Chance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty
Bienstock
Daniel
author
Columbia University. Industrial Engineering and Operations Research
Columbia University. Applied Physics and Applied Mathematics
Chertkov
Michael
author
Harnett
Sean
author
Columbia University. Industrial Engineering and Operations Research
originator
text
Articles
2012
English
When uncontrollable resources fluctuate, Optimum Power Flow (OPF), routinely used by the electric power industry to redispatch hourly controllable generation (coal, gas and hydro plants) over control areas of transmission networks, can result in grid instability, and, potentially, cascading outages. This risk arises because OPF dispatch is computed without awareness of major uncertainty, in particular fluctuations in renewable output. As a result, grid operation under OPF with renewable variability can lead to frequent conditions where power line flow ratings are significantly exceeded. Such a condition, which is borne by simulations of real grids, would likely resulting in automatic line tripping to protect lines from thermal stress, a risky and undesirable outcome which compromises stability. Smart grid goals include a commitment to large penetration of highly fluctuating renewables, thus calling to reconsider current practices, in particular the use of standard OPF. Our Chance Constrained (CC) OPF corrects the problem and mitigates dangerous renewable fluctuations with minimal changes in the current operational procedure. Assuming availability of a reliable wind forecast parameterizing the distribution function of the uncertain generation, our CCOPF satisfies all the constraints with high probability while simultaneously minimizing the cost of economic redispatch. CCOPF allows efficient implementation, e.g. solving a typical instance over the 2746bus Polish network in 20s on a standard laptop.
Submitted to Proceedings of the National Academy of Sciences of the United States of America.
An updated version of this article is available at http://academiccommons.columbia.edu/catalog/ac%3A156182
Industrial engineering
Operations research
http://hdl.handle.net/10022/AC:P:15118
NNC
NNC
2012-10-29 09:19:08 -0400
2013-02-05 10:51:50 -0500
9120
eng