Abstract
Frequency is a pivotal indicator of the balance between power generation and load. Frequency variations and frequency-related system behaviors and characteristics are crucial during cascading outages, but they have not been considered by many steady-state power-flow-based models used for simulating cascading outages. This chapter introduces a steady-state approach for simulating cascading outages (Ju et al., IEEE Access 6(1), 37784–37795 (2018)), named as Steady-State Simulation of Cascading Outages Considering Frequency (SSCOF). This SSCOF approach includes a power-flow-based model that comprehensively incorporates the static power-frequency characteristics of generators and loads. It enables the frequency deviation to be calculated resulting from active power imbalances during cascading outages. Additionally, the SSCOF approach considers an ac optimal power flow model that integrates the frequency deviation. This model facilitates the simulation of remedial control actions in response to a highly system condition against system collapse, signaled by power flow divergence. The SSCOF approach also enables an under-frequency load shedding and generator frequency protections to be modeled and simulated, enhancing the accuracy in steady-state simulation of cascading outages. Some case studies are presented to demonstrate the effectiveness of the SSCOF approach. First, the frequency calculated from the power-flow-based model is compared and benchmarked with the steady-state frequency obtained from the time-domain simulation on a two-area power system. Then, the SSCOF approach is demonstrated for cascading outage simulation on the IEEE 39-bus system and the Northeast Power Coordinating Council (NPCC) 48-machine, 140-bus power system. The simulated scenarios of cascading outages are compared with those generated from a conventional approach that uses the power flow and ac optimal power flow models without taking frequency into consideration. The results verify the superiority of the SSCOF approach. By accurately capturing frequency variations and considering frequency-related system behaviors during cascading outages, the SSCOF approach more precisely captures the mechanism of outage propagation and enhances the steady-state simulation of cascading outages. The conventional approaches that do not consider frequency might underestimate the risks and consequences of cascading outages.
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References
U.S.-Canada Power System Outage Task Force, Final report on the August 14, 2003 blackout in the United States and Canada: Causes and Recommendations, Apr. 2004
A. Atputharajah, T.K. Saha, Power system blackouts–Literature review, in Proc. 4th Int. Conf. Industrial and Information Syst., Sri Lanka, 2009
IEEE CAMS Task Force on Understanding, Prediction, Mitigation, Restoration of Cascading Failures, “Risk assessment of cascading outages: Methodologies and challenges. IEEE Trans. Power Syst. 27(2), 631–641 (2011)
R. Baldick, B. Chowdhury, I. Dobson et al., Initial review of methods for cascading failure analysis in electric power transmission systems, in IEEE PES General Meeting, Pittsburgh, PA, USA, Jul. 2008
M. Papic, K. Bell, Y. Chen et al., Survey of tools for risk assessment of cascading outages, in IEEE PES General Meeting, Detroit, MI, USA, Jul. 2011.
B.A. Carreras, D.E. Newman, I. Dobson, North American blackout time series statistics and implications for blackout risk. IEEE Trans. Power Syst. 31(6), 4406–4414 (2016)
J. Bialek et al., Benchmarking and validation of cascading failure analysis tools. IEEE Trans. Power Syst. 31(6), 4887–4900 (2016)
E. Ciapessoni et al., Benchmarking quasi-steady state cascading outage analysis methodologies, in Prob. Methods Applied to Power Syst., Boise, ID, USA, Jun. 2018
J. Qi, K. Sun, S. Mei, An interaction model for simulation and mitigation of cascading failures. IEEE Trans. Power Syst. 30(2), 804–819 (2015)
C. Asavathiratham, S. Roy, B. Lesieutre, G. Verghese, The influence model. IEEE Control Syst. Mag. 21(6), 52–64 (2001)
P. Hines, I. Dobson, E. Cotilla-Sanchez et al., “Dual graph” and “random chemistry” methods for cascading failure analysis, in Proc. 46th Hawaii Intl. Conf. System Sciences, Maui, HI, USA, Jan. 2013
P. Hines, I. Dobson, P. Rezaei, Cascading power outages propagate locally in an influence graph that is not the actual grid topology. IEEE Trans. Power Syst. 32(2), 958–967 (2017)
W. Ju, K. Sun, J. Qi, Multi-layer interaction graph for analysis and mitigation of cascading outages. IEEE Trans. Emerg. Sel. Topics Circuits Syst. 7(2), 239–249 (2017)
