@article{KempkeRehfeldtKoch2025, author = {Kempke, Nils-Christian and Rehfeldt, Daniel and Koch, Thorsten}, title = {A Massively Parallel Interior-Point-Method for Arrowhead Linear Programs}, journal = {SIAM Journal on Scientific Computing}, arxiv = {http://arxiv.org/abs/2412.07731}, year = {2025}, language = {en} } @misc{PedersenLindnerRehfeldtetal.2025, author = {Pedersen, Jaap and Lindner, Niels and Rehfeldt, Daniel and Koch, Thorsten}, title = {Integrated Wind Farm Design: Optimizing Turbine Placement and Cable Routing with Wake Effects}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-99218}, year = {2025}, abstract = {An accelerated deployment of renewable energy sources is crucial for a successful transformation of the current energy system, with wind energy playing a key role in this transition. This study addresses the integrated wind farm layout and cable routing problem, a challenging nonlinear optimization problem. We model this problem as an extended version of the Quota Steiner Tree Problem (QSTP), optimizing turbine placement and network connectivity simultaneously to meet specified expansion targets. Our proposed approach accounts for the wake effect - a region of reduced wind speed induced by each installed turbine - and enforces minimum spacing between turbines. We introduce an exact solution framework in terms of the novel Quota Steiner Tree Problem with interference (QSTPI). By leveraging an interference-based splitting strategy, we develop an advanced solver capable of tackling large-scale problem instances. The presented approach outperforms generic state-of-the-art mixed integer programming solvers on our dataset by up to two orders of magnitude. Moreover, we demonstrate that our integrated method significantly reduces the costs in contrast to a sequential approach. Thus, we provide a planning tool that enhances existing planning methodologies for supporting a faster and cost-efficient expansion of wind energy.}, language = {en} } @misc{KempkeKuntKatamishetal.2025, author = {Kempke, Nils-Christian and Kunt, Tim and Katamish, Bassel and Vanaret, Charlie and Sasanpour, Shima and Clarner, Jan-Patrick and Koch, Thorsten}, title = {Developing heuristic solution techniques for large-scale unit commitment models}, issn = {1438-0064}, arxiv = {http://arxiv.org/abs/2502.19012}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-99555}, year = {2025}, abstract = {Shifting towards renewable energy sources and reducing carbon emissions necessitate sophisticated energy system planning, optimization, and extension. Energy systems optimization models (ESOMs) often form the basis for political and operational decision-making. ESOMs are frequently formulated as linear (LPs) and mixed-integer linear (MIP) problems. MIPs allow continuous and discrete decision variables. Consequently, they are substantially more expressive than LPs but also more challenging to solve. The ever-growing size and complexity of ESOMs take a toll on the computational time of state-of-the-art commercial solvers. Indeed, for large-scale ESOMs, solving the LP relaxation -- the basis of modern MIP solution algorithms -- can be very costly. These time requirements can render ESOM MIPs impractical for real-world applications. This article considers a set of large-scale decarbonization-focused unit commitment models with expansion decisions based on the REMix framework (up to 83 million variables and 900,000 discrete decision variables). For these particular instances, the solution to the LP relaxation and the MIP optimum lie close. Based on this observation, we investigate the application of relaxation-enforced neighborhood search (RENS), machine learning guided rounding, and a fix-and-propagate (FP) heuristic as a standalone solution method. Our approach generated feasible solutions 20 to 100 times faster than GUROBI, achieving comparable solution quality with primal-dual gaps as low as 1\% and up to 35\%. This enabled us to solve numerous scenarios without lowering the quality of our models. For some instances that Gurobi could not solve within two days, our FP method provided feasible solutions in under one hour.}, language = {en} } @article{LePetkovic2025, author = {Le, Thi Thai and Petkovic, Milena}, title = {Predicting Fluid Interface Instability in Energy Systems for Sustainable Energy Transition}, journal = {Operations Research Proceedings 2023. OR 2023.}, publisher = {Springer}, doi = {10.1007/978-3-031-58405-3_45}, pages = {349 -- 356}, year = {2025}, abstract = {Due to the coexistence of different gases in underground storage, this work explores the interface stability's impact on energy storage, specifically during the injection and withdrawal of gases such as hydrogen and natural gas. A new approach of combing simulation and time series analysis is used to accurately predict instability modes in energy systems. Our simulation is based on the 2D Euler equations, solved using a second-order finite volume method with a staggered grid. The solution is validated by comparing them to experimental data and analytical solutions, accurately predicting the instability's behavior. We use time series analysis and state-of-the-art regime-switching methods to identify critical features of the interface dynamics, providing crucial insights into system optimization and design.