Improving the strategy for using rechargeable battery energy storage systems to enhance the economic efficiency of photovoltaic power plants in the electricity market

Authors

DOI:

https://doi.org/10.15587/1729-4061.2026.365476

Keywords:

photovoltaic power plant, electricity storage system, electricity market, optimization of operation

Abstract

This study investigates operational process involving rechargeable battery energy storage systems (ESSs) in a combination with photovoltaic power plants (PVPPs) in the competitive electricity market. The task addressed is to improve the functioning efficiency of electric power systems with deep PVPP penetration. This work analyzes scenarios and strategy optimization when using ESSs to increase the economic efficiency of PVPPs operating in a single complex. The research results established those factors that affect the technical and economic indicators of ESS application and helped define a combined management strategy.

Practical ESS management scenarios with different mechanisms for forming an economic effect were studied, in particular, the day-ahead market arbitrage scenario (DAM) and the PVPP balancing scenario. The latter, in addition to minimizing the costs of settling imbalances, provides for the sale of excess stored energy during hours of maximum price.

The use of an ensemble of machine learning methods (RandomForest + XGBoost + LightGBM) has made it possible to achieve high accuracy in forecasting PVPP generation. Energy indicators were optimized for each scenario of ESS control system; financial results were assessed based on the MILP and MPC methods. That made it possible to quantitatively compare the effectiveness of the arbitrage and balancing strategies and justify the choice of ESS operation mode depending on the volatility of the DAM prices, the level of forecast uncertainty of generation, and the current rules for regulating imbalances in the electricity market.

The results were verified on the example of a real 9.5 MW PVPP, supplemented by ESS with an energy capacity of 2 MWh, which made it possible to take into account the financial responsibility of the producer for imbalances and the impact of price asymmetry on the end result

