Distributed ANNs in a layered architecture for energy management and maintenance scheduling of renewable energy HPS microgrids

With increasing research on the field of alternative energy resources for sustainable development, more renewable energy sources are getting penetrated to the distributed network to form “microgrid”. This leads to more complexity in the distribution network in terms of energy management and realtime control. The objective of this paper is to design a system that forecasts the short (daily), medium (seasonal) and long term (yearly) load demand and the availability of energy resources at the microgrids. For scheduling the storage and transaction of electrical energy between neighboring microgrids, an energy management system (EMS) is designed. The EMS makes use of the forecasted data and real time data all together for managing an array of interconnected microgrids. The seasonal and yearly forecaster for a geographical boundary helps in maintenance scheduling and long term infrastructure development plan respectively. Recently, Artificial Neural Network (ANN) is found as a promising tool for statistical forecasting in real time applications. Hence, this paper makes use of ANN feature to forecast both load and availability of energy resources at microgrids in different scenarios like daily, seasonal, and yearly. The layered ANNs architecture is developed and trained with Levenberg-Marqurardt Back Propagation Algorithm. The entire design is simulated in MATLAB/Simulink. The proposed concept can be used in today's real time energy infrastructure to prevent future energy crises with improved reliability and smooth coordination among microgrids located at different areas.