Optimization of normal operation mode of an electric system with renewable energy sources in Mongolia

Cover Page

Cite item

Full Text

Abstract

This article is aimed at developing an algorithm for optimizing the operation modes of the electric power system of Mongolia, particularly the central power system that include not only conventional thermal power plants, but also renewable sources (wind and solar power plants). This power system accounts for a large share of electricity consumption and generation in Mongolia. The method of linear programming was chosen to minimize financial costs and active power losses during power generation at thermal power plants, while Newton’s method was used to minimize power losses. In addition, the article uses load schedules of each node of the studied power system for its modeling based on the ranking model. Load graphs are predicted using ensemble machine learning algorithms. After the optimization by the criterion of power loss minimization in the grid, power losses were found to be 3.05% of the total power consumption (with power losses in the basic variant of 3.12% and the average selling price of thermal power plants of 0.51 units). Thus, the reduction in losses amounted to 0.07 percentage points, or 2.24%. In terms of the cost minimization criterion, the average selling price of electricity was 0.49 units, i.e., decreased by 3.92%. Average losses of electric power in the grid decreased by 0.6%. According to empirical data, the suggested algorithms can be applied to the optimization of power distribution between thermal power plants by given criteria. The suggested algorithms are implemented using pandapower, a Python-based tool for power system analysis, thus creating a unified system of predictive analytics of power system operation modes

