Chance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty

Daniel Bienstock; Michael Chertkov; Sean Harnett

Chance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty
Bienstock, Daniel
Chertkov, Michael
Harnett, Sean
Industrial Engineering and Operations Research
Applied Physics and Applied Mathematics
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This version has been removed from Academic Commons at the request of the author. An updated version of this article is available at http://academiccommons.columbia.edu/catalog/ac%3A156182. Submitted to Proceedings of the National Academy of Sciences of the United States of America.
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.
Industrial engineering
Operations research
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Suggested Citation:
Daniel Bienstock, Michael Chertkov, Sean Harnett, , Chance Constrained Optimal Power Flow: Risk-Aware Network Control under Uncertainty, Columbia University Academic Commons, .

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