Microgrid Optimal Scheduling Using Particle Swarm Optimization Algorithm

In modern society, microgrids have become a crucial form of energy infrastructure. Consequently, optimal scheduling of microgrid operations has gained significant importance. The particle swarm optimization (PSO) algorithm provides an effective methodology for microgrid scheduling optimization. This approach enables optimization of power generation unit outputs while satisfying dual objectives of minimizing operational costs and maintaining environmental sustainability. The PSO algorithm implementation typically involves initializing particle positions representing potential solutions, where each particle's position vector corresponds to power output levels of different generation units. The algorithm employs velocity updates and position adjustments based on cognitive and social components to explore the solution space. Key functions include calculating fitness values that combine cost functions with environmental penalty terms, and iteratively updating global and personal best positions. This optimization method enhances microgrid efficiency by achieving optimal power dispatch among various generation sources (including renewable resources), resulting in significant energy conservation and positive environmental impacts. The algorithm's implementation can incorporate constraints through penalty functions or constraint-handling techniques to ensure feasible solutions while maintaining solution quality.