About the authors

A. G. Rusina

Novosibirsk State Technical University

Email: anastasiarusina@gmail.com
ORCID iD: 0000-0002-2591-4162

T. Osgonbaatar

Novosibirsk State Technical University

Email: o.tuvshin.21@gmail.com

G. S. Bondarchuk

Novosibirsk State Technical University

Email: djgleban1147@gmail.com

P. V. Matrenin

Novosibirsk State Technical University

Email: pavel.matrenin@gmail.com
ORCID iD: 0000-0001-5704-0976

References

  1. Сидорова А.В., Черемных А.А., Русина А.Г. Python как инструментарий оптимизации режима ГЭС в составе ЭЭС // Вестник Казанского государственного энергетического университета. 2021. Т. 13. № 2. С. 119–132. EDN: RXEUZU.
  2. Kim Insu, Kim Beopsoo, Sidorov D. Machine learning for energy systems optimization // Energies. 2022. Vol. 15. Iss. 11. P. 4116. https://doi.org/10.3390/en15114116.
  3. Лыгин М.М., Корнилов Г.П., Кожевников И.О. Анализ современных методов оптимизации // Энергетические и электротехнические системы: междунар. сб. науч. тр. / под ред. С.И. Лукьянова, Е.Г. Нешпоренко. Магнитогорск: Магнитогорский государственный технический университет им. Г.И. Носова, 2019. Вып. 6. С. 4–11. EDN: RRNVRC.
  4. Luo Xi, Liu Yanfeng, Feng Pingan, Gao Yuan, Guo Zhenxiang. Optimization of a solar-based integrated energy system considering interaction between generation, network, and demand side // Applied Energy. 2021. Vol. 294. P. 116931. https://doi.org/10.1016/j.apenergy.2021.116931.
  5. Niu Ming, Wan Can, Xu Zhao. A review on applications of heuristic optimization algorithms for optimal power flow in modern power systems // Journal of Modern Power Systems and Clean Energy. 2014. Vol. 2. Iss. 4. P. 289–297. https://doi.org/10.1007/s40565-014-0089-4.
  6. Zakaria A., Ismail F., Hossain M.S., Hannan M.A. Uncertainty models for stochastic optimization in renewable energy applications // Renewable Energy. 2020. Vol. 145. P. 1543–1571. https://doi.org/10.1016/j.renene.2019.07.081.
  7. Longo S., Montana F., Sanseverino E.R. A review on optimization and cost-optimal methodologies in low-energy buildings design and environmental considerations // Sustainable Cities and Society. 2019. Vol. 45. P. 87–104. https://doi.org/10.1016/j.scs.2018.11.027.
  8. Алехин Р.А., Кубарьков Ю.П., Замаков Д.В., Умяров Д.В. Обзор метаэвристических методов оптимизации, применяемых при решении электроэнергетических задач // Вестник Самарского государственного технического университета. Серия: Технические науки. 2019. № 3. T. 63. С. 6–19.
  9. Akbas B., Kocaman A.S., Nock D., Troffer P.A. Rural electrification: аn overview of optimization methods // Renewable and Sustainable Energy Reviews. 2022. Vol. 156. Р. 111935. https://doi.org/10.1016/j.rser.2021.111935.
  10. Thirunavukkarasu M., Sawle Y., Lala H. A comprehensive review on optimization of hybrid renewable energy systems using various optimization techniques // Renewable and Sustainable Energy Reviews. 2023. Vol. 176. P. 113192. https://doi.org/10.1016/j.rser.2023.113192.
  11. Alabi T.M., Aghimien E.I., Agbajor F.D., Yang Zaiyue, Lu Lin, Adeoye A.R., Gopaluni B. A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems // Renewable Energy. 2022. Vol. 194. P. 822–849. https://doi.org/10.1016/j.renene.2022.05.123.
  12. Manusov V., Beryozkina S., Nazarov M., Safaraliev M., Zicmane I., Matrenin P. Optimal management of energy consumption in an autonomous power system considering alternative energy sources // Mathematics. 2022. Vol. 10. Iss. 3. P. 525. https://doi.org/10.3390/math10030525.
  13. Альсова О.К., Артамонова А.В. Многокритериальная модель планирования водно-энергетических режимов Новосибирской ГЭС // Известия Самарского научного центра РАН. 2017. Т. 19. № 6. С. 112–117.
  14. Woo Jong Ha, Wu Lei, Park Jong-Bae, Roh Jae Hyung. Real-time optimal power flow using twin delayed deep deterministic policy gradient algorithm // IEEE Access. 2020. Vol. 8. P. 213611–213618. https://doi.org/10.1109/ACCESS.2020.3041007.
  15. Jo Haesung, Park Jaemin, Kim Insu, Haesung Jo. Environmentally constrained optimal dispatch method for combined cooling, heating, and power systems using two-stage optimization // Energies. 2021. Vol. 14. Iss. 14 P. 4135. https://doi.org/10.3390/en14144135.
  16. Zhang Ning, Hu Zhaoguang, Dai Daihong, Dang Shuping, Yao Mingtao, Zhou Yuhui. Unit commitment model in smart grid environment considering carbon emissions trading // IEEE Transactions on Smart Grid. 2015. Vol. 7. Iss. 1. P. 420– 427. https://doi.org/10.1109/TSG.2015.2401337.
  17. Hussen K., Koch S., Ulbig A., Andersson G. Energy storage in power system operation: The power nodes modeling framework // IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe). 2010. https://doi.org/10.1109/ISGTEUROPE.2010.5638865.
  18. Nazari-Heris M., Mohammadi-Ivatloo B., Gharehpetian G.B. A comprehensive review of heuristic optimization algorithms for optimal combined heat and power dispatch from economic and environmental perspectives // Renewable and Sustainable Energy Reviews. 2018. Vol. 81. P. 2128–2143. https://doi.org/10.1016/j.rser.2017.06.024.
  19. Поляхов Н.Д., Приходько И.А., Рубцов И.А., Швыров И.В. Оптимизация распределения потоков мощности в энергосистеме с помощью генетических алгоритмов // Современные проблемы науки и образования. 2012. № 3. С. 170. EDN: PAAFDB.
  20. Park Jaemin, Jo Haesung, Kim Insu. The selection of the most cost-efficient distributed generation type for a combined cooling heat and power system used for metropolitan residential customers // Energies. 2021. Vol. 14. Iss. 18. P. 5606. https://doi.org/10.3390/en14185606.
  21. Lambora A., Gupta K., Chopra K. Genetic algorithm-A literature review // International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (Faridabad, 14–16 February). Faridabad: IEEE, 2019. P. 380–384. https://doi.org/10.1109/COMITCon.2019.8862255.
  22. Клер А.М., Корнеева З.Р., Елсуков П.Ю. Оптимизация режимов энергосистем, включающих ТЭЦ и ГЭС, с использованием дерева сочетаний условий функционирования // Вестник Иркутского государственного технического университета. 2010. № 7. С. 170–175.
  23. Sulaiman M.H., Mustaffa Z. Solving optimal power flow problem with stochastic wind–solar–small hydro power using barnacles mating optimizer // Control Engineering Practice. 2021. Vol. 106. P. 104672. https://doi.org/10.1016/j.conengprac.2020.104672.
  24. Manusov V.Z., Matrenin P.V., Khasanzoda N. Swarm algorithms in dynamic optimization problem of reactive power compensation units control // International Journal of Electrical & Computer Engineering. 2019. Vol. 9. Iss. 5. P. 3967–3974. https://doi.org/10.11591/ijece.v9i5.pp3967-3974.
  25. Rabe M., Bilan Yu., Widera K., Vasa L. Application of the linear programming method in the construction of a mathematical model of optimization distributed energy // Energies. 2022. Vol. 15. Iss. 5. P. 1872. https://doi.org/10.3390/en15051872.
  26. Gbadamosi S.L., Nwulu N.I. Optimal power dispatch and reliability analysis of hybrid CHP-PV-wind systems in farming applications // Sustainability. 2020. Vol. 12. Iss. 19. P. 8199. https://doi.org/10.3390/su12198199.
  27. Брамм А.М., Хальясмаа А.И., Ерошенко С.А., Матренин П.В., Попкова Н.А., Секацкий Д.А. Оптимизация топологии сети с ВИЭ-генерацией на основе модифицированного адаптированного генетического алгоритма // Энергетика. Известия высших учебных заведений и энергетических объединений СНГ. 2022. Т. 65. № 4. С. 341–354. https://doi.org/10.21122/1029-7448-2022-65-4-341-354.
  28. Манусов В.З., Матренин П.В., Третьякова Е.С. Оптимизация размещения источников реактивной мощности с помощью алгоритма роя частиц с генетической адаптацией // Промышленная энергетика. 2016. № 8. С. 34–40. EDN: WMRCLZ.
  29. Manusov V.Z., Matrenin P.V., Ahyoev J.S., Atabaeva L.Sh. Optimization of power distribution networks in megacities // Earth and Environmental Science: IOP Conference Series. 2017. Vol. 72. Iss. 1. P. 012019. https://doi.org/10.1088/17551315/72/1/012019.
  30. Matrenin P.V., Osgonbaatar T., Sergeev N.N. Overview of renewable energy sources in Mongolia // IEEE International Multi-Conference on Engineering, Computer and Information Sciences. 2022. P. 700–703. https://doi.org/10.1109/SIBIRCON56155.2022.10016986.
  31. Osgonbaatar T., Matrenin P., Safaraliev M., Zicmane I., Rusina A., Kokin S. A rank analysis and ensemble machine learning model for load forecasting in the nodes of the central Mongolian power system // Inventions. 2023. Vol. 8. Iss. 5. P. 114. https://doi.org/10.3390/inventions8050114.

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).