Intelligent Load Balancing in Microgrids with AI Optimization

Abstract

Microgrids are a hopeful way to deal with problems in modern power systems because they allow energy to be generated, distributed, and used in smaller areas. But managing microgrid operations well is still a big problem, especially in places that are changing and aren't sure what will happen next. Intelligent load balance methods that use AI optimization techniques are a great way to improve the performance, stability, and efficiency of microgrids. This study suggests a new way to use AI-based optimization methods to make Smart load balance work in microgrids. The suggested framework uses cutting edge AI methods, like machine learning, deep learning, and evolutionary algorithms, to make the microgrid's load distribution, generation schedule, and energy storage use more efficient all the time. The system can predict changes in demand and output by using real-time data and predictive analytics. This lets proactive and adaptable load balance techniques work. Implementing AI-based decision-making systems also helps the microgrid adjust to changing working conditions, get the most out of green energy, keep costs low, and reduce the chance of system breakdowns. The suggested Smart load balance system works because it has been tested in the real world and in simulations for a long time period of time. Compared to standard methods, the results show big gains in system performance measures like load matching, voltage control, and general system stability. The proposed solution's ability to grow and stay strong is also tested in a number of different working conditions, such as when demand trends change, green energy is not available, and the grid experiences problems. Using AI optimization methods for smart load balance is a potential way to make microgrid operations more reliable and efficient, which will make it easier for microgrid technology to be widely used in future energy systems.