A Real-Time Electricity Scheduling for Residential Home Energy Management

The effect of home energy management system (HEMS) is even more pronounced at the edge of smart grid infrastructure. However, the isolated scheduling horizons and the uncertainty about scheduling inputs are the major challenges for HEMS. In this paper, a novel demand-side management system, namely, a real-time electricity scheduling (RTES) for residential home energy management, is presented to operate the smart home. The proposed management system attempts to achieve minimizing the cost payment by optimally scheduling smart appliances and improving the utilization of renewable energy. Most importantly, it considers the uncertainty in the renewable generation and the subjectivity in electricity consumption. Our RTES adopts a 24-h rolling horizon, and the optimization problem be solved by an effective genetic algorithm at regular intervals. Moreover, to reduce the impact caused by the discrepancy between the predictive information and the actual information, we design an effective real-time prediction method for the renewable generation, and update the inputs of scheduling system before each optimization calculation. Simulation results confirm that the proposed approach can improve the performance of the home electricity scheduling, reduce the impact of uncertainty on the system, and reduce the total energy costs.