IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics036054422403723x.html
   My bibliography  Save this article

Reliability impact of dynamic thermal line rating and electric vehicles on wind power integrated networks

Author

Listed:
  • Song, Tianhua
  • Teh, Jiashen
  • Alharbi, Bader

Abstract

Wind power, as a sustainable clean energy source, has been widely used in power systems. However, the intermittency of wind power poses a threat to the reliability of these systems. One solution to address this issue is the integration of electric vehicle (EV) scheduling. By using EVs with Vehicle-to-Grid (V2G) technology, grid reliability can be improved: EVs can charge when there is excess wind power and discharge when there is a shortage. Another technology that enhances wind power system reliability is Dynamic Thermal Line Rating (DTLR). The DTLR system increases transmission line capacity based on actual weather conditions, helping to alleviate network congestion. Despite the benefits of these technologies, their coordinated operation has not been jointly modeled for reliability purposes before. This study presents, for the first time, a reliability model for the coordinated operation of these two technologies and introduces a new reliability index, the Smart Regulation Reliability Index (SRRI). Simulations were conducted using the IEEE 24-bus reliability test system. The results show that the coordinated operation of these technologies reduces Expected Energy Not Supplied (EENS) by nearly 28.8 % and increases SRRI by about 12.9 %.

Suggested Citation

  • Song, Tianhua & Teh, Jiashen & Alharbi, Bader, 2024. "Reliability impact of dynamic thermal line rating and electric vehicles on wind power integrated networks," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s036054422403723x
    DOI: 10.1016/j.energy.2024.133945
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422403723X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.133945?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Olabi, A.G. & Abdelkareem, Mohammad Ali, 2022. "Renewable energy and climate change," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    2. Fernandez, E. & Albizu, I. & Bedialauneta, M.T. & Mazon, A.J. & Leite, P.T., 2016. "Review of dynamic line rating systems for wind power integration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 80-92.
    3. Peng, Xiaosheng & Wang, Hongyu & Lang, Jianxun & Li, Wenze & Xu, Qiyou & Zhang, Zuowei & Cai, Tao & Duan, Shanxu & Liu, Fangjie & Li, Chaoshun, 2021. "EALSTM-QR: Interval wind-power prediction model based on numerical weather prediction and deep learning," Energy, Elsevier, vol. 220(C).
    4. Yin, Wanjun & Jia, Leilei & Ji, Jianbo, 2024. "Energy optimal scheduling strategy considering V2G characteristics of electric vehicle," Energy, Elsevier, vol. 294(C).
    5. Zhao, Yang & Noori, Mehdi & Tatari, Omer, 2017. "Boosting the adoption and the reliability of renewable energy sources: Mitigating the large-scale wind power intermittency through vehicle to grid technology," Energy, Elsevier, vol. 120(C), pages 608-618.
    6. Karimi, Soheila & Musilek, Petr & Knight, Andrew M., 2018. "Dynamic thermal rating of transmission lines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 600-612.
    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. Glaum, Philipp & Hofmann, Fabian, 2023. "Leveraging the existing German transmission grid with dynamic line rating," Applied Energy, Elsevier, vol. 343(C).
    2. F. Gülşen Erdinç & Ozan Erdinç & Recep Yumurtacı & João P. S. Catalão, 2020. "A Comprehensive Overview of Dynamic Line Rating Combined with Other Flexibility Options from an Operational Point of View," Energies, MDPI, vol. 13(24), pages 1-30, December.
    3. Yanghe Liu & Hairong Zhang & Chuanfeng Wu & Mengxin Shao & Liting Zhou & Wenlong Fu, 2024. "A Short-Term Wind Speed Forecasting Framework Coupling a Maximum Information Coefficient, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise, Shared Weight Gated Memory Network with Im," Sustainability, MDPI, vol. 16(16), pages 1-19, August.
