Economic Operation Algorithm for Energy Storage System with Customer Baseline Load (CBL)-based Load Forecasting

Myung Hyun Shim, Hyeong Jin Choi, Seung Ho Song, Dong Jun Won

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

An operation of energy storage system (ESS) in buildings and factories is gaining popularity owing to its decreasing price and increasing efficiency. However, there is a financial risk in conducting ESS without evaluating its profit in advance. Therefore, this paper analyzes economic benefits of ESS before installation. A mathematical model of ESS and an ESS power scheduling algorithm are presented, which consider both a peak load and an energy cost to maximize user's benefit. The algorithm employs customer baseline load (CBL)-based load forecasting which will facilitate its engagement in Korean electricity market since it is used in order to evaluate load reduction in Korea. In addition, symmetric additive adjustment (SAA) is applied to improve low load forecasting accuracy of the CBL-based load prediction. That is, day ahead optimization is performed by the CBL-based load prediction and it is rescheduled hourly with corrected load by SAA during ESS operation. In case study, ESS scheduling results for an actual load, CBL-based load forecasting, and SAA applied load forecasting are compared for a normal load day and a peak load day.

Original languageEnglish
Title of host publicationProceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538629109
DOIs
StatePublished - 20 Nov 2018
Event53rd International Universities Power Engineering Conference, UPEC 2018 - Glasgow, United Kingdom
Duration: 4 Sep 20187 Sep 2018

Publication series

NameProceedings - 2018 53rd International Universities Power Engineering Conference, UPEC 2018

Conference

Conference53rd International Universities Power Engineering Conference, UPEC 2018
Country/TerritoryUnited Kingdom
CityGlasgow
Period4/09/187/09/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

  • Customer Baseline Load
  • Energy Storage System
  • Load Prediction
  • Peak Reduction

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