How to conduct near real-time analytics of streaming data in the smart grid? This talk offers a dynamic systems approach to utilizing emerging data for improved monitoring of the grid. The first example of the talk presents how to leverage the underlying spatio-temporal correlations of synchrophasors for early anomaly (e.g., subsynchronous oscillations) detection, localization, and data quality outlier detection. The second example presents a dynamic systems approach to modeling price responsive demand in real-time energy markets. The underlying theme of the work suggests the importance of integrating data with dynamic physics-based analytics in the context of electric energy systems.

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