Steffen Finck (Research Centre Business Informatics) and Peter Kepplinger (Research Centre Energy) serve as guest-editors.

Thermal or electrothermal storage systems provide flexibilities to shift the loads. At the same time, smart devices and smart grids provide access to more data than before, which fosters utilization of these flexibilities to schedule their deployment. In other fields, such dynamic and uncertain systems are successfully tackled with methods from the field of Evolutionary Computation and Machine Learning. These methods may be able to provide robust solutions, more adaptive systems, and can work in large data-driven environments.

This special issue therefore invites contributions that investigate the use of such methods for dealing with flexibility control problems within energy and power systems. Topics of interest include but are not limited to: Optimization and control of flexibilities; Demand side management; Load and power grid management; Prediction for prices or demands; Application of Evolutionary Computation or Machine Learning.

www.mdpi.com/journal/energies/special_issues/Modern_Computational_Methods_Flexibility_Control