NON–CONVEX ECONOMIC LOAD DISPATCH WITH ADAPTIVE ACCELERATED PARTICLE SWARM OPTIMIZATION

Abstract

Economic Load Dispatch (ELD) is essential for efficient power system operation. The objective is to satisfy demand at minimum cost while meeting op- erational constraints. Valve- point effects and multiple constraints make the problem nonlinear and nonconvex. These features limit the performance of conventional optimization methods. This paper proposes an advanced metaheuristic method called Adaptive Accelerated Particle Swarm Optimization (AAPSO). The approach combines dynamic adaptation of classical PSO parameters with an additional acceleration term. This strategy improves the balance between global exploration and local exploitation. It reduces premature convergence and enhances stability and accuracy in highly multimodal and constrained ELD problems. The proposed method was tested on standard 5-, 13-, and 40-unit systems with valve-point effects. Results show that AAPSO consistently achieves lower generation costs around 19,15% compared to other techniques and recent metaheuristic algorithms. All generator limits and power balance constraints are strictly satisfied. Convergence curves demonstrate fast convergence, robustness, and strong ability to avoid local optima.