Machinery & Energetics

Intelligent load management in dynamic power systems with a high share of RES and small modular reactors

Ievgen Alfimov
Download article
Abstract

The purpose of the study was to conduct a comprehensive assessment of the effectiveness of intelligent load management methods within Ukraine’s hybrid power systems, under conditions of an increasing proportion of renewable energy sources and the integration of small modular reactors, and to determine their impact on energy efficiency, operational stability, and the dynamics of daily load fluctuations. The methodology included a step-by-step analysis of energy-efficient control technologies, modelling of the operation of the 330/110 kV Kremenchuk substation based on actual operating profiles, and five-year load forecasting using a Long Short-Term Memory model and optimisation algorithms. The study established that traditional dispatch control mechanisms maintain stability only with low generation variability, while intelligent approaches provide a significant increase in accuracy, reduction of losses and improvement of mode stability. The results showed that optimisation based on a genetic algorithm reduces energy consumption by 12.4% and costs by 9.1%, while the Particle Swarm Optimisation algorithm demonstrated the highest efficiency, reducing energy consumption by up to 18.1%, reducing costs by up to 14.7% and providing the most accurate reproduction of daily profiles. Forecast calculations for the period up to 2030 showed an increase in the daily load amplitude and identified intervals of increased sensitivity in which the use of intelligent control strategies can reduce daily active power deviations by more than 40% and losses to be reduced by almost 17%. The practical significance of the results is determined by the fact that the established patterns can be used to modernise Ukrainian power grids, optimise the integration of renewable generation, increase the reliability of industrial power systems, and justify the introduction of smart technologies in grids with a growing share of unstable sources

Keywords

optimisation strategies, predictive models, multilevel modelling, stochastic generation, system stability, microgrids

