Abstract-Power utilities globally are increasingly upgrading to Smart Grids that use bi-directional communication with the consumer to enable an information-driven approach to distributed energy management. Clouds offer features well suited for Smart Grid software platforms and applications, such as elastic resources and shared services. However, the security and privacy concerns inherent in an informationrich Smart Grid environment are further exacerbated by their deployment on Clouds. Here, we present an analysis of security and privacy issues in a Smart Grids software architecture operating on different Cloud environments, in the form of a taxonomy. We use the Los Angeles Smart Grid Project that is underway in the largest U.S. municipal utility to drive this analysis that will benefit both Cloud practitioners targeting Smart Grid applications, and Cloud researchers investigating security and privacy.
Pervasive smart meters that continuously measure power usage by consumers within a smart (power) grid are providing utilities and power systems researchers with unprecedented volumes of information through streams that need to be processed and analyzed in near realtime. We introduce the use of Cloud platforms to perform scalable, latency sensitive stream processing for eEngineering applications in the smart grid domain. One unique aspect of our work is the use of adaptive rate control to throttle the rate of generation of power events by smart meters, which meets accuracy requirements of smart grid applications while consuming 50% lesser bandwidth resources in the Cloud.
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