Primal-Dual Decomposed State Estimation for Integrated Energy Systems Leveraging Variational Bayesian Approximation

Victor Levi, Yue Feng, Dragan Cetenovic, Junbo Zhao, Phil Taylor, Vladimir Terzija

Research output: Contribution to journalArticlepeer-review

Abstract

This paper proposes a new methodology for the tracking state estimation (SE) of multi-energy systems containing electricity, gas and heat networks. The three networks are modelled via quasi steady-state models, whereby different gas components form the gas mixture. The SE Kalman filter framework is extended to allow the application of the primal-dual decomposed constrained optimization. The primal problem is further decomposed into three sub-problems, corresponding to electricity, gas and heat networks. It is also proposed to solve the dual problem with a Newton – type second order method. Efficient detection of bad data, without further aggravation of the measurement redundancy, is achieved by incorporating the Variational Bayesian approximation into the decomposed Kalman filter SE and developing the computation algorithms. The proposed methodology is tested on the developed regional and national multi-energy systems and its advantages are highlighted
Original languageEnglish
Article number3328114
Pages (from-to)2696-2709
Number of pages14
JournalIEEE Transactions on Smart Grid
Volume15
Issue number3
DOIs
Publication statusPublished - May 2024

Keywords

  • Bad data, decomposition by sub-systems, multi energy system, primal-dual decomposition, tracking state estimation, variational Bayesian

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