| Abstrakt: |
With rising energy prices, it becomes increasingly important for industrial companies to consider energy costs in the activity of production planning and control. Reduced energy costs can both be realised through lower consumption and higher energy efficiency. Furthermore, the ability to flexibly consume energy, while adapting consumption to electricity availability or variable energy prices, as often referred to as 'demand response', might bring significant benefits. Two important requirements for demand response are the ability to predict consumption and the ability to precisely determine the potential flexible approaches available. For this purpose, a material flow model was implemented that simulates both the production process and the energy consumed by it. This model was parameterised based on real production data and energy consumption. Initial analysis reveals that manufacturing processes generally undergo interruptions, which cannot be predicted at the stage of production planning. These interruptions significantly reduce the accuracy of the model and were considered by means of probability density functions. In this paper, the forging shop of an automotive supplier is used as a use case. The main power consumption in this concrete case is caused by induction furnaces, which require different amounts of electricity, depending on the workpiece. Consequently, the loads can be adjusted by adapting the sequence of production orders. Using the simulation model developed, the peak loads of the forging shop were analysed, and an energy-orientated production plan was created and optimised using a genetic algorithm. Results show that the peak loads of the plant can be reduced significantly (by more than 10 %), despite the uncertainties of production, by means of stochastic modelling and optimisation. The application of these findings might contribute to both the stability of the electricity grid and the reliability of operations. [ABSTRACT FROM AUTHOR] |
