@article{HoppmannBaumHenningsZitteletal., author = {Hoppmann-Baum, Kai and Hennings, Felix and Zittel, Janina and Gotzes, Uwe and Spreckelsen, Eva-Maria and Spreckelsen, Klaus and Koch, Thorsten}, title = {An Optimization Approach for the Transient Control of Hydrogen Transport Networks}, series = {Mathematical Methods of Operations Research}, journal = {Mathematical Methods of Operations Research}, number = {Special Issue on Energy Networks}, language = {en} } @misc{HoppmannBaumHenningsZitteletal., author = {Hoppmann-Baum, Kai and Hennings, Felix and Zittel, Janina and Gotzes, Uwe and Spreckelsen, Eva-Maria and Spreckelsen, Klaus and Koch, Thorsten}, title = {From Natural Gas towards Hydrogen - A Feasibility Study on Current Transport Network Infrastructure and its Technical Control}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-79901}, abstract = {This study examines the usability of a real-world, large-scale natural gas transport infrastructure for hydrogen transport. We investigate whether a converted network can transport the amounts of hydrogen necessary to satisfy current energy demands. After introducing an optimization model for the robust transient control of hydrogen networks, we conduct computational experiments based on real-world demand scenarios. Using a representative network, we demonstrate that replacing each turbo compressor unit by four parallel hydrogen compressors, each of them comprising multiple serial compression stages, and imposing stricter rules regarding the balancing of in- and outflow suffices to realize transport in a majority of scenarios. However, due to the reduced linepack there is an increased need for technical and non-technical measures leading to a more dynamic network control. Furthermore, the amount of energy needed for compression increases by 364\% on average.}, language = {en} } @article{PetkovicChenGamrathetal., author = {Petkovic, Milena and Chen, Ying and Gamrath, Inken and Gotzes, Uwe and Hadjidimitrou, Natalia Selini and Zittel, Janina and Xu, Xiaofei and Koch, Thorsten}, title = {A hybrid approach for high precision prediction of gas flows}, series = {Energy Systems}, volume = {13}, journal = {Energy Systems}, doi = {10.1007/s12667-021-00466-4}, pages = {383 -- 408}, abstract = {About 23\% of the German energy demand is supplied by natural gas. Additionally, for about the same amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by transmissions system operators (TSOs). The number one priority of the TSOs is to ensure the security of supply. However, the TSOs have only very limited knowledge about the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany's largest TSO, operates a high-pressure transport network of about 12,000 km length. With the introduction of peak-load gas power stations, it is of great importance to predict in- and out-flow of the network to ensure the necessary flexibility and security of supply for the German Energy Transition ("Energiewende"). In this paper, we introduce a novel hybrid forecast method applied to gas flows at the boundary nodes of a transport network. This method employs an optimized feature selection and minimization. We use a combination of a FAR, LSTM and mathematical programming to achieve robust high-quality forecasts on real-world data for different types of network nodes.}, language = {en} } @inproceedings{PetkovicZakiyevaZittel, author = {Petkovic, Milena and Zakiyeva, Nazgul and Zittel, Janina}, title = {Statistical Analysis and Modeling for Detecting Regime Changes in Gas Nomination Time Series}, series = {Operations Research Proceedings 2021. OR 2021}, booktitle = {Operations Research Proceedings 2021. OR 2021}, publisher = {Springer, Cham}, doi = {10.1007/978-3-031-08623-6_29}, pages = {188 -- 193}, abstract = {As a result of the legislation for gas markets introduced by the European Union in 2005, separate independent companies have to conduct the transport and trading of natural gas. The current gas market of Germany, which has a market value of more than 54 billion USD, consists of Transmission System Operators (TSO), network users, and traders. Traders can nominate a certain amount of gas anytime and anywhere in the network. Such unrestricted access for the traders, on the other hand, increase the uncertainty in the gas supply management. Some customers' behaviors may cause abrupt structural changes in gas flow time series. In particular, it is a challenging task for the TSO operators to predict gas nominations 6 to 10 h-ahead. In our study, we aim to investigate the regime changes in time series of nominations to predict the 6 to 10 h-ahead of gas nominations.