基于灰狼优化(Gwo)算法的家庭能源管理系统中家电的最优调度

A. R. Jordehi
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引用次数: 8

摘要

在智能家居中,在基于价格或基于激励的需求响应计划下,家庭能源管理系统(HEMS)旨在确定电器的最佳时间表,以最大限度地减少家庭电费。这种调度问题通常被表述为具有整数决策变量的约束优化问题。元启发式算法是解决工程优化问题最流行的算法。灰狼优化算法(GWO)是一种基于群的元启发式优化算法,其灵感来自于狼的性能,在解决一些工程优化问题中显示出良好的性能。本文将GWO用于解决HEM系统中设备的最优调度问题。这个问题解决了两个不同的家庭,有不同的家电。对于每个家庭,使用不同的DR程序解决了两种情况下的问题。将GWO算法与已有的粒子群优化算法(PSO)进行了性能比较。结果表明,相对于PSO,所提出的GWO具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Scheduling of Home Appliances in Home Energy Management Systems Using Grey Wolf Optimisation (Gwo) Algorithm
In smart homes, under price-based or incentivebased demand response programs, home energy management system (HEMS) aims to determine optimal schedule of appliances in order to minimise electricity bill of the home. This scheduling problem is commonly formulated as a constrained optimisation problem with integer decision variables. Metaheuristics are the most popular algorithms for solving engineering optimisation problems. Grey wolf optimisation (GWO) is a swarm-based metaheuristic optimisation algorithm, inspired from the performance of wolves and has shown promising performance in solving some engineering optimisation problems. In this paper, GWO is used for solving the problem of optimal scheduling of appliances in HEM systems. The problem is solved for two different homes with different set of appliances. For each home, the problem is solved for two cases with different DR programs. The performance of GWO is compared with the well-established particle swarm optimisation (PSO) algorithm. The results indicate the outperformance of the proposed GWO with respect to PSO.
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