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Optimal Sizing of PV/Wind/Battery Hybrid Microgrids Considering Lifetime of Battery Banks

Author

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  • Ning Zhang

    (College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
    Department of Electrical Engineering, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li, Taoyuan 32003, Taiwan)

  • Nien-Che Yang

    (Department of Electrical Engineering, National Taiwan University of Science and Technology, 43, Keelung Road, Section 4, Taipei 10607, Taiwan)

  • Jian-Hong Liu

    (Department of Electrical Engineering, Yuan Ze University, 135, Yuan-Tung Road, Chung-Li, Taoyuan 32003, Taiwan)

Abstract

Power system scheduling of renewable energy sources has been studied extensively due to the severe increase in pollution caused by conventional energy sources. In this study, a multi-objective scheduling model of a hybrid microgrid is proposed to minimize the cost of hybrid microgrids and maximize the power supply reliability. The main power generation units such as the wind turbines, photovoltaic (PV) cells, and battery banks are used in this hybrid microgrid. In this study, the optimal sizing of PV panels and battery banks are obtained using multi-objective particle swarm optimization (MOPSO) for the proposed multi-objective scheduling model. The lifetime of battery banks is considered in the energy storage system (ESS) model. Finally, the practicality of the scheduling model proposed in this study is verified by four examples.

Suggested Citation

  • Ning Zhang & Nien-Che Yang & Jian-Hong Liu, 2021. "Optimal Sizing of PV/Wind/Battery Hybrid Microgrids Considering Lifetime of Battery Banks," Energies, MDPI, vol. 14(20), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6655-:d:656279
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    References listed on IDEAS

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    1. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2016. "Optimal allocation and sizing of PV/Wind/Split-diesel/Battery hybrid energy system for minimizing life cycle cost, carbon emission and dump energy of remote residential building," Applied Energy, Elsevier, vol. 171(C), pages 153-171.
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    4. Ju, Liwei & Tan, Zhongfu & Yuan, Jinyun & Tan, Qingkun & Li, Huanhuan & Dong, Fugui, 2016. "A bi-level stochastic scheduling optimization model for a virtual power plant connected to a wind–photovoltaic–energy storage system considering the uncertainty and demand response," Applied Energy, Elsevier, vol. 171(C), pages 184-199.
    5. Kaabeche, A. & Belhamel, M. & Ibtiouen, R., 2011. "Sizing optimization of grid-independent hybrid photovoltaic/wind power generation system," Energy, Elsevier, vol. 36(2), pages 1214-1222.
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    Cited by:

    1. Hajra Khan & Imran Fareed Nizami & Saeed Mian Qaisar & Asad Waqar & Moez Krichen & Abdulaziz Turki Almaktoom, 2022. "Analyzing Optimal Battery Sizing in Microgrids Based on the Feature Selection and Machine Learning Approaches," Energies, MDPI, vol. 15(21), pages 1-22, October.
    2. Anukriti Pokhriyal & José Luis Domínguez-García & Pedro Gómez-Romero, 2022. "Impact of Battery Energy System Integration in Frequency Control of an Electrical Grid with Wind Power," Clean Technol., MDPI, vol. 4(4), pages 1-15, October.
    3. Elkholy, M.H. & Senjyu, Tomonobu & Elymany, Mahmoud & Gamil, Mahmoud M. & Talaat, M. & Masrur, Hasan & Ueda, Soichiro & Lotfy, Mohammed Elsayed, 2024. "Optimal resilient operation and sustainable power management within an autonomous residential microgrid using African vultures optimization algorithm," Renewable Energy, Elsevier, vol. 224(C).
    4. Zhao, Yi-Bo & Dong, Xiao-Jian & Shen, Jia-Ni & He, Yi-Jun, 2024. "Simultaneous sizing and scheduling optimization for PV-wind-battery hybrid systems with a modified battery lifetime model: A high-resolution analysis in China," Applied Energy, Elsevier, vol. 360(C).

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