IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2466-d780863.html
   My bibliography  Save this article

Research on the Operational Strategy of the Hybrid Wind/PV/Small-Hydropower/Facility-Agriculture System Based on a Microgrid

Author

Listed:
  • Yan Ren

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
    Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China)

  • Linmao Ren

    (Railway Police College, Zhengzhou 450053, China)

  • Kai Zhang

    (Henan Province Agricultural Science and Technology Exhibition Hall, Zhengzhou 450002, China)

  • Dong Liu

    (School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xianhe Yao

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

  • Huawei Li

    (School of Electric Power, North China University of Water Resources and Electric Power, Zhengzhou 450045, China)

Abstract

The use of renewable energy sources, such as wind, photovoltaics (PV), and hydropower, to supply facility agriculture may effectively mitigate food and environmental pollution problems and ensure continuity of the energy supply. The operating conditions of a hybrid system are complex, so the operating strategy is very important for system configuration and scheduling purposes. In the current study, first, a hybrid wind/PV/small-hydropower/facility-agricultural system was constructed. Then, the chaotic particle swarm method was applied to optimize hybrid system operation, and a scheduling strategy of the hybrid system was proposed. Finally, combined with an example, according to wind and PV power output and load curves, supply-to-load curves for wind, PV, and small hydropower were obtained. The operational strategy proposed in this study maximizes the utilization of wind and solar resources and rationally allocates hydropower resources. The aforementioned operational strategy provides a basis for hybrid system capacity allocation and scheduling.

