A novel meta-heuristic technique for energy optimization in smart grid

Abstract

The increase in the energy consumption causes a serious crisis, especially during on-peak hours when the demand of energy consumption is high. Consequently, the peak to average ratio and electricity cost will be increased. This issue can be overcome by integrating Demand side management (DSM) with traditional Smart grid (SG), so that electricity utilization can be minimized during on-peak hours by efficiently distributing them into off-peak hours. In this paper, Crow search algorithm (CSA) is proposed to schedule the appliances for DSM and the performance of Home energy management system (HEMS) is assessed by two meta-heuristic techniques; Enhanced differential evolution and CSA. The reduction in cost and peak to average ratio along with increase in user comfort is mainly focused in this paper. Moreover, the electricity cost is based on real time pricing scheme. The main objective is to provide a comparative analysis of the aforementioned techniques for energy optimization using simulations in HEMS. The simulations results show that our proposed technique outperformed as compared to the existing meta-heuristic technique.