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 language | English |
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Title of host publication | 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 535-538 |
Number of pages | 4 |
ISBN (Print) | 9781479937950 |
DOIs | |
Publication status | Published - Jan 20 2014 |
Event | 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014 - Milwaukee, United States Duration: Oct 19 2014 → Oct 22 2014 |
Other
Other | 3rd International Conference on Renewable Energy Research and Applications, ICRERA 2014 |
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Country/Territory | United States |
City | Milwaukee |
Period | 10/19/14 → 10/22/14 |
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