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Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids
Abstract How does one evaluate the performance of a …
How does one evaluate the performance of a stochastic system in the absence of a perfect model (i.e. probability distribution)? We address this question under the framework of optimal uncertainty quantification (OUQ), which is an information-based approach for worst-case analysis of stochastic systems. We are able to generalize previous results and show that the OUQ problem can be solved using convex optimization when the function under evaluation can be expressed in a polytopic canonical form (PCF). We also propose iterative methods for scaling the convex formulation to larger systems. As an application, we study the problem of storage placement in power grids with renewable generation. Numerical simulation results for simple artificial examples as well as an example using the IEEE 14-bus test case with real wind generation data are presented to demonstrate the usage of OUQ analysis.
to demonstrate the usage of OUQ analysis.  +
Authors Shuo Han, Ufuk Topcu, Molei Tao, Houman Owhadi, Richard M. Murray  +
Funding Correct-by-Construction Synthesis of Control Protocols for Aerospace Systems +
ID 2012r  +
Source To appear, 2013 American Control Conference (ACC)  +
Tag han+13-acc  +
Title Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids +
Type Conference Paper  +
Categories Papers
Modification date
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15 May 2016 06:15:37  +
URL
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http://www.cds.caltech.edu/~murray/preprints/han+13-acc.pdf  +
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Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids + Title
 

 

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