AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home CSEE Journal of Power and Energy Systems Article
PDF (3.8 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter

Yanbo Chen, Yuan Yao( ), Yuzhang Lin, Xiaonan Yang
State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, School of Electrical & Electronic Engineering, North China Electric Power University, 102206 Beijing, China
Qinghai Key Laboratory of Efficient Utilization of Clean Energy, Tus-Institute for Renewable Energy, Qinghai University, Xining 810016, China
School of Engineering, Xining University, Xining 810016, China
School of Electrical & Electronic Engineering, North China Electric Power University, 102206 Beijing, China
Department of Electrical and Computer Engineering, University of Massachusetts, Lowell, MA 01852, USA
State Key Laboratory of Power Grid Safety and Energy Conservation, China Electric Power Research Institute, 100192 Beijing, China
Show Author Information

Abstract

In recent years, integrated electricity-gas systems (IEGSs) have attracted widespread attention. The unified scheduling and control of the IEGS depends on high-precision operating data. To this end, it is necessary to establish an appropriate state estimation (SE) model for IEGS to filter the raw measured data. Considering that power systems and natural gas systems have different time scales and sampling periods, this paper proposes a dynamic state estimation (DSE) method based on a Kalman filter that can consider the dynamic characteristics of natural gas pipelines. First, the standardized state transition equations for the gas system are developed by applying the finite difference method to the partial differential equations (PDEs) of the gas system; then the DSE model for IEGS is formulated based on a Kalman filter; also, the measurements from the electricity system and the gas system with different sampling periods are fused to ensure the observability of DSE by using the interpolation method. The IEEE 39-bus electricity system and the 18-nodes Belgium gas system are integrated as the test systems. Simulation results verify the proposed method’s accuracy and calculation efficiency.

Keywords

Dynamic state estimationintegrated electricity-gas systemKalman filtertwo time-scale measurementstransition state equation

