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• W3036691340 endingPage "826" @default.
• W3036691340 startingPage "812" @default.
• W3036691340 abstract "Renewable energy sources (RESs) are considered to be reliable and green electric power generation sources. Photovoltaics (PVs) and wind turbines (WTs) are used to provide electricity in remote areas. Optimal sizing of hybrid RESs is a vital challenge in a stand-alone environment. The meta-heuristic algorithms proposed in the past are dependent on algorithm-specific parameters for achieving an optimal solution. This paper proposes a hybrid algorithm of Jaya and a teaching–learning-based optimization (TLBO) named the JLBO algorithm for the optimal unit sizing of a PV–WT–battery hybrid system to satisfy the consumer’s load at minimal total annual cost (TAC). The reliability of the system is considered by a maximum allowable loss of power supply probability (LPSPmax) concept. The results obtained from the JLBO algorithm are compared with the original Jaya, TLBO, and genetic algorithms. The JLBO results show superior performance in terms of TAC, and the PV–WT–battery hybrid system is found to be the most economical scenario. This system provides a cost-effective solution for all proposed LPSPmax values as compared with PV–battery and WT–battery systems." @default.
• W3036691340 created "2020-06-25" @default.
• W3036691340 creator A5055380996 @default.
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• W3036691340 date "2020-07-01" @default.
• W3036691340 modified "2023-10-02" @default.
• W3036691340 title "Jaya Learning-Based Optimization for Optimal Sizing of Stand-Alone Photovoltaic, Wind Turbine, and Battery Systems" @default.
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• W3036691340 doi "https://doi.org/10.1016/j.eng.2020.06.004" @default.
• W3036691340 hasPublicationYear "2020" @default.
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