Journal Home > Volume 1 , issue 1

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.


menu
Abstract
Full text
Outline
About this article

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

Show Author's information Rui Yao1( )Dongbo Zhao1A. P. Sakis Meliopoulos2Chanan Singh3Joydeep Mitra4Feng 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

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:

Extended-term simulation, resilience, multi-timescale simulation, event-driven simulation, dynamics, quasi-steady-state (QSS), hybrid simulation, holomorphic embedding (HE), semi-analytical simulation (SAS)
Received: 06 December 2021 Revised: 27 January 2022 Accepted: 26 February 2022 Published: 25 March 2022 Issue date: March 2022
References(24)
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.

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.

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.

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
6

Vournas, C. D., Manos, G. A. (1998). Modelling of stalling motors during voltage stability studies. IEEE Transactions on Power Systems, 13: 775–781.

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.

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.

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.

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.

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.

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.

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.

15

Qiu, F., Li, P. J. (2017). An integrated approach for power system restoration planning. Proceedings of the IEEE, 105: 1234–1252.

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.

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.

20

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

21

Stahl, H. (1985). Extremal domains associated with an analytic function Ⅱ. Complex Variables, Theory and Application: an International Journal, 4: 325–338.

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
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.

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.

Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 06 December 2021
Revised: 27 January 2022
Accepted: 26 February 2022
Published: 25 March 2022
Issue date: March 2022

Copyright

© The author(s) 2022

Acknowledgements

Acknowledgements

This work was supported by the lab-directed research & development (LDRD) program of Argonne National Laboratory and U.S. DOE Advanced Grid Modeling Program grant DE-OE0000875. We also acknowledge the Laboratory Computing Resource Center of Argonne National Laboratory.

Rights and permissions

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/).

Reprints and Permission requests may be sought directly from editorial office.

Return