Intelligent based hierarchical control power electronics for distributed generation systems

There is an increasing adoption of distributed energy (DE) systems in industry where many issues related to, economics, electrical system optimization and long-term viability have been focused for understanding the integration of these systems with the electric power systems. It is well recognized how advanced power electronic (PE) interfaces allow furthering functionality through improved power quality and voltage/VAR support, increase electrical system compatibility by reducing DE fault contributions, and flexibility in operations with various other DE sources, while reducing overall interconnection costs. A distributed intelligent energy management system (DIEMS) is implemented to optimize operating costs. As the optimization greatly depends on the power generation and the power output from renewable sources strongly depends on the weather, the forecast of power generation is required for DIEMS. A neural network structure is used to predict hourly day-type outputs based on which generation can be forecasted. Depending on the forecast, an optimization scheme can be developed utilizing linear programming along with heuristics