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Distributionally Robust Chance-Constrained Optimal Power Flow Assuming Log-Concave Distribution

Optimally operating the power system under diverse uncertainties, for example, renewable and load forecast uncertainty, is a challenging task. To manage uncertainties, different approaches have been proposed, for example, using chance constraints to ensure that the system's physical constraints are satisfied at high confidence levels. Most of the approaches either assume known probability distributions or give over-conservative results. To get better trade-offs between performance and reliability, we develop a new tractable distributionally robust chance constrained optimal power flow model in which the chance constraints are satisfied over a family of plausible distributions. Specifically, we assume distributions are log-concave, which is satisfied by most real uncertainty distributions. This has not been studied in the existing distributionally robust optimization literature. To evaluate the performance of our approach, we will compare our new model against other conventional chance-constrained optimal power flow models.

Author(s):

Bowen Li    
University of Michigan
United States

Ruiwei Jiang    
University of Michigan
United States

Johanna Mathieu    
University of Michigan
United States

 

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