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

Yuan, Zhenyi; Cavraro, Guido; Cortes, Jorge

doi:10.1109/LCSYS.2023.3287985

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

Journal Article · 20 June 2023 · IEEE Control Systems Letters

DOI:https://doi.org/10.1109/LCSYS.2023.3287985· OSTI ID:1992510

Yuan, Zhenyi; Cavraro, Guido; Cortes, Jorge

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.

Research Organization:: National Renewable Energy Laboratory (NREL), Golden, CO (United States)

Sponsoring Organization:: USDOE National Renewable Energy Laboratory (NREL), Laboratory Directed Research and Development (LDRD) Program

DOE Contract Number:: AC36-08GO28308;

OSTI ID:: 1992510

Report Number(s):: NREL/JA-5D00-86542; MainId:87315; UUID:1ea93353-9a74-439d-8dc6-cc0821e919fc; MainAdminID:70027

Journal Information:: IEEE Control Systems Letters, Journal Name: IEEE Control Systems Letters Vol. 7

Country of Publication:: United States

Language:: English

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