Cost and comfort based optimization of residential load in smart grid

Abstract

In smart grid, several optimization techniques are developed for residential load scheduling purpose. Preliminary all the conventional techniques aimed at minimizing the electricity consumption cost. This paper mainly focuses on minimization of electricity cost and maximization of user comfort along with the reduction of peak power consumption. We develop a multi-residential load scheduling algorithm based on two heuristic optimization techniques: genetic algorithm and binary particle swarm optimization. The day-ahead pricing mechanism is used for this scheduling problem. The simulation results validate that the proposed model has achieved substantial savings in electricity bills with maximum user comfort. Moreover, results also show the reduction in peak power consumption. We analyzed that user comfort has significant effect on electricity consumption cost.