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Energies 2018, 11(2), 384; https://doi.org/10.3390/en11020384

Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes

1
COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
2
PMAS, Arid Agriculture University, Rawalpindi 46000, Pakistan
3
CIS, Higher Colleges of Technology, Fujairah 4114, UAE
4
Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
*
Author to whom correspondence should be addressed.
Received: 17 December 2017 / Revised: 1 February 2018 / Accepted: 2 February 2018 / Published: 7 February 2018
(This article belongs to the Special Issue Building Energy Use: Modeling and Analysis)
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Abstract

With the emergence of automated environments, energy demand by consumers is increasing rapidly. More than 80% of total electricity is being consumed in the residential sector. This brings a challenging task of maintaining the balance between demand and generation of electric power. In order to meet such challenges, a traditional grid is renovated by integrating two-way communication between the consumer and generation unit. To reduce electricity cost and peak load demand, demand side management (DSM) is modeled as an optimization problem, and the solution is obtained by applying meta-heuristic techniques with different pricing schemes. In this paper, an optimization technique, the hybrid gray wolf differential evolution (HGWDE), is proposed by merging enhanced differential evolution (EDE) and gray wolf optimization (GWO) scheme using real-time pricing (RTP) and critical peak pricing (CPP). Load shifting is performed from on-peak hours to off-peak hours depending on the electricity cost defined by the utility. However, there is a trade-off between user comfort and cost. To validate the performance of the proposed algorithm, simulations have been carried out in MATLAB. Results illustrate that using RTP, the peak to average ratio (PAR) is reduced to 53.02%, 29.02% and 26.55%, while the electricity bill is reduced to 12.81%, 12.012% and 12.95%, respectively, for the 15-, 30- and 60-min operational time interval (OTI). On the other hand, the PAR and electricity bill are reduced to 47.27%, 22.91%, 22% and 13.04%, 12%, 11.11% using the CPP tariff. View Full-Text
Keywords: home energy management; demand side; supply side; enhanced differential evolution; peak to average ratio; operational time interval home energy management; demand side; supply side; enhanced differential evolution; peak to average ratio; operational time interval
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Naz, M.; Iqbal, Z.; Javaid, N.; Khan, Z.A.; Abdul, W.; Almogren, A.; Alamri, A. Efficient Power Scheduling in Smart Homes Using Hybrid Grey Wolf Differential Evolution Optimization Technique with Real Time and Critical Peak Pricing Schemes. Energies 2018, 11, 384.

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