}, language = {en} } @inproceedings{PedersenHoppmannBaumZitteletal., author = {Pedersen, Jaap and Hoppmann-Baum, Kai and Zittel, Janina and Koch, Thorsten}, title = {Blending hydrogen into natural gas: An assessment of the capacity of the German gas grid}, series = {Operations Research Proceedings 2021}, booktitle = {Operations Research Proceedings 2021}, doi = {https://doi.org/10.1007/978-3-031-08623-6_28}, pages = {182 -- 187}, abstract = {In the transition towards a pure hydrogen infrastructure, repurposing the existing natural gas infrastructure is considered. In this study, the maximal technically feasible injection of hydrogen into the existing German natural gas transmission network is analysed with respect to regulatory limits regarding the gas quality. We propose a transient tracking model based on the general pooling problem including linepack. The analysis is conducted using real-world hourly gas flow data on a network of about 10,000 km length.}, language = {en} } @misc{HenningsHoppmannBaumZittel, author = {Hennings, Felix and Hoppmann-Baum, Kai and Zittel, Janina}, title = {Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-86842}, abstract = {Optimizing the transient control of gas networks is a highly challenging task. The corresponding model incorporates the combinatorial complexity of determining the settings for the many active elements as well as the non-linear and non-convex nature of the physical and technical principles of gas transport. In this paper, we present the latest improvements of our ongoing work to solve this problem for real-world, large-scale problem instances: By adjusting our mixed-integer non-linear programming model regarding the gas compression capabilities in the network, we reflect the technical limits of the underlying units more accurately while maintaining a similar overall model size. In addition, we introduce a new algorithmic approach that is based on splitting the complexity of the problem by first finding assignments for discrete variables and then determining the continuous variables as locally optimal solution of the corresponding non-linear program. For the first task, we design multiple different heuristics based on concepts for general time-expanded optimization problems that find solutions by solving a sequence of sub-problems defined on reduced time horizons. To demonstrate the competitiveness of our approach, we test our algorithm on particularly challenging historic demand scenarios. The results show that high-quality solutions are obtained reliably within short solving times, making the algorithm well-suited to be applied at the core of time-critical industrial applications.}, language = {en} } @misc{YuekselErguenMostWyrwolletal., author = {Yueksel-Erguen, Inci and Most, Dieter and Wyrwoll, Lothar and Schmitt, Carlo and Zittel, Janina}, title = {Modeling the transition of the multimodal pan-European energy system including an integrated analysis of electricity and gas transport}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-87774}, abstract = {The European energy system has been through a fundamental transformation since the Paris Agreement to reduce greenhouse gas emissions. The transition involves several energy-generating and consuming sectors emphasizing sector coupling. The increase in the share of renewable energy sources has revealed the need for flexibility in the electri city grid. Thus, holistic planning of pathways towards decarbonized energy systems also involves assessing the gas infrastructure to provide such a flexibility and support for the security of supply. In this paper, we propose a workflow to investigate such optimal energy transition pathways considering sector coupling. This workflow involves an integrated operational analysis of the electricity market, its transmission grid, and the gas grid in high spatio-temporal resolution. In a case study on a pan-European scale between 2020-2050, we show that carbon neutrality can be reached within feasible additional costs and in time. However, the manifestation of the potential pathways strongly depends on political and technological constraints. Sector coupling acts as an enabler of cross-border cooperation to achieve both, decarbonization and security of supply.