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Uncertainty Analysis of Middleware Services for Streaming Smart Grid Applications


Accuracy and responsiveness are two key properties of emerging cyber-physical energy systems that need to incorporate high throughput sensor streams for distributed monitoring and control applications. The electric power grid, which is a prominent example of such systems, is being integrated with high throughput sensors in order to support stable system dynamics that are provisioned to be utilized in real-time supervisory control applications. The end-to-end performance and overall scalability of cyber-physical energy applications depend on robust middleware services that are able to operate with variable resources and multi-source sensor data. This leads to uncertain behavior under highly variable sensor and middleware topologies. We present a parametric approach to modeling the middleware service architecture for distributed power applications and account for temporal satisfiability of system properties under network resource and data volume uncertainty. We present a heterogeneous modeling framework that combines Monte Carlo simulations of uncertainty parameters within an executable discrete-event middleware service model. By employing Monte Carlo simulations followed by regression analysis, we quantify system parameters that significantly affect behavior of middleware services and the achievability of temporal requirements.

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