Differential Evolution Optimization for a Residential Demand Response Application

In the smart grid era, when the power system is under stress, demand response (DR) is considered a viable and practical solution for smoothing the demand curve. DR is a procedure that is applied to provide changes in consumers power consumption. These changes can be obtained by optimization techniques producing solutions for the management of power profiles of consumers. In general, optimization techniques can be divided into two groups: the exact methods and the approximate methods. In this paper, an optimization DR problem is formulated and solved using an approximate method based on evolutionary computation. The differential evolution (DE) and one variant called hybrid-adaptive DE (HyDE), as well as the Particle swarm optimization (PSO) algorithms are used and their performance is compared. The results show that DE algorithms are superior to PSO for this application and their performance is close to that obtained with an exact method.The present work was done and funded in the scope of the following projects: SPEAR Project, CEECIND/02887/2017, UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT and PhD grant SFRH/BD/133086/2017