An Efficient Genetic Algorithm Based Demand Side Management Scheme for Smart Grid

In this paper, we propose a novel strategy for a Demand Side Management (DSM) in a Smart Grid (SG). In this strategy, three types of loads are considered, i.e., residential load, commercial load and industrial load. The larger number of appliances of different power rating for each type of load is considered in this work. The focus of this work is to minimize the Peak to Average Ratio (PAR) to increase the efficiency of SG, by increasing the utilization of spinning reserves. On the other hand, our aim is to minimize the electricity consumption cost. Tackling the large number of appliances in an SG is a challenging task, because it increases the complexity of the problem. However, in literature the focus is on small number of appliance. In this work, the load scheduling problem is mathematically formulated and solved by using genetic algorithm. The simulation results show that the propose algorithm reduces the cost, while reducing the peak load demand of the SG.