Volatility in the availability of energy resources is a key challenge to master in the transition to sustainable energy sources. Especially with a rising share of renewable energy sources the situation is expected to intensify. To leverage the situation within an industrial context, energy-aware scheduling could be used. However, state-of-the-art simulation techniques use state-based energy determination, which is merely suitable for recurring manufacturing of products. In order to enable energy-aware scheduling within a changeable manufacturing environment, an alternative approach is researched. The approach aims to disaggregate the used manufacturing entities and identify the individual load patterns. Based on the individual load patterns, a simulation and optimization architecture was derived to conduct energy-aware scheduling in future applications. The used methods include the analysis of current methods for scheduling and energy consumption prediction and derivation of the component-based simulation approach based on the state-of-the-art. Depending on the inclusion of environmental dependencies two optimization architectures were created. A case study with a small-scale demonstrator was conducted to present and validate the approach. Within the scope of the small-scale demonstrator the approach could be validated. The main contribution of the publication is the provision of an approach for scheduling orders for merely non-recurring jobs.