Scheduling optimization of smart homes based on demand response

Abstract

Demand response can potentially lead to economic and environmental advantages, but non-coordinated scheduling and operation of controllable devices in a set of smart homes will make peak rebounds at periods with lower electricity prices happen, which may damage the power grid, cause unforeseen disasters, and reduce the global profit. In this work, we advocate the use of a metaheuristic algorithm based on Cooperative Particle Swarm Optimization to optimize scheduling and operation of time-shiftable and power-shiftable devices in a set of smart homes of a district. By employing this method, user comfort is guaranteed, electricity cost is reduced and total load on the main grid is flattened so that the global energy efficiency is improved.