To achieve low-carbon and high-efficiency operation of the big data park， an integrated energy system （IES） coupling photovoltaic （PV） generation， free cooling， waste heat recovery， and multiple energy storage methods is constructed. An energy consumption model of the integrated energy system is established. Due to the characteristics of nonlinear， multivariable and multi-constraint conditions of the model， a rolling optimization control method based on a genetic algorithm is proposed to deal with the dynamic change of energy supply and demand. This method aims to minimize the system’s operational costs by considering factors such as peak-valley electricity pricing， renewable energy output characteristics， and partial load performance characteristics of equipment， thereby determining the optimal operational strategy for the system. By comparing the simulation results with the results of the rule-based control method， it is found that the rolling optimization can reduce system operating costs by 10.68%~12.63%. In addition， scenarios with different solar radiation intensities are selected for this research. The results show that the improvement in PV utilization rate is greatly affected by the battery operation mode. By adjusting the system operating parameters， the PV utilization rate can be further improved.