Data-Centric Situational Awareness and Management in Intelligent Power Systems

摘要

The rapid development of technology and society has made the current power system a much more complicated system than ever. The request for big data based situation awareness and management becomes urgent today. In this dissertation, to respond to the grand challenge, two data-centric power system situation awareness and management approaches are proposed to address the security problems in the transmission/distribution grids and social benefits augmentation problem at the distribution-customer lever, respectively.;To address the security problem in the transmission/distribution grids utilizing big data, the first approach provides a fault analysis solution based on characterization and analytics of the synchrophasor measurements. Specically, the optimal synchrophasor measurement devices selection algorithm (OSMDSA) and matching pursuit decomposition (MPD) based spatial-temporal synchrophasor data characterization method was developed to reduce data volume while preserving comprehensive information for the big data analyses. And the weighted Granger causality (WGC) method was investigated to conduct fault impact causal analysis during system disturbance for fault localization. Numerical results and comparison with other methods demonstrate the effectiveness and robustness of this analytic approach.;As more social effects are becoming important considerations in power system management, the goal of situation awareness should be expanded to also include achievements in social benefits. The second approach investigates the concept and application of social energy upon the University of Denver campus grid to provide management improvement solutions for optimizing social cost. Social element---human working productivity cost, and economic element---electricity consumption cost, are both considered in the evaluation of overall social cost. Moreover, power system simulation, numerical experiments for smart building modeling, distribution level real-time pricing and social response to the pricing signals are studied for implementing the interactive artificial-physical management scheme.