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.

本研究在智能微电网能源管理的背景下，探讨了基于模糊逻辑的控制计算与优化策略的集成，包括粒子群优化（PSO）、遗传算法（GA）、蚁群优化（ACO）和模拟退火（SA）。利用来自模拟微电网环境的实时数据来评估每种算法的执行情况，考虑的关键指标包括目标函数值、收敛速度、稳定性和配置质量。结果显示，模拟退火表现出卓越的优化性能，实现了1000的最低目标函数值，而遗传算法则呈现出快速的收敛速度。粒子群优化和遗传算法展示出较高的稳定性，能有效地适应各种微电网场景。这些发现为寻求根据特定微电网需求定制优化策略的微电网管理者和研究人员提供了有价值的见解。此外，该研究有助于更广泛地理解智能控制工具和优化算法在提高智能微电网系统的效率和灵活性方面的作用。