Demand Side Management Using Hybrid Genetic Algorithm and Pigeon Inspired Optimization Techniques

Abstract

In this paper, our goal is to minimize the electricity cost, electricity consumption at minimum user discomfort while considering the peak electricity consumption. Electricity consumption may not be the same in residential, commercial and industrial areas. It may vary from each and every area. It is a challenging task to maintain the balance between the conflicting objectives: electricity consumption and user comfort. To meet the rising electricity demand in residential area, scheduleable devices can be equally distributed to the available time slots on the basis of average power consumption. The main objective is to minimize the electricity usage during the electricity peak hours by distributing the electricity load during the off-peak hours. In this regard, Genetic Algorithm (GA), Pigeon Inspired Optimization (PIO) and our proposed hybridization of GA and PIO (HGP) in Demand Side Management(DSM) are applied for residential load management to optimize the fitness function. GA, PIO and HGP are evaluated on the basis of real time pricing scheme (RTP) for single home with three different operational time interval (OTI) and for multiple homes with a single OTI. Simulations results shows that GA, PIO and HGP are able to minimize electricity bill and electricity consumption while minimizing the user discomfort. The performance of HGP is better than GA, PIO with respect to PAR, electricity load and electricity cost for both single home and multiple homes scenario. The feasible region between electricity cost and electricity consumption is also represented. Moreover, the desired trade-off between electricity cost and user comfort is also achieved in both techniques.