Визначення впливу похибок прогнозування та уподобань щодо гнучкості в системах підтримки прийняття рішень для оптимального добового операційного планування просʼюмерів

The object of the study is operational planning in decision support systems (DSSs) for prosumers. The study addresses a lack of explicit flexibility modeling in DSSs and a limited understanding of how forecast quality impacts planning results. A novel control module for short-term planning of flexible energy demand and battery dispatch in prosumers is presented. The proposed solution improves prosumers’ information support by integrating consumption and generation forecasts, user-defined flexibility preferences, and battery constraints to reduce operational costs and increase profit from energy sales via optimal planning. Unlike methods that obscure decision logic, the module enables explicit flexibility modeling, enhancing transparency and better reflecting individual behaviors. Validation using real-world data across diverse prosumer segments confirms the module’s robustness and effectiveness in achieving cost savings. The module maintained positive cost improvements under realistic and extreme forecast errors (up to 75%) across most flexibility settings, with performance influenced by forecast accuracy and flexibility configuration. A linear dependency was found between forecast error and cost savings. In rare edge cases – very low flexibility and high forecast error – the control plans led to underperformance. Increasing flexibility relaxes accuracy requirements, highlighting an important trade-off. Higher flexibility led to stronger initial performance but faster degradation as forecast errors increased. Lower flexibility setups declined more slowly but were more prone to underperformance in edge conditions. These findings offer practical insights into flexibility modeling and forecast error tolerance, enabling improved planning and control design for prosumers