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

Area of Science:

• Electrical Engineering
• Computer Science
• Data Science

Background:

• Smart grids (SGs) face challenges with big data transactions, limiting optimal operation and management.
• A fast and reliable architecture is crucial for efficient big data management in SGs.

Purpose of the Study:

• To assess the optimal operation of SGs using cloud computing (CC), fog computing (FC), and resource allocation.
• To enhance SG big data management through the integration of CC and FC (CFC).

Main Methods:

• Proposed a CFC-driven framework for data management among agents.
• Implemented FC with features like position awareness, low latency, and mobility.
• Developed a hybrid gray wolf differential evolution optimization algorithm (HGWDE) combining GWO and IDE.
• Utilized load-balancing techniques for virtual machine allocation in FC.

Main Results:

• The HGWDE algorithm demonstrated superior performance in response time and processing time compared to PSO, DE, and GWO.
• HGWDE achieved response times 54 ms, 82.1 ms, and 81.6 ms faster than PSO, DE, and GWO, respectively.
• HGWDE achieved processing times 53 ms, 81.2 ms, and 80.6 ms faster than PSO, DE, and GWO, respectively.

Conclusions:

• The integration of CFC enhances SG efficiency by minimizing cloud burden and maximizing resource allocation.
• The proposed HGWDE algorithm is effective for managing high data transactions and reducing computational time in SGs.
• The CFC framework with HGWDE offers a promising solution for optimizing smart grid operations.