In this paper, an energy consumption management is considered for households (users) in a residential smart grid network. In each house, there are two types of demands, essential and flexible demands, where the flexible demands are further categorized into delay-sensitive and delay-tolerant demands. The delay-sensitive demands have higher priority to be served than the delay-tolerant demands. Meanwhile, in order to decrease the delay of delay-tolerant demands, such demands are allowed to be upgraded to the high-priority queue (i.e., the same queue that serves the delay-sensitive demands) with a given probability.
An optimization problem is then formulated to minimize the total electricity cost and the operation delay of flexible demands by obtaining the optimal energy management decisions. Based on adaptive dynamic programming, a centralized algorithm is proposed to solve the optimization problem. In addition, a distributed algorithm is designed for practical implementation and the neuralnetwork is employed to estimate the pricing or demands when such system information is not known. Simulation results show that the proposed schemes can provide effective management for household electricity usage and reduce the operation delay for the flexible demands.