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• W3207709544 endingPage "108663" @default.
• W3207709544 startingPage "108663" @default.
• W3207709544 abstract "Renewable energy resources are often suffered from the challenges such as non-uniform power generation as a result of weather and seasonal variations. Hybrid renewable energy systems (HRES) is a solution for efficient energy intensification of renewable resources in which several of them are combined to overcome the challenges arising in operating these in a stand-alone mode. This study proposes a multi-objective dynamic optimization of a candidate HRES by using a genetic algorithm in which the operating cost of HRES, use of nonrenewable power, and fuel emission are minimized simultaneously over a finite time length, subject to operational constraints. In optimization, three strategies are evaluated by considering the wind, solar, and load profiles for 24 h ahead (strategy 1), past and 1 h ahead (strategy 2), and 1 h ahead (strategy 3). Comparison of results illustrates that the power needs to be bought from the grid for strategy 1 is lower by 8.7% and 10.7% compared to strategy 2 and strategy 3 and also the power sold to the grid is 19% and 22% higher than strategy 2 and strategy 3, respectively while meeting the given load profile of 100 households." @default.
• W3207709544 created "2021-10-25" @default.
• W3207709544 creator A5005564013 @default.
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• W3207709544 date "2022-01-01" @default.
• W3207709544 modified "2023-10-04" @default.
• W3207709544 title "Multi-objective dynamic optimization of hybrid renewable energy systems" @default.
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• W3207709544 doi "https://doi.org/10.1016/j.cep.2021.108663" @default.
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