Economic Dispatch of Thermal Generators via Bio-Inspired Optimization Techniques

Abstract

This paper addresses the economic dispatch problem in thermal power systems using four metaheuristic optimization algorithms: Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), Salp Swarm Algorithm (SSA), and JAYA algorithm. A deterministic formulation is adopted to minimize the total generation cost over a 24-hour horizon while meeting generator operating constraints and ensuring load balance. A randomly generated dispatch strategy is also included as a baseline. Each algorithm is independently executed 100 times to evaluate robustness, repeatability, and associated CO2 emissions. Among all methods, PSO achieves the best performance, yielding the lowest total dispatch cost of $82,412.78 and the smallest relative standard deviation (0.12%), along with total CO2 emissions of 1901.65 kg. Compared to other techniques, PSO provides cost improvements of 0.20% over CSA, 0.28% over SSA, 0.94% over JAYA, and a substantial 29.23% reduction with respect to the random baseline. Moreover, all metaheuristic strategies significantly outperform the random dispatch, demonstrating their ability to generate high-quality and feasible solutions. The PSO-based dispatch strategy efficiently allocates hourly power outputs within technical constraints, introducing a controlled overgeneration margin to compensate for system losses. These results confirm the effectiveness of metaheuristic approaches in complex power system optimization tasks and establish a foundation for future work involving renewable integration, emission constraints, and uncertainty modeling.