An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization

Fahad Alsokhiry; Andres Annuk; Mohamed A Mohamed; Manoel Marinho

doi:10.3390/s23041752

An Innovative Cloud-Fog-Based Smart Grid Scheme for Efficient Resource Utilization

Sensors (Basel). 2023 Feb 4;23(4):1752. doi: 10.3390/s23041752.

Authors

Fahad Alsokhiry^{1

2}, Andres Annuk³, Mohamed A Mohamed⁴, Manoel Marinho⁵

Affiliations

¹ Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
² K. A. CARE Energy Research and Innovation Center, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
³ Institute of Forestry and Engineering, Estonian University of Life Sciences, 51006 Tartu, Estonia.
⁴ Electrical Engineering Department, Faculty of Engineering, Minia University, Minia 61519, Egypt.
⁵ Polytechnic School of Pernambuco, University of Pernambuco (UPE), Recife 50720-001, PE, Brazil.

Abstract

Smart grids (SGs) enhance the effectiveness, reliability, resilience, and energy-efficient operation of electrical networks. Nonetheless, SGs suffer from big data transactions which limit their capabilities and can cause delays in the optimal operation and management tasks. Therefore, it is clear that a fast and reliable architecture is needed to make big data management in SGs more efficient. This paper assesses the optimal operation of the SGs using cloud computing (CC), fog computing, and resource allocation to enhance the management problem. Technically, big data management makes SG more efficient if cloud and fog computing (CFC) are integrated. The integration of fog computing (FC) with CC minimizes cloud burden and maximizes resource allocation. There are three key features for the proposed fog layer: awareness of position, short latency, and mobility. Moreover, a CFC-driven framework is proposed to manage data among different agents. In order to make the system more efficient, FC allocates virtual machines (VMs) according to load-balancing techniques. In addition, the present study proposes a hybrid gray wolf differential evolution optimization algorithm (HGWDE) that brings gray wolf optimization (GWO) and improved differential evolution (IDE) together. Simulation results conducted in MATLAB verify the efficiency of the suggested algorithm according to the high data transaction and computational time. According to the results, the response time of HGWDE is 54 ms, 82.1 ms, and 81.6 ms faster than particle swarm optimization (PSO), differential evolution (DE), and GWO. HGWDE's processing time is 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO. Although GWO is a bit more efficient than HGWDE, the difference is not very significant.

Keywords: cloud computing; efficient resource utilization; fog computing; gray wolf optimization; improved differential evolution; smart grid.

Grants and funding

The authors acknowledge the support provided by King Abdullah City for Atomic and Renewable Energy (K.A.CARE) under the K.A.CARE-King Abdulaziz University Collaboration Program. The authors are also thankful to the Deanship of Scientific Research, King Abdulaziz University for providing financial support vide grant number (RG-49-135-42). Furthermore, the authors would like to thank the Estonian Centre of Excellence in Zero Energy and Resource Efficient Smart Buildings and Districts, ZEBE, for grant TK146, funded by the European Regional Development Fund to support this research.