Open AccessA real‐time state estimation framework for integrated energy system considering measurement delay
Author(s) -
Xu Dongliang,
Xu Junjun,
Wu Zaijun,
Hu Qinran
Publication year - 2022
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12399
Subject(s) - kalman filter , curse of dimensionality , computer science , energy management system , electric power system , energy (signal processing) , stability (learning theory) , extended kalman filter , filter (signal processing) , state (computer science) , node (physics) , noise (video) , energy management , control theory (sociology) , real time computing , power (physics) , engineering , algorithm , statistics , physics , mathematics , control (management) , structural engineering , quantum mechanics , machine learning , artificial intelligence , image (mathematics) , computer vision
The increasingly close connections between multiple heterogeneous energy subsystems, the integrated energy system (IES) will play a critical role in ensuring future energy generations and distributions. As an essential function of the energy management system, state estimation provides data support for energy management of IES. Unfortunately, the existing IES state estimation method does not consider the measurement delay, which is inconsistent with the measurement delay characteristics caused by the multi‐timescale characteristics of IES. Driven by this motivation, this paper proposes a real‐time state estimation framework for the gas‐electricity coupled system. Considering the characteristics of the gas pipelines and coupling elements, the dynamic model of the natural gas system is established. A dynamic state estimation algorithm to enhance numerical stability is adopted to solve the problem that real‐time estimation based on the traditional Kalman filter suffers from the curse of dimensionality. Finally, a modified unscented Kalman filter (UKF) based estimation method is designed based on unified time processing and delay noise synthesizing. The IEEE 39‐bus electrical system and the 20‐node Belgian gas system are coupled to form the test system in this paper. The case study shows the advantages of the proposed method in efficiency and accuracy compared with the existing methods.