Abstract
This paper proposes an improved enhanced differential evolution algorithm for implementing
demand response between aggregator and consumer. The proposed algorithm utilizes a
secondary population archive, which contains unfit solutions that are discarded by the primary
archive of the earlier proposed enhanced differential evolution algorithm. The secondary
archive initializes, mutates and recombines candidates in order to improve their fitness and
then passes them back to the primary archive for possible selection. The capability of this
proposed algorithm is confirmed by comparing its performance with three other wellperforming
evolutionary algorithms: enhanced differential evolution, multiobjective
evolutionary algorithm based on dominance and decomposition, and non-dominated sorting
genetic algorithm III. This is achieved by testing the algorithms’ ability to optimize a multiobjective
optimization problem representing a smart home with demand response aggregator.
Shiftable and non-shiftable loads are considered for the smart home which model energy usage
profile for a typical household in Johannesburg, South Africa. In this study, renewable sources
include battery bank and rooftop photovoltaic panels. Simulation results show that the proposed
algorithm is able to optimize energy usage by balancing load scheduling and contribution of
renewable sources, while maximizing user comfort and minimizing peak-to-average ratio.