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Energies 2017, 10(3), 319; doi:10.3390/en10030319

A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid

1
COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
2
Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
3
Institute of Management Sciences (IMS), Peshawar 25000, Pakistan
*
Author to whom correspondence should be addressed.
Academic Editor: K.T. Chau
Received: 8 November 2016 / Revised: 5 February 2017 / Accepted: 24 February 2017 / Published: 7 March 2017

Abstract

In recent years, demand side management (DSM) techniques have been designed for residential, industrial and commercial sectors. These techniques are very effective in flattening the load profile of customers in grid area networks. In this paper, a heuristic algorithms-based energy management controller is designed for a residential area in a smart grid. In essence, five heuristic algorithms (the genetic algorithm (GA), the binary particle swarm optimization (BPSO) algorithm, the bacterial foraging optimization algorithm (BFOA), the wind-driven optimization (WDO) algorithm and our proposed hybrid genetic wind-driven (GWD) algorithm) are evaluated. These algorithms are used for scheduling residential loads between peak hours (PHs) and off-peak hours (OPHs) in a real-time pricing (RTP) environment while maximizing user comfort (UC) and minimizing both electricity cost and the peak to average ratio (PAR). Moreover, these algorithms are tested in two scenarios: (i) scheduling the load of a single home and (ii) scheduling the load of multiple homes. Simulation results show that our proposed hybrid GWD algorithm performs better than the other heuristic algorithms in terms of the selected performance metrics. View Full-Text
Keywords: Demand side management; priority scheduling; user comfort; heuristic optimization Demand side management; priority scheduling; user comfort; heuristic optimization
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MDPI and ACS Style

Javaid, N.; Javaid, S.; Abdul, W.; Ahmed, I.; Almogren, A.; Alamri, A.; Niaz, I.A. A Hybrid Genetic Wind Driven Heuristic Optimization Algorithm for Demand Side Management in Smart Grid. Energies 2017, 10, 319.

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