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Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance

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dc.contributor.author Bagheri, Mehdi
dc.contributor.author Nurmanova, Venera
dc.contributor.author Abedinia, Oveis
dc.contributor.author Naderi, Mohammad Salay
dc.contributor.author Ghadimi, Noradin
dc.contributor.author Naderi, Mehdi Salay
dc.date.accessioned 2019-04-25T06:13:17Z
dc.date.available 2019-04-25T06:13:17Z
dc.date.issued 2019-01-24
dc.identifier.citation Bagheri, M.; Nurmanova, V.; Abedinia, O.; Salay Naderi, M.; Ghadimi, N.; Salay Naderi, M. Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance. Energies 2019, 12, 373. en_US
dc.identifier.uri http://dx.doi.org/10.3390/en12030373
dc.identifier.uri http://nur.nu.edu.kz/handle/123456789/3845
dc.description.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. en_US
dc.language.iso en en_US
dc.publisher MDPI en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject renewable energy sources en_US
dc.subject lead–acid battery en_US
dc.subject state of charge en_US
dc.subject feature selection en_US
dc.subject forecasting en_US
dc.title Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance en_US
dc.type Article en_US
workflow.import.source science


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Attribution-NonCommercial-ShareAlike 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 3.0 United States