}, language = {en} } @inproceedings{LindnerBartoszukDebguptaetal.2025, author = {Lindner, Niels and Bartoszuk, Karolina and Debgupta, Srinwanti and Gering, Marie-Claire and Muschner, Christoph and Zittel, Janina}, title = {Warm-starting modeling to generate alternatives for energy transition paths in the Berlin-Brandenburg area}, booktitle = {Operations Research Proceedings 2024. OR 2024}, doi = {10.1007/978-3-031-92575-7_35}, pages = {248 -- 253}, year = {2025}, abstract = {Energy system optimization models are key to investigate energy transition paths towards a decarbonized future. Since this approach comes with intrinsic uncertainties, it is insufficient to compute a single optimal solution assuming perfect foresight to provide a profound basis for decision makers. The paradigm of modeling to generate alternatives enables to explore the near-optimal solution space to a certain extent. However, large-scale energy models require a non-negligible computation time to be solved. We propose to use warm start methods to accelerate the process of finding close-to-optimal alternatives. In an extensive case study for the energy transition of the Berlin-Brandenburg area, we make use of the sector-coupled linear programming oemof-B3 model to analyze a scenario for the year 2050 with a resolution of one hour and 100\% reduction of greenhouse gas emissions. We demonstrate that we can actually achieve a significant computational speedup.}, language = {en} } @article{ChenKochPengetal.2025, author = {Chen, Ying and Koch, Thorsten and Peng, Hanqui and Zhang, Hongrui}, title = {Benchmarking of Quantum and Classical Computing in Large-Scale Dynamic Portfolio Optimization Under Market Frictions}, arxiv = {http://arxiv.org/abs/2502.05226}, year = {2025}, language = {en} } @inproceedings{TurnerBertholdBesancon2025, author = {Turner, Mark and Berthold, Timo and Besan{\c{c}}on, Mathieu}, title = {A Context-Aware Cutting Plane Selection Algorithm for Mixed-Integer Programming}, booktitle = {Operations Research Proceedings 2023. OR 2023.}, doi = {10.1007/978-3-031-58405-3_28}, pages = {217 -- 244}, year = {2025}, abstract = {The current cut selection algorithm used in mixed-integer programming solvers has remained largely unchanged since its creation. In this paper, we propose a set of new cut scoring measures, cut filtering techniques, and stopping criteria, extending the current state-of-the-art algorithm and obtaining a 5\\% performance improvement for SCIP over the MIPLIB 2017 benchmark set.}, language = {en} } @article{ShinanoFujii2025, author = {Shinano, Yuji and Fujii, Koichi}, title = {Introduction to Quadratic Programming Solvers}, volume = {64}, journal = {Journal of The Society of Instrument and Control Engineers}, number = {8}, doi = {10.11499/sicejl.64.532}, pages = {532 -- 540}, year = {2025}, language = {en} } @article{ChenXuKoch2020, author = {Chen, Ying and Xu, Xiuqin and Koch, Thorsten}, title = {Day-ahead high-resolution forecasting of natural gas demand and supply in Germany with a hybrid model}, volume = {262}, journal = {Applied Energy}, number = {114486}, doi = {https://doi.org/10.1016/j.apenergy.2019.114486}, year = {2020}, abstract = {As the natural gas market is moving towards short-term planning, accurate and robust short-term forecasts of the demand and supply of natural gas is of fundamental importance for a stable energy supply, a natural gas control schedule, and transport operation on a daily basis. We propose a hybrid forecast model, Functional AutoRegressive and Convolutional Neural Network model, based on state-of-the-art statistical modeling and artificial neural networks. We conduct short-term forecasting of the hourly natural gas flows of 92 distribution nodes in the German high-pressure gas pipeline network, showing that the proposed model provides nice and stable accuracy for different types of nodes. It outperforms all the alternative models, with an improved relative accuracy up to twofold for plant nodes and up to fourfold for municipal nodes. For the border nodes with rather flat gas flows, it has an accuracy that is comparable to the best performing alternative model.}, language = {en} } @misc{ZakiyevaPetkovic2023, author = {Zakiyeva, Nazgul and Petkovic, Milena}, title = {High-dimensional high-frequency time series prediction with a mixed integer optimisation method}, journal = {Operations Research Proceedings}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-93114}, year = {2023}, language = {en} }