Jorge Cortés
Professor
Cymer Corporation Endowed Chair
Constraints on OPF surrogates for learning stable
local Volt/Var controllers
Z. Yuan, G. Cavraro, J. Cortés
IEEE Control Systems Letters (7) (2023), 2533-2538
Abstract
We consider the problem of learning local Volt/Var controllers in
distribution grids (DGs). Our approach starts from learning separable
surrogates that take both local voltages and reactive powers as
arguments and predict the reactive power setpoints that approximate
optimal power flow (OPF) solutions. We propose an incremental control
algorithm and identify two different sets of slope conditions on the
local surrogates such that the network is collectively steered toward
desired configurations asymptotically. Our results reveal the
trade-offs between each set of conditions, with coupled voltage-power
slope constraints allowing arbitrary shape of surrogate functions but
risking limitations on exploiting generation capabilities, and
reactive power slope constraints taking full advantage of generation
capabilities but constraining the shape of surrogate functions.
Simulations on the IEEE 37-bus feeder illustrate their respective
advantages in two DG scenarios.
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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