With the rapid advancement in technology, electrical energy consumption is increasing rapidly. Especially, in the residential sector, more than 80% of electrical energy is being consumed because of consumer negligence. This brings the challenging task of maintaining the balance between the demand and supply of electric power. In this paper, we focus on the problem of load balancing via load scheduling under utility and rooftop photovoltaic (PV) units to reduce electricity cost and peak to average ratio (PAR) in demand-side management. For this purpose, we adopted genetic algorithm (GA), binary particle swarm optimization (BPSO), wind-driven optimization (WDO), and our proposed genetic WDO (GWDO) algorithm, which is a hybrid of GA and WDO, to schedule the household load. For energy cost estimation, combined real-time pricing (RTP) and inclined block rate (IBR) were used. The proposed algorithm shifts load from peak consumption hours to off-peak hours based on combined pricing scheme and generation from rooftop PV units. Simulation results validate our proposed GWDO algorithm in terms of electricity cost and PAR reduction while considering all three scenarios which we have considered in this work: (1) load scheduling without renewable energy sources (RESs) and energy storage system (ESS), (2) load scheduling with RESs, and (3) load scheduling with RESs and ESS. Furthermore, our proposed scheme reduced electricity cost and PAR by 22.5% and 29.1% in scenario 1, 47.7% and 30% in scenario 2, and 49.2% and 35.4% in scenario 3, respectively, as compared to unscheduled electricity consumption.

Cooperative communication (CC) is one of the best solutions to overcome channel fading and to improve channel capacity. However, most of the researchers evaluate its performance based on mathematical modeling or by simulations. These approaches are often unable to successfully capture many real-world radio signal propagation problems. Hardware based wireless communication test-bed provides reliable and accurate measurements, which are not attainable through other means. This research work investigates experimental performance analysis of CC over direct communication (DC) in the lab environment. The experimental setup is built using Universal Software Radio Peripheral (USRP) and Laboratory Virtual Instrument Engineering Workbench (LabVIEW). A text message is transmitted by using Phase Shift Keying (PSK) modulation schemes. The setup uses amplify and forward (AF) relaying mode and two time slot transmission protocols. The maximum ratio combining (MRC) technique is used for combining SNR at the receiver. Channel capacity analysis is performed in order to evaluate the performance of CC over DC with and without obstacle. Moreover, optimal position of the relay is also analyzed by varying the position of the relay. Extensive experiments are carried out in the lab environment to evaluate the performance of the system for different hardware setups. The results reveal that cooperative communication attains significant improvement in terms of channel capacity of the system.