Roles of Dynamic State Estimation in Power System Modeling, Monitoring and Operation

Journal Article · Mon May 31 04:00:00 EDT 2021 · IEEE Transaction on Power Systems

DOI:https://doi.org/10.1109/TPWRS.2020.3028047· OSTI ID:1804708

Zhao, Junbo ^[1]; Netto, Marcos ^[2]; Huang, Zhenyu ^[3]; Yu, Samson Shenglong ^[4]; Gómez-Expósito, Antonio ^[5]; Wang, Shaobu ^[3]; Kamwa, Innocent ^[6]; Akhlaghi, Shahrokh ^[7]; Mili, Lamine ^[2]; Terzija, Vladimir ^[8]; Meliopoulos, Sakis ^[9]; Pal, Bikash ^[10]; Singh, Abhinav Kumar ^[11]; Abur, Ali ^[12]; Bi, Tianshu S. ^[13]; Rouhani, Alireza ^[14]

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1. Mississippi State University
2. Virginia Tech
3. BATTELLE (PACIFIC NW LAB)
4. Deakin University
5. University of Seville
6. Hydro-Quebec Research Institute
7. State University of New York at Binghamton
8. The University of Manchester
9. Georgia Institute Of Technology
10. Imperial College London
11. University of Southampton
12. Northeastern University
13. China - Beijing Inst Of Aero M
14. Dominion Energy

—Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today’s synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems.

• Research Organization: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1804708
• Report Number(s): PNNL-SA-153500
• Journal Information: IEEE Transaction on Power Systems, Journal Name: IEEE Transaction on Power Systems Journal Issue: 3 Vol. 36
• Country of Publication: United States
• Language: English