A Robust Approach to Chance Constrained Optimal Power Flow With Renewable Generation

Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF u...

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Bibliographic Details
Published inIEEE transactions on power systems Vol. 31; no. 5; pp. 3840 - 3849
Main Authors Lubin, Miles, Dvorkin, Yury, Backhaus, Scott
Format Journal Article
LanguageEnglish
Published New York IEEE 01.09.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
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Summary:Optimal Power Flow (OPF) dispatches controllable generation at minimum cost subject to operational constraints on generation and transmission assets. The uncertainty and variability of intermittent renewable generation is challenging current deterministic OPF approaches. Recent formulations of OPF use chance constraints to limit the risk from renewable generation uncertainty, however, these new approaches typically assume the probability distributions which characterize the uncertainty and variability are known exactly. We formulate a robust chance constrained (RCC) OPF that accounts for uncertainty in the parameters of these probability distributions by allowing them to be within an uncertainty set. The RCC OPF is solved using a cutting-plane algorithm that scales to large power systems. We demonstrate the RRC OPF on a modified model of the Bonneville Power Administration network, which includes 2209 buses and 176 controllable generators. Deterministic, chance constrained (CC), and RCC OPF formulations are compared using several metrics including cost of generation, area control error, ramping of controllable generators, and occurrence of transmission line overloads as well as the respective computational performance.
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LA-UR-15-22280
USDOE
AC52-06NA25396
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2015.2499753