Machinery & Energetics

Economic efficiency of the Net Billing model for solar power plants in the private sector of Ukraine (using houses of 120-200 m² as an example)

Taras Bilas, Volodymyr Yaras
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Abstract

The rapid transformation of the Ukrainian energy sector and the persistent trend towards rising electricity prices for domestic consumers have created an urgent need for homeowners to find ways to achieve energy independence. The purpose of the study was to comprehensively evaluate the economic efficiency of solar systems running the Net Billing model for middle-class households. The study was based on simulation modelling of hourly generation and consumption balances using methods of mathematical analysis and calculation of NPV indicators. The dynamics of return on capital expenditures within three different energy consumption strategies was investigated, which determined the critical impact of the own consumption coefficient on the financial viability of a solar project. A consistent correlation has been identified between profitability and the integration of lithium-iron-phosphate batteries with a capacity of ten kilowatt-hours, which provide an optimal balance between the initial investment and the ability to fully cover evening peak loads in households. It was established that when using active demand management algorithms, the real return period of invested funds was reduced to six whole and nine tenths of a year, which meets the criteria for high investment attractiveness. Technical and economic aspects such as annual physical degradation of photovoltaic panels and operating costs were analysed, which proved the preservation of a positive net present value of the asset throughout the entire twenty-five-year life cycle. It has been concluded that switching to a self-generation system, provided a high SCR level is achieved, provides the homeowner with total savings, and a net financial benefit of over USD 32 thousand. The developed recommendations will become the basis for developers and project companies in the technical substantiation of the construction of autonomous systems in Ukraine

Keywords

prosumerism; energy independence; return on investment; decentralised generation; battery energy storage systems; demand management; photovoltaic systems

Suggested citation
Bilas, T., & Yaras, V. (2026). Economic efficiency of the Net Billing model for solar power plants in the private sector of Ukraine (using houses of 120-200 m² as an example). Machinery & Energetics, 17(2), 39-49. https://doi.org/10.31548/machinery/2.2026.3
References
  1. Benalcazar, P., Andrade, C., & Guamán, W. (2025). Prosumer policy options in developing countries: A comparative analysis of feed-in tariffs, net metering, and net billing for residential PV-battery systems. Energy Policy Journal, 28(1), 77-98. doi: 10.33223/epj/202801.
  2. Blinov, I.V., Trach, I.V., Parus, Y.V., Derevianko, D.G., & Khomenko, V.M. (2022). Voltage and reactive power regulation in distribution networks by the means of distributed renewable energy sources. Technical Electrodynamics, 2, 60-68. doi: 10.15407/techned2022.02.060.
  3. Buratynskyi, I., & Nechaieva, T. (2022). The least-cost optimization of PV-station’s DC/AC equipment using battery energy storage system. Latvian Journal of Physics and Technical Sciences, 59(1), 53-62. doi: 10.2478/lpts-2022-0006.
  4. Denysiuk, S., & Derevianko, D. (2021). The cost-based DSM methods in microgrids with DG sources. In 2021 IEEE 2nd KhPI week on advanced technology (KhPIWeek) (pp. 544-548). Kharkiv: IEEE. doi: 10.1109/KhPIWeek53812.2021.9570096.
  5. Fan, J., & Zhou, H. (2023). Optimization of a hybrid solar/wind/storage system with bio-generator for a household by emerging metaheuristic optimization algorithm. Journal of Energy Storage, 73, article number 108967. doi: 10.1016/j.est.2023.108967.
  6. Hassan, A.S., Cipcigan, L., & Jenkins, N. (2017). Optimal battery storage operation for PV systems with tariff incentives. Applied Energy, 203, 422-441. doi: 10.1016/j.apenergy.2017.06.043.
  7. Kabir, A., Sunny, M.R., & Siddique, N.I. (2021). Assessment of grid-connected residential pv-battery systems in Sweden – a techno-economic perspective. Kuala Lumpur: IEEE. doi: 10.1109/ICPEA51500.2021.9417835.
  8. Kurbatova, T., Sotnyk, I., Perederii, T., Prokopenko, O., Wit, B., Pysmenna, U., & Kubatko, O. (2024). On-grid hybrid wind-solar power plants in Ukraine’s residential sector: Economic justification of installation under different support schemes. Energies, 17(20), article number 5214. doi: 10.3390/en17205214.
  9. Kyrylenko, O.V. (2022). Measures and means of transforming Ukraine’s energy industry into an intelligent system. Visnyk of the National Academy of Sciences of Ukraine, 3, 18-31. doi: 10.15407/visn2022.03.018.
  10. Liu, X., & Gou, Z. (2025). Hybrid forecasting and optimization framework for residential photovoltaic-battery systems: Integrating data-driven prediction with multi-strategy scenario analysis. Building Simulation, 18, 1587-1609. doi: 10.1007/s12273-025-1319-6.
  11. Luthander, R., Widén, J., Nilsson, D., & Palm, J. (2015). Photovoltaic self-consumption in buildings: A review. Applied Energy, 142, 80-94. doi: 10.1016/j.apenergy.2014.12.028.
  12. Olczak, P., Matuszewska, D., Lishchenko, A., Zhydyk, I., Koval, V., & Iermakova, O. (2022). The economic efficiency of photovoltaic energy for energy prosumers. Polityka Energetyczna – Energy Policy Journal, 25(4), 95-114. doi: 10.33223/epj/155095.
  13. Ordóñez, A., Sánchez, E., Rozas, L., García, R., & Parra-Domínguez, J. (2022). Net-metering and net-billing in photovoltaic self-consumption: The cases of Ecuador and Spain. Sustainable Energy Technologies and Assessments, 53, article number 102434. doi: 10.1016/j.seta.2022.102434.
  14. Photovoltaic Geographical Information System (PVGIS). (n.d.). Retrieved from https://joint-research-centre.ec.europa.eu/photovoltaic-geographical-information-system-pvgis_en.
  15. Pysmenna, U.I., Trypolska, H.S., & Kubatko, O.V. (2023). Ukraine’s energy market and small producers: possibilities for integration. Economy and Society, 53. doi: 10.32782/2524-0072/2023-53-97.
  16. Scott, C., Ahsan, M., & Albarbar, A. (2023). Machine learning for forecasting a photovoltaic (PV) generation system. Energy, 278, article number 127807. doi: 10.1016/j.energy.2023.127807.
  17. Smiech, L., Kurbatova, T., & Kubatko, O. (2025). Assessing the impact of household energy efficiency and renewable energy developments on energy poverty reduction. Environmental Economics, 16(4), 83-94. doi: 10.21511/ee.16(4).2025.06.
  18. Sotnyk, I., Sasse, J.-P., & Trutnevyte, E. (2024). Decarbonizing Ukraine’s electricity sector in 2035: Scenario analysis. Energy and Climate Change, 6(3), article number 100170. doi: 10.1016/j.egycc.2024.100170.
  19. Trela, M., & Dubel, A. (2022). Net-Metering vs. net-billing from the investors perspective-impacts of changes in RES financing in Poland. Energies, 15(1), article number 227. doi: 10.3390/en15010227.
  20. Yagup, V.G., & Yagup, K.V. (2024). Analytical method of determining conditions for full compensation of reactive power in the power supply system. Electrical Engineering & Electromechanics, 2, 75-80. doi: 10.20998/2074-272X.2024.2.11.
  21. Zgurovets, O., & Kulyk, M. (2023). Application of energy storage for automatic load and frequency control. In Power systems research and operation. Studies in systems, decision and control (Vol. 220, pp. 75-85). Cham: Springer. doi: 10.1007/978-3-031-17554-1_4.