Оптимизация живучести энергетических комплексов
Ключевые слова:
энергетические комплексы, повышение живучести, синтез, методы структурно-параметрической оптимизации, многокритериальный анализ, испытательный стендАннотация
В настоящее время разработка подходов, повышающих живучесть энергетических комплексов, является весьма актуальным направлением исследований. Такие подходы основаны на структурной и параметрической оптимизации структуры исследуемой системы. Как правило, эти подходы тесно связаны с определенным пространственно-временным диапазоном и конкретным методом оптимизации. Применение разработанных подходов в иных диапазонах зачастую приводит к существенному увеличению времени вычислений и возможному снижению точности решения. Эта проблема обусловлена сложностью моделей оптимизации энергосистем и их различиями. Для решения этой проблемы нами разработана методология выбора наиболее подходящих методов проектирования живучих энергетических комплексов в заданном пространственно-временном диапазоне. Методология основана на методах тестирования в рамках специализированного испытательного стенда и многокритериальном анализе результатов испытаний. Критерии оценки методов включают как сводные метрики живучесть, так и параметры эффективности вычислительных ресурсов. Проиллюстрированы преимущества методологии для проектирования живучих национальных и локальных энергетических комплексов. Несколько десятков методов из известной библиотеки Parallel Global Multiobjective Optimizer были эффективно протестированы в течение 10 часов. Анализ результатов тестирования проводился с использованием различных многокритериальных алгоритмов с учетом приоритетности критериев.
Литература
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12. Hoffmann M., Priesmann J., Nolting L., Praktiknjo, A., Kotzur, L., Stolten, D. Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models. Applied Energy. 2021. vol. 304. pp. 117825. DOI: 10.1016/j.apenergy.2021.117825.
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15. Patin M., Bégot S., Gustin F., Lepiller V. Enhancing Residential Sustainability: Multi-objective optimization of hydrogen-based multi-energy system. International Journal of Hydrogen. Energy. 2024. vol. 67. pp. 875–887. DOI: 10.1016/j.ijhydene.2023.12.201.
16. Gabrielli P, Fürer F, Mavromatidis G., Mazzotti M. Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis. Applied Energy. 2019. vol. 238. pp. 1192–1210. DOI: 10.1016/j.apenergy.2019.01.064.
17. Baumgärtner N., Bahl B., Hennen M., Bardow A. RiSES3: Rigorous Synthesis of Energy Supply and Storage Systems via time-series relaxation and aggregation. Computers & Chemical Engineering. 2019. vol. 127. pp. 127–139. DOI: 10.1016/j.compchemeng.2019.02.006.
18. Fazlollahi S., Maréchal F. Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP). Applied Thermal Engineering. 2013. vol. 50(2). pp. 1504–1513. DOI: 10.1016/j.applthermaleng.2011.11.035.
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21. Tsvirkun A.D., Rezchikov A.F., Dranko O.I., Kushnikov V.A., Bogomolov A.S. Optimization and simulation approach to determining critical combinations of company parameters. Avtomatika i telemehanika. 2024. vol. 10. pp. 53–64. DOI: 10.31857/S0005231024100053. (In Russ.).
22. Maulén L., Castro M., Lorca Á., Negrete-Pincetic M. Optimization-based expansion planning for power and hydrogen systems with feedback from a unit commitment model. Applied Energy. 2023. vol. 343. pp. 121207. DOI: 10.1016/j.apenergy.2023.121207.
23. Cho S., Tovar-Facio J., Grossmann I.E. Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. Computers & Chemical Engineering. 2023. vol. 174. pp. 108243. DOI: 10.1016/j.compchemeng.2023.108243.
24. Teichgraeber H., Küpper L.E., Brandt A.R. Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation. Applied Energy. 2021. vol. 304. pp. 117696. DOI: 10.1016/j.apenergy.2021.117696.
25. Jing R., Wang X., Zhao Y., Zhou Y., Wu J., Lin J. Planning urban energy systems adapting to extreme weather. Advances in Applied Energy. 2021. vol. 3. pp. 100053. DOI: 10.1016/j.adapen.2021.100053.
