Constraints on OPF Surrogates for Learning Stable Local Volt/Var Controllers

Zhenyi Yuan, Guido Cavraro, Jorge Cortes

Research output: Contribution to journalArticlepeer-review

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 an 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. AC power flow simulations on the IEEE 37-bus feeder illustrate their guaranteed stability properties and respective advantages in two DG scenarios.

Original languageAmerican English
Pages (from-to)2533-2538
Number of pages6
JournalIEEE Control Systems Letters
Volume7
DOIs
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

NREL Publication Number

  • NREL/JA-5D00-86542

Keywords

  • asymptotic stability
  • neural networks
  • Volt/Var control
  • voltage regulation

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