An Efficient Resource Management Scheme for Smart Grid Using GBO Algorithm

The smart grid (SG) plays a vital role in the current energy infrastructure and reinforces the reliability, sustainability, efficiency, as well as economics of electricity services. The resourceful use of diverse smart devices in the front-end, including smart meters, is a demanding task and the processing of enormous data received out of these devices is also a major challenge. Cloud computing provides the on-demand services for the computational need but as it has latency issues, fog computing aids cloud computing in providing a favorable method to surmount the SG obstacles. Fog computing in conjunction with cloud computing facilitates energy-saving, cost-saving, flexibility, scalability, and agility. A major issue is the management of resources in SGs. This paper proposed a fog-aided-cloud-based model is proposed for managing the resources in SGs. For enhancing the performance of the proposed fog-aided cloud system, various load balancing techniques are employed. The load amid SG user's tasks and service providers is balanced by implementing four different meta-heuristic algorithms like particle swarm optimization, ant colony optimization (ACO),artificial bee colony (ABC), and gradient-based optimizer (GBO). Simulation results reveal that GBO which is a newly developed meta-heuristic optimization algorithm outperforms the others.