Unit commitment (UC) is a key optimization problem in power system operation, but its computational burden increases rapidly when a system includes many identical or similar generating units. The aggregation of identical units can reduce this burden by replacing unit-level binary variables with aggregated integer variables. However, this simplification may reduce the accuracy of the obtained schedule because the operation of individual generating units is not fully represented. This paper proposes a tractable unit commitment (TUC) formulation to improve the computational performance of UC while preserving the accuracy of the unit-level solution. The proposed formulation connects the conventional UC model and the aggregated representation through linking constraints that enforce consistency between unit-level and aggregated variables. The model is tested on scaled IEEE 24-bus and IEEE 118-bus systems under different renewable penetration levels and battery energy storage scenarios. The results show that the aggregation-based formulation provides the shortest CPU time, but it underestimates the operating cost in all cases. In contrast, the proposed TUC formulation keeps the cost error almost equal to zero, with a maximum absolute error of 0.0009%, while reducing CPU time compared with UC. Additional comparisons of generation schedules, commitment decisions, and battery charging and discharging profiles show that TUC closely follows the UC benchmark. These results confirm that the proposed TUC formulation provides a better balance between computational efficiency and solution accuracy than the conventional aggregation-based formulation.