Browse wiki
From MurrayWiki
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 informationbased approach for worstcase 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 14bus 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  CorrectbyConstruction Synthesis of Control Protocols for Aerospace Systems + 
ID  2012r + 
Source  To appear, 2013 American Control Conference (ACC) + 
Tag  han+13acc + 
Title  Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids + 
Type  Conference Paper + 
Categories  Papers 
Modification date This property is a special property in this wiki.

15 May 2016 06:15:37 + 
URL This property is a special property in this wiki.

http://www.cds.caltech.edu/~murray/preprints/han+13acc.pdf + 
hide properties that link here 
Convex optimal uncertainty quantification: Algorithms and a case study in energy storage placement for power grids +  Title 
