Energy efficiency in manufacturing processes is crucial for mitigating climate change and optimizing industrial operations. Therefore, managing energy efficiency in manufacturing by accurate power prediction plays a significant role in this effort. In particular, it is essential to effectively model the power demand of the milling process, one of the most common and energy-intensive manufacturing processes. However, the common power models for the milling process based on material removal rate (MRR) neglect the influence of tool wear, one of the most critical factors. Meanwhile, some power models that consider tool wear cannot be generalized across different materials and manufacturing conditions. To deal with these limitations, this study aims to propose a novel dynamic power model. Especially, we incorporate tool wear and workpiece material hardness as key factors in the power calculation. Then, for power simulation, we simulate the milling processes of three different workpiece materials (aluminum alloy, stainless steel, and titanium alloy) and apply the proposed power model to derive the time series of power demand. As the results of the power simulation, the milling process of titanium alloy, the hardest material, exhibits the highest average power and total energy consumption, whereas the peak power is highest in the aluminum alloy process, the softest material. These findings demonstrate that both tool wear progression and material hardness significantly influence power demand, impacting energy efficiency and tool replacement cycles. This study contributes to facilitating energy-efficient machining and informed decision-making in manufacturing operations by developing a more realistic and generalizable power model.