For the development and analysis of energy scenarios, the application of models is crucial. Considering the fact that energy systems analysis is an important instrument for policy advice, the question of the reliability of scenarios is essential, as these scenarios are subject to a large number of uncertainties. This challenge is addressed in UNSEEN. By inspecting a very large parameter space, a hitherto unattained number of model-based energy scenarios is to be evaluated. In particular, this also includes extreme and disruptive scenarios.

In the past, the main obstacle to applying extensive parameter variations to energy scenarios was computing time. This issue is also addressed in UNSEEN. In the predecessor project BEAM-ME, considerable success concerning the reduction of computing times of extensive and complex energy system optimization models has been achieved by the development and application of the open source solver PIPS-IPM++. This in general has laid the foundation for the application of high-performance computers to solve such large models.

However, PIPS-IPM++ is a solver suited for continuous, linear optimization problems.  For the modelling of real infrastructures and the derivation of concrete measures for the implementation of the energy system transformation, however, considering of discrete decisions is indispensable (for example, decisions for the investment in a certain storage power plant at a particular site are discrete). Solving the resulting optimization problems represents an additional challenge, which is addressed by the project’s central methodological approach – the training of neural networks  by “reinforcement learning". Among other things, this method should enable a fast "prediction" of the results of an optimization problem in order to provide a hot start solution for a deterministic solver algorithm. Based on PIPS-IPM++, this method should enable solving of so-called mixed-integer linear optimization problems on high-performance computers.

Based on the results of the parameter variations for energy system optimization problems, detailed analyses of the scenario space are performed by means of model couplings (e.g. with REMix and AMIRIS) and statistical analyses. For this purpose, indicators are determined which provide information on essential aspects of the adequacy, operational safety and economic efficiency of the corresponding scenarios of future energy systems.

Publications

2024
Cutting Plane Selection for Mixed-Integer Linear Programming Doctoral thesis, Technische Universität Berlin, Thorsten Koch (Advisor), 2024 Mark Turner BibTeX
UNSEEN
Interval constraint programming for globally solving catalog-based categorical optimization Journal of Global Optimization, Vol.89, pp. 457-476, 2024 Charlie Vanaret BibTeX
arXiv
DOI
UNSEEN
2023
A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming Operation Research Proceedings, 2023 (accepted for publication, preprint available as ZIB-Report 23-21) Mark Turner, Timo Berthold, Mathieu Besançon PDF (ZIB-Report)
BibTeX
UNSEEN
Adaptive Cut Selection in Mixed-Integer Linear Programming Open Journal of Mathematical Optimization, Vol.4, p. 5, 2023 (preprint available as ZIB-Report 22-04) Mark Turner, Thorsten Koch, Felipe Serrano, Michael Winkler PDF (ZIB-Report)
BibTeX
DOI
UNSEEN
Branching via Cutting Plane Selection: Improving Hybrid Branching 2023 (under review, preprint available as ZIB-Report 23-17) Mark Turner, Timo Berthold, Mathieu Besançon, Thorsten Koch PDF (ZIB-Report)
BibTeX
UNSEEN
Cutting Plane Selection with Analytic Centers and Multiregression Integration of Constraint Programming, Artificial Intelligence, and Operations Research. CPAIOR 2023., pp. 52-68, Vol.13884, Lecture Notes in Computer Science, 2023 (preprint available as ZIB-Report 22-28) Mark Turner, Timo Berthold, Mathieu Besançon, Thorsten Koch PDF (ZIB-Report)
BibTeX
DOI
UNSEEN
Evaluation of Uncertainties in Linear-Optimizing Energy System Models - Compendium DLR-Forschungsbericht, 2023 Karl-Kien Cao, Lovis Anderson, Aileen Böhme, Thomas Breuer, Jan Buschmann, Frederick Fiand, Ulrich Frey, Benjamin Fuchs, Nils-Christian Kempe, Kai von Krbek, Wided Medjroubi, Judith Riehm, Shima Sasanpour, Sonja Simon, Charlie Vanaret, Manuel Wetzel, Mengzhu Xiao, Janina Zittel BibTeX
DOI
UNSEEN
How Many Clues To Give? A Bilevel Formulation For The Minimum Sudoku Clue Problem ZIB-Report 23-15 Gennesaret Tjusila, Mathieu Besancon, Mark Turner, Thorsten Koch PDF
BibTeX
URN
UNSEEN
2022
Generative deep learning for decision making in gas networks Mathematical Methods of Operations Research, Vol.95, pp. 503-532, 2022 (preprint available as ZIB-Report 20-38) Lovis Anderson, Mark Turner, Thorsten Koch PDF (ZIB-Report)
PDF (ZIB-Report)
BibTeX
DOI
UNSEEN
Progress in mathematical programming solvers from 2001 to 2020 EURO Journal on Computational Optimization, Vol.10, p. 100031, 2022 (preprint available as ZIB-Report 21-20) Thorsten Koch, Timo Berthold, Jaap Pedersen, Charlie Vanaret PDF (ZIB-Report)
BibTeX
DOI
UNSEEN