  As global attention on renewable and clean energy grows, the research and
implementation of microgrids become paramount. This paper delves into the
methodology of exploring the relationship between the operational and
environmental costs of microgrids through multi-objective optimization models.
By integrating various optimization algorithms like Genetic Algorithm,
Simulated Annealing, Ant Colony Optimization, and Particle Swarm Optimization,
we propose an integrated approach for microgrid optimization. Simulation
results depict that these algorithms provide different dispatch results under
economic and environmental dispatch, revealing distinct roles of diesel
generators and micro gas turbines in microgrids. Overall, this study offers
in-depth insights and practical guidance for microgrid design and operation.
