Modern power systems exhibit an increasing penetration of variable renewable energy sources (RESs) alongside conventional generation units. Under such conditions, day‐ahead operational planning must ensure both cost‐efficient and reliable system operation, which requires an accurate representation of system flexibility and reserve allocation. This article presents a day‐ahead reserve‐constrained unit commitment (day‐ahead RCUC) model formulated as a mixed‐integer linear programming (MILP) problem and solved in MATLAB using commercial MILP solvers. The model captures the operational and flexibility characteristics of thermal, hydropower, and pumped‐storage hydropower (PSH) units. Balancing services, including primary reserve, automatic secondary reserve, and manual tertiary reserve, are modeled in both upward and downward directions. The formulation further introduces detailed co‐optimization of energy and reserves, enhanced representation of hydropower flexibility, integration of system‐specific balancing constraints, and reserve pricing based on opportunity cost in accordance with national regulatory practice. The model is applied to the prospective Serbian power system in 2030 under realistic projected operating conditions with high RES penetration and pronounced balancing challenges. Its performance is evaluated through a comparative analysis with a reference model developed in the PLEXOS software environment. The comparison highlights certain modeling differences between the proposed formulation and the PLEXOS framework, particularly with respect to the representation of reserve allocation and hydropower flexibility. The results show that the proposed model differs by 1.14% in objective function value compared to PLEXOS, while providing a more realistic reserve deployment pattern and achieving a computation time of ~32 s, which is suitable for day‐ahead operational planning. Finally, the analysis confirms that the ongoing energy transition requires the development of additional flexible resources to meet increasing balancing requirements and address the duck curve phenomenon. In this context, the integration of a variable‐speed PSH plant (VS‐PSH) significantly enhances system flexibility and improves overall economic performance.
Lazović et al. (Thu,) studied this question.