Forecasting Energy Storage Requirements for Energy Complex with Solar Power Plant and Battery Energy Storage System

Despite the many advantages of renewable energy sources, the stochastic nature of their generation creates a mismatch between the timing of electricity production and demand. Without appropriate storage solutions, surplus energy may remain unused. Therefore, the development of energy complexes based on solar power plants with the integration of battery energy storage systems, as well as the development of corresponding computational models for them, becomes critical for ensuring the stability, flexibility, reliability, and efficiency of power systems. Battery energy storage systems have become widely used due to their availability, high response speed, significant energy density, and sufficient power capacity. However, their cost remains relatively high. This paper proposes a methodology and a calculation model for determining the optimal forecasted capacity and the rational storage requirements of an energy complex consisting of a solar power plant and a battery energy storage system operating in parallel with the grid at constant power under short-term forecasting conditions (day-ahead or longer). The proposed approach makes it possible to minimise the costs of energy companies associated with the short-term lease of part of a battery energy storage system when they do not own one, or, if such a system is available, to lease out its unused capacity and obtain corresponding profits. The validation of the computational model was carried out using a dataset of hourly daily power outputs of solar power plants in the Integrated Power System of Ukraine for 2018. Statistical analysis of the obtained results showed that the probability of occurrence of maximum deviations for the optimal capacity of the energy complex (5.4%), as well as for the power and capacity of the battery energy storage system (13% and 18%, respectively), does not exceed 0.05 during the year. Although the proposed methodology was applied using solar power plant generation data for the national power system as a whole, it can also be used for individual solar power plants located in different regions and countries with different climatic conditions. Certainly, the calculated coefficients will differ, but the methodology itself and the sequence of its application will remain the same.