Application of Neural Network Algorithms in Networked Microgrids' Operation Optimization and Control

Neural Network algorithms have significant applications in microgrid operations optimization and control to provide cheap, robust, and reliable energy to end-users. These algorithms are inspired by artificial neural networks (ANNs). In this paper, we have proposed a neural network algorithm (NNA) based on the unique structure of ANNs. Neural network algorithms have the capability to generate new candidate solutions using the complicated structure of ANNs and their operators. Improvised exploitation and each parameter in the asymmetric interval are iteratively converged theoretically in the context of convergence proof. In this paper, we have demonstrated the scheduling problems for networked microgrids solved by using artificial neural networks (ANNs) along with the biological nervous systems approach. The neural network algorithm (NNA) is designed by using a specific structure of ANNs. NNA has the capability to take the benefits using complicated structure of ANNs to generate the enhanced solution. The designed code supports and implements a neural network-supported optimization algorithm. The proposed algorithm finds optimal solutions by utilizing solutions that are based on certain rules produced by machine learning neural networks.