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

Nadeem Javaid, Sakeena Javaid, Wadood Abdul, Imran Ahmed, Ahmad Almogren, Atif Alamri and Iftikhar Azim Niaz
Additional contact information
Nadeem Javaid: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Sakeena Javaid: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan
Wadood Abdul: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Imran Ahmed: Institute of Management Sciences (IMS), Peshawar 25000, Pakistan
Ahmad Almogren: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Atif Alamri: Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia
Iftikhar Azim Niaz: COMSATS Institute of Information Technology, Islamabad 44000, Pakistan

Energies, 2017, vol. 10, issue 3, 1-27

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.

Keywords: Demand side management; priority scheduling; user comfort; heuristic optimization (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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