Informacionnye TehnologiiInformacionnye TehnologiiИнформационные технологии1684-6400New Technologies Publishing House70228010.17587/it.31.346-355Modeling and optimizationМоделирование и оптимизацияResearch ArticleAutomation of calculations in multi-agent modeling of integrated energy systemsАвтоматизация вычислений при проведении мультиагентного моделирования интегрированных энергетических системBarakhtenkoE. A.БаpахтенкоЕ. А.
Russian Federation

PhD, Scientific Secretary

канд. техн. наук, доц., уч. секр.

barakhtenko@isem.irk.ruSokolovD. V.СоколовД. В.
Russian Federation

PhD, Senior Researcher

канд. техн. наук, ст. науч. сотр.

sokolov_dv@isem.irk.ruMayorovG. S.МайоровГ. С.
Russian Federation

PhD, Researcher

канд. техн. наук, науч. сотр.

mayorovgs@isem.irk.ruMelentiev Energy Systems Institute of Siberian Branch of the Russian Academy of SciencesФедеральное государственное бюджетное учреждение науки Институт систем энергетики им. Л. А. Мелентьева Сибирского отделения Российской академии наук (ИСЭМ СО РАН)150720253173463550602202606022026Copyright ©; 2025, Informacionnye TehnologiiCopyright ©; 2025, Информационные технологии2025Informacionnye TehnologiiИнформационные технологииhttps://journals.eco-vector.com/1684-6400/article/view/702280

In modern energy sector, the importance of integrated energy systems is constantly increasing, which is caused by the high significance of these systems for industry and the public utility sphere of modern society. In modern conditions, the optimal design of these systems, which has a scientific (technical and economic) justification, is becoming relevant. Designing integrated energy systems is a complex problem, which is due to the high complexity of the configuration of these systems, a wide range of equipment used and a diverse set of mathematical models and specialized software used for its modeling, the need to model a number of decision-making centers and objects with complex behavior. The use of a multi-agent approach allows one to effectively model various directions of their development in virtual space and ensures the creation of effective design solutions.

Carrying out multi-agent modeling requires the organization of a complex computational process, which is due to the wide variety of equipment used and models of subsystems of integrated energy systems, the complexity of programming and the need to adjust to the features of the modeled system. Automation of the construction of a multi-agent system allows one to overcome these difficulties and eliminate the labor-intensive stages of its formation and configuration. The article proposes a methodological approach that ensures automation of calculations during multi-agent modeling of integrated energy systems when solving the problem of their design. This approach includes the following components:

an architecture of the software system;

principles of software organization of the multi-agent system; principles of automated construction of the multi-agent system;

the structure of the ontology system;

a technique for solving the problem using the automation of multi-agent modeling.

The results of the computational experiment obtained using the developed methodological approach are presented. As a result of the experiment, an optimal configuration of the integrated energy system for energy supply to consumers was obtained.

The developed methodological approach can be used by research, design and operational organizations that design and develop integrated energy systems. Its application allows one to increase the efficiency of the design process, the quality of the resulting design solution and automate labor-intensive computational operations performed when determining the configuration of the designed integrated energy system and the characteristics of the equipment used.

Мультиагентный подход является эффективным средством решения актуальной задачи проектирования интегрированных энергетических систем, но его практическое применение осложняется трудностью построе­ния мультиагентных систем. В статье предложен методологический подход, который обеспечивает автома­тизацию вычислений при проведении мультиагентного моделирования интегрированных энергетических систем при решении задачи их проектирования.

