Optimal Real-Time Price in Smart Grid via Recurrent Neural Network

In this paper, we consider a smart power infrastructure which contains a single energy provider, several load subscribers and a regulatory authority. Considering the importance of energy pricing as an essential tool to develop efficient demand side management strategies, we propose a neural network modeled by a differential inclusion to solve real-time pricing problem in smart grid based on optimization theory. Compared with the existing algorithms, our model has fewer variables, our model is not only parallel computational model, but also can be implemented with the schematic block diagram. Moreover, the solution of proposed network converges to the feasible region in finite time and to the particular element in the optimal solution set with the smallest norm, which indicates that the proposed neural network is globally attractive. Finally, simulation results confirm the effectiveness and performance of the proposed network.