    4. Kamal, Md. Mustafa & Saini, R.P., 2023. "Performance investigations of hybrid hydrokinetic turbine rotor with different system and operating parameters," Energy, Elsevier, vol. 267(C).
    5. Shi, Ruifeng & Li, Shaopeng & Zhang, Penghui & Lee, Kwang Y., 2020. "Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization," Renewable Energy, Elsevier, vol. 153(C), pages 1067-1080.
    6. A. G. Olabi & Khaled Obaideen & Mohammad Ali Abdelkareem & Maryam Nooman AlMallahi & Nabila Shehata & Abdul Hai Alami & Ayman Mdallal & Asma Ali Murah Hassan & Enas Taha Sayed, 2023. "Wind Energy Contribution to the Sustainable Development Goals: Case Study on London Array," Sustainability, MDPI, vol. 15(5), pages 1-22, March.
    7. Han, X.C. & Xu, H.J. & Hua, W.S., 2023. "Decomposition performance and kinetics analysis of magnesium hydroxide regulated with C/N/Ti/Si additives for thermochemical heat storage," Applied Energy, Elsevier, vol. 344(C).
    8. Baxter L. M. Williams & R. J. Hooper & Daniel Gnoth & J. G. Chase, 2025. "Residential Electricity Demand Modelling: Validation of a Behavioural Agent-Based Approach," Energies, MDPI, vol. 18(6), pages 1-22, March.
    9. Son, Donghee & Song, Youngbin & Park, Shina & Oh, Junseok & Kim, Sang Woo, 2025. "Online state-of-charge and capacity co-estimation for lithium-ion batteries under aging and varying temperatures," Energy, Elsevier, vol. 316(C).
    10. Fan, Siyuan & Wang, Xiao & Wang, Zun & Sun, Bo & Zhang, Zhenhai & Cao, Shengxian & Zhao, Bo & Wang, Yu, 2022. "A novel image enhancement algorithm to determine the dust level on photovoltaic (PV) panels," Renewable Energy, Elsevier, vol. 201(P1), pages 172-180.
    11. Ye, Xiaoling & Liu, Chengcheng & Xiong, Xiong & Qi, Yinyi, 2025. "Recurrent attention encoder–decoder network for multi-step interval wind power prediction," Energy, Elsevier, vol. 315(C).
    12. Yun Deng & Xueling Ran & Hussien Elshareef & Renjie Dong & Yuguang Zhou, 2025. "Emergy, Environmental and Economic (3E) Assessment of Biomass Pellets from Agricultural Waste," Agriculture, MDPI, vol. 15(6), pages 1-17, March.
    13. Yang, Ting & Yang, Zhenning & Li, Fei & Wang, Hengyu, 2024. "A short-term wind power forecasting method based on multivariate signal decomposition and variable selection," Applied Energy, Elsevier, vol. 360(C).
    14. Shihao Xie & Yun Zeng & Jing Qian & Fanjie Yang & Youtao Li, 2023. "CPSOGSA Optimization Algorithm Driven Cascaded 3DOF-FOPID-FOPI Controller for Load Frequency Control of DFIG-Containing Interconnected Power System," Energies, MDPI, vol. 16(3), pages 1-18, January.
    15. Wang, Yun & Chen, Tuo & Zou, Runmin & Song, Dongran & Zhang, Fan & Zhang, Lingjun, 2022. "Ensemble probabilistic wind power forecasting with multi-scale features," Renewable Energy, Elsevier, vol. 201(P1), pages 734-751.
    16. Siavash Asiaban & Nezmin Kayedpour & Arash E. Samani & Dimitar Bozalakov & Jeroen D. M. De Kooning & Guillaume Crevecoeur & Lieven Vandevelde, 2021. "Wind and Solar Intermittency and the Associated Integration Challenges: A Comprehensive Review Including the Status in the Belgian Power System," Energies, MDPI, vol. 14(9), pages 1-41, May.
    17. Baxter Williams & Daniel Bishop & Patricio Gallardo & J. Geoffrey Chase, 2023. "Demand Side Management in Industrial, Commercial, and Residential Sectors: A Review of Constraints and Considerations," Energies, MDPI, vol. 16(13), pages 1-28, July.
    18. Li, Shenglin & Zhu, Jizhong & Dong, Hanjiang & Zhu, Haohao & Fan, Junwei, 2022. "A novel rolling optimization strategy considering grid-connected power fluctuations smoothing for renewable energy microgrids," Applied Energy, Elsevier, vol. 309(C).
    19. Zhong, Zewei & Hu, Wuyang & Zhao, Xiaoli, 2024. "Rethinking electric vehicle smart charging and greenhouse gas emissions: Renewable energy growth, fuel switching, and efficiency improvement," Applied Energy, Elsevier, vol. 361(C).
    20. Yang, Mao & Han, Chao & Zhang, Wei & Wang, Bo, 2024. "A short-term power prediction method for wind farm cluster based on the fusion of multi-source spatiotemporal feature information," Energy, Elsevier, vol. 294(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:eee:energy:v:313:y:2024:i:c:s036054422403723x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.