}, language = {en} } @article{PedersenSpreckelsenGotzesetal., author = {Pedersen, Jaap and Spreckelsen, Klaus and Gotzes, Uwe and Zittel, Janina and Koch, Thorsten}, title = {Beimischung von Wasserstoff zum Erdgas: Eine Kapazit{\"a}tsstudie des deutschen Gasnetzes}, series = {gwf Gas + Energie}, journal = {gwf Gas + Energie}, edition = {06/2023}, publisher = {Vulkan Verlag}, abstract = {Die europaische Gasinfrastruktur wird disruptiv in ein zukunftiges dekarbonisiertes Energiesystem ver{\"a}ndert; ein Prozess, der angesichts der j{\"u}ngsten politischen Situation beschleunigt werden muss. Mit einem wachsenden Wasserstoffmarkt wird der pipelinebasierte Transport unter Nutzung der bestehenden Erdgasinfrastruktur wirtschaftlich sinnvoll, tr{\"a}gt zur Erh{\"o}hung der {\"o}ffentlichen Akzeptanz bei und beschleunigt den Umstellungsprozess. In diesem Beitrag wird die maximal technisch machbare Einspeisung von Wasserstoff in das bestehende deutsche Erdgastransportnetz hinsichtlich regulatorischer Grenzwerte der Gasqualit{\"a}t analysiert. Die Analyse erfolgt auf Basis eines transienten Tracking-Modells, das auf dem allgemeinen Pooling-Problem einschließlich Linepack aufbaut. Es zeigt sich, dass das Gasnetz auch bei strengen Grenzwerten gen ̈ugend Kapazit{\"a}t bietet, um f{\"u}r einen großen Teil der bis 2030 geplanten Erzeugungskapazit{\"a}t f{\"u}r gr{\"u}nen Wasserstoff als garantierter Abnehmer zu dienen.}, language = {de} } @article{PedersenSpreckelsenGotzesetal., author = {Pedersen, Jaap and Spreckelsen, Klaus and Gotzes, Uwe and Zittel, Janina and Koch, Thorsten}, title = {Beimischung von Wasserstoff zum Erdgas: Eine Kapazit{\"a}tsstudie des deutschen Gasnetzes}, series = {3R - Fachzeitschrift f{\"u}r Rohrleitungssystem}, journal = {3R - Fachzeitschrift f{\"u}r Rohrleitungssystem}, number = {06/2023}, pages = {70 -- 75}, abstract = {Die europ{\"a}ische Gasinfrastruktur wird disruptiv in ein zuk{\"u}nftiges dekarbonisiertes Energiesystem ver{\"a}ndert; ein Prozess, der angesichts der j{\"u}ngsten politischen Situation beschleunigt werden muss. Mit einem wachsenden Wasserstoffmarkt wird der pipelinebasierte Transport unter Nutzung der bestehenden Erdgasinfrastruktur wirtschaftlich sinnvoll, tr{\"a}gt zur Erh{\"o}hung der {\"o}ffentlichen Akzeptanz bei und beschleunigt den Umstellungsprozess. In diesem Fachbeitrag wird die maximal technisch machbare Einspeisung von Wasserstoff in das bestehende deutsche Erdgastransportnetz hinsichtlich regulatorischer Grenzwerte der Gasqualit{\"a}t analysiert. Die Analyse erfolgt auf Basis eines transienten Tracking-Modells, das auf dem allgemeinen Pooling-Problem einschließlich Linepack aufbaut. Es zeigt sich, dass das Gasnetz auch bei strengen Grenzwerten gen{\"u}gend Kapazit{\"a}t bietet, um f{\"u}r einen großen Teil der bis 2030 geplanten Erzeugungskapazit{\"a}t f{\"u}r gr{\"u}nen Wasserstoff als garantierter Abnehmer zu dienen.}, language = {de} } @misc{PetkovicChenGamrathetal., author = {Petkovic, Milena and Chen, Ying and Gamrath, Inken and Gotzes, Uwe and Hadjidimitriou, Natalia Selini and Zittel, Janina and Xu, Xiaofei and Koch, Thorsten}, title = {A Hybrid Approach for High Precision Prediction of Gas Flows}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-73525}, abstract = {About 20\% of the German energy demand is supplied by natural gas. Ad- ditionally, for about twice the amount Germany serves as a transit country. Thereby, the German network represents a central hub in the European natural gas transport network. The transport infrastructure is operated by so-called transmissions system operators or TSOs. The number one priority of the TSOs is to ensure security of supply. However, the TSOs have no knowledge of the intentions and planned actions of the shippers (traders). Open Grid Europe (OGE), one of Germany's largest TSO, operates a high- pressure transport network of about 12.000 km length. Since flexibility and security of supply is of utmost importance to the German Energy Transition ("Energiewende") especially with the introduction of peak-load gas power stations, being able to predict in- and out-flow of the network is of great importance. In this paper we introduce a new hybrid forecast method applied to gas flows at the boundary nodes of a transport network. The new method employs optimized feature minimization