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Wind-Thermal Integrated Power System Scheduling Problem Using Cuckoo Search Algorithm

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  • K. Chandrasekaran

    (Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, India)

  • Sishaj P. Simon

    (Department of Electrical and Electronics Engineering, National Institute of Technology, Tiruchirappalli, India)

Abstract

A new nature inspired metaheuristic algorithm known as the cuckoo search algorithm (CSA) is presented in this paper, to solve the unit commitment problem (UCP) for hybrid power system. The utilization of wind energy sources is increasing throughout the world. It is therefore important to develop the protocol for the integration of wind generation system with conventional thermal unit generation system. High wind penetration can lead to high-risk level in power system reliability. In order to maintain the system reliability, wind power dispatch is usually restricted and energy storage is considered for smoothing out the fluctuations. On solving UCP, the proposed binary coded CSA finds the ON/OFF status of the generating units while the economic dispatch problem (EDP) is solved using the real coded CSA. The proposed methodology is tested and validated on 3, 4, 9, 12 38 and 100 unit systems for 24 hour scheduling horizon. The effectiveness of the proposed technique is demonstrated by comparing its performance with the other methods reported in the literature.

Suggested Citation

  • K. Chandrasekaran & Sishaj P. Simon, 2014. "Wind-Thermal Integrated Power System Scheduling Problem Using Cuckoo Search Algorithm," International Journal of Operations Research and Information Systems (IJORIS), IGI Global, vol. 5(3), pages 81-109, July.
    • Handle: RePEc:igg:joris0:v:5:y:2014:i:3:p:81-109
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    Cited by:

    1. Ismail Marouani & Tawfik Guesmi & Hsan Hadj Abdallah & Badr M. Alshammari & Khalid Alqunun & Ahmed S. Alshammari & Salem Rahmani, 2022. "Combined Economic Emission Dispatch with and without Consideration of PV and Wind Energy by Using Various Optimization Techniques: A Review," Energies, MDPI, vol. 15(12), pages 1-35, June.

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