Abstract
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.
Highlights
The global energy demand drastically increases on a daily basis, and fossil fuels are limited and being exhausted
We develop the genetic wind-driven optimization (GWDO) algorithm, which is a hybrid of genetic algorithm (GA) and wind-driven optimization (WDO) algorithm, for load scheduling under a combined real-time pricing (RTP) and inclined block rate (IBR) environment to reduce electricity cost and peak to average ratio (PAR)
To validate the performance of our proposed genetic WDO (GWDO) algorithm, simulations are carried out and results are compared with GA, binary particle swarm optimization (BPSO), WDO, and unscheduled electricity consumption in terms of electricity cost and PAR
Summary
The global energy demand drastically increases on a daily basis, and fossil fuels are limited and being exhausted. Residential load scheduling has attracted significant attention, but an important challenge for residential load scheduling is that users are unable to respond to the price incentives To handle this problem, an energy management control unit (EMCU) based on heuristic algorithms can be implemented in order to make users respond efficiently to utility price incentives. The work presented in [5,6,7] schedule residential load using different optimization techniques in order to reduce the electricity cost. Hybrid of genetic algorithm (GA) and wind-driven optimization (WDO) algorithm, for load scheduling under a combined RTP and inclined block rate (IBR) environment to reduce electricity cost and PAR.
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