Optimal Residential Load Scheduling Under Utility and Rooftop Photovoltaic Units

Ghulam Hafeez, Nadeem Javaid, Sohail Iqbal and Farman Ali Khan
Additional contact information
Ghulam Hafeez: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Nadeem Javaid: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Sohail Iqbal: SEECS, National University of Science and Technology, Islamabad 44000, Pakistan
Farman Ali Khan: COMSATS Institute of Information Technology, Attock 43600, Pakistan

Energies, 2018, vol. 11, issue 3, 1-27

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.

Keywords: rooftop photovoltaic units; demand-side management; heuristic techniques; real-time pricing tariff; inclined block rate; energy storage system; load scheduling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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