%0 Journal Article %1 IlicXieEtAl10tsmca %A Ilić, Marija D. %A Xie, Le %A Khan, Usman A. %A Moura, José M. F. %D 2010 %J IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans %K v1500 ieee paper embedded ai sensor data processing energy optimize zzz.cps %N 4 %P 825-838 %R 10.1109/TSMCA.2010.2048026 %T Modeling of Future Cyber-Physical Energy Systems for Distributed Sensing and Control %V 40 %X This paper proposes modeling the rapidly evolving energy systems as cyber-based physical systems. It introduces a novel cyber-based dynamical model whose mathematical description depends on the cyber technologies supporting the physical system. This paper discusses how such a model can be used to ensure full observability through a cooperative information exchange among its components; this is achieved without requiring local observability of the system components. This paper also shows how this cyber-physical model is used to develop interactive protocols between the controllers embedded within the system layers and the network operator. Our approach leads to a synergistic framework for model-based sensing and control of future energy systems. The newly introduced cyber-physical model has network structure-preserving properties that are key to effective distributed decision making. The aggregate load modeling that we develop using data mining techniques and novel sensing technologies facilitates operations of complex electric power systems.

@article{IlicXieEtAl10tsmca, abstract = {This paper proposes modeling the rapidly evolving energy systems as cyber-based physical systems. It introduces a novel cyber-based dynamical model whose mathematical description depends on the cyber technologies supporting the physical system. This paper discusses how such a model can be used to ensure full observability through a cooperative information exchange among its components; this is achieved without requiring local observability of the system components. This paper also shows how this cyber-physical model is used to develop interactive protocols between the controllers embedded within the system layers and the network operator. Our approach leads to a synergistic framework for model-based sensing and control of future energy systems. The newly introduced cyber-physical model has network structure-preserving properties that are key to effective distributed decision making. The aggregate load modeling that we develop using data mining techniques and novel sensing technologies facilitates operations of complex electric power systems.}, added-at = {2012-11-30T14:23:39.000+0100}, author = {Ili\'{c}, Marija D. and Xie, Le and Khan, Usman A. and Moura, Jos\'{e} M. F.}, biburl = {https://www.bibsonomy.org/bibtex/2f603d07e650c2a66a16f31e549bfe71e/flint63}, doi = {10.1109/TSMCA.2010.2048026}, file = {IEEE Digital Library:2010/IlicXieEtAl10tsmca.pdf:PDF}, groups = {public}, interhash = {c837a57c9db20430e3a71fc1ad16eb75}, intrahash = {f603d07e650c2a66a16f31e549bfe71e}, issn = {1083-4427}, journal = {IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans}, keywords = {v1500 ieee paper embedded ai sensor data processing energy optimize zzz.cps}, month = {#jul#}, number = 4, pages = {825-838}, timestamp = {2018-04-16T11:54:53.000+0200}, title = {Modeling of Future Cyber-Physical Energy Systems for Distributed Sensing and Control}, username = {flint63}, volume = 40, year = 2010 }