ipolytechiPolytech JournaliPolytech Journal2782-40042782-6341Irkutsk National Research Technical University10.21285/1814-3520-2023-4-760-772OSHSWUipolytech-761Research ArticleЭНЕРГЕТИКАPOWER ENGINEERINGОптимизация нормального режима работы электрической системы с возобновляемыми источниками энергии на примере МонголииOptimization of normal operation mode of an electric system with renewable energy sources in Mongoliahttps://orcid.org/0000-0002-2591-4162РусинаА. Г.RusinaA. G.

Русина Анастасия Георгиевна, д.т.н., доцент, заведующий кафедрой электрических станций

630073, г. Новосибирск, пр. К. Маркса, 20

Anastasiya G. Rusina, Dr. Sci. (Eng.), Associate Professor, Head of the Power Plants Department

20, Prospekt K. Marksa, Novosibirsk 630073

anastasiarusina@gmail.comОсгонбаатарТ.OsgonbaatarT.

Осгонбаатар Тувшин, аспирант

630073, г. Новосибирск, пр. К. Маркса, 20

Tuvshin Osgonbaatar, Postgraduate Student

20, Prospekt K. Marksa, Novosibirsk 630073

o.tuvshin.21@gmail.comБондарчукГ. С.BondarchukG. S.

Бондарчук Глеб Сергеевич, студент

630073, г. Новосибирск, пр. К. Маркса, 20

Gleb S. Bondarchuk, Student

20, Prospekt K. Marksa, Novosibirsk 630073

djgleban1147@gmail.comhttps://orcid.org/0000-0001-5704-0976МатрёнинП. В.MatreninP. V.

Матрёнин Павел Викторович, к.т.н., доцент, доцент кафедры систем электроснабжения предприятий

630073, г. Новосибирск, пр. К. Маркса, 20

Pavel V. Matrenin, Cand. Sci. (Eng.), Associate Professor, Associate Professor of the Department of Industrial Power Supply Systems

20, Prospekt K. Marksa, Novosibirsk 630073

pavel.matrenin@gmail.comНовосибирский государственный технический университетРоссияNovosibirsk State Technical UniversityRussian Federation202310012024274760772Copyright © Русина А.Г., Осгонбаатар Т., Бондарчук Г.С., Матрёнин П.В., 20242024Русина А.Г., Осгонбаатар Т., Бондарчук Г.С., Матрёнин П.В.Rusina A.G., Osgonbaatar T., Bondarchuk G.S., Matrenin P.V.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ipolytech.elpub.ru/jour/article/view/761

Цель – разработка алгоритма оптимизации режимов работы электроэнергетической системы Монголии. Объектом исследований выбрана центральная энергосистема Монголии, включающая традиционные тепловые электростанции и возобновляемые источники (ветровые и солнечные электростанции). На нее приходится большая доля потребления и генерации электрической энергии в Монголии. Для минимизации финансовых расходов и потерь активной мощности при производстве электроэнергии на тепловых электростанциях был выбран метод линейного программирования, для минимизации потерь мощности – метод Ньютона. Также в работе использованы графики нагрузки каждого узла исследуемой энергосистемы для ее моделирования на основе ранговой модели. Графики нагрузки прогнозируются с помощью ансамблевых алгоритмов машинного обучения. Показано, что после оптимизации по критерию минимизации потерь мощности в сети потери электроэнергии составили 3,05% от общего электропотребления (при потерях электроэнергии в базовом варианте 3,12% и средней цене продажи тепловых электростанций 0,51 единицы). Таким образом, снижение потерь составило 0,07 процентных пункта или 2,24%. Также по критерию минимизации затрат средняя цена продажи электроэнергии составила 0,49 единицы, то есть уменьшилась на 3,92%. Cредние потери электрической энергии в сети снизились на 0,6%. Экспериментально обосновано, что предложенные алгоритмы могут быть применены к оптимизации распределения мощности между тепловыми электростанциями по заданным критериям. Программная реализация предложенных алгоритмов выполнена с помощью библиотеки Pandapower на языке программирования Python, что позволяет создать единую систему предиктивной аналитики режимов работы энергосистемы.

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

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The authors declare that there are no conflicts of interest present.