“20th PSCC 2018 papers submission and review platform

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Variance-aware optimal power flow

We present data-driven methods for robust and distributionally robust, variance-aware OPF variants. The variance-aware feature seeks to control variance of important parameters, such as cost, which serves to reduce overall system stochasticity. The optimization models we consider a data driven and obtained through machine-learning methodologies applied to PMU data.

Author(s):

Daniel Bienstock    
Columbia University
United States

Apurv Shukla    
Columbia University
United States

 

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