Author Biographies

Volodymyr Kulyk, Vinnytsia National Technical University

Doctor of Technical Sciences, Associate Professor

Department of Electric Power Stations and Systems

Maksym Zatkhei, Vinnytsia National Technical University

PhD Student

Department of Electric Power Stations and Systems

Vira Teptia, Vinnytsia National Technical University

Candidate of Technical Sciences, Associate Professor

Department of Electric Power Stations and Systems

Yurii Hrytsiuk, Lutsk National Technical University

Candidate of Technical Sciences, Associate Professor

Department of Electrical Engineering

Sviatoslav Vishnevskyi, Vinnytsia National Technical University

Candidate of Technical Sciences

Department of Electric Power Stations and Systems

Iryna Hrytsiuk, Lutsk National Technical University

Candidate of Technical Sciences, Associate Professor

Department of Electrical Engineering

References

  1. Pro zatverdzhennia Pravyl rynku. Postanova 14.03.2018 No. 307. Natsionalna komisiya, shcho zdiysniuie derzhavne rehuliuvannia u sferakh enerhetyky ta komunalnykh posluh. Available at: https://zakon.rada.gov.ua/laws/show/v0307874-18#Text
  2. Pedro, H. T. C., Coimbra, C. F. M. (2012). Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 86 (7), 2017–2028. https://doi.org/10.1016/j.solener.2012.04.004
  3. Voyant, C., Notton, G., Kalogirou, S., Nivet, M.-L., Paoli, C., Motte, F., Fouilloy, A. (2017). Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569–582. https://doi.org/10.1016/j.renene.2016.12.095
  4. Yang, D., Kleissl, J., Gueymard, C. A., Pedro, H. T. C., Coimbra, C. F. M. (2018). History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining. Solar Energy, 168, 60–101. https://doi.org/10.1016/j.solener.2017.11.023
  5. Fountoulakis, I., Kosmopoulos, P., Papachristopoulou, K., Raptis, I.-P., Mamouri, R.-E., Nisantzi, A. et al. (2021). Effects of Aerosols and Clouds on the Levels of Surface Solar Radiation and Solar Energy in Cyprus. Remote Sensing, 13 (12), 2319. https://doi.org/10.3390/rs13122319
  6. Poliukhov, A., Gvozdeva, A., Piskunova, D. (2026). The influence of CAMS aerosol climatology on shortwave irradiance and temperature forecasts in the ICON NWP model verified against observational data. Atmospheric Research, 338, 109025. https://doi.org/10.1016/j.atmosres.2026.109025
  7. Skoplaki, E., Palyvos, J. A. (2009). Operating temperature of photovoltaic modules: A survey of pertinent correlations. Renewable Energy, 34 (1), 23–29. https://doi.org/10.1016/j.renene.2008.04.009
  8. Jordan, D. C., Kurtz, S. R. (2012). Photovoltaic Degradation Rates – An Analytical Review. National Renewable Energy Laboratory. Available at: https://www.nrel.gov/docs/fy13osti/51664.pdf
  9. Cole, W., Frazier, A., Augustine, C. (2021). Cost Projections for Utility-Scale Battery Storage: 2021. National Renewable Energy Laboratory. https://doi.org/10.2172/1786976
  10. Akrami, E., Ameri, M., Rocco, M. V., Sanvito, F. D., Colombo, E. (2020). Thermodynamic and exergo-economic analyses of an innovative semi self-feeding energy system synchronized with waste-to-energy technology. Sustainable Energy Technologies and Assessments, 40, 100759. https://doi.org/10.1016/j.seta.2020.100759
  11. Batteries and Secure Energy Transitions (2024). IEA. Available at: https://www.iea.org/reports/batteries-and-secure-energy-transitions
  12. Baumgarte, F., Glenk, G., Rieger, A. (2020). Business Models and Profitability of Energy Storage. iScience, 23 (10), 101554. https://doi.org/10.1016/j.isci.2020.101554
  13. Inman, R. H., Pedro, H. T. C., Coimbra, C. F. M. (2013). Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, 39 (6), 535–576. https://doi.org/10.1016/j.pecs.2013.06.002
  14. Diagne, M., David, M., Lauret, P., Boland, J., Schmutz, N. (2013). Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renewable and Sustainable Energy Reviews, 27, 65–76. https://doi.org/10.1016/j.rser.2013.06.042
  15. Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, 78–111. https://doi.org/10.1016/j.solener.2016.06.069
  16. Denholm, P., Jorgenson, J., Hummon, M., Jenkin, T., Palchak, D., Kirby, B. et al. (2013). The Value of Energy Storage for Grid Applications. National Renewable Energy Laboratory. https://doi.org/10.2172/1220050
  17. Staffell, I., Rustomji, M. (2016). Maximising the value of electricity storage. Journal of Energy Storage, 8, 212–225. https://doi.org/10.1016/j.est.2016.08.010
  18. Hesse, H., Schimpe, M., Kucevic, D., Jossen, A. (2017). Lithium-Ion Battery Storage for the Grid – A Review of Stationary Battery Storage System Design Tailored for Applications in Modern Power Grids. Energies, 10 (12), 2107. https://doi.org/10.3390/en10122107
  19. Parra, D., Gillott, M., Norman, S. A., Walker, G. S. (2015). Optimum community energy storage system for PV energy time-shift. Applied Energy, 137, 576–587. https://doi.org/10.1016/j.apenergy.2014.08.060
  20. Zhang, Y., Wang, J., Wang, X. (2014). Review on probabilistic forecasting of wind power generation. Renewable and Sustainable Energy Reviews, 32, 255–270. https://doi.org/10.1016/j.rser.2014.01.033
  21. Burger, S., Chaves-Ávila, J. P., Batlle, C., Pérez-Arriaga, I. J. (2017). A review of the value of aggregators in electricity systems. Renewable and Sustainable Energy Reviews, 77, 395–405. https://doi.org/10.1016/j.rser.2017.04.014
  22. Kulyk, V. V., Zatkhei, M. V. (2024). Improving the Accuracy of the Forecast of Electricity Production by Photovoltaic Power Station Based on the Random Forest Method. Visnyk of Vinnytsia Politechnical Institute, 177 (6), 52–61. https://doi.org/10.31649/1997-9266-2024-177-6-52-61
Improving the strategy for using rechargeable battery energy storage systems to enhance the economic efficiency of photovoltaic power plants in the electricity market

Downloads

Published

2026-06-30

How to Cite

Kulyk, V., Zatkhei, M., Teptia, V., Hrytsiuk, Y., Vishnevskyi, S., & Hrytsiuk, I. (2026). Improving the strategy for using rechargeable battery energy storage systems to enhance the economic efficiency of photovoltaic power plants in the electricity market. Eastern-European Journal of Enterprise Technologies, 3(8 (141), 15–27. https://doi.org/10.15587/1729-4061.2026.365476

Issue

Section

Energy-saving technologies and equipment