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• W2912458207 abstract "In this study, the influence of using acid batteries as part of green energy sources, such as wind and solar electric power generators, is investigated. First, the power system is simulated in the presence of a lead–acid battery, with an independent solar system and wind power generator. In the next step, in order to estimate the output power of the solar and wind resources, a novel forecast model is proposed. Then, the forecasting task is carried out considering the conditions related to the state of charge (SOC) of the batteries. The optimization algorithm used in this model is honey bee mating optimization (HBMO), which operates based on selecting the best candidates and optimization of the prediction problem. Using this algorithm, the SOC of the batteries will be in an appropriate range, and the number of on-or-off switching’s of the wind turbines and photovoltaic (PV) modules will be reduced. In the proposed method, the appropriate capacity for the SOC of the batteries is chosen, and the number of battery on/off switches connected to the renewable energy sources is reduced. Finally, in order to validate the proposed method, the results are compared with several other methods." @default.
• W2912458207 created "2019-02-21" @default.
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• W2912458207 date "2019-01-24" @default.
• W2912458207 modified "2023-10-16" @default.
• W2912458207 title "Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance" @default.
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• W2912458207 doi "https://doi.org/10.3390/en12030373" @default.
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