Novel AI Based Energy Management System for Smart Grid With RES Integration

The different energy assets such as solar panels and batteries help electrical engineers to manage and meet the increasing demand. The amalgamation of renewable energy resources with artificial intelligence is the key focus of providing high energy efficiency with alternative sources. This solution will not only meet electricity demand but also help in reducing greenhouse gas emissions as a result the efficient, sustainable and eco-friendly solution can be achieved which would contribute a lot to the smart grid environment. Here, a modified grey wolf optimizer approach is utilized to develop a novel energy management system for SPV-based microgrid considering modern power grid interactions. The proposed approach aims to provide a proficient microgrid that utilizes solar photovoltaic technology, and energy storage systems using an artificial intelligence algorithm-based microgrid control for optimal dispatch of energy in grid-connected systems. The performance of this novel energy management system is validated under sunny day and cloudy day, to emulate the stochastic nature of solar photovoltaic systems. A comparative study with mixed linear programming is also conducted that indicates towards the savings in 23.34% and 45.55% of the rolling cost for a clear and cloudy day respectively.

Automated summary

The study focuses on utilizing solar energy and energy storage systems for microgrid energy management, with optimal energy dispatch achieved through artificial intelligence algorithms. Experimental results demonstrate cost savings of 23.34% and 45.55% compared to mixed linear programming for sunny and cloudy days respectively.