JOURNAL ARTICLE

Optimal resource allocation in Cognitive Smart Grid Networks

Abstract

Taking advantage of information and communication technologies, the power industry is moving towards the next generation power grid, the smart grid. This information-based power grid is expected to change the way electricity is generated, distributed, and transmitted to the consumers by enhancing the reliability, efficiency, sustainability, and economics of the grid. However, due to the high volume and high granularity of the data generated by smart electricity meters, careful planning and management of this communication network is necessary. Given the large scale future deployment of smart grid, utility companies face possible network capacity constraints. Due to this scarcity, an efficient spectrum allocation is often difficult, thus resulting in low overall bandwidth utilization in Smart Grid Networks (SGN). Hence, an efficient utilization of this communication network should be studied. Cognitive Radio Networks (CRN) enable Secondary Users (SU) to coexist with existing network infrastructures. Cognitive Smart Grid Networks (CSGN) use CRN to optimize resource allocation in SGNs. However, efficient utilization of available channel bandwidth by SUs, without interfering with the Primary Users (PU), remains an important open problem in CSGN. In this paper, we focus on CSGN as the Secondary Network (SN), coexisting with a Primary Network, and outlining the applicability of Code Division Multiple Access for overcoming the low Number of SUs (NSU) in SN. We propose a novel resource allocation technique to improve NSU in CSGN by using a specific kind of Orthogonal Chip Sequence (OCS) allocation in spread spectrum communications for SU transmissions. By means of extensive simulations and analysis, we show that our technique improves NSU on SN (or CSGNs) significantly.

Keywords:
Computer science Smart grid Cognitive radio Resource allocation Grid Computer network Bandwidth (computing) Distributed computing Cognitive network Telecommunications Wireless Engineering

Metrics

14
Cited By
2.33
FWCI (Field Weighted Citation Impact)
32
Refs
0.90
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Cognitive Radio Networks and Spectrum Sensing
Physical Sciences →  Computer Science →  Computer Networks and Communications
Power Line Communications and Noise
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Advanced MIMO Systems Optimization
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
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