Optimized Particle Swarm Algorithm Incorporating Diverse Distributed Energy Resources

This paper presents an improved particle swarm optimization (PSO) algorithm designed to handle multiple distributed energy resources. PSO is a widely-used metaheuristic optimization technique that simulates collective foraging behavior in biological populations. Each particle represents a potential solution and dynamically adjusts its position in search space to converge toward optimal solutions. Unlike conventional PSO implementations that typically model single-source systems, our enhanced algorithm incorporates heterogeneous distributed energy resources (DERs) including solar photovoltaics, wind turbines, and energy storage systems. The implementation involves: 1. Particle Initialization: We initialize particle positions using Latin Hypercube Sampling to ensure comprehensive coverage of the solution space, with velocity vectors randomized within [-Vmax, Vmax] bounds. 2. Multi-Objective Fitness Function: The evaluation function integrates power balance constraints, generation costs, and environmental factors through weighted summation approach. 3. Adaptive Velocity Update: Particles update positions using modified equations: v_i(t+1) = w*v_i(t) + c1*r1*(pbest_i - x_i(t)) + c2*r2*(gbest - x_i(t)) where inertia weight w decreases linearly from 0.9 to 0.4 over iterations. 4. Constraint Handling: We employ penalty functions for boundary constraints and system reliability requirements. The algorithm's performance is evaluated through convergence analysis and comparative studies with traditional optimization methods. Potential applications include microgrid scheduling, renewable energy integration, and smart grid management. Future enhancements may involve hybrid algorithms combining PSO with local search techniques and machine learning-based parameter tuning.