26. Oster M.R., Amburg I., Chatterjee S., Eisenberg D.A., Thomas D.G., Pan F., Ganguly A.R. A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events. IEEE International Symposium on Technologies for Homeland Security (HST). IEEE. Boston. 2024. vol. 47. pp. 1–3. DOI: 10.1109/HST53381.2021.9619824.
27. Pfetsch M.E., Schmitt A. A generic optimization framework for resilient systems. Optimization Methods and Software. 2023. vol. 38(2). pp. 356–385. DOI: 10.1080/10556788.2022.2142581.
28. Cao K.K., Von Krbek K., Wetzel M., Cebulla F., Schreck S. Classification and evaluation of concepts for improving the performance of applied energy system optimization models. Energies. 2019. vol. 12(24). pp. 4656. DOI: 10.3390/en12244656.
29. Biscani F., Izzo D. A parallel global multiobjective framework for optimization: pagmo. Journal of Open Source Software. 2020. vol. 5(53). pp. 2338. DOI: 10.21105/joss.02338.
30. Mikoni S.V., Sokolov B.V., Yusupov R.M. Kvalimetriya modeley i polimodel'nykh kompleksov [Qualimetry of Models and Polymodel Complexes]. Moscow: RAS, 2018. 314 p. (In Russ.).
31. Valkman Y.R., Rykhalsky A.Y. Architecture of model parametric space: hierarchy in Simon's Architecture of Complexity. Proceedings of the 2nd International Conference on Inductive Modelling (ICTM' 2008). Kyiv. 2008. pp. 58–59.
32. Danilov G., Voskoboinikov M. Testbed-based approach to testing a library for evaluating network reliability algorithms. Proceedings of the International Workshop on Critical Infrastructures in the Digital World (IWCI-2024). Irkutsk, 2024. pp. 3–4.
33. Safonov G., Potashnikov V., Lugovoy O., Safonov M., Dorina A., Bolotov A. The low carbon development options for Russia. Climatic Change. 2020. vol. 162. pp. 1929–1945. DOI: 10.1007/s10584-020-02780-9.
34. Edeleva O., Edelev A., Voskoboinikov M., Feoktistov A. Scientific Workflow-Based Synthesis of Optimal Microgrid Configurations. Energies. 2024. vol. 17(23). pp. 1–25. DOI: 10.3390/en17236138.
35. Maulik A., Das D. Optimal operation of microgrid using four different optimization techniques. Sustainable Energy Technologies and Assessments. 2017. vol. 21. pp. 100–120, DOI: 10.1016/j.seta.2017.04.005.
36. Priesmann J., Nolting L., Praktiknjo A. Are complex energy system models more accurate? An intra-model comparison of power system optimization models. Applied Energy. 2019. vol. 255. pp. 113783. DOI: 10.1016/j.apenergy.2019.113783.
2. Kemabonta T. Grid Resilience analysis and planning of electric power systems: The case of the 2021 Texas electricity crises caused by winter storm Uri (# TexasFreeze). The Electricity Journal. 2021. vol. 34(10). pp. 107044. DOI: 10.1016/j.tej.2021.107044.
3. Mancarella P. MES (multi-energy systems): An overview of concepts and evaluation models. Energy. 2014. vol. 65. pp. 1–17. DOI: 10.1016/j.energy.2013.10.041.
4. Poulin C.R., Kane M.B. Infrastructure resilience curves: Performance measures and summary metrics. Reliability Engineering & System Safety. 2021. vol. 216. pp. 107926. DOI: 10.1016/j.ress.2021.107926.
5. Dehghani A., Sedighizadeh M., Haghjoo F. An overview of the assessment metrics of the concept of resilience in electrical grids. International Transactions on Electrical Energy Systems. 2021. vol. 31(12). pp. e13159. DOI: 10.1002/2050-7038.13159.
6. Monakov Y., Tarasov A., Ivannikov A., Murzintsev, A., Shutenko, N. Optimization of equipment operation in power systems based on the use in the design of frequency-dependent models. Energies. 2023. vol. 16(18). pp. 6756. DOI: 10.3390/en16186756.
7. Karamov D.N., Suslov K.V. Structural optimization of autonomous photovoltaic systems with storage battery replacements. Energy Reports. 2021. vol. 7. pp. 349–358. DOI: 10.1016/j.egyr.2021.01.059.
