The papers presented in this open access book address diverse challenges in decarbonizing energy systems, ranging from operational to investment planning problems, from market economics to technical and environmental considerations, from distribution grids to transmission grids, and from theoretical considerations to data provision concerns and applied case studies. While most papers have a clear methodological focus, they address policy-relevant questions at the same time. The target audience therefore includes academics and experts in industry as well as policy makers, who are interested in state-of-the-art quantitative modelling of policy relevant problems in energy systems. The 2nd International Symposium on Energy System Optimization (ISESO 2018) was held at the Karlsruhe Institute of Technology (KIT) under the symposium theme "Bridging the Gap Between Mathematical Modelling and Policy Support" on October 10th and 11th 2018. ISESO 2018 was organized by the KIT, the Heidelberg Institute for Theoretical Studies (HITS), the Heidelberg University, the German Aerospace Center and the University of Stuttgart. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.

The computation of the optimum of a dynamical or multi-period Optimal Power Flow problem assuming an Interior Point Method (IPM) leads to linear systems of equations whose size is proportional to the number of considered time steps. In this preprint we investigate a possibility to reduce the amount of time steps needed to be taken into account: Assuming that the power grid's dynamic is mainly determined by changes of the residual demand, we drop time steps in case it does not change much. Hence, the size of the linear systems can be reduced. We tested this method for the German Power Grid of the year 2023 and a synthetic 960 h profile. We were able to reduce the amount of time steps by 40% without changing the objective function's value significantly.