The more large‐scale data centers infrastructure costs increase, the more simulation‐based evaluations are needed to understand better the trade‐off between energy and performance and support the development of new energy‐aware resource allocation policies. Specifically, in the cloud computing field, various simulators are able to predict and measure the behavior of applications on different architectures using different resource allocation policies. Yet, only a few of them have the ability to simulate energy‐saving strategies, and none of them support the complete advanced configuration and power interface (ACPI) specification. ACPI defines a terminology for all possible power states of a machine and their associated power rate. The hardware industry has relied on ACPI to provide up‐to‐date standard interfaces for hardware discovery, configuration, power management, and monitoring, enabling a better understanding of the energy consumption level of different hardware states, referred to as ACPI G‐states, S‐states, and P‐states. In this paper, we improve the modeling and simulation of the ACPI G/S‐states and show not only that these states offer different energy‐saving levels but also that state transitions consume energy. In addition, we model the latency to transit between two states and the effects on the turnaround time when the transitions are not performed conservatively. Furthermore, the equations provide essential information to quantify the trade‐off between energy consumption and performance and assist in the analysis/decision on which strategy fits better in the environment and how it could be refined. Our expanded energy model was implemented in CloudSim and validated with simulation‐based experiments with a very high level of accuracy, with a standard deviation of at most 6%. Copyright © 2016 John Wiley & Sons, Ltd.