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Cloud computing’s simulation and modeling capabilities are crucial for big data analysis in smart grid power; they are the key to finding practical insights, making the grid resilient, and improving energy management. Due to issues with data scalability and real-time analytics, advanced methods are required to extract useful information from the massive, ever-changing datasets produced by smart grids. This research proposed a Dynamic Resource Cloud-based Processing Analytics (DRC-PA), which integrates cloud-based processing and analytics with dynamic resource allocation algorithms. Computational resources must be able to adjust the changing grid circumstances, and DRC-PA ensures that big data analysis can scale as well. The DRC-PA method has several potential uses, including power grid optimization, anomaly detection, demand response, and predictive maintenance. Hence the proposed technique enables smart grids to proactively adjust to changing conditions, boosting resilience and sustainability in the energy ecosystem. A thorough simulation analysis is carried out using realistic circumstances within smart grids to confirm the usefulness of the DRC-PA approach. The methodology is described in the intangible, showing how DRC-PA is more efficient than traditional methods because it is more accurate, scalable, and responsive in real-time. In addition to resolving existing issues, the suggested method changes the face of contemporary energy systems by paving the way for innovations in grid optimization, decision assistance, and energy management.
Padmanaban et al. (Wed,) studied this question.