IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i18p6427-d1233472.html
   My bibliography  Save this article

Multi-Angle Reliability Evaluation of Grid-Connected Wind Farms with Energy Storage Based on Latin Hypercube Important Sampling

Author

Listed:
  • Weixin Yang

    (North China Electric Power Research Institute Co., Ltd., Beijing 100045, China)

  • Yangfan Zhang

    (North China Electric Power Research Institute Co., Ltd., Beijing 100045, China)

  • Yu Wang

    (North China Electric Power Research Institute Co., Ltd., Beijing 100045, China)

  • Kai Liang

    (North China Electric Power Research Institute Co., Ltd., Beijing 100045, China)

  • Hongshan Zhao

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Ao Yang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

Abstract

Aiming to combat the problems of slow speed and poor accuracy of reliability evaluation of the power system in wind farms with energy storage, this paper proposes a method of reliability evaluation based on Latin hypercube important sampling (LHIS). Firstly, we aimed to establish the Latin hypercube important sampling evaluation model by combining the Latin hypercube sampling method with the important sampling method. Secondly, we aimed to optimize the sample probability distribution of the components and conduct hierarchical sampling of the system. Then, the comprehensive risk indicator (CRI) was proposed to evaluate the operational risk and the wind storage generation interrupted energy benefit (WSGIEB) was proposed to evaluate the contribution of the reliability. Finally, simulation experiments were carried out through various power system operation scenarios. The simulation results show that the proposed method is 47% higher than the improving importance sampling method (IM-IS) in evaluation speed and 33% higher than the improving importance sampling method in calculation accuracy.

Suggested Citation

  • Weixin Yang & Yangfan Zhang & Yu Wang & Kai Liang & Hongshan Zhao & Ao Yang, 2023. "Multi-Angle Reliability Evaluation of Grid-Connected Wind Farms with Energy Storage Based on Latin Hypercube Important Sampling," Energies, MDPI, vol. 16(18), pages 1-18, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6427-:d:1233472
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/18/6427/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/18/6427/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sk. A. Shezan & Innocent Kamwa & Md. Fatin Ishraque & S. M. Muyeen & Kazi Nazmul Hasan & R. Saidur & Syed Muhammad Rizvi & Md Shafiullah & Fahad A. Al-Sulaiman, 2023. "Evaluation of Different Optimization Techniques and Control Strategies of Hybrid Microgrid: A Review," Energies, MDPI, vol. 16(4), pages 1-30, February.
    Full references (including those not matched with items on IDEAS)

    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. Hanaa Feleafel & Jovana Radulovic & Michel Leseure, 2024. "Should We Have Selfish Microgrids?," Energies, MDPI, vol. 17(16), pages 1-24, August.
    2. Belqasem Aljafari & Gunapriya Devarajan & Sivaranjani Subramani & Subramaniyaswamy Vairavasundaram, 2023. "Intelligent RBF-Fuzzy Controller Based Non-Isolated DC-DC Multi-Port Converter for Renewable Energy Applications," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    3. Osman Akbulut & Muhammed Cavus & Mehmet Cengiz & Adib Allahham & Damian Giaouris & Matthew Forshaw, 2024. "Hybrid Intelligent Control System for Adaptive Microgrid Optimization: Integration of Rule-Based Control and Deep Learning Techniques," Energies, MDPI, vol. 17(10), pages 1-23, May.
    4. Mustafa Kamal & Renzon Daniel Cosme Pecho & Hassan Falah Fakhruldeen & Hailer Sharif & Vedran Mrzljak & Saber Arabi Nowdeh & Igor Poljak, 2023. "Photovoltaic/Hydrokinetic/Hydrogen Energy System Sizing Considering Uncertainty: A Stochastic Approach Using Two-Point Estimate Method and Improved Gradient-Based Optimizer," Sustainability, MDPI, vol. 15(21), pages 1-30, November.
    5. Diego Peña & Paul Arevalo & Yadyra Ortiz & Franciso Jurado, 2024. "Survey of Optimization Techniques for Microgrids Using High-Efficiency Converters," Energies, MDPI, vol. 17(15), pages 1-24, July.
    6. Yang Li & Feilong Hong & Xiaohui Ge & Xuesong Zhang & Bo Zhao & Feng Wu, 2023. "Optimal Capacity Configuration of Pumped-Storage Units Used to Retrofit Cascaded Hydropower Stations," Energies, MDPI, vol. 16(24), pages 1-23, December.

    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:16:y:2023:i:18:p:6427-:d:1233472. 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.