IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v147y2021ics1364032121004652.html
   My bibliography  Save this article

Challenges and opportunities of inertia estimation and forecasting in low-inertia power systems

Author

Listed:
  • Heylen, Evelyn
  • Teng, Fei
  • Strbac, Goran

Abstract

Accurate inertia estimates and forecasts are crucial to support the system operation in future low-inertia power systems. A large literature on inertia estimation methods is available. This paper aims to provide an overview and classification of inertia estimation methods. The classification considers the time horizon the methods are applicable to, i.e., offline post mortem, online real time and forecasting methods, and the scope of the inertia estimation, e.g., system-wide, regional, generation, demand, individual resource. The framework presented in this paper facilitates objective comparisons of the performance of newly developed or improved inertia estimation methods with the state-of-the-art methods in their respective time horizon and with their respective scope. Moreover, shortcomings of the existing inertia estimation methods have been identified and suggestions for future work have been made.

Suggested Citation

  • Heylen, Evelyn & Teng, Fei & Strbac, Goran, 2021. "Challenges and opportunities of inertia estimation and forecasting in low-inertia power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    • Handle: RePEc:eee:rensus:v:147:y:2021:i:c:s1364032121004652
    DOI: 10.1016/j.rser.2021.111176
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032121004652
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2021.111176?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. Tielens, Pieter & Van Hertem, Dirk, 2016. "The relevance of inertia in power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 999-1009.
    2. Zhang, Yao & Wang, Jianxue & Wang, Xifan, 2014. "Review on probabilistic forecasting of wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 32(C), pages 255-270.
    3. 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.
    4. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    5. Xiang, Yue & Wu, Gang & Shen, Xiaodong & Ma, Yuhang & Gou, Jing & Xu, Weiting & Liu, Junyong, 2021. "Low-carbon economic dispatch of electricity-gas systems," Energy, Elsevier, vol. 226(C).
    6. 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).
    7. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
    8. Amela Ajanovic & Marina Siebenhofer & Reinhard Haas, 2021. "Electric Mobility in Cities: The Case of Vienna," Energies, MDPI, vol. 14(1), pages 1-18, January.
    9. 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.
    10. 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).
    11. Wu, Wei & Lin, Boqiang, 2021. "Benefits of electric vehicles integrating into power grid," Energy, Elsevier, vol. 224(C).
    12. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    13. Gonzalez, Elena & Stephen, Bruce & Infield, David & Melero, Julio J., 2019. "Using high-frequency SCADA data for wind turbine performance monitoring: A sensitivity study," Renewable Energy, Elsevier, vol. 131(C), pages 841-853.
    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. Zhang, Mingze & Li, Weidong & Yu, Samson Shenglong & Wang, Haixia & Ba, Yu, 2024. "Optimal day-ahead large-scale battery dispatch model for multi-regulation participation considering full timescale uncertainties," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Xingyong Zhao & Penghui Qin & Zhen Tang, 2022. "Equivalent Inertia Estimation of Asynchronous Motor and Its Effect on Power System Frequency Response," Energies, MDPI, vol. 15(22), pages 1-16, November.
    3. L., Lavanya & Swarup, K.S., 2024. "Inertia monitoring in power systems: Critical features, challenges, and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    4. Daniele Linaro & Federico Bizzarri & Davide Giudice & Cosimo Pisani & Giorgio M. Giannuzzi & Samuele Grillo & Angelo M. Brambilla, 2023. "Continuous estimation of power system inertia using convolutional neural networks," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Stelios C. Dimoulias & Eleftherios O. Kontis & Grigoris K. Papagiannis, 2022. "Inertia Estimation of Synchronous Devices: Review of Available Techniques and Comparative Assessment of Conventional Measurement-Based Approaches," Energies, MDPI, vol. 15(20), pages 1-30, October.

    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. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    2. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
    3. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    4. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1533-1547.
    5. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
    6. Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020. "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, vol. 36(2), pages 466-479.
    7. Debanjan, Mukherjee & Karuna, Kalita, 2022. "An Overview of Renewable Energy Scenario in India and its Impact on Grid Inertia and Frequency Response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    8. Claudio Monteiro & Ignacio J. Ramirez-Rosado & L. Alfredo Fernandez-Jimenez, 2018. "Probabilistic Electricity Price Forecasting Models by Aggregation of Competitive Predictors," Energies, MDPI, vol. 11(5), pages 1-25, April.
    9. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    10. Zhang, Hong & Nguyen, Hoang & Bui, Xuan-Nam & Pradhan, Biswajeet & Mai, Ngoc-Luan & Vu, Diep-Anh, 2021. "Proposing two novel hybrid intelligence models for forecasting copper price based on extreme learning machine and meta-heuristic algorithms," Resources Policy, Elsevier, vol. 73(C).
    11. Zoran Gligorić & Svetlana Štrbac Savić & Aleksandra Grujić & Milanka Negovanović & Omer Musić, 2018. "Short-Term Electricity Price Forecasting Model Using Interval-Valued Autoregressive Process," Energies, MDPI, vol. 11(7), pages 1-17, July.
    12. Maciej Kostrzewski & Jadwiga Kostrzewska, 2021. "The Impact of Forecasting Jumps on Forecasting Electricity Prices," Energies, MDPI, vol. 14(2), pages 1-17, January.
    13. Junfeng Qi & Fei Tang & Jiarui Xie & Xinang Li & Xiaoqing Wei & Zhuo Liu, 2022. "Research on Frequency Response Modeling and Frequency Modulation Parameters of the Power System Highly Penetrated by Wind Power," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    14. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
    15. Katarzyna Maciejowska & Bartosz Uniejewski & Tomasz Serafin, 2020. "PCA Forecast Averaging—Predicting Day-Ahead and Intraday Electricity Prices," Energies, MDPI, vol. 13(14), pages 1-19, July.
    16. Joanna Janczura & Aleksandra Michalak, 2020. "Optimization of Electric Energy Sales Strategy Based on Probabilistic Forecasts," Energies, MDPI, vol. 13(5), pages 1-16, February.
    17. Luis M. López-Manrique & E. V. Macias-Melo & O. May Tzuc & A. Bassam & K. M. Aguilar-Castro & I. Hernández-Pérez, 2018. "Assessment of Resource and Forecast Modeling of Wind Speed through An Evolutionary Programming Approach for the North of Tehuantepec Isthmus (Cuauhtemotzin, Mexico)," Energies, MDPI, vol. 11(11), pages 1-22, November.
    18. Martínez – Lucas, Guillermo & Sarasua, José Ignacio & Fernández – Guillamón, Ana & Molina – García, Ángel, 2021. "Combined hydro-wind frequency control scheme: Modal analysis and isolated power system case example," Renewable Energy, Elsevier, vol. 180(C), pages 1056-1072.
    19. Gabrielli, Paolo & Wüthrich, Moritz & Blume, Steffen & Sansavini, Giovanni, 2022. "Data-driven modeling for long-term electricity price forecasting," Energy, Elsevier, vol. 244(PB).
    20. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.

    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:rensus:v:147:y:2021:i:c:s1364032121004652. 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.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.