Comparative study of microgrid optimal scheduling under multi-optimization algorithm fusion

TL;DR

This paper compares multiple optimization algorithms for microgrid scheduling, analyzing their impact on operational and environmental costs, and providing insights for better microgrid design and management.

Contribution

It introduces an integrated approach combining various optimization algorithms to enhance microgrid scheduling and analyzes their effects on operational and environmental outcomes.

Findings

Different algorithms yield distinct dispatch results.

Diesel generators and micro gas turbines play specific roles.

The study offers practical guidance for microgrid operation.

Abstract

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.