8. Wang L., Yang Z., Sharma S., et al. A review of evaluation, optimization and synthesis of energy systems: methodology and application to thermal power plants. Energies. 2018. vol. 12(1). pp. 73. DOI: 10.3390/en12010073.
9. Mencarelli L., Chen Q., Pagot A., Grossmann I.E. A review on superstructure optimization approaches in process system engineering. Computers & Chemical Engineering. 2020. vol. 136. pp. 106808. DOI: 10.1016/j.compchemeng.2020.106808.
10. Lin S., Zhao L., Deng S., Zhao D., Wang W., Chen M. Intelligent collaborative attainment of structure configuration and fluid selection for the Organic Rankine cycle. Applied energy. 2020. vol. 264. pp. 114743. DOI: 10.1016/j.apenergy.2020.114743.
11. Lazzaretto A., Manente G., Toffolo A. SYNTHSEP: A general methodology for the synthesis of energy system configurations beyond superstructures. Energy. 2018. vol. 147. pp. 924–949. DOI: 10.1016/j.energy.2018.01.075.
12. Hoffmann M., Priesmann J., Nolting L., Praktiknjo, A., Kotzur, L., Stolten, D. Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models. Applied Energy. 2021. vol. 304. pp. 117825. DOI: 10.1016/j.apenergy.2021.117825.
13. Reinert C., Nilges B., Baumgärtner N., Bardow A. This is SpArta: Rigorous optimization of regionally resolved energy systems by spatial aggregation and decomposition. Applied Energy. 2024. vol. 367. pp. 123323. DOI: 10.1016/j.apenergy.2024.123323.
14. Castelli A.F., Pilotti L., Monchieri A., Martelli E. Optimal design of aggregated energy systems with (n-1) reliability: MILP models and decomposition algorithms. Applied Energy. 2024. vol. 356. pp. 122002. DOI: 10.1016/j.apenergy.2023.122002.
15. Patin M., Bégot S., Gustin F., Lepiller V. Enhancing Residential Sustainability: Multi-objective optimization of hydrogen-based multi-energy system. International Journal of Hydrogen. Energy. 2024. vol. 67. pp. 875–887. DOI: 10.1016/j.ijhydene.2023.12.201.
16. Gabrielli P, Fürer F, Mavromatidis G., Mazzotti M. Robust and optimal design of multi-energy systems with seasonal storage through uncertainty analysis. Applied Energy. 2019. vol. 238. pp. 1192–1210. DOI: 10.1016/j.apenergy.2019.01.064.
17. Baumgärtner N., Bahl B., Hennen M., Bardow A. RiSES3: Rigorous Synthesis of Energy Supply and Storage Systems via time-series relaxation and aggregation. Computers & Chemical Engineering. 2019. vol. 127. pp. 127–139. DOI: 10.1016/j.compchemeng.2019.02.006.
18. Fazlollahi S., Maréchal F. Multi-objective, multi-period optimization of biomass conversion technologies using evolutionary algorithms and mixed integer linear programming (MILP). Applied Thermal Engineering. 2013. vol. 50(2). pp. 1504–1513. DOI: 10.1016/j.applthermaleng.2011.11.035.
19. Schmeling L., Schönfeldt P., Klement P., Vorspel L., Hanke B., von Maydell K., Agert C. A generalised optimal design methodology for distributed energy systems. Renewable Energy. 2022. vol. 200. pp. 1223–1239. DOI: 10.1016/j.renene.2022.10.029.
20. Honarmand H.A., Rashid S. M. A sustainable framework for long-term planning of the smart energy hub in the presence of renewable energy sources, energy storage systems and demand response program. Journal of Energy Storage. 2022. vol. 52. pp. 105009. DOI: 10.1016/j.est.2022.105009.
21. Tsvirkun A.D., Rezchikov A.F., Dranko O.I., Kushnikov V.A., Bogomolov A.S. Optimization and simulation approach to determining critical combinations of company parameters. Avtomatika i telemehanika. 2024. vol. 10. pp. 53–64. DOI: 10.31857/S0005231024100053. (In Russ.).
22. Maulén L., Castro M., Lorca Á., Negrete-Pincetic M. Optimization-based expansion planning for power and hydrogen systems with feedback from a unit commitment model. Applied Energy. 2023. vol. 343. pp. 121207. DOI: 10.1016/j.apenergy.2023.121207.
