Comparative analysis between particle swarm optimization algorithms applied to price-based demand response

Abstract

Demand-side management is a useful and necessary strategy in the context of smart grids, as it allows to reduce electricity consumption in periods of increased demand, ensuring system reliability and minimizing resources wastage. In its range of activities, Demand Response programs have received great attention in recent years due to their potential impact measured in several studies. In this work, different approaches of the Particle Swarm Optimization algorithm are applied to the autonomous and distributed demand response optimization model based on energy price. In addition, a stochastic mechanism is proposed to mitigate the structural bias problem that such algorithm presents, boosting its application in the analyzed problem. Results provided by computational simulations show that the proposed approach contributes significantly to reduce the energy consumption costs in relation to tariff variations, as well as minimizing the use of residential equipment during peak hours of a group of consumers.