Construction of a model for optimal scheduling of energy storage systems in the day-ahead market considering market strategies and technical constraints - Havva, Oleksandr; Parus, Yevgene; Blinov, Ihor; Evdokimov, Volodymyr (Eastern-European Journal of Enterprise Technologies, 2026)
This study investigates the process of optimal planning of energy storage system operating schedules in the day-ahead market. The task addressed is to prevent unprofitable operation of energy storage systems. A comprehensive mathematical model of daily planning of energy storage operation modes has been built, which takes into account technological efficiency, hardware wear processes, as well as asymmetry of market payments. This forms an adaptive filter with cutting off cycles of economically inefficient arbitrage. The approach is based on separation of the vector of control variables into two independent charging and discharging flows. This has made it possible to take into account the asymmetry of tariffs and the structure of losses in a differentiated manner. The study on the sensitivity of the model revealed the existence of areas of adaptive regulation and parametric insensitivity to fluctuations in price signals. In the area of parametric insensitivity, the model forms a stable cost-effective solution regardless of price fluctuations. In the area of adaptive regulation, the model changes the volumes of a separate operating cycle depending on a change in the market price spread. It has been established that ignoring the accompanying payments for the area of adaptive regulation leads to an overestimation of income by 35.6 percent and the formation of unprofitable operations. Under conditions of low market price spread, the deviation in the financial result reaches 91.6 percent; however, the parametric insensitivity of the model guarantees profitable operation. The scope of model’s practical implementation is decision support systems for operators of energy storage systems. The conditions of use assume the presence of deterministic forecasts of market price signals for the calculated operating day. Implementing the model contributes to the extension of the life cycle and increase in the operational profitability of energy storage systems