Suggested citation
Alfimov, Ie. (2025). Intelligent load management in dynamic power systems with a high share of RES and small modular reactors. Machinery & Energetics, 16(4), 76-88. https://doi.org/10.31548/machinery/4.2025.76
References
  1. Ahmad, S., Shafiullah, Ahmed, C.B., & Alowaifeer, M. (2023). A review of microgrid energy management and control strategies. IEEE Access, 11, 21729-21757. doi: 10.1109/ACCESS.2023.3248511.
  2. Al-Ghussain, L., Samu, R., Taylan, O., & Fahrioglu, M. (2020). Sizing renewable energy systems with energy storage systems in microgrids for maximum cost-efficient utilization of renewable energy resources. Sustainable Cities and Society, 55, article number 102059. doi: 10.1016/j.scs.2020.102059.
  3. An, B., Kang, D., Kim, J., & Thu, M.K. (2025). Small modular reactors (SMRs) and smart energy cities: Techno-socioeconomic analysis of global energy transition plans. SSRN. doi: 10.2139/ssrn.5150425.
  4. Aslam, S., Herodotou, H., Mohsin, S.M., Javaid, N., Ashraf, N., & Aslam, S. (2021). A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews, 144, article number 110992. doi: 10.1016/j.rser.2021.110992.
  5. Biswal, B., Deb, S., Datta, S., Ustun, T.S., & Cali, U. (2024). Review on smart grid load forecasting for smart energy management using machine learning and deep learning techniques. Energy Reports, 12, 3654-3670. doi: 10.1016/j.egyr.2024.09.056.
  6. Blinov, I.V. (2025). Development of distributed energy in Ukraine using microgrid technologies. Visnyk of the National Academy of Sciences of Ukraine, 5, 35-44. doi: 10.15407/visn2025.05.035.
  7. Cherep, O., Osmakovska, K., & Lyshenko, O. (2023). Feasibility of using energy-efficient technologies and renewable energy sources. Modeling the Development of the Economic Systems, 2, 203-207. doi: 10.31891/mdes/2023-8-27.
  8. Ukrainian Wind Energy Association. (2024). Data on Ukraine’s wind and solar potential now available on the re data explorer. Retrieved from https://uwea.com.ua/en/news/entry/dan-schodo-vtrovogo-sonyachnogo-potencalu-ukrani-vdteper-dostupn-na-re-data.
  9. DSTU ISO 50001:2020. (2020). Energy management systems. Requirements and guidance for use. Retrieved from https://online.budstandart.com/ua/catalog/doc-page.html?id_doc=90178.
  10. European Environment Agency. (n.d.). Analysis and data. Retrieved from https://www.eea.europa.eu/en/analysis.
  11. Faraji, J., Ketabi, A., Hashemi-Dezaki, H., Shafie-Khah, M., & Catalão, J.P.S. (2020). Optimal day-ahead self-scheduling and operation of prosumer microgrids using hybrid machine learning-based weather and load forecasting. IEEE Access, 8, 157284-157305. doi: 10.1109/ACCESS.2020.3019562.
  12. Hlushko, A. (2024). Strengthening Ukraine’s energy security. Economics and Region, 3(94), 157-163. doi: 10.26906/EiR.2024.3(94).3494.
  13. Holland, J.H. (1975). Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. Ann Arbor: University of Michigan Press.
  14. IAEA TECDOC-1937. (2020). Probabilistic safety assessment for seismic events. Retrieved from https://www-pub.iaea.org/MTCD/Publications/PDF/TE-1937_web.pdf.
  15. IEC 61850:2023 (2023). Communication networks and systems for power utility automation all parts Retrieved from https://www.bsbedge.com/standard/communication-networks-and-systems-for-power-utility-automation-all-parts/IEC61850-2023.
  16. IEC 62559-2:2015. (2015). Use case methodology – Part 2: Definition of the templates for use cases, actor list and requirements list. Retrieved from https://cdn.standards.iteh.ai/samples/20300/c3c2f3905fd045cba1b81c615be6d3c3/IEC-62559-2-2015.pdf.
  17. IEC TR 61850-90-4:2020. (2020). Communication networks and systems for power utility automation – part 90-4: Network engineering guidelines. Retrieved from https://webstore.iec.ch/en/publication/64801.
  18. International Energy Agency. (2025). Global energy review 2025. Retrieved from https://www.iea.org/reports/global-energy-review-2025.
  19. ISO 14044:2006. (2006). Environmental management – life cycle assessment – requirements and guidelines. Retrieved from https://www.iso.org/standard/38498.html.
  20. JSC “Market Operator”. (n.d.). Reporting. Retrieved from https://www.oree.com.ua/index.php/web/1005?lang=english.
  21. Kassab, F.A., Rodriguez, R., Celik, B., Locment, F., & Sechilariu, M. (2024). A comprehensive review of sizing and energy management strategies for optimal planning of microgrids with PV and other renewable integration. Applied Sciences, 14(22), article number 10479. doi: 10.3390/app142210479.
  22. Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95 – international conference on neural networks (pp. 1942-1948). Perth: IEEE. doi: 10.1109/ICNN.1995.488968.
  23. Khan, M.R., Haider, Z.M., Malik, F.H., Almasoudi, F.M., Alatawi, K.S.S., & Bhutta, M.S. (2024). A comprehensive review of microgrid energy management strategies considering electric vehicles, energy storage systems, and AI techniques. Processes, 12(2), article number 270. doi: 10.3390/pr12020270.
  24. Kharazishvili, Y., Kwilinski, A., Sukhodolia, O., Dzwigol, H., Bobro, D., & Kotowicz, J. (2021). The systemic approach for estimating and strategizing energy security: The case of Ukraine. Energies, 14(8), article number 2126. doi: 10.3390/en14082126.
  25. Kondaiah, V.Y., Saravanan, B., Sanjeevikumar, P., & Khan, B. (2022). A review on short-term load forecasting models for micro-grid application. Journal of Engineering, 2022(7), 665-689. doi: 10.1049/tje2.12151.