References

[1]
P. J. Hibbard and T. Schatzki, “The interdependence of electricity and natural gas: current factors and future prospects,”The Electricity Journal, vol. 25, no. 4, pp. 6–17, May 2012.
Google Scholar
[2]
B. V. Mathiesen, H. Lund, D. Connolly, H. Wenzel, P. A. Østergaard, B. Möller, S. Nielsen, I. Ridjan, P. Karnøe, K. Sperling, and F. K. Hvelplund, “Smart Energy Systems for coherent 100% renewable energy and transport solutions,”Applied Energy, vol. 145, pp. 139–154, May 2015.
Google Scholar
[3]
F. C. Schweppe, “Power system static-state estimation, part III: implementation,”IEEE Transactions on Power Apparatus and Systems, vol. PAS-89, no. 1, pp. 130–135, Jan. 1970.
Google Scholar
[4]
Y. B. Chen, Z. Zhang, H. F. Fang, Y. S. Lang, J. Ma, and S. L. Zheng, “Generalised-fast decoupled state estimator,” IET Generation, Transmission &Distribution, vol. 12, no. 22, pp. 5928–5938, Dec. 2018.
Google Scholar
[5]
Y. B. Chen, F. Liu, S. W. Mei, and J. Ma, “A robust WLAV state estimation using optimal transformations,”IEEE Transactions on Power Systems, vol. 30, no. 4, pp. 2190–2191, Jul. 2015.
Google Scholar
[6]
Y. B. Chen, J. Ma, P. Zhang, F. Liu, and S. W. Mei, “Robust state estimator based on maximum exponential absolute value,”IEEE Transactions on Smart Grid, vol. 8, no. 4, pp. 1537–1544, Jul. 2017.
Google Scholar
[7]
A. S. Debs and R. E. Larson, “A dynamic estimator for tracking the state of a power system,”IEEE Transactions on Power Apparatus and Systems, vol. PAS-89, no. 7, pp. 1670–1678, Sep. 1970.
Google Scholar
[8]
G. Valverde and V. Terzija, “Unscented kalman filter for power system dynamic state estimation,” IET Generation,Transmission & Distribution, vol. 5, no. 1, pp. 29–37, Jan. 2011.
Google Scholar
[9]
J. B. Zhao, “Dynamic state estimation with model uncertainties using H∞ extended kalman filter,”IEEE Transactions on Power Systems, vol. 33, no. 1, pp. 1099–1100, Jan. 2018.
Google Scholar
[10]
H. C. Yan, J. Y. Sun, H. Zhang, X. S. Zhan, and F. W. Yang, “ent-triggered H∞ state estimation of 2-DOF quarter-car suspension systems with nonhomogeneous markov switching,”EE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 9, pp. 3320–3329, Sep. 2020.
Google Scholar
[11]
H. C. Yan, H. Zhang, F. W. Yang, C. Z. Huang, and S. M. Chen, “Distributed H∞ filtering for switched repeated scalar nonlinear systems with randomly occurred sensor nonlinearities and asynchronous switching,”IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, no. 12, pp. 2263–2270, Dec. 2018.
Google Scholar
[12]
H. Zhang, Z. P. Wang, H. C. Yan, F. W. Yang, and X. Zhou, “Adaptive event-triggered transmission scheme and H∞ filtering co-design over a filtering network with switching topology,”IEEE Transactions on Cybernetics, vol. 49, no. 12, pp. 4296–4307, Dec. 2019.
Google Scholar
[13]
H. C. Yan, X. P Zhou,, H. Zhang, F. W. Yang, and Z. G. Wu, “A novel sliding mode estimation for microgrid control with communication time delays,”IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1509–1520, Mar. 2019.
Google Scholar
[14]
Y. X. Du, W. Zhang, and T. T. Zhang, “ADMM-based distributed state estimation for integrated energy system,”CSEE Journal of Power and Energy Systems, vol. 5, no. 2, pp. 275–283, Jun. 2019.
Google Scholar
[15]
S. Chen, Z. N. Wei, G. Q. Sun, H. X. Zang, Y. Zhang, and Z. J. Ma, “An interval state estimation for electricity-gas urban energy systems,”in Proceedings of 2017 IEEE Conference on Energy Internet and Energy System Integration (EI2), 2017, pp. 1–5.
Google Scholar
[16]
K. Sundar and A. Zlotnik, “State and parameter estimation for natural gas pipeline networks using transient state data,”IEEE Transactions on Control Systems Technology, vol. 27, no. 5, pp. 2110–2124, Sep. 2019.
Google Scholar
[17]
İ. Durgut and M. K. Leblebicioğlu, “State estimation of transient flow in gas pipelines by a Kalman filter-based estimator,”Journal of Natural Gas Science and Engineering, vol. 35, pp. 189–196, Sep. 2016.
Google Scholar
[18]
H. P. Reddy, S. Narasimhan, and S. M. Bhallamudi, “Simulation and state estimation of transient flow in gas pipeline networks using a transfer function model,”Industrial and Engineering Chemistry Research, vol. 45, no. 11, pp. 3853–3863, May 2006.
Google Scholar
[19]
J. W. Yang, N. Zhang, C. Q. Kang, and Q. Xia, “Effect of natural gas flow dynamics in robust generation scheduling under wind uncertainty,”IEEE Transactions on Power Systems, vol. 33, no. 2, pp. 2087–2097, Mar. 2018.
Google Scholar
[20]
J. K. Fang, Q. Zeng, X. M. Ai, Z. Chen, and J. Y. Wen, “Dynamic optimal energy flow in the integrated natural gas and electrical power systems,”IEEE Transactions on Sustainable Energy, vol. 9, no. 1, pp. 188–198, Jan. 2018.
Google Scholar
[21]
W. J. Liu, P. Y. Li, W. T. Yang, and C. Y. Chung, “Optimal energy flow for integrated energy systems considering gas transients,”IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 5076–5079, Nov. 2019.
Google Scholar
[22]
A. Gómez-Expósito, C. Gómez-Quiles, and I. Džafić, “State estimation in two time scales for smart distribution systems,”IEEE Transactions on Smart Grid, vol. 6, no. 1, pp. 421–430, Jan. 2015.
Google Scholar
[23]
D. De Wolf and Y. Smeers, “The gas transmission problem solved by an extension of the simplex algorithm,”Management Science, vol. 46, no. 11, pp. 1454–1465, Nov. 2000.
Google Scholar
[24]
A. Abur and A. G. Expósito, Power System State Estimation: Theory and Implementation. Boca Raton, FL, USA: CRC Press, 2004.
[25]
H. A. Behrooz and R. B. Boozarjomehry, “Modeling and state estimation for gas transmission networks,”Journal of Natural Gas Science and Engineering, vol. 22, pp. 551–570, Jan. 2015.
Google Scholar
[26]
A. M. L. da Silva, M. B. D. C. Filho, and J. F. de Queiroz, “State forecasting in electric power systems,”IEE Proceedings C - Generation, Transmission and Distribution, vol. 130, no. 5, pp. 237–244, Sep. 1983.
Google Scholar
CSEE Journal of Power and Energy Systems
Volume 8 Issue 1,
January 2022
Pages 293-303
DOI: 10.17775/CSEEJPES.2020.02050
Cite this article:
Chen Y, Yao Y, Lin Y, et al. Dynamic State Estimation for Integrated Electricity-gas Systems Based on Kalman Filter. CSEE Journal of Power and Energy Systems, 2022, 8(1): 293-303. https://doi.org/10.17775/CSEEJPES.2020.02050

822

Views

16

Downloads

16

Crossref

N/A

Web of Science

49

Scopus

6

CSCD

Altmetrics
Received: 22 May 2020
Revised: 27 July 2020
Accepted: 30 August 2020
Published: 06 October 2020
© 2020 CSEE
Return