Solving Power System Economic Dispatch Using Adaptive Particle Swarm Optimization (APSO)

Power system economic dispatch represents one of the core operational challenges in power systems, aiming to optimally schedule generator outputs under various constraints to minimize total system operating costs. Traditional optimization algorithms like linear programming and dynamic programming often encounter computational complexity issues and local optimum traps when solving such problems. In recent years, intelligent optimization algorithms such as Adaptive Particle Swarm Optimization (APSO) have gained widespread application in power system economic dispatch due to their superior global search capability and adaptability. The APSO algorithm mimics bird flock foraging behavior by employing particle swarm cooperative search to find optimal solutions. In power system economic dispatch applications, each particle represents a potential generator output combination. The algorithm iteratively updates particle velocities and positions using velocity update equations and position update rules, gradually converging toward the optimal solution. Compared to traditional PSO, APSO features adaptive adjustment mechanisms for inertia weights and learning factors through dynamic parameter tuning functions, achieving better balance between global exploration and local exploitation capabilities. When handling constraints, the APSO algorithm must address multiple constraint types including power balance constraints, generator output limits, and ramp rate constraints. These constraints can be incorporated through penalty function methods that add constraint violation penalties to the objective function, or feasible solution preservation strategies that maintain solution feasibility throughout iterations. Particularly for nonlinear, non-convex generator cost characteristics, APSO effectively handles such complex objective functions using fitness evaluation functions that calculate total generation costs. The application of APSO enables more optimized solutions for power system economic dispatch problems, especially in large-scale complex systems where its parallel search characteristics significantly improve computational efficiency through population-based parallel processing. In practical implementations, APSO can be combined with other optimization techniques to form hybrid optimization strategies, such as integrating local search algorithms for refinement, further enhancing solution quality through hybrid optimization frameworks.