ABSTRACT
The convergence of technologies and business process benefits with smart grids has brought a focus on competitive advantage and the urgency to apply grid analytics. The theme in smart grid analytics involves continuous data transformation into actionable information, which can take place at multiple levels of the grid management hierarchy. Amid all the complexity and evolution, the high-level core elements of grid analytics stay the same: the right data need to be collected, processed, and turned into actionable insight and information, and made available to the right person or application at the right time, and in the right format. Key requirements that drive the tools' adoption at utilities are ease of use, integration with geographical data and visualization, and ease of data access and manipulation for analysis. Understanding the general latency differences and relationship between data acquisition and analytical processes is an important consideration in designing a robust architecture that integrates and analyzes data with suitable performance, speed, and scalability.
