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Open Access

Impact Increment Based Hybrid Reliability Assessment Method for Transmission Systems

Yunkai Lei ( ), Yeguang Sun, Kai Hou, Pei Zhang, Lewei Zhu, Xiaoxi Yang, Xiaonan Liu
State Grid Sichuan Economic Research Institute, Chengdu, 610000 China
School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
State Grid Customer Service Center, Tianjin 300072, China
School of Electrical and Automation Engineering, East China Jiaotong Unviersity, Nanchang 330052, China
Maritime College, Tianjin University of Technology, Tianjin 300384, China
College of Economics and Management, Southwest Petroleum University, Chengdu 610000, China
State Grid Tianjin Electric Power Research Institute, Tianjin 300072, China
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Abstract

This paper proposes an impact-increment-based hybrid (IIHybrid) reliability assessment approach for power transmission systems. The proposed approach integrates the advantages of the impact-increment-based state enumeration method (IISE) and impact-increment-based Monte Carlo simulation (IIMC) to improve computational efficiency and accuracy. The IISE can efficiently assess the impacts of low-order contingencies. The accuracy is, however, sacrificed as high-order contingencies are usually neglected. The IIMC is more suitable for large-scale contingency spaces compared with IISE, although the calculation process is time-consuming. In this paper, the proposed IIHybrid takes advantage of its strengths while avoiding its shortcomings. The IISE and the IIMC are applied to lower and higher contingency spaces respectively. The high-order contingencies elimination technique proposed in our previous studies is still applicable to the IIHybrid. In addition, efficiency can be controlled by modifying the preset parameters to adapt to various scenarios. Case studies are performed on the IEEE 118-bus test system and PEGASE System. The results show that the proposed approach is more efficient and practicable than traditional methods.

