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An Online Joint Optimization–Estimation Architecture for Distribution Networks

Guo, Yi·Zhou, Xinyang·Zhao, Changhong·Chen, Lijun·Hug, Gabriela·Summers, Tyler Holt
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Abstract

Here in this article, we propose an optimal joint optimization-estimation architecture for distribution networks, which jointly solves the optimal power flow (OPF) problem and static state estimation (SE) problem through an online gradient-based feedback algorithm. The main objective is to enable a fast and timely interaction between the OPF decisions and state estimators with limited sensor measurements. First, convergence and optimality of the proposed algorithm are analytically established. Then, the proposed gradient-based algorithm is modified by introducing statistical information of the inherent estimation and linearization errors for an improved and robust performance of the online OPF decisions. Overall, the proposed method eliminates the traditional separation of operation and monitoring, where optimization and estimation usually operate at distinct layers and different time scales. Hence, it enables a computationally affordable, efficient, and robust online operational framework for distribution networks under time-varying settings.

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24 POWER TRANSMISSION AND DISTRIBUTIONconvergence and optimality analysisdistribution networksonline optimization algorithmsoperational architectureoptimal power flowpower systemsstate estimation

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Guo, Yi, Zhou, Xinyang, Zhao, Changhong, Chen, Lijun, Hug, Gabriela, Summers, Tyler Holt. 2023-03-27. An Online Joint Optimization–Estimation Architecture for Distribution Networks. https://doi.org/10.1109/tcst.2023.3254137

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