Adaptive Teaching Learning Based OptimizationAlgorithm for Solving Unit Commitment Problem with Wind Farm

Authors

  • Sarika Tade Department of Electrical Engineering, Government College of Engineering, Chandrapur, India
  • Amol Kalage Department of Electrical Engineering, Sinhgad Institute of Technology, Lonavala, India
  • Haripriya Kulkarni Department of Electrical Engineering, D. Y. Patil College of Engineering, Pimpri, Pune, India
  • Sahebrao Patil Department of Electrical Engineering, Sinhgad Institute of Technology, Lonavala, India
  • Vidula Jape Department of Electrical Engineering, Modern College of Engineering, Pune, India Corresponding Author: Sarika Vijaykumar Tade
  • Sachin Datey Department of Electrical Engineering, Sinhgad Institute of Technology, Lonavala, India

Keywords:

Unit Commitment, TLBO, Wind Farm Power system optimization

Abstract

Now a day’s electrical power system is suffering from many dificulities like limited availability of thermal generation, increasing power demand as well as fuel cost. Unpredictable load demand becomes more challenging for power system operator in case of thermal wind system due to fluctuations of wind energy. Smart grid system plays a vital role in reducing the problems associated with existing popwer system with intelligent computational techniques. In this paper, integration of wind farm ia presented to overcome the problems associated with power system. The classical unit commitment problem is modified by penetrating the cost model of fluctuating wind power. Teaching Learning Based Optimization algorithm is used to find the solution of this modified optimization problem for 10 unit system. Unit commitment in smart grid environment shows a significant reduction in the total cost.

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Published

24.03.2024

How to Cite

Tade, S. ., Kalage, A. ., Kulkarni, H. ., Patil, S. ., Jape, V. ., & Datey, S. . (2024). Adaptive Teaching Learning Based OptimizationAlgorithm for Solving Unit Commitment Problem with Wind Farm. International Journal of Intelligent Systems and Applications in Engineering, 12(20s), 276–282. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5139

Issue

Vol. 12 No. 20s (2024)

Section

Research Article

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