and selection. We use a combination of an FAR, LSTM DNN and mathematical programming to achieve robust high quality forecasts on real world data for different types of network nodes. Keywords: Gas Forecast, Time series, Hybrid Method, FAR, LSTM, Mathematical Optimisation}, language = {en} } @misc{CharoussetBrignolvanAckooijOudjaneetal., author = {Charousset-Brignol, Sandrine and van Ackooij, Wim and Oudjane, Nadia and Daniel, Dominique and Noceir, Slimane and Haus, Utz-Uwe and Lazzaro, Alfio and Frangioni, Antonio and Lobato, Rafael and Ghezelsoflu, Ali and Iardella, Niccol{\`o} and Galli, Laura and Gorgone, Enrico and dell'Amico, Mauro and Giannelos, Spyros and Moreira, Alex and Strbac, Goran and Borozan, Stefan and Falugi, Paula and Pudjianto, Danny and Wyrwoll, Lothar and Schmitt, Carlo and Franken, Marco and Beulertz, Daniel and Schwaeppe, Henrik and Most, Dieter and Y{\"u}ksel-Erg{\"u}n, Inci and Zittel, Janina and Koch, Thorsten}, title = {Synergistic approach of multi-energy models for a European optimal energy system management tool}, series = {The Project Repository Journal}, volume = {9}, journal = {The Project Repository Journal}, pages = {113 -- 116}, language = {en} } @misc{ClarnerTawfikKochetal., author = {Clarner, Jan-Patrick and Tawfik, Christine and Koch, Thorsten and Zittel, Janina}, title = {Network-induced Unit Commitment - A model class for investment and production portfolio planning for multi-energy systems}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-87607}, abstract = {In light of the energy transition production planning of future decarbonized energy systems lead to very large and complex optimization problems. A widely used modeling paradigm for modeling and solving such problems is mathematical programming. While there are various scientific energy system models and modeling tools, most of them do not provide the necessary level of detail or the modeling flexibility to be applicable for industrial usage. Industrial modeling tools, on the other hand, provide a high level of detail and modeling flexibility. However, those models often exhibit a size and complexity that restricts their scope to a time horizon of several months, severely complicating long-term planning. As a remedy, we propose a model class that is detailed enough for real-world usage but still compact enough for long-term planning. The model class is based on a generalized unit commitment problem on a network with investment decisions. The focus lies on the topological dependency of different energy production and transportation units.}, language = {en} } @article{YuekselErguenMostWyrwolletal., author = {Yueksel-Erguen, Inci and Most, Dieter and Wyrwoll, Lothar and Schmitt, Carlo and Zittel, Janina}, title = {Modeling the transition of the multimodal pan-European energy system including an integrated analysis of electricity and gas transport}, series = {Energy Systems}, journal = {Energy Systems}, doi = {10.1007/s12667-023-00637-5}, abstract = {Most recently, the European energy system has undergone a fundamental transformation to meet decarbonization targets without compromising the security of the energy supply. The transition involves several energy-generating and consuming sectors emphasizing sector coupling. The increase in the share of renewable energy sources has revealed the need for flexibility in supporting the electricity grid to cope with the resulting high degree of uncertainty. The new technologies accompanying the energy system transition and the recent political crisis in Europe threatening the security of the energy supply have invalidated the experience from the past by drastically changing the conventional scenarios. Hence, supporting strategic planning tools with detailed operational energy network models with appropriate mathematical precision has become more important than ever to understand the impacts of these disruptive changes. In this paper, we propose a workflow to investigate optimal energy transition pathways considering sector coupling. This workflow involves an integrated operational analysis of the electricity market, its transmission grid, and the gas grid in high spatio-temporal resolution. Thus, the workflow enables decision-makers to evaluate the reliability of high-level models even in case of disruptive events. We demonstrate the capabilities of the proposed workflow using results from a pan-European case study. The case study, spanning 2020-2050, illustrates that feasible potential pathways to carbon neutrality are heavily influenced by political and technological constraints. Through integrated operational analysis, we identify scenarios where strategic decisions become costly or infeasible given the existing electricity and gas networks.