Optimal household appliances scheduling of multiple smart homes using an improved cooperative algorithm

• Published in: Energy (Oxford) Vol. 171; pp. 944 - 955
• Main Authors: Zhu, Jiawei, Lin, Yishuai, Lei, Weidong, Liu, Youquan, Tao, Mengling
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.03.2019; Elsevier BV
• Subjects: Algorithms; Case studies; Combinatorial analysis; Cooperative particle swarm optimization; Demand response; Disasters; Electric appliances; Electric power demand; Electricity; Electricity consumption; Electricity distribution; Electricity pricing; Energy consumption; Energy efficiency; Formulations; Heuristic methods; Household appliances; Load scheduling; Optimization; Peak load; Residential energy; Schedules; Scheduling; Smart buildings; Smart grid; Smart home; Smart houses; Virtual energy storage system; Demand response; Cooperative particle swarm optimization; Load scheduling; Virtual energy storage system; Smart grid; Smart home
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2019.01.025

It will easily make peak loads happen with the increasing usage of residential high-power appliances, which may damage the power grid, cause unforeseen disasters, and reduce the global profit. Towards the optimization of energy consumption, this paper aims to provide an attempt to schedule the operations of household appliances considering their characteristics as well as customer convenience. Bottom-up engineering models that can obtain better understanding of residential electricity demand patterns are developed. Since the formulations are nonlinear complex combinatorial problems, the scheduling of household appliances within multiple smart homes is a challenging optimization problem. In order to solve this challenging optimization problem efficiently, an improved cooperative heuristic approach is proposed to achieve a near optimal solution with better performance. Experimental results confirm the effectiveness of the proposed algorithm. Moreover, a case study is conducted to show that by employing this proposed approach, user comfort is guaranteed, electricity cost is reduced and total loads on the main grid are flattened so that the global energy efficiency is improved. •Aggregate and schedule multiple smart homes to shift peak load.•Develop accurate engineering models for residential electricity demand.•Propose an optimization method for appliance scheduling.•Reduce electricity bill and generation cost without bringing discomfort.