Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/3/373 |
| _version_ | 1817992578274951168 |
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| author | Mehdi Bagheri Venera Nurmanova Oveis Abedinia Mohammad Salay Naderi Noradin Ghadimi Mehdi Salay Naderi |
| author_facet | Mehdi Bagheri Venera Nurmanova Oveis Abedinia Mohammad Salay Naderi Noradin Ghadimi Mehdi Salay Naderi |
| author_sort | Mehdi Bagheri |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T01:27:39Z |
| format | Article |
| id | doaj.art-e60969a939fc477aae7b3672383acbe6 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:27:39Z |
| publishDate | 2019-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e60969a939fc477aae7b3672383acbe62022-12-22T02:20:20ZengMDPI AGEnergies1996-10732019-01-0112337310.3390/en12030373en12030373Renewable Energy Sources and Battery Forecasting Effects in Smart Power System PerformanceMehdi Bagheri0Venera Nurmanova1Oveis Abedinia2Mohammad Salay Naderi3Noradin Ghadimi4Mehdi Salay Naderi5Department of Electrical and Computer Engineering, Nazarbayev University, Astana 010000, KazakhstanDepartment of Electrical and Computer Engineering, Nazarbayev University, Astana 010000, KazakhstanDepartment of Electric Power Eng., Budapest University of Technology and Economics, Budapest 1111, HungaryElectrical and Computer Engineering Department, Tehran North Branch, Islamic Azad University, Tehran 1651153311, IranYoung Researchers and Elite Club, Islamic Azad University, Ardabil Branch, Ardabil 5615731567, IranIran Grid Secure Operation Research Center, Amirkabir University of Technology, Tehran 158754413, IranIn 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.https://www.mdpi.com/1996-1073/12/3/373renewable energy sourceslead–acid batterystate of chargefeature selectionforecasting |
| spellingShingle | Mehdi Bagheri Venera Nurmanova Oveis Abedinia Mohammad Salay Naderi Noradin Ghadimi Mehdi Salay Naderi Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance Energies renewable energy sources lead–acid battery state of charge feature selection forecasting |
| title | Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance |
| title_full | Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance |
| title_fullStr | Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance |
| title_full_unstemmed | Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance |
| title_short | Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance |
| title_sort | renewable energy sources and battery forecasting effects in smart power system performance |
| topic | renewable energy sources lead–acid battery state of charge feature selection forecasting |
| url | https://www.mdpi.com/1996-1073/12/3/373 |
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