A Robust Smart Grid-Aware Cloud Computing Framework for Sustainable Energy Management

The growing use of renewable energy as a part of a smart grid infrastructure has raised new challenges relating to the coordination of the computational workload scheduling and the availability of intermittent energy in a distributed cloud infrastructure. The traditional cloud scheduling systems do not work with knowledge of the current pattern of renewable generation and thus the people have to rely more on the non-renewable grid energy and the intensity of carbon emissions is also high. In order to overcome this drawback, the current study is a proposal of a Smart Grid-Aware Cloud Computing Framework, with an embedded Grid-Aware Adaptive Scheduling Algorithm to dynamically schedule the execution of the computational workload of renewable-sustainable cloud nodes. The suggested framework incorporates the knowledge of renewable availability, estimation of sustainability threshold, and migration control with carbon awareness into the scheduling of tasks. Experimental evaluation conducted under heterogeneous workload demand and renewable generation conditions demonstrates improved Renewable Utilization Ratio of 0.79 compared to 0.66 achieved by reinforcement learning–based adaptive scheduling methods. The proposed framework further reduces normalized computational energy consumption to 0.81 and lowers carbon emission index to 0.52, while maintaining acceptable scheduling latency of 1.07 under renewable-aware workload migration. These findings suggest that the introduction of renewable conscious scheduling tools in cloud infrastructures can make the execution performance in smart grid settings to be greatly more sustainable.