Load Leveling Trainer For A Physical Microgrid

Improving the reliability and efficiency of microgrids to handle diverse load types will lead to more dependable electrical grids in small communities, which can reduce costs, for both consumers and utility companies, and reduce carbon emissions by decreasing reliance on traditional energy sources, such as burning coal. Better demand side management (DSM) has been proposed as a solution to creating more efficient microgrids as well as preventing the need to increase power generation capacity and transmission. In this research, various algorithms for achieving better DSM through load leveling have been explored, adapted to a physical 45kW microgrid, and evaluated in order to create a load leveling trainer. A simulated case study, based on the electrical load needs of small communities in Bangladesh, was ran and has shown that the load leveling trainer can be used to better balance electrical load demands. This research can be expanded on in future work by adding renewable energy sources and energy storage to the microgrid’s profile, in addition to providing a foundation for continuing to develop more advanced algorithms for load leveling through optimization techniques. The load leveling trainer can also be reapplied in more case studies based on different small communities with different load types, which will broaden our understanding of how to implement DSM as it is applied to varying grid needs.