Enhanced leader particle swarm optimisation (ELPSO): a new algorithm for optimal scheduling of home appliances in demand response programs - Rezaee Jordehi, Ahmad (The Artificial intelligence review, 2020)
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.