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Intermittent Smoothing Approaches for Wind Power Output: A Review

Author

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  • Muhammad Jabir

    (Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia)

  • Hazlee Azil Illias

    (Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia)

  • Safdar Raza

    (Department of Electrical Engineering, NFC IET, Multan 60000, Pakistan)

  • Hazlie Mokhlis

    (Department of Electrical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur, Malaysia)

Abstract

Wind energy is one of the most common types of renewable energy resource. Due to its sustainability and environmental benefits, it is an emerging source for electric power generation. Rapid and random changes of wind speed makes it an irregular and inconsistent power source when connected to the grid, causing different technical problems in protection, power quality and generation dispatch control. Due to these problems, effective intermittent smoothing approaches for wind power output are crucially needed to minimize such problems. This paper reviews various intermittent smoothing approaches used in smoothing the output power fluctuations caused by wind energy. Problems associated with the inclusion of wind energy resources to grid are also briefly reviewed. From this review, it has been found that battery energy storage system is the most suitable and effective smoothing approach, provided that an effective control strategy is available for optimal utilization of battery energy system. This paper further demonstrates different control strategies built for battery energy storage system to obtain the smooth output wind power.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1572-:d:114760
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    References listed on IDEAS

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    1. Rui Hou & Thai-Thanh Nguyen & Hak-Man Kim & Huihui Song & Yanbin Qu, 2017. "An Energy-Based Control Strategy for Battery Energy Storage Systems: A Case Study on Microgrid Applications," Energies, MDPI, vol. 10(2), pages 1-20, February.
    2. Howlader, Abdul Motin & Urasaki, Naomitsu & Yona, Atsushi & Senjyu, Tomonobu & Saber, Ahmed Yousuf, 2013. "A review of output power smoothing methods for wind energy conversion systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 26(C), pages 135-146.
    3. Acuña, Luceny Guzmán & Padilla, Ricardo Vasquez & Mercado, Alcides Santander, 2017. "Measuring reliability of hybrid photovoltaic-wind energy systems: A new indicator," Renewable Energy, Elsevier, vol. 106(C), pages 68-77.
    4. Deepak, M. & Abraham, Rajesh Joseph & Gonzalez-Longatt, Francisco M. & Greenwood, David M. & Rajamani, Haile-Selassie, 2017. "A novel approach to frequency support in a wind integrated power system," Renewable Energy, Elsevier, vol. 108(C), pages 194-206.
    5. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    6. Liu, Hui & Tian, Hongqi & Liang, Xifeng & Li, Yanfei, 2015. "New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, Mind Evolutionary Algorithm and Artificial Neural Networks," Renewable Energy, Elsevier, vol. 83(C), pages 1066-1075.
    7. Yoldaş, Yeliz & Önen, Ahmet & Muyeen, S.M. & Vasilakos, Athanasios V. & Alan, İrfan, 2017. "Enhancing smart grid with microgrids: Challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 205-214.
    8. Shahbazi, Mahmoud & Poure, Philippe & Saadate, Shahrokh & Zolghadri, Mohammad Reza, 2011. "Five-leg converter topology for wind energy conversion system with doubly fed induction generator," Renewable Energy, Elsevier, vol. 36(11), pages 3187-3194.
    9. 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.
    10. 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.
    11. Shukur, Osamah Basheer & Lee, Muhammad Hisyam, 2015. "Daily wind speed forecasting through hybrid KF-ANN model based on ARIMA," Renewable Energy, Elsevier, vol. 76(C), pages 637-647.
    12. Chowdhury, M.A. & Hosseinzadeh, N. & Shen, W.X., 2012. "Smoothing wind power fluctuations by fuzzy logic pitch angle controller," Renewable Energy, Elsevier, vol. 38(1), pages 224-233.
    13. Mousavi G, S.M. & Faraji, Faramarz & Majazi, Abbas & Al-Haddad, Kamal, 2017. "A comprehensive review of Flywheel Energy Storage System technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 477-490.
    14. Khalid, M. & Savkin, A.V., 2014. "Minimization and control of battery energy storage for wind power smoothing: Aggregated, distributed and semi-distributed storage," Renewable Energy, Elsevier, vol. 64(C), pages 105-112.
    15. Nian Shi & Yi Luo, 2017. "Energy Storage System Sizing Based on a Reliability Assessment of Power Systems Integrated with Wind Power," Sustainability, MDPI, vol. 9(3), pages 1-20, March.
    16. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.
    17. Bouzgou, Hassen & Benoudjit, Nabil, 2011. "Multiple architecture system for wind speed prediction," Applied Energy, Elsevier, vol. 88(7), pages 2463-2471, July.
    18. Shi, Rui-jing & Fan, Xiao-chao & He, Ying, 2017. "Comprehensive evaluation index system for wind power utilization levels in wind farms in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 461-471.
    19. Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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    Cited by:

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    2. Guangyi Wu & Xiangxin Shao & Hong Jiang & Shaoxin Chen & Yibing Zhou & Hongyang Xu, 2020. "Control Strategy of the Pumped Storage Unit to Deal with the Fluctuation of Wind and Photovoltaic Power in Microgrid," Energies, MDPI, vol. 13(2), pages 1-23, January.
    3. Diego Jose da Silva & Edmarcio Antonio Belati & Jesús M. López-Lezama, 2023. "A Mathematical Programming Approach for the Optimal Operation of Storage Systems, Photovoltaic and Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-24, January.
    4. Lai, Chun Sing & Locatelli, Giorgio, 2021. "Economic and financial appraisal of novel large-scale energy storage technologies," Energy, Elsevier, vol. 214(C).
    5. 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).
    6. Jae Woong Shim & Heejin Kim & Kyeon Hur, 2019. "Incorporating State-of-Charge Balancing into the Control of Energy Storage Systems for Smoothing Renewable Intermittency," Energies, MDPI, vol. 12(7), pages 1-13, March.
    7. 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.
    8. Li, Chaolei & Wu, Anqi & Xi, Chengqiao & Guan, Wanbing & Chen, Liang & Singhal, Subhash C., 2022. "High reversible cycling performance of carbon dioxide electrolysis by flat-tube solid oxide cell," Applied Energy, Elsevier, vol. 314(C).
    9. Barelli, Linda & Pelosi, Dario & Bidini, Gianni & Di Donato, Graziano & Navarra, Maria Assunta & Passerini, Stefano, 2023. "Na-seawater battery technology integration with renewable energies: The case study of Sardinia Island," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
    10. 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.
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