}, language = {en} } @inproceedings{YuekselErguenKochZittel, author = {Yueksel-Erguen, Inci and Koch, Thorsten and Zittel, Janina}, title = {Consistent flow scenario generation based on open data for operational analysis of European gas transport networks}, series = {Operations Research Proceedings 2023}, booktitle = {Operations Research Proceedings 2023}, abstract = {In recent years, European gas transport has been affected by major disruptive events like political issues such as, most recently, the Russian war on Ukraine. To incorporate the impacts of such events into decision-making during the energy transition, more complex models for gas network analysis are required. However, the limited availability of consistent data presents a significant obstacle in this endeavor. We use a mathematical-modeling-based scenario generator to deal with this obstacle. The scenario generator consists of capacitated network flow models representing the gas network at different aggregation levels. In this study, we present the coarse-to-fine approach utilized in this scenario generator.}, language = {en} } @inproceedings{PetkovicZittel, author = {Petkovic, Milena and Zittel, Janina}, title = {Resilient Forecasting of High-Dimensional Network Time Series in the Energy Domain: A Hybrid Approach}, series = {Operations Research Proceedings 2023}, booktitle = {Operations Research Proceedings 2023}, abstract = {Energy systems are complex networks consisting of various interconnected components. Accurate energy demand and supply forecasts are crucial for efficient system operation and decision-making. However, high-dimensional data, complex network structures, and dynamic changes and disruptions in energy networks pose significant challenges for forecasting models. To address this, we propose a hybrid approach for resilient forecasting of network time series (HRF-NTS) in the energy domain. Our approach combines mathematical optimization methods with state-of-the-art machine learning techniques to achieve accurate and robust forecasts for high-dimensional energy network time series. We incorporate an optimization framework to account for uncertainties and disruptive changes in the energy system. The effectiveness of the proposed approach is demonstrated through a case study of forecasting energy demand and supply in a complex, large-scale natural gas transmission network. The results show that the hybrid approach outperforms alternative prediction models in terms of accuracy and resilience to structural changes and disruptions, providing stable, multi-step ahead forecasts for different short to mid-term forecasting horizons.}, language = {en} } @misc{CaoAndersonBoehmeetal., author = {Cao, Karl-Kien and Anderson, Lovis and B{\"o}hme, Aileen and Breuer, Thomas and Buschmann, Jan and Fiand, Frederick and Frey, Ulrich and Fuchs, Benjamin and Kempe, Nils-Christian and von Krbek, Kai and Medjroubi, Wided and Riehm, Judith and Sasanpour, Shima and Simon, Sonja and Vanaret, Charlie and Wetzel, Manuel and Xiao, Mengzhu and Zittel, Janina}, title = {Evaluation of Uncertainties in Linear-Optimizing Energy System Models - Compendium}, series = {DLR-Forschungsbericht}, journal = {DLR-Forschungsbericht}, number = {DLR-FB-2023-15}, doi = {10.57676/w2rq-bj85}, pages = {95}, abstract = {F{\"u}r die Energiesystemforschung sind Software-Modelle ein Kernelement zur Analyse von Szenarien. Das Forschungsprojekt UNSEEN hatte das Ziel eine bisher unerreichte Anzahl an modellbasierten Energieszenarien zu berechnen, um Unsicherheiten - vor allem unter Nutzung linear optimierender Energiesystem-Modelle - besser bewerten zu k{\"o}nnen. Hierf{\"u}r wurden umfangreiche Parametervariationen auf Energieszenarien angewendet und das wesentliche methodische Hindernis in diesem Zusammenhang adressiert: die rechnerische Beherrschbarkeit der zu l{\"o}senden mathematischen Optimierungsprobleme. Im Vorl{\"a}uferprojekt BEAM-ME wurde mit der Entwicklung und Anwendung des Open-Source-L{\"o}sers PIPS-IPM++ die Grundlage f{\"u}r den Einsatz von High-Performance-Computing (HPC) zur