Battery time of discharge setting for maximum effectiveness in a distribution smart grid application

Nasser Hosseinzadeh, Peter Wolfs

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

1 Citation (Scopus)

Abstract

Distributed Generation (DG) is a feature of smart grids in power distribution networks. The DG comprises of various types of renewable energy. Battery storages may be used along with the DG sources to store their produced energy and then release it at a proper time. Most of the current schemes discharge the stored energy based on a timer, which normally start the discharging cycle at a fixed expected peak time. But, the peak time in a distribution network does not remain at a fixed time. This paper proposes a novel intelligent method to determine a suitable time for discharging a battery based on a dynamic forecast of the peak time. A combination of fuzzy logic and artificial neural network has been used to forecast electrical power load up to four hours ahead. Another FLS is used to estimate the possibility of the current time being close to a peak period, which is represented by a factor called peak possibility factor (PPF). Based on the maximum forecasted power output of the ANN among the four outputs, i.e. 1 hour ahead to 4 hours ahead forecasts, and the calculated PPF, the starting time of the discharge cycle will be decided.

Original languageEnglish
Title of host publication3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages535-538
Number of pages4
ISBN (Print)9781479937950
DOIs
Publication statusPublished - Jan 20 2014
Event3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014 - Milwaukee, United States
Duration: Oct 19 2014Oct 22 2014

Other

Other3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014
CountryUnited States
CityMilwaukee
Period10/19/1410/22/14

Fingerprint

Distributed power generation
Electric power distribution
Fuzzy logic
Neural networks

Keywords

  • battery discharge cycle
  • battery storage system
  • distributed generation
  • fuzzy-logic system (FLS)
  • recursive neural network (RNN)
  • short-term load forecasting (STLF)
  • Smart grid

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

Cite this

Hosseinzadeh, N., & Wolfs, P. (2014). Battery time of discharge setting for maximum effectiveness in a distribution smart grid application. In 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014 (pp. 535-538). [7016442] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRERA.2014.7016442

Battery time of discharge setting for maximum effectiveness in a distribution smart grid application. / Hosseinzadeh, Nasser; Wolfs, Peter.

3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 535-538 7016442.

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

Hosseinzadeh, N & Wolfs, P 2014, Battery time of discharge setting for maximum effectiveness in a distribution smart grid application. in 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014., 7016442, Institute of Electrical and Electronics Engineers Inc., pp. 535-538, 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014, Milwaukee, United States, 10/19/14. https://doi.org/10.1109/ICRERA.2014.7016442
Hosseinzadeh N, Wolfs P. Battery time of discharge setting for maximum effectiveness in a distribution smart grid application. In 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 535-538. 7016442 https://doi.org/10.1109/ICRERA.2014.7016442
Hosseinzadeh, Nasser ; Wolfs, Peter. / Battery time of discharge setting for maximum effectiveness in a distribution smart grid application. 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 535-538
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