23. Cho S., Tovar-Facio J., Grossmann I.E. Disjunctive optimization model and algorithm for long-term capacity expansion planning of reliable power generation systems. Computers & Chemical Engineering. 2023. vol. 174. pp. 108243. DOI: 10.1016/j.compchemeng.2023.108243.
24. Teichgraeber H., Küpper L.E., Brandt A.R. Designing reliable future energy systems by iteratively including extreme periods in time-series aggregation. Applied Energy. 2021. vol. 304. pp. 117696. DOI: 10.1016/j.apenergy.2021.117696.
25. Jing R., Wang X., Zhao Y., Zhou Y., Wu J., Lin J. Planning urban energy systems adapting to extreme weather. Advances in Applied Energy. 2021. vol. 3. pp. 100053. DOI: 10.1016/j.adapen.2021.100053.
26. Oster M.R., Amburg I., Chatterjee S., Eisenberg D.A., Thomas D.G., Pan F., Ganguly A.R. A tri-level optimization model for interdependent infrastructure network resilience against compound hazard events. IEEE International Symposium on Technologies for Homeland Security (HST). IEEE. Boston. 2024. vol. 47. pp. 1–3. DOI: 10.1109/HST53381.2021.9619824.
27. Pfetsch M.E., Schmitt A. A generic optimization framework for resilient systems. Optimization Methods and Software. 2023. vol. 38(2). pp. 356–385. DOI: 10.1080/10556788.2022.2142581.
28. Cao K.K., Von Krbek K., Wetzel M., Cebulla F., Schreck S. Classification and evaluation of concepts for improving the performance of applied energy system optimization models. Energies. 2019. vol. 12(24). pp. 4656. DOI: 10.3390/en12244656.
29. Biscani F., Izzo D. A parallel global multiobjective framework for optimization: pagmo. Journal of Open Source Software. 2020. vol. 5(53). pp. 2338. DOI: 10.21105/joss.02338.
30. Mikoni S.V., Sokolov B.V., Yusupov R.M. Kvalimetriya modeley i polimodel'nykh kompleksov [Qualimetry of Models and Polymodel Complexes]. Moscow: RAS, 2018. 314 p. (In Russ.).
31. Valkman Y.R., Rykhalsky A.Y. Architecture of model parametric space: hierarchy in Simon's Architecture of Complexity. Proceedings of the 2nd International Conference on Inductive Modelling (ICTM' 2008). Kyiv. 2008. pp. 58–59.
32. Danilov G., Voskoboinikov M. Testbed-based approach to testing a library for evaluating network reliability algorithms. Proceedings of the International Workshop on Critical Infrastructures in the Digital World (IWCI-2024). Irkutsk, 2024. pp. 3–4.
33. Safonov G., Potashnikov V., Lugovoy O., Safonov M., Dorina A., Bolotov A. The low carbon development options for Russia. Climatic Change. 2020. vol. 162. pp. 1929–1945. DOI: 10.1007/s10584-020-02780-9.
34. Edeleva O., Edelev A., Voskoboinikov M., Feoktistov A. Scientific Workflow-Based Synthesis of Optimal Microgrid Configurations. Energies. 2024. vol. 17(23). pp. 1–25. DOI: 10.3390/en17236138.
35. Maulik A., Das D. Optimal operation of microgrid using four different optimization techniques. Sustainable Energy Technologies and Assessments. 2017. vol. 21. pp. 100–120, DOI: 10.1016/j.seta.2017.04.005.
36. Priesmann J., Nolting L., Praktiknjo A. Are complex energy system models more accurate? An intra-model comparison of power system optimization models. Applied Energy. 2019. vol. 255. pp. 113783. DOI: 10.1016/j.apenergy.2019.113783.
Опубликован
2025-06-25
Как цитировать
Бычков, И. В., Феоктистов, А. Г., Воскобойников, М. Л., Еделев, А. В., Береснева, Н. М., & Еделева, О. А. (2025). Оптимизация живучести энергетических комплексов. Информатика и автоматизация, 24(3), 951-981. https://doi.org/10.15622/ia.24.3.8
Раздел
Математическое моделирование и прикладная математика
Copyright (c) Михаил Леонтьевич Воскобойников
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