Scholarlink

This research presents a hybrid optimization technique that integrates an adaptive genetic algorithm (AGA) with particle swarm optimization (PSO) to improve energy management in grid-tied microgrids that incorporate solar panels, wind turbines, and energy storage systems (ESS). By dynamically adjusting crossover and mutation rates, AGA + PSO effectively handles the variability of renewable energy, delivering a cost-efficient and reliable microgrid configuration. The method achieves an optimal setup of 150.00 kW solar, 120.00 kW wind, and 220.00 kWh ESS, with a total cost of ₹7,664,847,749.77, surpassing the standard genetic algorithm by 1.2% in cost savings. The design ensures efficient resource use, with 17.07% solar, 80.04% wind, and 2.89% ESS contributions over 24 h. Compared to standard GA and GA + PSO, AGA + PSO offers faster convergence and better adaptability to fluctuating energy demands, providing a robust solution for sustainable microgrids in regions dependent on renewable energy sources.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Adaptive Genetic Algorithm and Particle Swarm Optimization for Efficient Energy Management in Grid-Tied Microgrids

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Abhishek Kumar,
  • Karan Kishor Kumar,
  • Amitesh,
  • Divya Kumar

摘要

This research presents a hybrid optimization technique that integrates an adaptive genetic algorithm (AGA) with particle swarm optimization (PSO) to improve energy management in grid-tied microgrids that incorporate solar panels, wind turbines, and energy storage systems (ESS). By dynamically adjusting crossover and mutation rates, AGA + PSO effectively handles the variability of renewable energy, delivering a cost-efficient and reliable microgrid configuration. The method achieves an optimal setup of 150.00 kW solar, 120.00 kW wind, and 220.00 kWh ESS, with a total cost of ₹7,664,847,749.77, surpassing the standard genetic algorithm by 1.2% in cost savings. The design ensures efficient resource use, with 17.07% solar, 80.04% wind, and 2.89% ESS contributions over 24 h. Compared to standard GA and GA + PSO, AGA + PSO offers faster convergence and better adaptability to fluctuating energy demands, providing a robust solution for sustainable microgrids in regions dependent on renewable energy sources.