Economic Dispatch of Thermal Generators via Bio-Inspired Optimization Techniques - Patiño-Cataño, Cristian; Bolaños, Rubén Iván; Guzmán-Henao, Jhony Andrés; Grisales-Noreña, Luis Fernando; Montoya, Oscar Danilo (Statistics, Optimization & Information Computing, 2025)
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.