IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i16p5850-d886271.html
   My bibliography  Save this article

Ramp Rate Limitation of Wind Power: An Overview

Author

Listed:
  • Guglielmo D’Amico

    (Department of Economics, University G. D’Annunzio, 65127 Pescara, Italy)

  • Filippo Petroni

    (Department of Management, Marche Polytechnic University, 60121 Ancona, Italy)

  • Salvatore Vergine

    (Department of Neurosciences, Imaging and Clinical Sciences, University G. D’Annunzio, 66100 Chieti, Italy)

Abstract

A run for increasing the integration of renewable energy sources in the electricity network has been seen in recent years because of the big concern about environmental issues and pollution from controllable power units. This paper aims to give a general overview of the concept of ramp rate limitation and its principal applications in the literature regarding the field of control strategies, which deal with smoothing the wind power output. Wind power is one of the most-used renewable energy sources, and the objective of limiting the ramp rate of the power output is to produce more stable power. The studies of ramp rate limitation applied in wind power production deal with the definition and detection of this phenomenon in the real data, the methodologies used to forecast it, its application for managing grids and microgrids, the different actions aimed at physically implementing the restriction, and some of the grid code requirements used in different nations.

Suggested Citation

  • Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:16:p:5850-:d:886271
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/16/5850/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/15/16/5850/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    2. Hansen, Anca D. & Sørensen, Poul & Iov, Florin & Blaabjerg, Frede, 2006. "Centralised power control of wind farm with doubly fed induction generators," Renewable Energy, Elsevier, vol. 31(7), pages 935-951.
    3. Ochoa, Danny & Martinez, Sergio, 2018. "Frequency dependent strategy for mitigating wind power fluctuations of a doubly-fed induction generator wind turbine based on virtual inertia control and blade pitch angle regulation," Renewable Energy, Elsevier, vol. 128(PA), pages 108-124.
    4. Dorado-Moreno, M. & Cornejo-Bueno, L. & Gutiérrez, P.A. & Prieto, L. & Hervás-Martínez, C. & Salcedo-Sanz, S., 2017. "Robust estimation of wind power ramp events with reservoir computing," Renewable Energy, Elsevier, vol. 111(C), pages 428-437.
    5. Cui, Yang & He, Yingjie & Xiong, Xiong & Chen, Zhenghong & Li, Fen & Xu, Taotao & Zhang, Fanghong, 2021. "Algorithm for identifying wind power ramp events via novel improved dynamic swinging door," Renewable Energy, Elsevier, vol. 171(C), pages 542-556.
    6. EunJi Ahn & Jin Hur, 2022. "A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems," Energies, MDPI, vol. 15(7), pages 1-16, April.
    7. Kakran, Sandeep & Chanana, Saurabh, 2018. "Smart operations of smart grids integrated with distributed generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 524-535.
    8. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & Xiong, Yi & Huang, Heming, 2017. "Model of selecting prediction window in ramps forecasting," Renewable Energy, Elsevier, vol. 108(C), pages 98-107.
    9. Frate, G.F. & Cherubini, P. & Tacconelli, C. & Micangeli, A. & Ferrari, L. & Desideri, U., 2019. "Ramp rate abatement for wind power plants: A techno-economic analysis," Applied Energy, Elsevier, vol. 254(C).
    10. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2021. "An Analysis of a Storage System for a Wind Farm with Ramp-Rate Limitation," Energies, MDPI, vol. 14(13), pages 1-25, July.
    11. Muhammad Jabir & Hazlee Azil Illias & Safdar Raza & Hazlie Mokhlis, 2017. "Intermittent Smoothing Approaches for Wind Power Output: A Review," Energies, MDPI, vol. 10(10), pages 1-23, October.
    12. Hadjipaschalis, Ioannis & Poullikkas, Andreas & Efthimiou, Venizelos, 2009. "Overview of current and future energy storage technologies for electric power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1513-1522, August.
    13. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    14. Taylor, James W., 2017. "Probabilistic forecasting of wind power ramp events using autoregressive logit models," European Journal of Operational Research, Elsevier, vol. 259(2), pages 703-712.
    15. Alvarado-Barrios, Lázaro & Rodríguez del Nozal, Álvaro & Boza Valerino, Juan & García Vera, Ignacio & Martínez-Ramos, Jose L., 2020. "Stochastic unit commitment in microgrids: Influence of the load forecasting error and the availability of energy storage," Renewable Energy, Elsevier, vol. 146(C), pages 2060-2069.
    16. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    17. Headley, Alexander J. & Copp, David A., 2020. "Energy storage sizing for grid compatibility of intermittent renewable resources: A California case study," Energy, Elsevier, vol. 198(C).
    18. Anca Daniela Hansen & Kaushik Das & Poul Sørensen & Pukhraj Singh & Andrea Gavrilovic, 2021. "European and Indian Grid Codes for Utility Scale Hybrid Power Plants," Energies, MDPI, vol. 14(14), pages 1-15, July.
    19. James W. Taylor & Keming Yu, 2016. "Using auto-regressive logit models to forecast the exceedance probability for financial risk management," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 179(4), pages 1069-1092, October.
    20. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2014. "Wind speed and energy forecasting at different time scales: A nonparametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 59-66.
    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. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    2. Edisson Villa-Ávila & Paul Arévalo & Roque Aguado & Danny Ochoa-Correa & Vinicio Iñiguez-Morán & Francisco Jurado & Marcos Tostado-Véliz, 2023. "Enhancing Energy Power Quality in Low-Voltage Networks Integrating Renewable Energy Generation: A Case Study in a Microgrid Laboratory," Energies, MDPI, vol. 16(14), pages 1-23, July.

