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Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids

1
Australian Maritime College, University of Tasmania, Newnham, TAS 7248, Australia
2
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
3
Center for Renewable Energy and Power System School of Engineering, University of Tasmania, Hobart, TAS 7005, Australia
*
Author to whom correspondence should be addressed.
Energies 2018, 11(4), 847; https://doi.org/10.3390/en11040847
Received: 26 February 2018 / Revised: 27 March 2018 / Accepted: 3 April 2018 / Published: 4 April 2018
In the revolution of green energy development, microgrids with renewable energy sources such as solar, wind and fuel cells are becoming a popular and effective way of controlling and managing these sources. On the other hand, owing to the intermittency and wide range of dynamic responses of renewable energy sources, battery energy-storage systems have become an integral feature of microgrids. Intelligent energy management and battery sizing are essential requirements in the microgrids to ensure the optimal use of the renewable sources and reduce conventional fuel utilization in such complex systems. This paper presents a novel approach to meet these requirements by using the grey wolf optimization (GWO) technique. The proposed algorithm is implemented for different scenarios, and the numerical simulation results are compared with other optimization methods including the genetic algorithm (GA), particle swarm optimization (PSO), the Bat algorithm (BA), and the improved bat algorithm (IBA). The proposed method (GWO) shows outstanding results and superior performance compared with other algorithms in terms of solution quality and computational efficiency. The numerical results show that the GWO with a smart utilization of battery energy storage (BES) helped to minimize the operational costs of microgrid by 33.185% in comparison with GA, PSO, BA and IBA. View Full-Text
Keywords: battery energy storage sizing; optimization; energy management systems; economic load dispatch; grey wolf optimization (GWO); microgrid battery energy storage sizing; optimization; energy management systems; economic load dispatch; grey wolf optimization (GWO); microgrid
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MDPI and ACS Style

Nimma, K.S.; Al-Falahi, M.D.A.; Nguyen, H.D.; Jayasinghe, S.D.G.; Mahmoud, T.S.; Negnevitsky, M. Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids. Energies 2018, 11, 847. https://doi.org/10.3390/en11040847

AMA Style

Nimma KS, Al-Falahi MDA, Nguyen HD, Jayasinghe SDG, Mahmoud TS, Negnevitsky M. Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids. Energies. 2018; 11(4):847. https://doi.org/10.3390/en11040847

Chicago/Turabian Style

Nimma, Kutaiba S.; Al-Falahi, Monaaf D.A.; Nguyen, Hung D.; Jayasinghe, S. D.G.; Mahmoud, Thair S.; Negnevitsky, Michael. 2018. "Grey Wolf Optimization-Based Optimum Energy-Management and Battery-Sizing Method for Grid-Connected Microgrids" Energies 11, no. 4: 847. https://doi.org/10.3390/en11040847

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