Ant-Lion Optimizer algorithm and recurrent neural network for energy management of micro grid connected system

Abstract In this paper, an intelligent technique for EMS based on Recurrent Neural Network (RNN) with aid of Ant-Lion Optimizer (ALO) algorithm is presented to find energy scheduling in MG. The optimal operation programming of electrical systems through the minimization of production cost as well as better utilization of renewable energy resources, such as the PV system, WT, and storage system. The objective of the proposed method is utilized to the optimum operation of micro-sources for decreasing the electricity production cost by hourly day-ahead and real-time scheduling. The proposed method is able to analyze the technical and economic time-dependent constraints. The proposed method attempts to meet the required load demand with minimum energy cost. To accomplish this aim, demand response (DR) is evaluated by utilizing the RNN and additional indices for evaluating customer response, such as consumers information based on the offered priority, DR magnitude, duration, and minimum cost of energy (COE). Finally, the ALO algorithm is developed to solve the economic dispatch issues for determining the generation, storage, and responsive load offers. The proposed method is implemented in MATLAB/Simulink working platform and their performances are tested with the existing methods such as GA, ABC, and BFA respectively.