LAUSR

This article aims to develop a multiobjective optimization portfolio for real-time energy management in a smart home equipped with battery associated rooftop solar panels, lighting loads, air conditioners, and other smart home appliances. The energy management problem is framed for simultaneous minimization of the monetary energy cost and total dissatisfaction due to regulation in power consumption. The entire optimization portfolio is designed as a time average stochastic problem, which is simplified by the combination of queueing theory and Lyapunov optimization. The revised problem takes form of a mixed integer convex nonlinear programming, which is further solved using outer approximation approach. The proposed real-time home energy management framework needs only the current data regarding the random input parameters like renewable generation, energy price, and aggregated load demand, and does not call for their probabilistic estimation. Case study is carried out on a practical home data to proof efficacy of the proposed strategy and also the simulation outcomes are compared with one of the popular real-time energy management process named greedy algorithm.