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Processes 2019, 7(3), 142; https://doi.org/10.3390/pr7030142

An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms

1
Department of Electrical Engineering, Capital University of Science and Technology, Islamabad 44000, Pakistan
2
Army Public College of Management & Sciences (APCOMS), Rawalpindi 46000, Pakistan
3
School of Engineering, University of Glasgow, Glasgow G12 8QQ, UK
*
Author to whom correspondence should be addressed.
Received: 29 January 2019 / Revised: 24 February 2019 / Accepted: 1 March 2019 / Published: 7 March 2019
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Abstract

Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme. View Full-Text
Keywords: appliance scheduling techniques; bacterial foraging algorithm (BFA); energy management system; energy optimization algorithms; grasshopper optimization algorithm (GOA); smart grid appliance scheduling techniques; bacterial foraging algorithm (BFA); energy management system; energy optimization algorithms; grasshopper optimization algorithm (GOA); smart grid
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Ullah, I.; Khitab, Z.; Khan, M.N.; Hussain, S. An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms. Processes 2019, 7, 142.

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