Suggested Citation

  • Yan Ren & Linmao Ren & Kai Zhang & Dong Liu & Xianhe Yao & Huawei Li, 2022. "Research on the Operational Strategy of the Hybrid Wind/PV/Small-Hydropower/Facility-Agriculture System Based on a Microgrid," Energies, MDPI, vol. 15(7), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2466-:d:780863
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2466/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2466/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xu, Xiao & Hu, Weihao & Cao, Di & Huang, Qi & Chen, Cong & Chen, Zhe, 2020. "Optimized sizing of a standalone PV-wind-hydropower station with pumped-storage installation hybrid energy system," Renewable Energy, Elsevier, vol. 147(P1), pages 1418-1431.
    2. Wang, Tianyue & Wu, Gaoxiang & Chen, Jiewei & Cui, Peng & Chen, Zexi & Yan, Yangyang & Zhang, Yan & Li, Meicheng & Niu, Dongxiao & Li, Baoguo & Chen, Hongyi, 2017. "Integration of solar technology to modern greenhouse in China: Current status, challenges and prospect," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1178-1188.
    3. Abdelkader, Abbassi & Rabeh, Abbassi & Mohamed Ali, Dami & Mohamed, Jemli, 2018. "Multi-objective genetic algorithm based sizing optimization of a stand-alone wind/PV power supply system with enhanced battery/supercapacitor hybrid energy storage," Energy, Elsevier, vol. 163(C), pages 351-363.
    4. Yuzhu Wang & Huanna Niu & Lu Yang & Weizhou Wang & Fuchao Liu, 2018. "An Optimization Method for Local Consumption of Photovoltaic Power in a Facility Agriculture Micro Energy Network," Energies, MDPI, vol. 11(6), pages 1-20, June.
    5. Tuballa, Maria Lorena & Abundo, Michael Lochinvar, 2016. "A review of the development of Smart Grid technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 710-725.
    6. Xu, Weiwei & Zhou, Dan & Huang, Xiaoming & Lou, Boliang & Liu, Dong, 2020. "Optimal allocation of power supply systems in industrial parks considering multi-energy complementarity and demand response," Applied Energy, Elsevier, vol. 275(C).
    7. Marucci, Alvaro & Cappuccini, Andrea, 2016. "Dynamic photovoltaic greenhouse: Energy balance in completely clear sky condition during the hot period," Energy, Elsevier, vol. 102(C), pages 302-312.
    8. Andrea Colantoni & Danilo Monarca & Alvaro Marucci & Massimo Cecchini & Ilaria Zambon & Federico Di Battista & Diego Maccario & Maria Grazia Saporito & Margherita Beruto, 2018. "Solar Radiation Distribution inside a Greenhouse Prototypal with Photovoltaic Mobile Plant and Effects on Flower Growth," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Irshad, Ahmad Shah & Samadi, Wais Khan & Fazli, Agha Mohammad & Noori, Abdul Ghani & Amin, Ahmad Shah & Zakir, Mohammad Naseer & Bakhtyal, Irfan Ahmad & Karimi, Bashir Ahmad & Ludin, Gul Ahmad & Senjy, 2023. "Resilience and reliable integration of PV-wind and hydropower based 100% hybrid renewable energy system without any energy storage system for inaccessible area electrification," Energy, Elsevier, vol. 282(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhi Li & Akira Yano & Marco Cossu & Hidekazu Yoshioka & Ichiro Kita & Yasuomi Ibaraki, 2018. "Electrical Energy Producing Greenhouse Shading System with a Semi-Transparent Photovoltaic Blind Based on Micro-Spherical Solar Cells," Energies, MDPI, vol. 11(7), pages 1-23, June.
    2. Simona Moretti & Alvaro Marucci, 2019. "A Photovoltaic Greenhouse with Variable Shading for the Optimization of Agricultural and Energy Production," Energies, MDPI, vol. 12(13), pages 1-15, July.
    3. Yano, Akira & Cossu, Marco, 2019. "Energy sustainable greenhouse crop cultivation using photovoltaic technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 116-137.
    4. La Notte, Luca & Giordano, Lorena & Calabrò, Emanuele & Bedini, Roberto & Colla, Giuseppe & Puglisi, Giovanni & Reale, Andrea, 2020. "Hybrid and organic photovoltaics for greenhouse applications," Applied Energy, Elsevier, vol. 278(C).
    5. Fioriti, Davide & Pintus, Salvatore & Lutzemberger, Giovanni & Poli, Davide, 2020. "Economic multi-objective approach to design off-grid microgrids: A support for business decision making," Renewable Energy, Elsevier, vol. 159(C), pages 693-704.
    6. Fares, Dalila & Fathi, Mohamed & Mekhilef, Saad, 2022. "Performance evaluation of metaheuristic techniques for optimal sizing of a stand-alone hybrid PV/wind/battery system," Applied Energy, Elsevier, vol. 305(C).
    7. Islam, M.S. & Das, Barun K. & Das, Pronob & Rahaman, Md Habibur, 2021. "Techno-economic optimization of a zero emission energy system for a coastal community in Newfoundland, Canada," Energy, Elsevier, vol. 220(C).
    8. Fernández, Eduardo F. & Villar-Fernández, Antonio & Montes-Romero, Jesús & Ruiz-Torres, Laura & Rodrigo, Pedro M. & Manzaneda, Antonio J. & Almonacid, Florencia, 2022. "Global energy assessment of the potential of photovoltaics for greenhouse farming," Applied Energy, Elsevier, vol. 309(C).
    9. Carlos Toledo & Alessandra Scognamiglio, 2021. "Agrivoltaic Systems Design and Assessment: A Critical Review, and a Descriptive Model towards a Sustainable Landscape Vision (Three-Dimensional Agrivoltaic Patterns)," Sustainability, MDPI, vol. 13(12), pages 1-38, June.
    10. Mohd Bilal & Ibrahim Alsaidan & Muhannad Alaraj & Fahad M. Almasoudi & Mohammad Rizwan, 2022. "Techno-Economic and Environmental Analysis of Grid-Connected Electric Vehicle Charging Station Using AI-Based Algorithm," Mathematics, MDPI, vol. 10(6), pages 1-40, March.
    11. Arévalo, Paul & Benavides, Dario & Tostado-Véliz, Marcos & Aguado, José A. & Jurado, Francisco, 2023. "Smart monitoring method for photovoltaic systems and failure control based on power smoothing techniques," Renewable Energy, Elsevier, vol. 205(C), pages 366-383.
    12. Skroufouta, S. & Baltas, E., 2021. "Investigation of hybrid renewable energy system (HRES) for covering energy and water needs on the Island of Karpathos in Aegean Sea," Renewable Energy, Elsevier, vol. 173(C), pages 141-150.
    13. Bossink, Bart A.G., 2017. "Demonstrating sustainable energy: A review based model of sustainable energy demonstration projects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1349-1362.
    14. Jaszczur, Marek & Hassan, Qusay & Palej, Patryk & Abdulateef, Jasim, 2020. "Multi-Objective optimisation of a micro-grid hybrid power system for household application," Energy, Elsevier, vol. 202(C).
    15. Ge, Quanwu & Ke, Zhixin & Liu, Yutong & Chai, Fu & Yang, Wenhua & Zhang, Zhili & Wang, Yang, 2023. "Low-carbon strategy of demand-based regulating heating and lighting for the heterogeneous environment in beijing Venlo-type greenhouse," Energy, Elsevier, vol. 267(C).
    16. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
    17. Choi, Kwang Hun & Kwon, Gyu Hyun, 2023. "Strategies for sensing innovation opportunities in smart grids: In the perspective of interactive relationships between science, technology, and business," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    18. Zhu, Jiaoyiling & Hu, Weihao & Xu, Xiao & Liu, Haoming & Pan, Li & Fan, Haoyang & Zhang, Zhenyuan & Chen, Zhe, 2022. "Optimal scheduling of a wind energy dominated distribution network via a deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 201(P1), pages 792-801.
    19. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    20. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2466-:d:780863. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.