Paper Info
Title
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 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.
Year
DOI
Venue
2013
10.1109/ACC.2013.6579988
ACC
Keywords
Field
DocType
polytopic canonical form,ieee 14-bus test,ouq,convex optimal uncertainty quantification,stochastic system,pcf,worst-case analysis,renewable generation,wind generation data,numerical simulation,energy storage placement,power grids,probability distribution,energy storage,wind power,iterative methods,probability,cost function,uncertainty
Mathematical optimization,Uncertainty quantification,Computer simulation,Iterative method,Computer science,Algorithm,Regular polygon,Control engineering,Canonical form,Probability distribution,Convex optimization,Wind power
Conference
ISSN
ISBN
Citations 
0743-1619
978-1-4799-0177-7
0
PageRank 
References 
Authors
0.34
6
5
Name
Order
Citations
PageRank
Shuo Han1436.24
Ufuk Topcu21032115.78
Molei Tao3165.64
Houman Owhadi424721.02
Richard M. Murray5123221223.70