Comparative study of microgrid optimal scheduling under multi-optimization algorithm fusion
This study provides practical guidance for microgrid design and operation, but it is incremental as it combines existing optimization methods without introducing new algorithms.
This paper tackles microgrid optimal scheduling by integrating multiple optimization algorithms (Genetic Algorithm, Simulated Annealing, Ant Colony Optimization, Particle Swarm Optimization) into a multi-objective model to explore trade-offs between operational and environmental costs. The simulation results show that these algorithms produce different dispatch outcomes, highlighting distinct roles of diesel generators and micro gas turbines in microgrids.
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.