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, Iftikhar Azim Niaz

Research output: Contribution to journalArticlepeer-review

148 Citations (Scopus)

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.

Original languageEnglish
Article number319
JournalEnergies
Volume10
Issue number3
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Demand side management
  • Heuristic optimization
  • Priority scheduling
  • User comfort

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