Demand Side Management and Optimization of Power System

Abstract

In the power system, different types of optimization techniques are generated for the consumer's load scheduling purpose at the user's end side. Most of these traditional techniques are just to reduce power consumption cost. Keeping a fairness among multiple contrasting or conflicting goals: Power consumption cost, consumers comfort and PAR is still the most challenging job to achieve. Hence in this research, we focus on the minimization of the power consumption cost, consumers dissatisfactory and mitigation of PAR. In this scheme we scrutinize and implement the performance of conventional methods; both heuristic optimization schemes genetic algorithm (GA) and artificial immune algorithm for residential load, based on these two methods, we propose a scheme for residential load scheduling for the optimization of the multi objective problems. The proposed model is validated through two pricing schemes: RTEP (Real-time electricity pricing) and CPP (Critical Peak Pricing) for single day. The proposed method reflects the sufficient savings in power consumption along with minimum user discomfort and PAR.