Multi-stage optimal scheduling of multi-microgrids using deep-learning artificial neural network and cooperative game approach.

This article proposes a two-stage system for the daily energy management of micro-grids (MGs) in the presence of wind turbines, photovoltaic (PV) panels, and electrical energy storage systems (ESSs). Each MG uses historical data to predict its consumers' load demand, wind speed, and solar irradiance in the first stage. In the second stage, the cooperative game method is used to determine the MG's daily dispatch and energy transaction. The paper develops a prediction model using artificial neural network (ANN) and rough neuron water cycle (RNWC) algorithms, called deep learning artificial neural network (DLANN), which is a combination of technology from the artificial neural network and WC algorithm in order to predict uncertain parameters. The above model is implemented in the 33bus power distribution system; the simulation results show that the DLANN method provides more accurate predictions than the ANN method. The results also show that a MG can achieve energy cost savings through an alliance of MGs using the cooperative game approach. Furthermore, analysis of the impact of the ESS on the operation of the MG shows that the absence of the ESS will reduce the power output of the wind turbine. • Providing of a novel deep-learning ANN to develop forecasting model. • Solving multi micro-grid day-ahead scheduling problem using the game theory. • Analyzing the effect of energy storage system on the micro-grids operating cost. • Studying the effect of demand response program on the micro-grids operating cost. [ABSTRACT FROM AUTHOR]