C. Chen, W. Ju, K. Sun, S. Ma, Mitigation of cascading outages using a dynamic interaction graph-based optimal power flow model. IEEE Access 7, 168,637–168,648 (2019)
K. Zhou, I. Dboson, A Markovian influence graph formed from utility line outage data to mitigate large cascades. IEEE Trans. Power Syst. 35(4), 3224–3235 (2020)
K. Sun, Y. Hou, W. Sun, J. Qi, Power System Control Under Cascading Failures: Understanding, Mitigation, and System Restoration (Wiley-IEEE Press, 2019)
U. Nakarmi, M. Rahnamay-Naeini, M.J. Hossain, M.A. Hasnat, Interaction graphs for reliability analysis of power grids: A survey. Preprint (2019). arXiv:1911.00475 [physics.soc-ph]
Z. Ma, C. Shen, F. Liu, S. Mei, Fast screening of vulnerable transmission lines in power grids: A PageRank-based approach. IEEE Trans. Smart Grid 10(2), 1982–1991 (2019)
Y. Yang, T. Nishikawa, A.E. Motter, Vulnerability and cosusceptibility determine the size of network cascades. Phys. Rev. Lett. 118(4), 048301 (2017)
U. Nakarmi, M. Rahnamay-Naeini, H. Khamfroush, Critical component analysis in cascading failures for power grids using community structures in interaction graphs. IEEE Trans. Netw. Sci. Eng. 7(3), 1079–1093 (2019)
W. Ju, R. Yao, K. Sun, Interaction graph-based active islanding to mitigate cascading outage, in IEEE Power Energy Society General Meeting, Atlanta GA, Aug. 2019
C. Chen, S. Ma, K. Sun, Mitigation of cascading outages by breaking inter-regional linkages in the interaction graph. IEEE Trans. Power Syst. 38(2), 1501–1511 (2022)
B.A. Carreras, V.E. Lynch, I. Dobson, D.E. Newman, Dynamical and probabilistic approaches to the study of blackout vulnerability of the power transmission grid, in 37th HICSS, Hawaii, 2004
I. Dobson, B.A. Carreras, D.E. Newman, A loading-dependent model of probabilistic cascading failure. Probab. Eng. Inf. Sci. 19(1), 15–32 (2005)
P. Rezaei, P.D.H. Hines, M.J. Eppstein, Estimating cascading failure risk with random chemistry. IEEE Trans. Power Syst. 30(5), 2726–2735 (2015)
I. Dobson, J. Kim, et al., Testing branching process estimators of cascading failure with data from a simulation of transmission line outages. Risk Anal. 30, 650–662 (2010)
J. Qi, W. Ju, K. Sun, Estimating the propagation of interdependent cascading outages with multi-type branching processes. IEEE Trans. Power Syst. 32(2), 1212–1223 (2017)
I. Dobson, B.A. Carreras, et al., An initial model for complex dynamics in electric power system blackouts, in 34th HICSS, Hawaii, 2001
S. Mei, F. He, X. Zhang, et al., An improved OPA model and blackout risk assessment. IEEE Trans. Power Syst. 24(2), 814–823 (2009)
S. Mei, Y. Ni, G. Wang, et al., A study of self-organized criticality of power system under cascading failures based on AC-OPA with voltage stability margin. IEEE Trans. Power Syst. 23(4), 1719–1726 (2008)
M. Bhavaraju, N. Nour, TRELSS: A computer program for transmission reliability evaluation of large-scale systems, in Electr. Power Res. Inst., Palo Alto, CA, USA, Tech. Rep. EPRI-TR-100566, 1992
D.P. Nedic, I. Dobson, D.S. Kirschen, B.A. Carreras, V.E. Lynch, Criticality in a cascading failure blackout model. Electr. Power Energy Syst. 28(9), 627–633 (2006)
J. Chen, J.S. Thorp, I. Dobson, Cascading dynamics and mitigation assessment in power system disturbances via a hidden failure model. Int. J. Elect. Power Energy Syst. 27(4), 318–326 (2005)
R. Yao, S. Huang, K. Sun, et al., A multi-timescale quasi-dynamic model for simulation of cascading outages. IEEE Trans. Power Syst. 31(4), 3189–3201 (2016)
J. Song, E. Cotilla-Sanchez, G. Ghanavati, P.D.H. Hines, Dynamic modeling of cascading failure in power systems. IEEE Trans. Power Syst. 31(2), 1360–1368 (2016)
P. Henneaux, P.-E. Labeau, J.-C. Maun, L. Haarla, A two-level probabilistic risk assessment of cascading outages. IEEE Trans. Power Syst. 31(3), 2393–2403 (2016)
Q. Shi, F. Li, Q. Hu, Z. Wang, Dynamic demand control for system frequency regulation: concept review, algorithm comparison, and future vision. Electr. Power Syst. Res. 154, 75–87 (2018)
H. Pulgar-Painemal, Y. Wang, H. Silva-Saravia, On inertia distribution, inter-area oscillations and location of electronically-interfaced resources. IEEE Trans. Power Syst. 33(1), 995–1003 (2018)
G. Andersson, P. Donalek, R. Farmer, et al., Causes of the 2003 major grid blackouts in North America and Europe, and recommended means to improve system dynamic performance. IEEE Trans. Power Syst. 20(4), 1922–1928 (2005)
M. Okamura, S. Hayashi, K. Uemura, et al., A new power flow model and solution method including load and generator characteristics and effects of system control devices. IEEE Trans. Power Apparatus Syst. 94, 1042–1050 (1975)
