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Cuckoo search algorithm for short-term hydrothermal scheduling

Author

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  • Nguyen, Thang Trung
  • Vo, Dieu Ngoc
  • Truong, Anh Viet

Abstract

This paper proposes a cuckoo search algorithm (CSA) for solving short-term fixed-head hydrothermal scheduling (HTS) problem considering power losses in transmission systems and valve point loading effects in fuel cost function of thermal units. The CSA method is a new meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other host birds of other species for solving optimization problems. The advantages of the CSA method are few control parameters and effective for optimization problems with complicated constraints. The effectiveness of the proposed CSA has been tested on different hydrothermal systems and the obtained test results have been compared to those from other methods in the literature. The result comparison has shown that the CSA can obtain higher quality solutions than many other methods. Therefore, the proposed CSA can be an efficient method for solving short-term fixed head hydrothermal scheduling problems.

Suggested Citation

  • Nguyen, Thang Trung & Vo, Dieu Ngoc & Truong, Anh Viet, 2014. "Cuckoo search algorithm for short-term hydrothermal scheduling," Applied Energy, Elsevier, vol. 132(C), pages 276-287.
    • Handle: RePEc:eee:appene:v:132:y:2014:i:c:p:276-287
    DOI: 10.1016/j.apenergy.2014.07.017
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    References listed on IDEAS

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    1. Basu, M. & Chowdhury, A., 2013. "Cuckoo search algorithm for economic dispatch," Energy, Elsevier, vol. 60(C), pages 99-108.
    2. Basu, M., 2011. "Artificial immune system for fixed head hydrothermal power system," Energy, Elsevier, vol. 36(1), pages 606-612.
    3. Ahmed, Jubaer & Salam, Zainal, 2014. "A Maximum Power Point Tracking (MPPT) for PV system using Cuckoo Search with partial shading capability," Applied Energy, Elsevier, vol. 119(C), pages 118-130.
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    Cited by:

    1. Özyön, Serdar & Yaşar, Celal, 2018. "Gravitational search algorithm applied to fixed head hydrothermal power system with transmission line security constraints," Energy, Elsevier, vol. 155(C), pages 392-407.
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    7. Zhao, Jing & Guo, Zhen-Hai & Su, Zhong-Yue & Zhao, Zhi-Yuan & Xiao, Xia & Liu, Feng, 2016. "An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed," Applied Energy, Elsevier, vol. 162(C), pages 808-826.
    8. Kaboli, S. Hr. Aghay & Selvaraj, J. & Rahim, N.A., 2016. "Long-term electric energy consumption forecasting via artificial cooperative search algorithm," Energy, Elsevier, vol. 115(P1), pages 857-871.
    9. Chuanxiong Kang & Min Guo & Jinwen Wang, 2017. "Short-Term Hydrothermal Scheduling Using a Two-Stage Linear Programming with Special Ordered Sets Method," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(11), pages 3329-3341, September.
    10. Kaboli, S. Hr. Aghay & Fallahpour, A. & Selvaraj, J. & Rahim, N.A., 2017. "Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming," Energy, Elsevier, vol. 126(C), pages 144-164.
    11. Zhang, Jingrui & Lin, Shuang & Liu, Houde & Chen, Yalin & Zhu, Mingcheng & Xu, Yinliang, 2017. "A small-population based parallel differential evolution algorithm for short-term hydrothermal scheduling problem considering power flow constraints," Energy, Elsevier, vol. 123(C), pages 538-554.
    12. Cui Zheyuan & Ali Thaeer Hammid & Ali Noori Kareem & Mingxin Jiang & Muamer N. Mohammed & Nallapaneni Manoj Kumar, 2021. "A Rigid Cuckoo Search Algorithm for Solving Short-Term Hydrothermal Scheduling Problem," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    13. Ming, Bo & Liu, Pan & Guo, Shenglian & Cheng, Lei & Zhou, Yanlai & Gao, Shida & Li, He, 2018. "Robust hydroelectric unit commitment considering integration of large-scale photovoltaic power: A case study in China," Applied Energy, Elsevier, vol. 228(C), pages 1341-1352.
    14. Yin, Hao & Wu, Fei & Meng, Xin & Lin, Yicheng & Fan, Jingmin & Meng, Anbo, 2020. "Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs," Energy, Elsevier, vol. 203(C).
    15. Weibo Zhao & Dongxiao Niu, 2017. "Prediction of CO 2 Emission in China’s Power Generation Industry with Gauss Optimized Cuckoo Search Algorithm and Wavelet Neural Network Based on STIRPAT model with Ridge Regression," Sustainability, MDPI, vol. 9(12), pages 1-15, December.
    16. Nazari-Heris, M. & Mohammadi-Ivatloo, B. & B. Gharehpetian, G., 2017. "Short-term scheduling of hydro-based power plants considering application of heuristic algorithms: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 116-129.
    17. Amjad Ali & K. Almutairi & Muhammad Zeeshan Malik & Kashif Irshad & Vineet Tirth & Salem Algarni & Md. Hasan Zahir & Saiful Islam & Md Shafiullah & Neeraj Kumar Shukla, 2020. "Review of Online and Soft Computing Maximum Power Point Tracking Techniques under Non-Uniform Solar Irradiation Conditions," Energies, MDPI, vol. 13(12), pages 1-37, June.
    18. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    19. Thang Trung Nguyen & Nguyen Vu Quynh & Minh Quan Duong & Le Van Dai, 2018. "Modified Differential Evolution Algorithm: A Novel Approach to Optimize the Operation of Hydrothermal Power Systems while Considering the Different Constraints and Valve Point Loading Effects," Energies, MDPI, vol. 11(3), pages 1-30, March.

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