Economical Energy Storage Systems Scheduling Based on Load Forecasting Using Deep Learning

Seon Hyeog Kim, Gyul-Lee, Yong June Shin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Energy storage system is a key device for load-leveling which can shift the load from on-peak time to offpeak time in time-of-use. Customers of the behind-the-meter energy storage system can schedule charging/discharging of energy storage system for electricity cost saving at peak-load. In order to maximize the reduction of electricity cost, smart charging and discharging algorithms based on accurate load forecasting are needed. This paper proposes an energy storage system scheduling algorithm based on water filling optimization followed by short-term load forecasting by using long short-term memory neural network. The proposed method is expected to reduce electricity cost for customers in behind-the-meter by scheduling charging and discharging of an energy storage system. For practical implementation, the satisfaction index of the optimization and the daily electricity cost are compared according to the change of scheduling intervals. Finally, case studies are conducted to confirm the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538677896
DOIs
Publication statusPublished - 2019 Apr 1
Event2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Kyoto, Japan
Duration: 2019 Feb 272019 Mar 2

Publication series

Name2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings

Conference

Conference2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019
CountryJapan
CityKyoto
Period19/2/2719/3/2

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All Science Journal Classification (ASJC) codes

  • Information Systems and Management
  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems

Cite this

Kim, S. H., Gyul-Lee, & Shin, Y. J. (2019). Economical Energy Storage Systems Scheduling Based on Load Forecasting Using Deep Learning. In 2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings [8679319] (2019 IEEE International Conference on Big Data and Smart Computing, BigComp 2019 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIGCOMP.2019.8679319