    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. Hu, Jianming & Zhang, Liping & Tang, Jingwei & Liu, Zhi, 2023. "A novel transformer ordinal regression network with label diversity for wind power ramp events forecasting," Energy, Elsevier, vol. 280(C).
    2. Junwei Fu & Yuna Ni & Yuming Ma & Jian Zhao & Qiuyi Yang & Shiyi Xu & Xiang Zhang & Yuhua Liu, 2023. "A Visualization-Based Ramp Event Detection Model for Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-16, January.
    3. He, Yaoyao & Zhu, Chuang & An, Xueli, 2023. "A trend-based method for the prediction of offshore wind power ramp," Renewable Energy, Elsevier, vol. 209(C), pages 248-261.
    4. Andrea Mannelli & Francesco Papi & George Pechlivanoglou & Giovanni Ferrara & Alessandro Bianchini, 2021. "Discrete Wavelet Transform for the Real-Time Smoothing of Wind Turbine Power Using Li-Ion Batteries," Energies, MDPI, vol. 14(8), pages 1-32, April.
    5. Abdullah Al-Shereiqi & Amer Al-Hinai & Mohammed Albadi & Rashid Al-Abri, 2021. "Optimal Sizing of Hybrid Wind-Solar Power Systems to Suppress Output Fluctuation," Energies, MDPI, vol. 14(17), pages 1-16, August.
    6. Tang, Zhenhao & Zhao, Gengnan & Ouyang, Tinghui, 2021. "Two-phase deep learning model for short-term wind direction forecasting," Renewable Energy, Elsevier, vol. 173(C), pages 1005-1016.
    7. EunJi Ahn & Jin Hur, 2022. "A Practical Metric to Evaluate the Ramp Events of Wind Generating Resources to Enhance the Security of Smart Energy Systems," Energies, MDPI, vol. 15(7), pages 1-16, April.
    8. Frate, G.F. & Cherubini, P. & Tacconelli, C. & Micangeli, A. & Ferrari, L. & Desideri, U., 2019. "Ramp rate abatement for wind power plants: A techno-economic analysis," Applied Energy, Elsevier, vol. 254(C).
    9. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    10. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    11. Bernardina Algieri & Arturo Leccadito & Pietro Toscano, 2021. "A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements," Forecasting, MDPI, vol. 3(2), pages 1-16, May.
    12. Brian Loza & Luis I. Minchala & Danny Ochoa-Correa & Sergio Martinez, 2024. "Grid-Friendly Integration of Wind Energy: A Review of Power Forecasting and Frequency Control Techniques," Sustainability, MDPI, vol. 16(21), pages 1-22, November.
    13. Sales-Setién, Ester & Peñarrocha-Alós, Ignacio, 2020. "Robust estimation and diagnosis of wind turbine pitch misalignments at a wind farm level," Renewable Energy, Elsevier, vol. 146(C), pages 1746-1765.
    14. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    15. Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M., 2017. "Fuel cell hybrid electric vehicles: A review on power conditioning units and topologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 76(C), pages 268-291.
    16. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    17. Masebinu, S.O. & Akinlabi, E.T. & Muzenda, E. & Aboyade, A.O., 2017. "Techno-economics and environmental analysis of energy storage for a student residence under a South African time-of-use tariff rate," Energy, Elsevier, vol. 135(C), pages 413-429.
    18. Díaz-González, Francisco & Sumper, Andreas & Gomis-Bellmunt, Oriol & Villafáfila-Robles, Roberto, 2012. "A review of energy storage technologies for wind power applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2154-2171.
    19. Mehrabankhomartash, Mahmoud & Rayati, Mohammad & Sheikhi, Aras & Ranjbar, Ali Mohammad, 2017. "Practical battery size optimization of a PV system by considering individual customer damage function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 36-50.
    20. Rafiq Asghar & Francesco Riganti Fulginei & Hamid Wadood & Sarmad Saeed, 2023. "A Review of Load Frequency Control Schemes Deployed for Wind-Integrated Power Systems," Sustainability, MDPI, vol. 15(10), pages 1-29, May.

    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:16:p:5850-:d:886271. 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.