Keywords

Hybrid methodimpact-incrementreliability assessmenttransmission system

References

[1]
T. A. M. Sharaf and G. J. Berg, “Loadability in composite generation/transmission power-system reliability evaluation,”IEEE Transactions on Reliability, vol. 42, no. 3, pp. 393–400, Sep. 1993.
Crossref Google Scholar
[2]
Y. Zhang, “The evaluation method of fuzzy reliability in electric power network planning,”Proceedings of the CSEE, vol. 20, no. 11, pp. 77–80, Nov. 2000.
Google Scholar
[3]
T. H. Wang, P. Y. Wang, and M. T. Fan, “Optimal feeder automation planning using binary programming,”Proceedings of the CSEE, vol. 20, no. 5, pp. 54–58, May 2000.
Google Scholar
[4]
X Li, R. F. Zhang, L. Q. Bai, G. Q. Li, T. Jiang, and H. H. Chen, “Stochastic low-carbon scheduling with carbon capture power plants and coupon-based demand response,”Applied Energy, vol. 210, pp. 1219–1228, Jan. 2018.
Crossref Google Scholar
[5]
X. Li, G. D. Tian, Q. X. Shi, T. Jiang, F. X. Li, and H. J. Jia, “Security region of natural gas network in electricity-gas integrated energy system,” International Journal of Electrical Power &Energy Systems, vol. 117, pp. 105601, May 2020.
Crossref Google Scholar
[6]
T. Jiang, Z. N. Li, X. L. Jin, H. H. Chen, X. Li, and Y. F. Mu, “Flexible operation of active distribution network using integrated smart buildings with heating, ventilation and air-conditioning systems,”Applied Energy, vol. 226, pp. 181–196, Sep. 2018.
Crossref Google Scholar
[7]
T. Jiang, H. W. Deng, L. Q. Bai, R. F. Zhang, X. Li, and H. H. Chen, “Optimal energy flow and nodal energy pricing in carbon emission-embedded integrated energy systems,”CSEE Journal of Power and Energy Systems, vol. 4, no. 2, pp. 179–187, Jun. 2018.
Crossref Google Scholar
[8]
X. Li, W. M. Li, R. F. Zhang, T. Jiang, H. H. Chen, and G. Q. Li, “Collaborative scheduling and flexibility assessment of integrated electricity and district heating systems utilizing thermal inertia of district heating network and aggregated buildings,”Applied Energy, vol. 258, pp. 114021, Jan. 2020.
Crossref Google Scholar
[9]
Y. Teng, Z. Wang, Y. Li, Q. Ma, Q. Hui and S. Li, “Multi-energy storage system model based on electricity heat and hydrogen coordinated optimization for power grid flexibility,”CSEE Journal of Power and Energy Systems, vol. 5, no. 2, pp. 266–274, June 2019.
Google Scholar
[10]
W. Liu, T. Ma and Y. Yang, “Reliability assessment of integrated energy system based on coupling energy flow and thermal inertia,”CSEE Journal of Power and Energy Systems, .
Crossref Google Scholar
[11]
H. Abunima, J. Teh, C. M. Lai, and H. J. Jabir, “A systematic review of reliability studies on composite power systems: a coherent taxonomy motivations, open challenges, recommendations, and new research directions,”Energies, vol. 11, no. 9, pp. 2417, Sep. 2018.
Crossref Google Scholar
[12]
S. Nikolovski, Š. Damir, and B. Štević, “State enumeration approach in reliability assessment of the eastern Croatia bulk power system,”in Proceedings of the IEEE International Conference on Electric Power Engineering (PowerTECH ’99), Budapest, Hungary, 1999, pp. 138.
Google Scholar
[13]
R. Billinton and W. Y. Li, Reliability Assessment of Electric Power Systems Using Monte Carlo Methods. New York: Springer, 1994.
Crossref
[14]
Y. B. Jia, P. Wang, X. Q. Han, J. Y. Tian, and C. Singh, “A fast contingency screening technique for generation system reliability evaluation,”IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4127–4133, Nov. 2013.
Crossref Google Scholar
[15]
R. Billinton and R. N. Allan, Reliability Assessment of Large Electric Power Systems. Boston: Kluwer Academic Publishers, 1988.
Crossref
[16]
R. Billinton and R. N. Allan, Reliability Evaluation of Power Systems. New York: Plenum Press, 1984.
Crossref
[17]
M. V. F. Pereira and N. J. Balu, “Composite generation/transmission reliability evaluation,”Proceedings of the IEEE, vol. 80, no. 4, pp. 470–491, Apr. 1992.
Crossref Google Scholar
[18]
B. R. Nandini, R. Prakash, and , “Analysis of “Importance sampling” in electric power system security,” in Proceedings of the National Conference on Challenges in Research & Technology in the Coming Decades(CRT 2013), Ujire, 2013, pp. 1–6.
Crossref Google Scholar
[19]
W. H. Shi and Z. H. Bie, “Applications of markov chain monte carlo in large-scale system reliability evaluation,”Proceedings of the CSEE, vol. 28, no. 4, pp. 9–15, Feb. 2008.
Google Scholar
[20]
