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Article | Open Access

Advanced extended-term simulation approach with flexible quasisteady-state and dynamic semi-analytical simulation engines

Rui Yao1( ), Dongbo Zhao1, A. P. Sakis Meliopoulos2, Chanan Singh3, Joydeep Mitra4, Feng Qiu1
Division of Energy Systems, Argonne National Laboratory, Lemont, IL 60439, USA
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
Department of Electrical and Computer Engineering, Texas A & M University, College Station, TX 77843, USA
Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824, USA
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Abstract

Power system simulations that extend over a time period of minutes, hours, or even longer are called extended-term simulations. As power systems evolve into complex systems with increasing interdependencies and richer dynamic behaviors across a wide range of timescales, extended-term simulation is needed for many power system analysis tasks (e.g., resilience analysis, renewable energy integration, cascading failures), and there is an urgent need for efficient and robust extended-term simulation approaches. The conventional approaches are insufficient for dealing with the extended-term simulation of multi-timescale processes. This paper proposes an extended-term simulation approach based on the semi-analytical simulation (SAS) methodology. Its accuracy and computational efficiency are backed by SAS's high accuracy in event-driven simulation, larger and adaptive time steps, and flexible switching between full-dynamic and quasi-steady-state (QSS) models. We used this proposed extended-term simulation approach to evaluate bulk power system restoration plans, and it demonstrates satisfactory accuracy and efficiency in this complex simulation task.

Keywords

dynamicsresilienceExtended-term simulationmulti-timescale simulationevent-driven simulationquasi-steady-state (QSS)hybrid simulationholomorphic embedding (HE)semi-analytical simulation (SAS)

References

1

Chen, J. J., Crow, M. L. (2008). A variable partitioning strategy for the multirate method in power systems. IEEE Transactions on Power Systems, 23: 259–266.
Crossref Google Scholar
2

Yao, R., Huang, S. W., Sun, K., Liu, F., Zhang, X. M., Mei, S. W. (2016). A multi-timescale quasi-dynamic model for simulation of cascading outages. IEEE Transactions on Power Systems, 31: 3189–3201.
Crossref Google Scholar
3
Ma, F., Luo, X. C. (2013). SimAGC—An open-source power system dynamic simulator for AGC study. In: Proceedings of the 2013 IEEE Power & Energy Society General Meeting, Vancouver, BC, Canada.
4

Chen, L. Z., Zhang, H. X., Wu, Q. W., Terzija, V. (2018). A numerical approach for hybrid simulation of power system dynamics considering extreme icing events. IEEE Transactions on Smart Grid, 9: 5038–5046.
Crossref Google Scholar
5
Korkali, M., Min, L. (2017). GMLC extreme event modeling—Slow-dynamics models for renewable energy resources. Technical report, Lawrence Livermore National laboratory (LLNL), Livermore, CA, USA.https://doi.org/10.2172/1352116
Crossref
6

Vournas, C. D., Manos, G. A. (1998). Modelling of stalling motors during voltage stability studies. IEEE Transactions on Power Systems, 13: 775–781.
Crossref Google Scholar
7
Fu, C. (2011). High-speed extended-term time-domain simulation for online cascading analysis of power systemalysis of power system. PhD Thesis, Iowa State University, USA.
8

Yao, R., Liu, Y., Sun, K., Qiu, F., Wang, J. H. (2020). Efficient and robust dynamic simulation of power systems with holomorphic embedding. IEEE Transactions on Power Systems, 35: 938–949.
Crossref Google Scholar
9

Rao, S., Feng, Y., Tylavsky, D. J., Subramanian, M. K. (2016). The holomorphic embedding method applied to the power-flow problem. IEEE Transactions on Power Systems, 31: 3816–3828.
Crossref Google Scholar
10

Liu, C. X., Wang, B., Hu, F. K., Sun, K., Bak, C. L. (2017). Online voltage stability assessment for load areas based on the holomorphic embedding method. IEEE Transactions on Power Systems, 33: 3720–3734.
Crossref Google Scholar
11

Wang, C., Hou, Y. H., Qiu, F., Lei, S. B., Liu, K. (2017). Resilience enhancement with sequentially proactive operation strategies. IEEE Transactions on Power Systems, 32: 2847–2857.
Crossref Google Scholar
12

Panteli, M., Mancarella, P. (2015). The grid: Stronger, bigger, smarter?: Presenting a conceptual framework of power system resilience. IEEE Power and Energy Magazine, 13: 58–66.
Crossref Google Scholar
13

Huang, G., Wang, J. H., Chen, C., Qi, J. J., Guo, C. X. (2017). Integration of preventive and emergency responses for power grid resilience enhancement. IEEE Transactions on Power Systems, 32: 4451–4463.
Crossref Google Scholar
14

Song, J. J., Cotilla-Sanchez, E., Ghanavati, G., Hines, P. D. H. (2016). Dynamic modeling of cascading failure in power systems. IEEE Transactions on Power Systems, 31: 2085–2095.
Crossref Google Scholar
15

Qiu, F., Li, P. J. (2017). An integrated approach for power system restoration planning. Proceedings of the IEEE, 105: 1234–1252.
Crossref Google Scholar
16
Qureshi, M. (2019). A fast quasi-static time series simulation method using sensitivity analysis to evaluate distributed pv impacts. PhD thesis, Georgia Institute of Technology, USA.
17

Yao, R., Sun, K., Qiu, F. (2019). Vectorized efficient computation of padé approximation for semi-analytical simulation of large-scale power systems. IEEE Transactions on Power Systems, 34: 3957–3959.
Crossref Google Scholar
18
Dobson, I., McCalley, J. (2008). Risk of cascading outages. PSERC public report S-26. Power Systems Engineering Research Center, USA.
19

Ju, W. Y., Sun, K., Yao, R. (2018). Simulation of cascading outages using a power-flow model considering frequency. IEEE Access, 6: 37784–37795.
Crossref Google Scholar
20

Yao, R., Qiu, F. (2020). Novel AC distribution factor for efficient outage analysis. IEEE Transactions on Power Systems, 35: 4960–4963.
Crossref Google Scholar
21

Stahl, H. (1985). Extremal domains associated with an analytic function Ⅱ. Complex Variables, Theory and Application: an International Journal, 4: 325–338.
Crossref Google Scholar
22
Hou, Y. H., Liu, C. C., Sun, K., Zhang, P., Liu, S. S., Mizumura, D. (2011). Computation of milestones for decision support during system restoration. In: Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA.https://doi.org/10.1109/PES.2011.6039162
Crossref
23

Basiri-Kejani, M., Gholipour, E. (2017). Holomorphic embedding load-flow modeling of thyristor-based FACTS controllers. IEEE Transactions on Power Systems, 32: 4871–4879.
Crossref Google Scholar
24

Xu, X., Yao, R., Sun, K., Qiu, F. (2022). A semi-analytical solution approach for solving constant-coefficient first-order partial differential equations. IEEE Control Systems Letters, 6: 704–709.
Crossref Google Scholar
iEnergy
Volume 1 Issue 1,
March 2022
Pages 124-132
DOI: 10.23919/IEN.2022.0006
Cite this article:
Yao R, Zhao D, Sakis Meliopoulos AP, et al. Advanced extended-term simulation approach with flexible quasisteady-state and dynamic semi-analytical simulation engines. iEnergy, 2022, 1(1): 124-132. https://doi.org/10.23919/IEN.2022.0006

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Received: 06 December 2021
Revised: 27 January 2022
Accepted: 26 February 2022
Published: 25 March 2022
© The author(s) 2022

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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