L{\"o}sung dieser Modelle gelegt. In UNSEEN war dieser L{\"o}ser die zentrale Komponente eines Workflows, welcher zur Generierung, L{\"o}sung und multi-kriteriellen Bewertung von Energieszenarien auf dem Hochleistungscomputer JUWELS am Forschungszentrum J{\"u}lich implementiert wurde. Zur effizienten Generierung und Kommunikation von Modellinstanzen f{\"u}r Methoden der mathematischen Optimierung auf HPC wurde eine weitere Workflow-Komponente von der GAMS Software GmbH entwickelt: der Szenariogenerator. Bei der Weiterentwicklung von L{\"o}sungsalgorithmen f{\"u}r linear optimierende Energie-Systemmodelle standen gemischt-ganzzahlige Optimierungsprobleme im Fokus, welche f{\"u}r die Modellierung konkreter Infrastrukturen und Maßnahmen zur Umsetzung der Energiewende gel{\"o}st werden m{\"u}ssen. Die in diesem Zusammenhang stehenden Arbeiten zur Entwicklung von Algorithmen wurden von der Technischen Universit{\"a}t Berlin verantwortet. Bei Design und Implementierung dieser Methoden wurde sie vom Zuse Instituts Berlin unterst{\"u}tzt.}, language = {en} } @misc{YuekselErguenKochZittel, author = {Yueksel-Erguen, Inci and Koch, Thorsten and Zittel, Janina}, title = {Mathematical optimization based flow scenario generation for operational analysis of European gas transport networks based on open data}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-95789}, abstract = {The decarbonization of the European energy system demands a rapid and comprehensive transformation while securing energy supplies at all times. Still, natural gas plays a crucial role in this process. Recent unexpected events forced drastic changes in gas routes throughout Europe. Therefore, operational-level analysis of the gas transport networks and technical capacities to cope with these transitions using unconventional scenarios has become essential. Unfortunately, data limitations often hinder such analyses. To overcome this challenge, we propose a mathematical model-based scenario generator that enables operational analysis of the European gas network using open data. Our approach focuses on the consistent analysis of specific partitions of the gas transport network, whose network topology data is readily available. We generate reproducible and consistent node-based gas in/out-flow scenarios for these defined network partitions to enable feasibility analysis and data quality assessment. Our proposed method is demonstrated through several applications that address the feasibility analysis and data quality assessment of the German gas transport network. By using open data and a mathematical modeling approach, our method allows for a more comprehensive understanding of the gas transport network's behavior and assists in decision-making during the transition to decarbonization.}, language = {en} } @misc{PedersenLeKochetal., author = {Pedersen, Jaap and Le, Thi Thai and Koch, Thorsten and Zittel, Janina}, title = {Optimal discrete pipe sizing for tree-shaped CO2 networks}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-87574}, abstract = {While energy-intensive industries like the steel industry plan to switch to renewable energy sources, other industries, such as the cement industry, have to rely on carbon capture storage and utilization technologies to reduce the inevitable carbon dioxide (CO2) emissions of their production processes. In this context, we investigate the problem of finding optimal pipeline diameters from a discrete set of diameters for a tree-shaped network transporting captured CO2 from multiple sources to a single sink. The general problem of optimizing arc capacities in potential-based fluid networks is a challenging mixed-integer nonlinear program. Additionally, the behaviour of CO2 is highly sensitive and nonlinear regarding temperature and pressure changes. We propose an iterative algorithm splitting the problem into two parts: a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and b) the thermophysical modelling including mixing effects, the Joule-Thomson effect, and heat exchange with the surrounding environment. We show the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO2 network in Western Germany.