Integration of Cloud-Fog Based Platform for Load Balancing Using Hybrid Genetic Algorithm Using Bin Packing Technique

The smart girds (SGs) are used to accommodate the growing demand of electric systems and monitor the power consumption with bidirectional communication and power flows. Smart buildings as key partners of the smart grid for the energy transition. Smart grids co-ordinate the needs and capabilities of all generators, grid operators, end-users and electricity market stakeholders to operate all parts of the system as efficiently as possible, minimising costs and environmental impacts while maximising system reliability, resilience and stability. The users demand for energy varies dynamically in different time slots. The power grids needs ideal load balancing for supply and demand of electricity between end-users and utility providers. The main characteristics of the SGs are its heterogeneous architecture that includes reduce the costly impact of blackouts, help measure and reduce energy consumption, reduce their carbon footprint and provides the power quality for the range of needs. The cloud-fog based computing model is used to achieve the objective of load balancing in the SG. The cloud layer provides on-demand delivery of resources. The fog layer is the extension of the cloud that lies between the cloud and end-user layer. The fog layer minimizes the latency, enhances the reliability of cloud facilities and reduced the load on the cloud because fog is an edge computing and it analyzing data close to the device that collected the data can make the difference between averting disaster and a cascading system failure. The end-users required electricity through the Macrogrids (MGs) and Utilities installed on fog and cloud layer respectively. The cloud-fog computing framework uses different algorithms for load balancing objective. In this paper, three algorithms are used such as Round Robin (RR), throttled and Hybrid Genetic Algorithm using Bin Packing Technique for load balancing.