Hybrid-integer algorithm for a multi-objective optimal home energy management system

Abstract Most of the energy produced in world is consumed by commercial and residential buildings. With growth global economy demographics, this demand has become increasingly important. This led to higher unit electricity prices, frequent stresses on main grid carbon emissions due inefficient management. paper presents an energy-consumption management system based time-shifting loads according dynamic day-ahead pricing. simultaneously reduces bill peaks, while maintaining user comfort terms operating waiting time appliances. The proposed optimization problem formulated mathematically multi-objective integer non-linear programming, which involves constraints consumer preferences. For optimal scheduling, solved using hybridization particle swarm algorithm branch-and-bound algorithm. Two techniques are manage trade-off between conflicting objectives. first technique Pareto-optimal solutions classification supervised learning methods. second called lexicographic method. simulations were performed building consumption, time-of-use pricing (TOU) critical peak (CPP). algorithms implemented Python. results current work show that approach effective can reduce peak-to-average ratio (PAR) 28% 49.32%, respectively, for TOU tariff rate, 48.91% 47.87% CPP rate taking into account consumer’s level.