Hierarchical Optimal Power Flow with Improved Gradient Evaluation

Liang, Heng; Zhou, Xinyang; Zhao, Changhong

doi:10.23919/ACC53348.2022.9867623

Hierarchical Optimal Power Flow with Improved Gradient Evaluation

Conference · Mon Sep 05 04:00:00 EDT 2022

DOI:https://doi.org/10.23919/ACC53348.2022.9867623· OSTI ID:1898010

Liang, Heng; Zhou, Xinyang; Zhao, Changhong

Existing algorithms to solve alternating-current optimal power flow (AC-OPF) often exploit linear approximations to simplify system models and accelerate computations. In this paper, we improve a recent hierarchical OPF algorithm, which rested on primal-dual gradients evaluated in a linearized distribution power flow model. Specifically, we identify a risk of voltage violation arising from the model linearization, and propose a more accurate gradient evaluation method to eliminate that risk. We further develop a hierarchical primal-dual algorithm to solve OPF based on the proposed gradient evaluation method. Numerical results on IEEE networks show that our algorithm can enhance voltage safety with satisfactory computational efficiency.

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

Sponsoring Organization:: USDOE National Renewable Energy Laboratory (NREL)

DOE Contract Number:: AC36-08GO28308

OSTI ID:: 1898010

Report Number(s):: NREL/CP-5D00-84186; MainId:84959; UUID:cba88637-37c8-48ed-a78e-35de232ce754; MainAdminID:67967

Country of Publication:: United States

Language:: English

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