Power System Economic Dispatch Using Nested Particle Swarm Optimization (Nested PSO) Algorithm

The application of Particle Swarm Optimization (PSO) in power system economic dispatch provides an efficient optimization solution for energy management. This algorithm simulates bird flock foraging behavior, utilizing swarm intelligence to find optimal solutions, making it particularly suitable for solving nonlinear, multi-constraint optimization problems in power systems. Nested PSO (Nested Particle Swarm Optimization) as an enhanced version improves global search capability through its hierarchical structure. In power system economic dispatch, it can simultaneously consider multiple objectives including generation costs, unit constraints, and network losses. When implemented on the MATLAB platform, the algorithm first establishes an economic dispatch model for generation units, incorporating fuel cost functions and power balance constraints. The implementation typically involves defining particle positions representing generation outputs and velocities for search direction adjustments. The position update formula iteratively searches for optimal generation schemes through velocity and position updates using cognitive and social components. The outer PSO layer handles global optimization by exploring the solution space broadly, while the inner PSO layer performs local refinement for precise adjustments. Key MATLAB functions involved include initializing swarm parameters (population size, inertia weight), calculating fitness values using cost functions, and implementing constraint handling mechanisms. The algorithm's advantage lies in its fast convergence rate and reduced susceptibility to local optima, providing a reliable computational tool for power system economic operation. In practical applications, additional constraints such as unit commitment costs, ramp rate limits, and minimum up/down times must be incorporated to enhance the practicality of dispatch solutions. The MATLAB implementation would typically include penalty functions or constraint handling techniques to manage these additional requirements while maintaining solution feasibility.