Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Learning to pursue AC optimal power flow
solutions with feasibility guarantees
D. Ajeyemi, Y. Chen, A. Colot, J. Cortés, E. Dall'Anese
IEEE Transactions on Smart Grid, submitted
Abstract
This paper focuses on an AC optimal power flow (OPF) problem for
distribution feeders equipped with controllable distributed energy
resources (DERs). We consider a solution method that is based on a
continuous approximation of the projected gradient flow -- referred to
as the safe gradient flow -- that incorporates voltage and current
information obtained either through real-time measurements or power
flow computations. These two setups enable both online and offline
implementations. The safe gradient flow involves the solution of
convex quadratic programs (QPs). To enhance computational efficiency,
we propose a novel framework that employs a neural network
approximation of the optimal solution map of the QP. The resulting
method has two key features: (a) it ensures that the DERs' setpoints
are practically feasible, even for an online implementation or when an
offline algorithm has an early termination; (b) it ensures convergence
to a neighborhood of a strict local optimizer of the AC OPF. The
proposed method is tested on a 93-node distribution system with
realistic loads and renewable generation. The test shows that our
method successfully regulates voltages within limits during periods
with high renewable generation.
pdf
Mechanical and Aerospace Engineering,
University of California, San Diego
9500 Gilman Dr,
La Jolla, California, 92093-0411
Ph: 1-858-822-7930
Fax: 1-858-822-3107
cortes at ucsd.edu
Skype id:
jorgilliyo