operation schedule to minimize the peak load on the feeder of a distribution network. To reduce the peak load, the feeder load profile needs to be predicted. A deterministic prediction is not reliable, however, because it may contain errors. This study proposes the use of prediction intervals (PIs) of estimated error based on prior predictions. The proposed algorithm is intended for the determination of an optimal ESS schedule using the PIs. To demonstrate the method's validity, a case study is presented where a proposed optimal ESS schedule determined from PIs reduces the peak load during network operations over a one-year period. The performance of the proposed method is superior to that of the conventional method which uses deterministic load prediction.

Original languageEnglish
Article number8883183
Pages (from-to)2208-2217
Number of pages10
JournalIEEE Transactions on Smart Grid
Volume11
Issue number3
DOIs
StatePublished - May 2020

Keywords

  • Distribution network
  • energy storage system
  • peak shaving
  • prediction interval
  • probabilistic load prediction

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