An Online Joint Optimization–Estimation Architecture for Distribution Networks
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AbstractIn 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.
All Author(s) ListYi Guo, Xinyang Zhou, Changhong Zhao, Lijun Chen, Gabriela Hug, Tyler Holt Summers
Journal nameIEEE Transactions on Control Systems Technology
Year2023
Month11
Volume Number31
Issue Number6
PublisherIEEE
Pages2303 - 2318
ISSN1063-6536
eISSN1558-0865
LanguagesEnglish-United States
KeywordsDistribution networks, Optimization, Convergence, Real-time systems, Load flow, State estimation, Power systems, Convergence and optimality analysis, distribution networks, online optimization algorithms, operational architecture, optimal power flow (OPF), power systems, state estimation (SE)

Last updated on 2024-09-04 at 00:41