}, language = {en} } @article{PedersenLeKochetal., author = {Pedersen, Jaap and Le, Thi Thai and Koch, Thorsten and Zittel, Janina}, title = {Optimal discrete pipe sizing for tree-shaped CO2 networks}, series = {OR Spectrum}, journal = {OR Spectrum}, doi = {10.1007/s00291-024-00773-z}, abstract = {For industries like the cement industry, switching to a carbon-neutral production process is impossible. They must rely on carbon capture, utilization and storage (CCUS) technologies to reduce their production processes' inevitable carbon dioxide (CO2) emissions. For transporting continuously large amounts of CO2, utilizing a pipeline network is the most effective solution; however, building such a network is expensive. Therefore minimizing the cost of the pipelines to be built is extremely important to make the operation financially feasible. In this context, we investigate the problem of finding optimal pipeline diameters from a discrete set of diameters for a tree-shaped network transporting captured CO2 from multiple sources to a single sink. The general problem of optimizing arc capacities in potential-based fluid networks is already a challenging mixed-integer nonlinear optimization problem. The problem becomes even more complex when adding the highly sensitive nonlinear behavior of CO2 regarding temperature and pressure changes. We propose an iterative algorithm splitting the problem into two parts: a) the pipe-sizing problem under a fixed supply scenario and temperature distribution and b) the thermophysical modeling, including mixing effects, the Joule-Thomson effect, and heat exchange with the surrounding environment. We demonstrate the effectiveness of our approach by applying our algorithm to a real-world network planning problem for a CO2 network in Western Germany. Further, we show the robustness of the algorithm by solving a large artificially created set of network instances.}, language = {en} } @misc{PetkovicKochZittel, author = {Petkovic, Milena and Koch, Thorsten and Zittel, Janina}, title = {Deep learning for spatio-temporal supply and demand forecasting in natural gas transmission networks}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-81221}, abstract = {Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. Germany's high-pressure gas pipeline network is roughly 40,000 km long, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25km/h, an adequate high-precision, high-frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio-temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high-pressure transmission network. Experiments show that our model effectively captures comprehensive spatio-temporal correlations through modeling gas networks and consistently outperforms state-of-the-art benchmarks on real-world data sets by at least 21\$\\%\$. The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.}, language = {en} } @article{PetkovicKochZittel, author = {Petkovic, Milena and Koch, Thorsten and Zittel, Janina}, title = {Deep learning for spatio-temporal supply anddemand forecasting in natural gas transmission networks}, series = {Energy Science and Engineering}, journal = {Energy Science and Engineering}, doi = {https://doi.org/10.1002/ese3.932}, abstract = {Germany is the largest market for natural gas in the European Union, with an annual consumption of approx. 95 billion cubic meters. Germany's high-pressure gas pipeline network is roughly 40,000 km long, which enables highly fluctuating quantities of gas to be transported safely over long distances. Considering that similar amounts of gas are also transshipped through Germany to other EU states, it is clear that Germany's gas transport system is essential to the European energy supply. Since the average velocity of gas in a pipeline is only 25km/h, an adequate high-precision, high-frequency forecasting of supply and demand is crucial for efficient control and operation of such a transmission network. We propose a deep learning model based on spatio-temporal convolutional neural networks (DLST) to tackle the problem of gas flow forecasting in a complex high-pressure transmission network. Experiments show that our model effectively captures comprehensive spatio-temporal correlations through modeling gas networks and consistently outperforms state-of-the-art benchmarks on real-world data sets by at least 21\%. The results demonstrate that the proposed model can deal with complex nonlinear gas network flow forecasting with high accuracy and effectiveness.