Comparison of particle swarm optimization and differential evolution for aggregators’ profit maximization in the demand response system

ABSTRACT

Demand response (DR) refers to changes in the electricity use patterns of end-users in response to incentive payment designed to prompt lower electricity use during peak periods. Typically, there are three players in the DR system: an electric utility operator, a set of aggregators and a set of end-users. The DR model used in this study aims to minimize the operator’s operational cost and offer rewards to aggregators, while profit-maximizing aggregators compete to sell DR services to the operator and provide compensation to end-users for altering their consumption profiles. This article presents the first application of two metaheuristics in the DR system: particle swarm optimization (PSO) and differential evolution (DE). The objective is to optimize the incentive payments during various periods to satisfy all stakeholders. The results show that DE significantly outperforms PSO, since it can attain better compensation rates, lower operational costs and higher aggregator profits.

KEYWORDS:

• Load aggregators
• demand response
• particle swarm optimization
• differential evolution

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Warisa Wisittipanich http://orcid.org/0000-0003-4515-3273