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A novel fuzzy control algorithm for reducing the peak demands using energy storage system

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  • Chua, Kein Huat
  • Lim, Yun Seng
  • Morris, Stella

Abstract

Commercial and industrial customers are subject to the monthly maximum demand charges which can be as high as 30% of the total electricity bills. Battery-based energy storage system (BESS) can be used to reduce the monthly maximum demand charges. A number of control strategies have been developed for the BESS to reduce the daily peak demands. A fuzzy control algorithm is developed to reduce the daily peak demands with the limited capacity of the BESS. Its performance is evaluated at two different buildings, namely building A and B. The fuzzy controller forecasts the load profile one day in advance using the historical load data. Then during the day of peak reduction, the controller will adjust the power output of BESS using the latest state of charge and operation time. The performance of the fuzzy controller is compared with other two controllers developed in the past. The experimental results show that fuzzy controller is the most effective approach for the peak reduction under the limited capacity of the BESS.

Suggested Citation

  • Chua, Kein Huat & Lim, Yun Seng & Morris, Stella, 2017. "A novel fuzzy control algorithm for reducing the peak demands using energy storage system," Energy, Elsevier, vol. 122(C), pages 265-273.
    • Handle: RePEc:eee:energy:v:122:y:2017:i:c:p:265-273
    DOI: 10.1016/j.energy.2017.01.063
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    as
    1. Berg, Sandford V. & Savvides, Andreas, 1983. "The theory of maximum kW demand charges for electricity," Energy Economics, Elsevier, vol. 5(4), pages 258-266, October.
    2. Bekele, Getachew & Palm, Björn, 2010. "Feasibility study for a standalone solar-wind-based hybrid energy system for application in Ethiopia," Applied Energy, Elsevier, vol. 87(2), pages 487-495, February.
    3. Chao Lu & Hanchen Xu & Xin Pan & Jie Song, 2014. "Optimal Sizing and Control of Battery Energy Storage System for Peak Load Shaving," Energies, MDPI, vol. 7(12), pages 1-15, December.
    4. Chen, Jun & Garcia, Humberto E., 2016. "Economic optimization of operations for hybrid energy systems under variable markets," Applied Energy, Elsevier, vol. 177(C), pages 11-24.
    5. of England, Bank, 2016. "Markets and operations," Bank of England Quarterly Bulletin, Bank of England, vol. 56(4), pages 212-221.
    6. Walawalkar, Rahul & Apt, Jay & Mancini, Rick, 2007. "Economics of electric energy storage for energy arbitrage and regulation in New York," Energy Policy, Elsevier, vol. 35(4), pages 2558-2568, April.
    7. Garcia, Humberto E. & Chen, Jun & Kim, Jong S. & Vilim, Richard B. & Binder, William R. & Bragg Sitton, Shannon M. & Boardman, Richard D. & McKellar, Michael G. & Paredis, Christiaan J.J., 2016. "Dynamic performance analysis of two regional Nuclear Hybrid Energy Systems," Energy, Elsevier, vol. 107(C), pages 234-258.
    Full references (including those not matched with items on IDEAS)

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