R. Ramanathan, Dynamic load flow technique for power system simulators. IEEE Trans. Power Syst. 1(3), 25–30 (1986)
I. Roytelman, S.M. Shahidehpour, A comprehensive long term dynamic simulation for power system recovery. IEEE Trans. Power Syst. 9(3), 1427–1433 (1994)
M.S. Ćalović, V.C. Strezoski, Calculation of steady-state load flows incorporating system control effects and consumer self-regulation characteristics. Int. J. Elect. Power Energy Syst. 3, 65–74 (1981)
Y. Ping, A fast load flow model for a dispatcher training simulator considering frequency deviation effects. Electr. Power Energy Syst. 20(3), 177–182 (1998)
Y.Q. Hai, X. Wei, W.X. Fen, The improvement of dynamic power flow calculation in dispatcher training simulator. Autom. Elect. Power Syst. 23(23), 20–22 (1999)
D.P. Popović, An efficient methodology for steady-state security assessment of power systems. Int. J. Elect. Power Energy Syst. 10, 110–116 (1988)
Y. Duan, B. Zhang, Security risk assessment using fast probabilistic power flow considering static power-frequency characteristics of power systems. Electr. Power Syst. Res. 60, 53–58 (2014)
X. Ye, W. Zhong, X. Song, et al., Power system risk assessment method based on dynamic power flow, in International Conference on Probabilistic Methods Applied to Power Systems, 2016
P. Bei, B. Zhang, H. Li, et al., Probabilistic dynamic load flow algorithm considering static security risk of the power system, in International Conference on Electric Utility Deregulation and Restructuring and Power Technologies, Changsha, China, 2015
Y.H. Liu, Z.Q. Wu, S.J. Lin, et al., Application of the power flow calculation method to islanding microgrids, in International Conference on Sustainable Power Generation and Supply, Nanjing China, 2009
L. Rese, A.S. Costa, A.S. e Silva, A modified load flow algorithm for microgrids operating in islanded mode, in IEEE PES Conference on Innovative Smart Grid Technologies, DC Washington, 2013
Y. Duan, B. Zhang, An improved fast decoupled power flow model considering static power–frequency characteristic of power systems with large-scale wind power. IEEE Trans. Electr. Electron. Eng. 9(2), 151–157 (2014)
S. Li, W. Zhang, Z. Wang, Improved dynamic power flow model with DFIGs participating in frequency regulation. IEEE Trans. Electr. Energy Syst. 27, 1–13 (2017)
O.A. Mousavi, et al., Blackouts risk evaluation by Monte Carlo Simulation regarding cascading outages and system frequency deviation. Electr. Power Syst. Res. 89, 157–164 (2012)
O.A. Mousavi, et al., Inter-area frequency control reserve assessment regarding dynamics of cascading outages and blackouts. Electr. Power Syst. Res. 107, 144–152 (2014)
W. Ju, K. Sun, R. Yao, Simulation of cascading outages considering frequency using a dynamic power flow model. IEEE Access 6(1), 37784–37795 (2018)
Standard PRC-006-NPCC-1 Automatic Underfrequency Load Shedding, February 9, 2012. [online] available: http://www.nerc.com/files/PRC-006-NPCC-1.pdf
S. Imai, T. Yasuda, UFLS program to ensure stable island operation, in IEEE PES Power Systems Conference and Exposition, 2004
IEEE Standard C37.106–2003, in IEEE Guide for Abnormal Frequency Protection for Power Generating Plants, 2004
A.G. Exposito, J.L.M. Ramos, J.R. Santos, Slack bus selection to minimize the system power imbalance in load-flow studies. IEEE Trans. Power Syst. 19(2), 987–995 (2004)
P. Kundur, Power System Stability and Control (McGraw-Hill Education, New York, 1994)
W. Ju, J. Qi, K. Sun, Simulation and analysis of cascading failures on an NPCC power system test bed, in IEEE Power and Energy Society General Meeting, Denver CO, Jul. 2015
M. Variani, S. Wang, K. Tomsovic, Study of flatness-based Automatic Generation Control Approach on an NPCC system model, in IEEE Power and Energy Society General Meeting, Denver CO, Jul. 2015
P.M. Anderson, M.A. Mirheydar, A low-order system frequency response model. IEEE Trans. Power Syst. 5(3), 720–729 (1990)
PSS/E V32 User Manual, Siemens Power Transmission & Distribution, Inc., Dec 2007
A. Melman, Geometry and convergence of Euler’s and Halley’s methods. SIAM Rev. 39(4), 728–735 (1997)
I. Shames, F. Farokhi, M. Cantoni, Guaranteed maximum power point tracking by scalar iterations with quadratic convergence rate, in IEEE 55th Conference on Decision and Control, Las Vegas NV, 2016
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Ju, W., Sun, K., Yao, R. (2024). Steady-State Simulation of Cascading Outages Considering Frequency. In: Sun, K. (eds) Cascading Failures in Power Grids. Power Electronics and Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-48000-3_7
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