E. Tómasson and L. Söder, “Improved importance sampling for reliability evaluation of composite power systems,”IEEE Transactions on Power Systems, vol. 32, no. 3, pp. 2426–2434, May 2017.
Crossref Google Scholar
[21]
L. Geng, Y. Zhao, and W. Y. Li, “Enhanced cross entropy method for composite power system reliability evaluation,”IEEE Transactions on Power Systems, vol. 34, no. 4, pp. 3129–3139, Jul. 2019.
Crossref Google Scholar
[22]
Y. Zhao, Y. Tang, W. Y. Li, and J. Yu, “Composite power system reliability evaluation based on enhanced sequential cross-entropy monte carlo simulation,”IEEE Transactions on Power Systems, vol. 34, no. 5, pp. 3891–3901, Sep. 2019.
Crossref Google Scholar
[23]
B. W. Hua, Z. H. Bie, S. K. Au, W. Y. Li, and X. F. Wang, “Extracting rare failure events in composite system reliability evaluation via subset simulation,”IEEE Transactions on Power Systems, vol. 30, no. 2, pp. 753–762, Mar. 2015.
Crossref Google Scholar
[24]
R. Billinton and X. Tang, “Selected considerations in utilizing Monte Carlo simulation in quantitative reliability evaluation of composite power systems,”Electric Power Systems Research, vol. 69, no. 2–3, pp. 205–211, May 2004.
Crossref Google Scholar
[25]
A. Sankarakrishnan and R. Billinton, “Sequential monte carlo simulation for composite power system reliability analysis with time varying loads,”IEEE Transactions on Power Systems, vol. 10, no. 3, pp. 1540–1545, Aug. 1995.
Crossref Google Scholar
[26]
Z. Ren, J. J. He, J. Chen, W. Y. Huang, and B. H. Li, “Monte Carlo-FD comprehensive method for reliability evaluation of AC/DC hybrid network systems,”Power System Technology, vol. 24, no. 5, pp. 13–19, 51, May 2000.
Google Scholar
[27]
P. C. Xu, Q. Chen, D. G. Shi, and H. Zhang, “Reliability evaluation of distribution system including DG based on hybrid method,” Electrical &Energy Management Technology, no. 6, pp. 42–46, 75, 2018.
Google Scholar
[28]
Z. H. Bie, X. L. Wang, and X. F. Wang, “Study of hybrid method on power system reliability evaluation,”Electric Power, vol. 34, no. 3, pp. 25–28, Mar. 2001.
Google Scholar
[29]
J. He, Y. Sun, D. S. Kirschen, C. Singh, and L. Cheng, “State-space partitioning method for composite power system reliability assessment,” IET Generation, Transmission &Distribution, vol. 4, no. 7, pp. 780–792, Jul. 2010.
Crossref Google Scholar
[30]
H. T. Lei and C. Singh, “Non-sequential monte carlo simulation for cyber-induced dependent failures in composite power system reliability evaluation,”IEEE Transactions on Power Systems, vol. 32, no. 2, pp. 1064–1072, Mar. 2017.
Crossref Google Scholar
[31]
D. Urgun and C. Singh, “A hybrid monte carlo simulation and multi label classification method for composite system reliability evaluation,”IEEE Transactions on Power Systems, vol. 34, no. 2, pp. 908–917, Mar. 2019.
Crossref Google Scholar
[32]
K. Hou, H. J. Jia, X. D. Yu, L. W. Zhu, X. D. Xu, and X. Li, “An impact increments-based state enumeration reliability assessment approach and its application in transmission systems,”in Proceedings of the 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, 2016, pp. 1–5.
Google Scholar
[33]
Y. K. Lei, K. Hou, H. J. Jia, P. Zhang, Y. F. Mu, , and L. W. Zhu, “An impact-increment based Monte Carlo simulation reliability assessment approach for transmission systems,”in Proceedings of 2017 IEEE Power & Energy Society General Meeting, Chicago, IL, 2017, pp. 1–5.
Crossref Google Scholar
[34]
W. Y. Li, Risk Assessment of Power Systems: Models, Methods, and Applications. Piscataway, NJ: John Wiley & Sons, 2005.
[35]
IEEE 118-Bus System, IIIinois center for a smarter electric grid (ICSEG). 2015 [Online]. Available:http://publish.illinois.edu/smartergrid/ieee-118-bus-system/.
[36]
S. Fliscounakis, P. Panciatici, F. Capitanescu, and L. Wehenkel, “Contingency ranking with respect to overloads in very large power systems taking into account uncertainty, preventive, and corrective actions,”IEEE Transactions on Power Systems, vol. 28, no. 4, pp. 4909–4917, Nov. 2013.
Crossref Google Scholar
CSEE Journal of Power and Energy Systems
Volume 8 Issue 1,
January 2022
Pages 317-328
DOI: 10.17775/CSEEJPES.2020.01280
Cite this article:
Lei Y, Sun Y, Hou K, et al. Impact Increment Based Hybrid Reliability Assessment Method for Transmission Systems. CSEE Journal of Power and Energy Systems, 2022, 8(1): 317-328. https://doi.org/10.17775/CSEEJPES.2020.01280

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Received: 17 April 2020
Revised: 11 July 2020
Accepted: 30 August 2020
Published: 06 October 2020
© 2020 CSEE
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