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

Author

Listed:
  • Mehdi Bagheri

    (Department of Electrical and Computer Engineering, Nazarbayev University, Astana 010000, Kazakhstan)

  • Venera Nurmanova

    (Department of Electrical and Computer Engineering, Nazarbayev University, Astana 010000, Kazakhstan)

  • Oveis Abedinia

    (Department of Electric Power Eng., Budapest University of Technology and Economics, Budapest 1111, Hungary
    Young Researchers and Elite Club, Islamic Azad University, Ardabil Branch, Ardabil 5615731567, Iran)

  • Mohammad Salay Naderi

    (Electrical and Computer Engineering Department, Tehran North Branch, Islamic Azad University, Tehran 1651153311, Iran)

  • Noradin Ghadimi

    (Young Researchers and Elite Club, Islamic Azad University, Ardabil Branch, Ardabil 5615731567, Iran
    Department of Electrical engineering, Faculty of Technical Engineering, University of Mohaghegh Ardabili, Ardabil 5615731567, Iran)

  • Mehdi Salay Naderi

    (Iran Grid Secure Operation Research Center, Amirkabir University of Technology, Tehran 158754413, Iran)

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.

Suggested Citation

  • Mehdi Bagheri & Venera Nurmanova & Oveis Abedinia & Mohammad Salay Naderi & Noradin Ghadimi & Mehdi Salay Naderi, 2019. "Renewable Energy Sources and Battery Forecasting Effects in Smart Power System Performance," Energies, MDPI, vol. 12(3), pages 1-18, January.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:373-:d:200643
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    References listed on IDEAS

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    1. Rodrigues, E.M.G. & Godina, R. & Catalão, J.P.S., 2017. "Modelling electrochemical energy storage devices in insular power network applications supported on real data," Applied Energy, Elsevier, vol. 188(C), pages 315-329.
    2. Niknam, Taher & Mojarrad, Hasan Doagou & Meymand, Hamed Zeinoddini & Firouzi, Bahman Bahmani, 2011. "A new honey bee mating optimization algorithm for non-smooth economic dispatch," Energy, Elsevier, vol. 36(2), pages 896-908.
    3. Zhou, Wei & Yang, Hongxing & Fang, Zhaohong, 2008. "Battery behavior prediction and battery working states analysis of a hybrid solar–wind power generation system," Renewable Energy, Elsevier, vol. 33(6), pages 1413-1423.
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    Cited by:

    1. Mostafa Kermani & Domenico Luca Carnì & Sara Rotondo & Aurelio Paolillo & Francesco Manzo & Luigi Martirano, 2020. "A Nearly Zero-Energy Microgrid Testbed Laboratory: Centralized Control Strategy Based on SCADA System," Energies, MDPI, vol. 13(8), pages 1-15, April.
    2. Philippe de Bekker & Sho Cremers & Sonam Norbu & David Flynn & Valentin Robu, 2023. "Improving the Efficiency of Renewable Energy Assets by Optimizing the Matching of Supply and Demand Using a Smart Battery Scheduling Algorithm," Energies, MDPI, vol. 16(5), pages 1-26, March.
    3. Ding, Pan & Liu, Xiaojuan & Li, Huiqin & Huang, Zequan & Zhang, Ke & Shao, Long & Abedinia, Oveis, 2021. "Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    4. Yao, Fang & He, Wenxuan & Wu, Youxi & Ding, Fei & Meng, Defang, 2022. "Remaining useful life prediction of lithium-ion batteries using a hybrid model," Energy, Elsevier, vol. 248(C).

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