Real-time Optimal Power Flow
Future power networks are expected to incorporate a large number of distributed energy resources, which introduce randomness and fluctuations as well as fast control capabilities. But traditional optimal power flow methods are only appropriate for applications that operate on a slow timescale. In this paper, we build on recent work to develop a real-time algorithm for AC optimal power flow, based on quasi-Newton methods. The algorithm uses second order information to provide suboptimal solutions on a fast timescale, and can be shown to track the optimal power flow solution when the estimated second order information is sufficiently accurate. We also give a specific implementation based on L-BFGS-B method, and show by simulation that the proposed algorithm has good performance and is computationally efficient.
© 2017 IEEE. Manuscript received November 1, 2016; revised March 18, 2017 and May 6, 2017; accepted May 7, 2017. Date of publication May 16, 2017; date of current version October 19, 2017. This work was supported in part by the NSF under Grant CCF 1637598, Grant ECCS 1619352, and Grant CNS 1545096, in part by ARPA-E under Grant DE-AR0000699, and in part by DTRA under Grant HDTRA 1-15-1-0003. Paper no. TSG-01523-2016.