With current trends in Data Center power consumption, and the increase of AI oriented Data Centers with power hungry GPU nodes, the management and control of power consumption has become one of the key points in keeping resources in check. Power is a limited resource as is the number of available CPUs or GPUs. However, power is a dynamic resource and a computational node can consume more or less power depending on the workload and hardware settings (CPU and GPU frequency for example). There are multiple potential strategies to manage the total available power in a cluster but all of them have a certain impact on job performance and on workload performance. In this paper, we present the EAR hard-powercap strategy and the simulation framework we have implemented to evaluate, as accurately as possible, the performance impact of the EAR hard-powercap strategy when running large workloads in big Data Centers. The performance results will be compared against a static powercap strategy. Since power and performance relationship is not constant, we have introduced the concept of power profiles as part of the simulations and several use cases, for each workload, have been simulated. Both strategies have been evaluated using workload trace files + power usage profiles and simulated in an evaluation framework composed by a Job scheduler simulator (Batsim + Batsched) + ClusterSim (N Node manager simulated in a single process) + EAR System power manager. Results show a substantial reduction on both wait time and execution time, especially as the cluster’s power constraints increase when using EAR’s strategy.