Fuzzy Logic-Based Control Algorithm for Smart Microgrid Energy Management

This research investigates the integration of fuzzy logic-based control calculations with optimization strategies, counting Molecule Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Simulated Annealing (SA), within the setting of keen microgrid vitality administration. Real-time information from a simulated microgrid environment was utilized to assess the execution of each calculation, considering key measurements such as objective work esteem, merging rate, strength, and arrangement quality. Comes about uncovered that Simulated Annealing displayed predominant optimization, accomplishing the least objective work esteem of 1000, whereas the Genetic Algorithm illustrated a quick merging rate. Molecule Swarm Optimization and Hereditary Calculation showcased tall strength, adjusting viably to assorted microgrid scenarios. These discoveries give profitable experiences for microgrid administrators and analysts looking for to tailor optimization procedures to particular microgrid necessities. Also, the study contributes to the broader understanding of cleverly controlled instruments and optimization calculations in improving the proficiency and flexibility of savvy microgrid frameworks.