Economic Dispatch for 40 Generating Units Using Particle Swarm Optimization Algorithm

This study applies the Particle Swarm Optimization (PSO) algorithm to optimize economic dispatch across 40 generating units while accounting for wind power integration. PSO is a metaheuristic algorithm based on swarm intelligence that simulates the collective behavior of biological populations such as bird flocks or insect swarms during food foraging or predator avoidance. The algorithm operates through iterative updates of particle positions and velocities using key mathematical formulations: velocity_update = inertia_weight * current_velocity + cognitive_component * (pbest - position) + social_component * (gbest - position). Wind power represents a clean energy source; however, its inherent intermittency and unpredictability necessitate careful consideration in dispatch optimization to ensure grid stability and security. The PSO implementation features fitness function evaluation that combines fuel cost minimization with constraint handling for power balance, unit capacity limits, and wind power variability. Through systematic iteration and convergence mechanisms, the algorithm identifies optimal generation schedules that enhance dispatch efficiency while maintaining system reliability under wind power uncertainties.