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

Energies

Nadeem Javaid¹  Sakeena Javaid¹  Iftikhar Azim Niaz¹  Imran Ahmed²  Wadood Abdul³  Ahmad Almogren³  Atif Alamri³ 

[1] COMSATS Institute of Information Technology, Islamabad 44000, Pakistan;Institute of Management Sciences (IMS), Peshawar 25000, Pakistan;Research Chair of Pervasive and Mobile Computing, College of Computer and Information Sciences, King Saud University, Riyadh 11633, Saudi Arabia;

关键词: Demand side management;    priority scheduling;    user comfort;    heuristic optimization;   

DOI  :  10.3390/en10030319

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown