IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v253y2025ics0960148125010997.html

Correlation-segregated joint pdf for stochastic assessment of synthetic inertia in wind turbines

Author

Listed:
  • Yoo, Yeuntae
  • Jung, Seungmin

Abstract

The increasing integration of inverter-based resources (IBRs) into power systems is creating significant challenges for maintaining frequency response and stability. To address these concerns, recent research has explored leveraging the fast-response characteristics of IBRs, particularly by implementing synthetic inertia through advanced control algorithms. Although synthetic inertia from IBRs is promising for enhancing real-time system inertia and operation strategies, a noticeable gap remains in effectively integrating synthetic inertia considerations into long-term generation expansion planning.

Suggested Citation

  • Yoo, Yeuntae & Jung, Seungmin, 2025. "Correlation-segregated joint pdf for stochastic assessment of synthetic inertia in wind turbines," Renewable Energy, Elsevier, vol. 253(C).
    • Handle: RePEc:eee:renene:v:253:y:2025:i:c:s0960148125010997
    DOI: 10.1016/j.renene.2025.123437
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148125010997
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2025.123437?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

    for a different version of it.

    References listed on IDEAS

    as
    1. Díaz-González, Francisco & Hau, Melanie & Sumper, Andreas & Gomis-Bellmunt, Oriol, 2014. "Participation of wind power plants in system frequency control: Review of grid code requirements and control methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 551-564.
    2. Wais, Piotr, 2017. "A review of Weibull functions in wind sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1099-1107.
    3. Bidong Liu & Jakub Nowotarski & Tao Hong & Rafal Weron, 2015. "Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts," HSC Research Reports HSC/15/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
    4. Ratnam, Kamala Sarojini & Palanisamy, K. & Yang, Guangya, 2020. "Future low-inertia power systems: Requirements, issues, and solutions - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    5. Kim, Gyeongmin & Hur, Jin, 2023. "A probabilistic approach to potential estimation of renewable energy resources based on augmented spatial interpolation," Energy, Elsevier, vol. 263(PA).
    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. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    2. Li, Le & Zhu, Donghai & Zou, Xudong & Hu, Jiabing & Kang, Yong & Guerrero, Josep M., 2023. "Review of frequency regulation requirements for wind power plants in international grid codes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    3. Lim, Sunghoon & Kim, Taewan & Lee, Kwang Y. & Choi, Donghee & Park, Jung-Wook, 2025. "Impact of high penetration of renewables on power system frequency response: A review and verification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
    4. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics.
    5. Ana Fernández-Guillamón & Guillermo Martínez-Lucas & Ángel Molina-García & Jose-Ignacio Sarasua, 2020. "Hybrid Wind–PV Frequency Control Strategy under Variable Weather Conditions in Isolated Power Systems," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    6. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
    7. Cheng, Yi & Azizipanah-Abarghooee, Rasoul & Azizi, Sadegh & Ding, Lei & Terzija, Vladimir, 2020. "Smart frequency control in low inertia energy systems based on frequency response techniques: A review," Applied Energy, Elsevier, vol. 279(C).
    8. Li, Pengfei & Hu, Weihao & Hu, Rui & Huang, Qi & Yao, Jun & Chen, Zhe, 2019. "Strategy for wind power plant contribution to frequency control under variable wind speed," Renewable Energy, Elsevier, vol. 130(C), pages 1226-1236.
    9. Fernández-Guillamón, Ana & Gómez-Lázaro, Emilio & Muljadi, Eduard & Molina-García, Ángel, 2019. "Power systems with high renewable energy sources: A review of inertia and frequency control strategies over time," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    10. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
    11. Liu, Nianxin & Yang, Kangyuan & Zhao, Liang & Ye, Zhencheng, 2025. "Stochastic dual dynamic programming for multi-stage stochastic programming of sustainable utility systems," Energy, Elsevier, vol. 338(C).
    12. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    13. Yang, YeHa & Yang, SoYoung & Moon, HyungBin & Woo, JongRoul, 2024. "Analyzing heterogeneous electric vehicle charging preferences for strategic time-of-use tariff design and infrastructure development: A latent class approach," Applied Energy, Elsevier, vol. 374(C).
    14. Li, Yong & He, Li & Liu, Fang & Tan, Yi & Cao, Yijia & Luo, Longfu & Shahidehpour, Mohammod, 2018. "A dynamic coordinated control strategy of WTG-ES combined system for short-term frequency support," Renewable Energy, Elsevier, vol. 119(C), pages 1-11.
    15. Dreidy, Mohammad & Mokhlis, H. & Mekhilef, Saad, 2017. "Inertia response and frequency control techniques for renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 144-155.
    16. Lobato, E. & Doenges, K. & Egido, I. & Sigrist, L., 2020. "Limits to wind aggregation: Empirical assessment in the Spanish electricity system," Renewable Energy, Elsevier, vol. 147(P1), pages 1321-1330.
    17. Katinas, Vladislovas & Gecevicius, Giedrius & Marciukaitis, Mantas, 2018. "An investigation of wind power density distribution at location with low and high wind speeds using statistical model," Applied Energy, Elsevier, vol. 218(C), pages 442-451.
    18. Ratnam Kamala Sarojini & Kaliannan Palanisamy & Enrico De Tuglie, 2022. "A Fuzzy Logic-Based Emulated Inertia Control to a Supercapacitor System to Improve Inertia in a Low Inertia Grid with Renewables," Energies, MDPI, vol. 15(4), pages 1-23, February.
    19. Gonzalez Silva, Jean & Ferrari, Riccardo & van Wingerden, Jan-Willem, 2023. "Wind farm control for wake-loss compensation, thrust balancing and load-limiting of turbines," Renewable Energy, Elsevier, vol. 203(C), pages 421-433.
    20. Christos Hadjichristofi & Spyridon Diochnos & Kyriakos Andresakis & Vassilios Vescoukis, 2024. "Using Time-Series Databases for Energy Data Infrastructures," Energies, MDPI, vol. 17(21), pages 1-23, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:renene:v:253:y:2025:i:c:s0960148125010997. 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/renewable-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.