Online decentralized tracking for nonlinear time-varying optimal power flow of coupled transmission-distribution grids

TL;DR

This paper introduces an online decentralized method for real-time tracking of the nonlinear, time-varying optimal power flow in coupled transmission and distribution grids, addressing privacy and dynamic challenges.

Contribution

It presents a novel decentralized algorithm that directly handles nonlinear power flow equations and incorporates a prediction term for improved tracking accuracy.

Findings

Effective real-time tracking demonstrated in numerical tests

Algorithm outperforms existing methods in speed and accuracy

No need for convex relaxations or linearization techniques

Abstract

The coordinated alternating current optimal power flow (ACOPF) for coupled transmission-distribution grids has become crucial to handle problems related to high penetration of renewable energy sources (RESs). However, obtaining all system details and solving ACOPF centrally is not feasible because of privacy concerns. Intermittent RESs and uncontrollable loads can swiftly change the operating condition of the power grid. Existing decentralized optimization methods can seldom track the optimal solutions of time-varying ACOPFs. Here, we propose an online decentralized optimization method to track the time-varying ACOPF of coupled transmission-distribution grids. First, the time-varying ACOPF problem is converted to a dynamic system based on Karush-Kuhn-Tucker conditions from the control perspective. Second, a prediction term denoted by the partial derivative with respect to time is developed to improve the tracking accuracy of the dynamic system. Third, a decentralized implementation for solving the dynamic system is designed based on only a few information exchanges with respect to boundary variables. Moreover, the proposed algorithm can be used to directly address nonlinear power flow equations without relying on convex relaxations or linearization techniques. Numerical test results reveal the effectiveness and fast-tracking performance of the proposed algorithm.