methodological approachmulti-agent modelingmulti-agent systemautomation of computingintegrated energy systemapplied ontologiessoftware engineeringenergy system designprosumerautomation of modelingметодологический подходмультиагентное моделированиемультиагентная системаавтоматизация вычисленийинтегрированная энергетическая системаприкладные онтологиипрограммная инженерияпроектирование энергосистемактивный потребительавтоматизация моделированияРоссийский научный фондRussian Science Foundation24-29-00823The research was performed at the Melentiev Energy Systems Institute of Siberia Branch of the Russian Academy of Sciences under the support of the Russian Science Foundation (Grant number 24-29-00823).Исследование выполнено в Институте систем энергетики им. Л.А. Мелентьева Сибирского отделения Российской академии наук при поддержке Российского научного фонда (грант № 24-29-00823).Stennikov V., Barakhtenko E., Mayorov G., Sokolov D., Zhou B. Coordinated management of centralized and distributed generation in an integrated energy system using a multi-agent approach // Applied Energy. 2022. Vol. 309. 118487.Stennikov V., Barakhtenko E., Mayorov G. An approach to energy distribution between sources in a hierarchical integrated energy system using multi-agent technologies // Energy Reports. 2023. Vol. 9. P. 856—865.Bakken B., Haugstad A., Hornnes K. S. Simulation and Optimization of Systems with Multiple Energy Carriers // Conference of the Scandinavian Simulation Society Linkoping, Sweden. 1999.Voropai N., Stennikov V., Senderov S., Barakhtenko E., Voitov O., Ustinov A. Modeling of Integrated Energy Supply Systems: Main Principles, Model, and Applications // Journal of Energy Engineering. 2017. Vol. 143, N. 5. P. 1—11.Voropai N. I., Stennikov V. A., Barakhtenko E. A. Integrated Energy Systems: Challenges, Trends, Philosophy // Studies on Russian Economic Development. 2017. Vol. 28, N. 5. P. 492—499.Wooldridge M., Jennings N. Intelligent Agents: Theory and Practice // The Knowledge Engineering Review. 1995. Vol. 10, N. 2. P. 115—152.Fisher K., Muller J. P., Heimig I., Scheer A-W. Intelligent Agents in Virtual Enterprises // In Proceedings of the First International. Conference "The Practical Application of Intelligent Agents and Multi-Agent Technology", London, UK. 1996. P. 205—224.Gomes L., Vale Z., Corchado J. M. Microgrid management system based on a multi-agent approach: An office building pilot // Measurement. 2020. Vol. 154. 107427.Khan M. W., Wang J., Xiong L., Ma M. Modelling and optimal management of distributed microgrid using multiagent systems // Sustainable Cities and Society. 2018. Vol. 41. P. 154—169.Logenthiran T., Srinivasan D., Khambadkone A. M. Multi-agent system for energy resource scheduling of integrated microgrids in a distributed system // Electric Power Systems Research. 2011. Vol. 81. P. 138—148.Zhang B., Hu W., Ghias A. M. Y. M., Xu X., Chen Z. Two-timescale autonomous energy management strategy based on multi-agent deep reinforcement learning approach for residential multicarrier energy system // Applied Energy. 2023. Vol. 351. 121777.Li S., Hu W., Cao D., Chen Z., Huang Q., Blaabjerg F., Liao K. Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning // Applied Energy. 2023. Vol. 346. 121359.Booch G., Rumbaugh J., Jacobson I. The Unified Modeling Language User Guide. Boston: Addison Wesley, 1999.Booch G. Object-Oriented Analysis and Design with Applications. Boston: Addison Wesley, 1994.Rodrigues da Silva A. R. Model-driven engineering: A survey supported by the unified conceptual model // Computer Languages, Systems & Structures. 2015. Vol. 43. P. 139—155.Brambilla M. Cabot J., Wimmer M. Model-Driven Software Engineering in Practice. Springer Cham. 2017. P. 191.Seixas J., Ribeiro A., Rodrigues da Silva A. A Model-Driven Approach for Developing Responsive Web Apps // In Proceedings of the 14th International Conference ENASE, Heraklion, Crete, Greece. 2019. Vol. 1. P. 257—264.Akdur D., Garousi V., Demirors O. A survey on modeling and model-driven engineering practices in the embedded software industry // Journal of Systems Architecture. 2018. Vol. 91. P. 62—82.Boussaid I., Siarry P., Ahmed-Nacer M. A survey on search-based model-driven engineering // Automated Software Engineering. 2017. Vol. 24. P. 233—294.Al-Azzoni I., Blank J., Petrovic N. A Model-Driven Approach for Solving the Software Component Allocation Problem // Algorithms. 2021. Vol. 14, N. 12. 354.Araujo Silva E., Valentin E., Carvalho J. R. H., Silva Barreto R. A survey of Model Driven Engineering in robotics // Journal of Computer Languages. 2021. Vol. 62. 101021.Stennikov V. A., Barakhtenko E. A., Sokolov D. V., Mayorov G. S. Automation of Calculations in the Design of an Integrated Energy System Based on its Digital Twin, Informacionnye Tehnologii, 2024, vol. 30, no. 4, pp. 140—149. DOI: 10.17587/it.30.140-149.Hazzard K., Bock J. Metaprogramming in.NET. Manning Publications, NY, 2013. P. 360.Limmel R. Software Languages: Syntax, Semantics, and Metaprogramming. Cham: Springer, 2018. P. 454.