“20th PSCC 2018 papers submission and review platform

Full Program »

Statistical Learning for DC Optimal Power Flow

The optimal power flow problem plays an important role in the market clearing and operation of electric power systems. However, with increasing uncertainty from renewable energy operation, the optimal operating point of the system changes more significantly in real-time.
In this paper, we aim at developing control policies that are able to track the optimal set-point with high probability. The approach is based on the observation that the OPF solution corresponding to a certain uncertainty realization is a basic feasible solution, which provides an affine control policy. The optimality of this basis policy is restricted to uncertainty realizations that share the same set of active constraints. We propose an ensemble control policy that combines several basis policies to improve performance. Although the number of possible bases is exponential in the size of the system, we show that only a few of them are relevant to system operation. We adopt a statistical learning approach to learn these important bases, and provide theoretical results that validate our observations. For most systems, we observe that efficient ensemble policies constructed using as few as ten bases, are able to obtain optimal solutions with high probability.

Author(s):

Yee Sian Ng    
Massachusets Institute of Technology
United States

Sidhant Misra    
Los Alamos National Laboratory
United States

Line Roald    
Los Alamos National Laboratory
United States

Scott Backhaus    
Los Alamos National Laboratory
United States

 

Powered by OpenConf®
Copyright ©2002-2014 Zakon Group LLC