Clustering of Electric Power Systems into Reliability Zones in Adequacy Assessment. Part 2

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Дәйексөз келтіру

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Аннотация

The article presents an analysis of clustering methods in relation to the problem of the formation of energy calculation models (ECM) of electric power systems (EPS). Classical clustering methods are considered, such as: the k-means method, the shortest path method, as well as methods for detecting communities in graphs according to given features, which are the most suitable for solving the problem. Based on the analysis, it was found that for graph clustering, the most adequate result can be obtained by applying the Leiden method. The experimental part of the article presents the results of applying the Leiden method for the formation of the ECM of the unified energy system of Siberia.

Толық мәтін

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Авторлар туралы

D. Krupenev

Melentiev Energy Systems Institute Siberian Branch of the Russian Academy of Sciences

Хат алмасуға жауапты Автор.
Email: krupenev@isem.irk.ru
Ресей, Irkutsk

N. Belyaev

JSC “STC UPS”

Email: belyaev.na@yandex.ru

Department for Electricity and Power

Ресей, Moscow

D. Boyarkin

Melentiev Energy Systems Institute Siberian Branch of the Russian Academy of Sciences

Email: krupenev@isem.irk.ru
Ресей, Irkutsk

D. Iakubovskii

Melentiev Energy Systems Institute Siberian Branch of the Russian Academy of Sciences

Email: krupenev@isem.irk.ru
Ресей, Irkutsk

Әдебиет тізімі

  1. Руденко Ю.Н., Чельцов М.Б. Надежность и резервирование в электроэнергетических системах. Изд.-во “Наука” Сибирское отделение, Новосибирск. 1974. 262 с.
  2. Ковалев Г.Ф., Крупенев Д.С., Лебедева Л.М. Системная надежность ЕЭС России на уровне 2030 г. Электрические станции, 2011. № 2. С. 44–47.
  3. Probabilistic Adequacy and Measures. Technical Reference Report Final, NERC, July, 2018.
  4. Long-Term Reliability Assessment. NERC, 2021. P. 126.
  5. Ковалев Г.Ф., Лебедева Л.М. Надежность систем электроэнергетики. Новосибирск: Наука. 2015.
  6. Krupenev D., Boyarkin D., Iakubovskii D. Improvement in the computational efficiency of a technique for assessing the reliability of electric power systems based on the Monte Carlo method. Reliability Engineering & System Safety. № 204. 10.1016/j.ress.2020.107171' target='_blank'>https://doi: 10.1016/j.ress.2020.107171
  7. Rouhani M., Mohammadi M., Aiello M. Soft clustering based probabilistic power flow with correlated inter temporal events // Electric Power Systems Research. № 204. 2022. 107677. https://doi.org/10.1016/j.epsr.2021.107677.
  8. Gheorghe G., Scarlatache F., Neagu B. Clustering in Power Systems. Applications. 2016.
  9. Справочник по проектированию электрических сетей / под ред. Д.Л. Файбисовича – Изд. 4-е, перераб. и доп. – М.: Изд-во НЦ ЭНАС, 2012. 376 с.
  10. Мандель И.Д. Кластерный анализ. М.: Финансы и статистика. 1988. 176 с.
  11. MacQueen J. Some methods for classification and analysis of multivariate observations/ J. MacQueen // In Proc. 5th Berkeley Symp. Оn Math. Statistics and Probability, 1967. С. 281–297.
  12. Sarkar A., Ramalingam S. Graph Clustering: A Review. 2020.
  13. Kaufman L., Rousseeuw P.J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis (1 ed.). New York: John Wiley. ISBN 0-471-87876-6.
  14. Blondel V.D., Guillaume J.-L., Lambiotte R. & Lefebvre E. Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 10008. 6. https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).
  15. Newman M. E. J. and M. Girvan. Finding and evaluating community structure in networks. Phys. Rev. E 69. 026113. 2004.
  16. Ester M., Kriegel H.P., Sander J. and Xu X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, AAAI Press. 1996. P. 226–231.
  17. Emmons S., Kobourov S., Gallant M., Börner K. (2016) Analysis of Network Clustering Algorithms and Cluster Quality Metrics at Scale. PLoS ONE 11(7): e0159161. https://doi.org/10.1371/journal.pone.0159161
  18. Beligianni F., Tsatsaronis G., Palpanas T. A Survey on Clustering Techniques for Graph Structured Data. 2019.
  19. Palla G., Derényi I., Farkas I. et al. Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 2005. P. 814–818. https://doi.org/10.1038/nature03607
  20. Rosvall M., Bergstrom C.T. Maps of information flow reveal community structure in complex networks. PNAS. 105. 1118. 2008.
  21. Орлов А.О., Чеповский А.А. О свойствах модулярности и актуальных корректировках алгоритма Блонделя // Вестник НГУ. Серия: Информационные технологии. 2017. № 3.
  22. Metropolis N., Rosenbluth A.W., Rosenbluth M.N., Teller A.H., and Teller E. Equation of State Calculations by Fast Computer Machines // J. Chemical Physics. 21. 6. June. 1953. P. 1087–1092.
  23. Lancichinetti A., Fortunato S. Community detection algorithms: A comparative analysis. Phys. Rev. E 80, 056117. https://doi.org/10.1103/PhysRevE.80.056117 (2009).
  24. Traag V.A., Waltman L., van Eck N.J. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9. 5233. 2019. https://doi.org/10.1038/s41598-019-41695-z
  25. Приказ Минэнерго России от 28.02.2022 №146 “Об утверждении схемы и программы развития Единой энергетической системы России на 2022–2028 годы”.
  26. Крупенев Д.С., Беляев Н.А., Бояркин Д.А. Кластеризация электроэнергетических систем на зоны надежности при оценке балансовой надежности. Часть 1 // Известия Российской академии наук. Энергетика. 2024. № 1. С. 12–21.

Қосымша файлдар

Қосымша файлдар
Әрекет
1. JATS XML
2. Fig. 1. Visualization of the operation of the Leiden algorithm.

Жүктеу (30KB)
3. Fig. 2. Graphical representation of the assignment of Siberian ECO nodes to reliability zones based on the application of the Leiden algorithm.

Жүктеу (53KB)
4. Fig. 3. The result of clustering of the Siberian ECO on the ZN based on the application of the Leiden algorithm (geographical reference).

Жүктеу (47KB)

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