Architecture and Distributed Management for Reliable Mega-scale Smart Grids
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Abstract:
A primary objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications. Under this common theme, the PIs have taken a data analytics perspective to explore rigorous approaches in modeling, optimization, and control of wind generation integration. Short-term forecast of wind farm generation is studied by applying spatio-temporal analysis to extensive measurement data collected (over two consecutive years) from a large wind farm. Specifically, graph- learning based spatial analysis is carried out to characterize the statistical distribution of the overall wind farm generation and time series analysis is used to quantify the level crossing rate. Built on these characterizations, finite-state Markov chains are constructed for each epoch of three hours, which account for the diurnal non-stationarity and the seasonality of wind generation. Exploiting the Markovian property of the forecast model, the joint optimization of economic dispatch (ED) and interruptible load management is cast as a Markov decision process (MDP) problem. Numerical studies, via using the IEEE Reliability Test System – 1996 and realistic wind measurement data from an actual wind farm, demonstrate the significant benefits obtained by integrating the above forecast model and the interruptible load management, compared with conventional wind-speed-based forecast methods. In another research thrust, PMU measurements from multiple locations, are used for learning, characterizing and classifying event-specific spatial signatures, and probabilistic models are developed to subsume measurement data. Both the decision tree approach and dimensionality reduction approach are applied to identify impending signatures of catastrophic events to provide early warning to power system operators. To handle missing PMU data, randomized attribute subsets are used: 1) to reduce the impact of missing PMU data and 2) to reduce the complexity of training small DTs. Further, to attain high reliability, a trustworthy middleware tailored towards smart grid design, is devised to shield the grid design from the complexities of the underlying software world, using automatic generation of invariants for software validation.
Submitted by Junshan Zhang
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Abstract:
A primary objective of this research is to establish a foundational framework for smart grids that enables significant penetration of renewable DERs and facilitates flexible deployments of plug-and-play applications. Under this common theme, the PIs have taken a data analytics perspective to explore rigorous approaches in modeling, optimization, and control of wind generation integration. Short-term forecast of wind farm generation is studied by applying spatio-temporal analysis to extensive measurement data collected (over two consecutive years) from a large wind farm. Specifically, graph- learning based spatial analysis is carried out to characterize the statistical distribution of the overall wind farm generation and time series analysis is used to quantify the level crossing rate. Built on these characterizations, finite-state Markov chains are constructed for each epoch of three hours, which account for the diurnal non-stationarity and the seasonality of wind generation. Exploiting the Markovian property of the forecast model, the joint optimization of economic dispatch (ED) and interruptible load management is cast as a Markov decision process (MDP) problem. Numerical studies, via using the IEEE Reliability Test System – 1996 and realistic wind measurement data from an actual wind farm, demonstrate the significant benefits obtained by integrating the above forecast model and the interruptible load management, compared with conventional wind-speed-based forecast methods. In another research thrust, PMU measurements from multiple locations, are used for learning, characterizing and classifying event-specific spatial signatures, and probabilistic models are developed to subsume measurement data. Both the decision tree approach and dimensionality reduction approach are applied to identify impending signatures of catastrophic events to provide early warning to power system operators. To handle missing PMU data, randomized attribute subsets are used: 1) to reduce the impact of missing PMU data and 2) to reduce the complexity of training small DTs. Further, to attain high reliability, a trustworthy middleware tailored towards smart grid design, is devised to shield the grid design from the complexities of the underlying software world, using automatic generation of invariants for software validation.