Comparative Analysis of Chameleon Swarm and Particle Swarm Optimization to Optimize Energy Scheduling in Power System

DOI: https://doie.org/10.10399/JBSE.2025540584

Dhanesh S Patil, Dr. Vijay M Deshmukh

Keywords:

Energy Scheduling, Chameleon Swarm Algorithm, Particle Swarm Optimization, Convergence Patterns, Renewable Energy Integration, Metaheuristic Algorithms, Power System Optimization, Global Optima, Convergence Speed, Solution Accuracy.

Abstract:

Energy scheduling is a key function in optimizing the generation, transmission, and consumption of power, especially with the growing adoption of renewable energy sources in today's power systems. This article is a comparative analysis of two popular metaheuristic algorithms—Chameleon Swarm Algorithm (CSA) and Particle Swarm Optimization (PSO) for optimized energy scheduling. Overall consensus of this research is on the observation of the convergence patterns of CSA and PSO when used on energy scheduling problems, as in minimizing generation costs and exploiting the usage of renewable energy. The performance of the two algorithms is tested through numerical simulations on the basis of their convergence rate, solution quality, and resilience under different system scenarios. The findings highlight the variation between each algorithm to arrive at the optimum resolution, special consideration to efficiency of algorithms in coping with intricate power system constraints. The research proves that although CSA and PSO are efficient energy scheduling solutions, they possess different kinds of convergence behaviors—CSA is more performance-oriented and quicker in convergence but reliability, while PSO is superior in detecting global optima with fewer steps. The findings of the study are valuable in terms of knowledge regarding all the algorithms' merits and shortcomings, and offering insight for determining the most proper optimization technique that should be implemented for energy scheduling in different applications of power systems.

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