Towards Fast Response, Reduced Processing and Balanced Load in Fog-Based Data-Driven Smart Grid

Rasool Bukhsh, Nadeem Javaid, Zahoor Ali Khan, Farruh Ishmanov, Muhammad Khalil Afzal and Zahid Wadud
Additional contact information
Rasool Bukhsh: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Nadeem Javaid: Department of Computer Science, COMSATS University Islamabad, Islamabad 44000, Pakistan
Zahoor Ali Khan: CIS, Higher Colleges of Technology, Fujairah 4114, UAE
Farruh Ishmanov: Department of Electronics and Communication Engineering, Kwangwoon University, Seoul 01897, Korea
Muhammad Khalil Afzal: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantonment 47040, Pakistan
Zahid Wadud: Faculty of Computer Systems Engineering, University of Engineering and Technology Peshawar, Peshawar 25000, Pakistan

Energies, 2018, vol. 11, issue 12, 1-21

Abstract: The integration of the smart grid with the cloud computing environment promises to develop an improved energy-management system for utility and consumers. New applications and services are being developed which generate huge requests to be processed in the cloud. As smart grids can dynamically be operated according to consumer requests (data), so, they can be called Data-Driven Smart Grids . Fog computing as an extension of cloud computing helps to mitigate the load on cloud data centers. This paper presents a cloud–fog-based system model to reduce Response Time (RT) and Processing Time (PT). The load of requests from end devices is processed in fog data centers. The selection of potential data centers and efficient allocation of requests on Virtual Machines (VMs) optimize the RT and PT. A New Service Broker Policy (NSBP) is proposed for the selection of a potential data center. The load-balancing algorithm, a hybrid of Particle Swarm Optimization and Simulated Annealing (PSO-SA), is proposed for the efficient allocation of requests on VMs in the potential data center. In the proposed system model, Micro-Grids (MGs) are placed near the fogs for uninterrupted and cheap power supply to clusters of residential buildings. The simulation results show the supremacy of NSBP and PSO-SA over their counterparts.

Keywords: response time; processing time; microgrid; recurring cost; data-driven smart grid; resource allocation; residential buildings; energy management; demand side; cloud-fog based smart grid (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/11/12/3345/pdf (application/pdf)
https://www.mdpi.com/1996-1073/11/12/3345/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:11:y:2018:i:12:p:3345-:d:186781

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().