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Electric power plants generally operate on the Rankine cycle where the steam from the turbine exhaust is cooled and condensed. A significant amount of water is used as the coolant for the condensation process, and it becomes a challenge where water resources are scarce. Use of air-cooled condensers can help solve the problem. However, the air-cooled condensers can be less efficient in the hottest months of the year and this issue must be addressed to increase their adoption in power plants. One method to overcome this second law limitation is to use an air pre-cooler coupled with a phase change material (PCM) based thermal energy storage (TES) to cool the air supplied to the air-cooled condenser during peak day time temperatures. The TES is recharged at night when ambient air temperature drops below the PCM freezing temperature. In the present study, a computational model is developed to simulate the working of a TES unit coupled with an air pre-cooler using an Eulerian approach. The model was developed using MATLAB and the numerical results are validated against the available experimental data. The energy storage capacities of the two units considered in the study are 100 kJ and 1 MJ. The hot fluid entering the TES unit enables the PCM to undergo melting (extraction). The heat absorbed from the fluid is stored in the PCM and is released during the daytime when the air temperatures are high. The flow direction is switched based on certain conditions and the cold fluid is passed through the opposite direction in the TES unit. This enables the PCM to freeze (charging) and be ready for the next cycle. A parametric study involving the effect of changing PCM melt temperatures and mass flow rates has been carried out for the system. Results show that the PCM melting temperature significantly affects the performance of the TES. A change in the mass flow rate of the heat transfer fluid between the air pre-cooler and the TES can change the melt fraction of the PCM. The combined TES with the pre-cooler is an efficient way to reduce water usage in power plants. The developed model can be used to obtain optimized conditions at different locations by using appropriate ambient temperature conditions.