In this work, the generation aware production planning for a paint thickener production is investigated. In this production, products of different grades and therefore varying energy requirements are produced by electrified twin-screw extruders. The energy grid information is based on the German grid in 2020. For the investigated method, the results of the optimization of a very detailed and computationally demanding process model are used to build a computationally efficient surrogate model. This model maps the optimal minimal energy requirement to a given throughput and product grade at the optimal operating conditions. Based on this surrogate model and the price information, the optimization problem for the production planning is formulated. Constraints for the optimization problems are the satisfaction of the production demands of quantity and grade. The objective function is the minimization of the energy costs and the carbon footprint of the product caused by different energy mixes of the grid. To increase the industrial acceptance and to obtain a smoother solution, the use of a coarser time discretization is compared to penalizing the throughput changes in the cost function. The optimization problem is solved for a weekly production schedule and hourly changing energy costs and carbon footprints. The results are discussed and the performance is compared to a non-generation aware production planning. Lastly, the application at a larger scale with multiple parallel production machines is discussed.