Smart grids enable the residential consumers to have an active role in the management of their electricity consumption through home energy management (HEM) systems. HEM systems adjust the ON–OFF status and/or operation modes of home appliances under demand response programs, typically in a way that the electricity bill of the home is minimised and/or the peak load is minimised. This represents a constrained multi-objective optimisation problem with integer decision variables. The existing methodologies for optimal scheduling of home appliances have two drawbacks; most of them have not taken the consumers’ comfort into account and also powerful optimisation algorithms have not been used for solving this problem. In this paper, the problem of optimal scheduling of home appliances in HEM systems is formulated as a constrained, multi-objective optimisation problem with integer decision variables and a powerful variant of particle swarm optimisation, named as enhanced leader particle swarm optimisation (ELPSO) is proposed for solving this problem. Optimal scheduling of appliances is done for ten different scenarios that consider different demand response programs. The problem is solved for two different smart homes respectively with 10 and 11 appliances, both including electric vehicle as a big residential load. The results indicate the superiority of ELPSO over basic PSO, artificial bee colony, backtracking search algorithm, gravitational search algorithm and dragonfly algorithm. In the proposed multi-objective formulation, the effect of weight factor on optimal electricity bill of the home and optimal comfort of the consumers is meticulously investigated.

中文翻译：

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增强型领导粒子群优化（ELPSO）：一种在需求响应程序中优化家电调度的新算法

智能电网使住宅消费者能够通过家庭能源管理 (HEM) 系统在其电力消耗管理中发挥积极作用。HEM 系统在需求响应程序下调整家用电器的开关状态和/或操作模式，通常以最小化家庭电费和/或最小化峰值负载的方式。这表示具有整数决策变量的约束多目标优化问题。现有的家电优化调度方法有两个缺点：他们大多没有考虑到消费者的舒适度，也没有使用强大的优化算法来解决这个问题。在本文中，HEM 系统中家用电器的优化调度问题被表述为一个受约束的，为了解决这个问题，提出了具有整数决策变量的多目标优化问题和粒子群优化的强大变体，称为增强型领导粒子群优化（ELPSO）。针对考虑不同需求响应程序的十种不同场景，对设备进行了优化调度。分别有10个和11个电器的两个不同的智能家居解决了这个问题，其中包括电动汽车作为一个大的住宅负载。结果表明ELPSO优于基本PSO、人工蜂群、回溯搜索算法、引力搜索算法和蜻蜓算法。在提出的多目标公式中，仔细研究了权重因子对家庭最佳电费和消费者最佳舒适度的影响。