Cost and Reliability Optimization in Multi-Source Renewable Energy Microgrids Using Advanced Evolutionary Algorithms

This research addresses the challenge of optimizing micro grid Operations driven by renewable energy— wind, solar, hydro, and biomass—to minimize operational costs while ensuring high system reliability. We evaluate three optimization techniques: Non-dominated Sorting Genetic Algorithm II (NSGA-II),Particle Swarm Optimization (PSO),and Genetic Algorithm (GA). Comparative analysis reveals that PSO provides the most effective trade-off between price reduction and Dependability improvement, outperforming NSGA-II and GA. These findings demonstrate the superiority of PSO in enhancing microgrid performance, offering valuable insights for cost-effective and reliable energy management.