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This paper describes technical challenges in supporting large-scale real-time data analysis for future power grid systems and discusses various design options to address these challenges. Even though the existing U.S. power grid has served the nation remarkably well over the last 120 years, big changes are in the horizon. The widespread deployment of renewable generation, smart grid controls, energy storage, plug-in hybrids, and new conducting materials will require fundamental changes in the operational concepts and principal components. The whole system becomes highly dynamic and needs constant adjustments based on real time data. Even though millions of sensors such as phase measurement units (PMUs) and smart meters are being widely deployed, a data layer that can support this amount of data in real time is needed. Unlike the data fabric in cloud services, the data layer for smart grids must address some unique challenges. This layer must be scalable to support millions of sensors and a large number of diverse applications and still provide real time guarantees. Moreover, the system needs to be highly reliable and highly secure because the power grid is a critical piece of infrastructure. No existing systems can satisfy all the requirements at the same time. We examine various design options. In particular, we explore the special characteristics of power grid data to meet both scalability and quality of service requirements. Our initial prototype can improve performance by orders of magnitude over existing general-purpose systems. The prototype was demonstrated with several use cases from PNNL's FPGI and was shown to be able to integrate huge amount of data from a large number of sensors and a diverse set of applications.
Yin et al. (Fri,) studied this question.