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The energy demand is increasing day by day due to the huge amount of residential appliance’s energy consumption, which creates more shortage of electricity. Industrial and commercial areas are also consuming large amount of energy, but residential energy demand is more flexible as compared to other two. Nowadays, many of the techniques are presented for scheduling of Smart Appliances to reduce the peak to average ratio (PAR) and consumer delay time. However, they didn’t consider the total electricity cost and consumer waiting time. In this paper, we reduce the cost through load shifting techniques. In order to consider above objective, we employed some feature of the Jaya algorithm (JA) on a bat algorithm (BA) to develop a candidate solution updation algorithm (CSUA). Simulation was conducted to compare the result of existing BA and Jaya for single smart home with 15 smart appliances. We used time of use (ToU) and critical peak price (CPP). The result depicts that successful achievement of load shifting from higher price time slot to lower price time slot, which basically bring out the reduction in electricity bills.

Recently a massive increase in the demand of energy has been reported in residential, industrial and commercial sectors. Traditional Grid (TG) with the aging infrastructure is unable to address the increasing demand problem. Smart Grid (SG) enhanced the TG by adopting information and communication based technological solutions to address the increasing electricity demand. Smart Home Energy Management System (SHEMS) plays an important role in the efficacy of SG. In this paper, an Improved Algorithm for Peak to average ratio Reduction (IAPR) in SHEMS is developed. To validate the effectiveness of the IAPR, comparison is made with the renowned meta-heuristic optimization approaches namely Strawberry Algorithm (SA) and Salp Swarm Algorithm (SSA) using two different pricing scheme. It is illustrated by simulations results that the IAPR reduced the PAR to a greater degree as compare to SA and SSA.

As the energy demand for consumption is comparably higher than the generation of energy, which produce the shortage of energy. Many new schemes are being developed to fulfill the energy consumer demand. In this paper, we proposed our meta-heuristic algorithm Runner Updation Optimization Algorithm (RUOA) to schedule the consumption pattern of residential appliances. We compared the results of our scheme with other meta-heuristic algorithm Strawberry Algorithm (SBA) and Firefly Algorithm (FA). Critical Peak Price (CPP) and Real Time Price (RTP) are the two electricity pricing scheme that we used in this paper for calculation of electricity cost. The main objective of this paper is to minimize the electricity cost and Peak to Average Ratio (PAR). However, consumer comfort is not satisfied.