Synergistic Optimization of Unit Commitment Using PSO and Random Search

Authors

  • Rajasekhar Vatambeti Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India https://orcid.org/0009-0003-5396-1825
  • P. K. Dhal Department of Electrical and Electronics Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai 600062, India https://orcid.org/0000-0002-9449-6099

DOI:

https://doi.org/10.37256/cm.5120243638

Keywords:

optimization, thermal units, PSO, random search algorithm, hybrid method

Abstract

Optimizing the order of thermal units for power generation plays a pivotal role in meeting load demand while minimizing fuel consumption. This paper introduces an enhanced hybrid method designed to schedule generating units with the simultaneous objectives of cost and emission reduction, which often pose a trade-off challenge. The hybrid approach integrates the parametric adaptation of particle swarm optimization (PSO) with the randomness of a random search algorithm. The introduction of intermediate variables enhances the performance of particles in the PSO framework, contributing to more effective optimization. To update the individual population’s locations within the PSO process, randomness is judiciously introduced using a random search method. To assess the potential of the proposed method, it is applied to the IEEE-39 bus system and a four-unit thermal system. The results obtained through the proposed approach are compared with those achieved by existing methods, demonstrating its effectiveness in achieving optimal solutions for the unit commitment problem.

Downloads

Published

2024-03-12

How to Cite

1.
Vatambeti R, Dhal PK. Synergistic Optimization of Unit Commitment Using PSO and Random Search. Contemp. Math. [Internet]. 2024 Mar. 12 [cited 2024 Apr. 18];5(1):698-710. Available from: https://ojs.wiserpub.com/index.php/CM/article/view/3638