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Fuzzy scheduling of a non-isolated micro-grid with renewable resources

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  • Banaei, Mohsen
  • Rezaee, Babak

Abstract

This paper proposes a new energy management scheduling model for optimizing operation costs of a non-isolated micro-grid. It's assumed that the micro-grid includes some renewable energy resources like wind turbines and photovoltaic panels, fuel cell, and energy storage systems, and is connected to the macro-grid through a distributed transmission line with limited capacity. Different possible uncertainties associated with different elements of the micro-grid like forecasted outputs of renewable resources, a maximum capacity of the fuel cell and batteries, a maximum capacity of distributed line and hourly demands are considered in the proposed model. Fuzzy sets concept is used to model these uncertainties in the micro-grid and a three stage optimization method is applied to find the optimal scheduling of the micro-grid under the uncertainties. The proposed method is implemented to a real case study. Impacts of variations in each element of the micro-grid and its associated uncertainty on the results are investigated through a sensitivity analysis.

Suggested Citation

  • Banaei, Mohsen & Rezaee, Babak, 2018. "Fuzzy scheduling of a non-isolated micro-grid with renewable resources," Renewable Energy, Elsevier, vol. 123(C), pages 67-78.
    • Handle: RePEc:eee:renene:v:123:y:2018:i:c:p:67-78
    DOI: 10.1016/j.renene.2018.01.088
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    References listed on IDEAS

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    1. R. E. Bellman & L. A. Zadeh, 1970. "Decision-Making in a Fuzzy Environment," Management Science, INFORMS, vol. 17(4), pages 141-164, December.
    2. Fathima, A. Hina & Palanisamy, K., 2015. "Optimization in microgrids with hybrid energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 431-446.
    3. Malheiro, André & Castro, Pedro M. & Lima, Ricardo M. & Estanqueiro, Ana, 2015. "Integrated sizing and scheduling of wind/PV/diesel/battery isolated systems," Renewable Energy, Elsevier, vol. 83(C), pages 646-657.
    4. Morais, Hugo & Kádár, Péter & Faria, Pedro & Vale, Zita A. & Khodr, H.M., 2010. "Optimal scheduling of a renewable micro-grid in an isolated load area using mixed-integer linear programming," Renewable Energy, Elsevier, vol. 35(1), pages 151-156.
    5. Khodr, H.M. & El Halabi, N. & García-Gracia, M., 2012. "Intelligent renewable microgrid scheduling controlled by a virtual power producer: A laboratory experience," Renewable Energy, Elsevier, vol. 48(C), pages 269-275.
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    Citations

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    Cited by:

    1. Torkan, Ramin & Ilinca, Adrian & Ghorbanzadeh, Milad, 2022. "A genetic algorithm optimization approach for smart energy management of microgrids," Renewable Energy, Elsevier, vol. 197(C), pages 852-863.
    2. Md Mainul Islam & Mahmood Nagrial & Jamal Rizk & Ali Hellany, 2021. "General Aspects, Islanding Detection, and Energy Management in Microgrids: A Review," Sustainability, MDPI, vol. 13(16), pages 1-45, August.
    3. Vaziri, Shabnam Mahmoudzadeh & Rezaee, Babak & Monirian, Masoud Amel, 2020. "Utilizing renewable energy sources efficiently in hospitals using demand dispatch," Renewable Energy, Elsevier, vol. 151(C), pages 551-562.
    4. Jinke Yao & Jiachen Xu & Ning Zhang & Yajuan Guan, 2023. "Model-Based Reinforcement Learning Method for Microgrid Optimization Scheduling," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    5. Yu, Shiwei & Zhou, Shuangshuang & Zheng, Shuhong & Li, Zhenxi & Liu, Lancui, 2019. "Developing an optimal renewable electricity generation mix for China using a fuzzy multi-objective approach," Renewable Energy, Elsevier, vol. 139(C), pages 1086-1098.

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