A recurrent neural network for optimal real-time price in smart grid

Recently, some algorithms have been proposed for optimal real-time price in smart grid based on optimization theory. In this paper, a recurrent neural network modeled by means of a differential inclusion is proposed for solving this problem. Compared with the existing algorithms, recurrent neural network as parallel computational models for real-time optimization and applications have received substantial attention in the literature. Our model has the least number of state variables and simple structure. Using nonsmooth analysis, the theory of differential inclusions and Lyapunov-like method, the equilibrium point of the proposed neural networks can converge to an optimal solution of optimal real-time price under certain conditions. Finally, simulation results on two numerical examples show the effectiveness and performance of the proposed neural network.