Currently many high-performance computers are used to finish operational numerical computing cyclically. The main maintenance cost originates from the large amount of electric energy, and reducing energy consumption can reduce the maintenance cost significantly. The core units for operational systems are microprocessors, and the current microprocessors prevalently support the low power technique of the dynamic voltage and frequency scaling (DVFS). DVFS reduces the energy consumption by decreasing the supply voltage and execution frequency, which generally leads to performance reduction. This paper models energy consumption of operational applications confined by time constraints, and present energy optimization techniques by DVFS in the operational systems. Differing on the way to obtain the program execution information, two energy optimization models, SEOM and CEOM are evolved. The execution information of SEOM is obtained directly from testing, and the execution information of CEOM is obtained from compiler-directed program profile. The models have been investigated in representative computer platforms, and the results show that they can save 12% the largest reduction of energy consumption.