  26. Krasnostanova, N., Yaromich, S., & Yatsenko, O. (2024). Ways of implementing energy management at Ukrainian enterprises. Kyiv Economic Scientific Journal, 6, 70-77. doi: 10.32782/2786-765X/2024-6-10.
  27. Lee, J.I. (2024). Review of small modular reactors: Challenges in safety and economy to success. Korean Journal of Chemical Engineering, 41(10), 2761-2780. doi: 10.1007/s11814-024-00207-0.
  28. Li, L., & Wang, X. (2021). Design and operation of hybrid renewable energy systems: Current status and future perspectives. Current Opinion in Chemical Engineering, 31, article number 100669. doi: 10.1016/j.coche.2021.100669.
  29. Ma, P., Cui, S., Chen, M., Zhou, S., & Wang, K. (2023). Review of family-level short-term load forecasting and its application in household energy management system. Energies, 16(15), article number 5809. doi: 10.3390/en16155809.
  30. Manghale, A.B., Mahajan, G.K., & Temburnikar, G.P. (2017). Dynamic load management system for smart micro-grid system. International Journal of Engineering Trends and Technology, 43(2), 122-125. doi: 10.14445/22315381/IJETT-V43P220.
  31. Mazauric, A.-L., Sciora, P., Pascal, V., Droin, J.-B., Bésanger, Y., Hadjsaïd, N., Tran, Q.T., & Guyonneau, N. (2022). Simplified approach to determine the requirements of a “flexible nuclear reactor” in power system with high insertion of variable renewable energy sources. EPJ Nuclear Sciences & Technologies, 8, article number 5. doi: 10.1051/epjn/2021026.
  32. Ministry of Energy of Ukraine. (2022). Energy strategy. Retrieved from https://mev.gov.ua/en/reforma/energy-strategy.
  33. National Commission for State Regulation of Energy and Utilities. (n.d.). Register of NEURC decisions. Retrieved from https://www.nerc.gov.ua/npasearch?&category=4&tags=postanovi.
  34. National Power Company Ukrenergo. (n.d.). Reporting. Retrieved from https://ua.energy/about_us/reporting/.
  35. Onteru, R.R., & Sandeep, V. (2024). An intelligent model for efficient load forecasting and sustainable energy management in sustainable microgrids. Discover Sustainability, 5, article number 170. doi: 10.1007/s43621-024-00356-6.
  36. Order of the Cabinet of Ministers of Ukraine No. 730-2018-r “On Approval of the Project “Reconstruction of open switchgear 330 kV, 150 kV, 110 kV, Substation 330 kV “Novokyivska”, Substation 330 kV “Zhovtneva”, Substation 330 kV “Zhytomyrska”, Substation 330 kV “Cherkaska”, Substation 330 kV “Sumy”, Substation 330 kV “Kremenchuk” 6th stage “Reconstruction of open switchgear 330 kV (inv. No. 657/1), 150 kV (inv. No. 658/1) Substation 330/150/10 kV “Kremenchuk”, Poltava st., 2B, Kyyashky village, Horishni Plavni city, Poltava region”. (2018, October). Retrieved from https://zakon.rada.gov.ua/laws/show/730-2018-%D1%80#Text.
  37. Quiñones, J.J., Pineda, L.R., Ostanek, J., & Castillo, L. (2023). Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources. Energy Conversion and Management, 293, article number 117440. doi: 10.1016/j.enconman.2023.117440.
  38. Rodriguez-Gil, J.A., Mojica-Nava, E., Vargas-Medina, D., Arevalo-Castiblanco, M.F., Cortes, C.A., Rivera, S., & Cortes-Romero, J. (2024). Energy management system in networked microgrids: An overview. Energy Systems. doi: 10.1007/s12667-024-00676-6.
  39. Shafiullah, Refat, A.M., Haque, M.E., Chowdhury, D.M.H., Hossain, S., Alharbi, A.G., Alam, S., Ali, A., & Hossain, S. (2022). Review of recent developments in microgrid energy management strategies. Sustainability, 14(22), article number 14794. doi: 10.3390/su142214794.
  40. Tan, S., Cheng, S., Wang, K., Liu, X., Cheng, H., & Wang, J. (2023). The development of micro and small modular reactor in the future energy market. Frontiers in Energy Research, 11, article number 1149127. doi: 10.3389/fenrg.2023.1149127.
  41. Tkalenko, S.I., Liubachivska, R.Z., & Makedon, H.M. (2024). Modeling of the energy security of the country in the context of sustainable development: The case of Ukraine. IOP Conference Series: Earth and Environmental Science, 1415, article number 012061. doi: 10.1088/1755-1315/1415/1/012061.
  42. Turner, A. (2025). What’s new in Python 3.12. Retrieved from https://docs.python.org/dev/whatsnew/3.12.html.
  43. Uddin, M., Mo, H., Dong, D., Elsawah, S., Zhu, J., & Guerrero, J.M. (2023). Microgrids: A review, outstanding issues and future trends. Energy Strategy Reviews, 49, article number 101127. doi: 10.1016/j.esr.2023.101127.
  44. Voronenko, V., Kubatko, O., Karintseva, O., Konovalenko, I., & Mishchenko, Y. (2025). Energy efficiency as a way to ensure Ukraine’s energy security. Scientific Bulletin of the International Humanitarian University, 62, 24-30. doi: 10.32782/2413-2675/2025-62-4.
  45. Wazirali, R., Yaghoubi, E., Abujazar, M.S.S., Ahmad, R., & Vakili, A.H. (2023). State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques. Electric Power Systems Research, 225, article number 109792. doi: 10.1016/j.epsr.2023.109792.
  46. Zhao, X., Liu, W., Deng, Y., & Zhu, J.Y. (2017). Low-temperature microbial and direct conversion of lignocellulosic biomass to electricity: Advances and challenges. Renewable & Sustainable Energy Reviews, 71, 268-282. doi: 10.1016/j.rser.2016.12.055.
  47. Zhiliaeva, A. (2024). AnyLogic 8.9: Enhanced performance and collaboration features. Retrieved from https://www.anylogic.com/blog/anylogic-8-9-enhanced-performance-and-collaboration-features/.