}, language = {en} } @misc{PedersenHoppmannBaumZitteletal., author = {Pedersen, Jaap and Hoppmann-Baum, Kai and Zittel, Janina and Koch, Thorsten}, title = {Blending hydrogen into natural gas: An assessment of the capacity of the German gas grid; Technical Report}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-82838}, abstract = {In the transition towards a pure hydrogen infrastructure, utilizing the existing natural gas infrastructure is a necessity. In this study, the maximal technically feasible injection of hydrogen into the existing German natural gas transmission network is analysed with respect to regulatory limits regarding the gas quality. We propose a transient tracking model based on the general pooling problem including linepack. The analysis is conducted using real-world hourly gas flow data on a network of about 10,000 km length.}, language = {en} } @misc{PetkovicZakiyevaZittel, author = {Petkovic, Milena and Zakiyeva, Nazgul and Zittel, Janina}, title = {Statistical analysis and modeling for detecting regime changes in gas nomination time series}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-82988}, abstract = {As a result of the legislation for gas markets introduced by the European Union in 2005, separate independent companies have to conduct the transport and trading of natural gas. The current gas market of Germany, which has a market value of more than 54 billion USD, consists of Transmission System Operators (TSO), network users, and traders. Traders can nominate a certain amount of gas anytime and anywhere in the network. Such unrestricted access for the traders, on the other hand, increase the uncertainty in the gas supply management. Some customers' behaviors may cause abrupt structural changes in gas flow time series. In particular, it is a challenging task for the TSO operators to predict gas nominations 6 to 10 hours ahead. In our study, we aim to investigate the regime changes in the time series of nominations to predict the 6 to 10 hours ahead of gas nominations.}, language = {en} } @incollection{DiekerhofMontiLebedevaetal., author = {Diekerhof, M. and Monti, A. and Lebedeva, E. and Tkaczyk, A. H. and Y{\"u}ksel-Erg{\"u}n, I. and Zittel, J. and Escudero, L. F. and Soroudi, A. and Helmberg, C. and Kanov{\´i}c, Ž. and Petkovic, M. and Lacalandra, F. and Frangioni, A. and Lee, J. and De Filippo, A. and Lombardi, M. and Milano, M. and Ezran, P. and Haddad, Y.}, title = {Production and Demand Management}, series = {Mathematical Optimization for Efficient and Robust Energy Networks}, volume = {4}, booktitle = {Mathematical Optimization for Efficient and Robust Energy Networks}, publisher = {Springer}, isbn = {978-3-030-57442-0}, doi = {https://doi.org/10.1007/978-3-030-57442-0_1}, abstract = {Demand Side Management (DSM) is usually considered as a process of energy consumption shifting from peak hours to off-peak times. DSM does not always reduce total energy consumption, but it helps to meet energy demand and supply. For example, it balances variable generation from renewables (such as solar and wind) when energy demand differs from renewable generation.}, language = {en} } @inproceedings{PetkovicZittel, author = {Petkovic, Milena and Zittel, Janina}, title = {Forecasting and modeling the dynamics of large-scale energy networks under the supply and demand balance constraint}, series = {AIRO Springer Series: International Conference on Optimization and Decision Science}, booktitle = {AIRO Springer Series: International Conference on Optimization and Decision Science}, abstract = {With the emergence of "Big Data" the analysis of large data sets of high-dimensional energy time series in network structures have become feasible. However, building large-scale data-driven and computationally efficient models to accurately capture the underlying spatial and temporal dynamics and forecast the multivariate time series data remains a great challenge. Additional constraints make the problem more challenging to solve with conventional methods. For example, to ensure the security of supply, energy networks require the demand and supply to be balanced. This paper introduces a novel large-scale Hierarchical Network Regression model with Relaxed Balance constraint (HNR-RB) to investigate the network dynamics and predict multistep-ahead flows in the natural gas transmission network, where the total in- and out-flows of the network have to be balanced over a period of time. We concurrently address three main challenges: high dimensionality of networks with more than 100 nodes, unknown network dynamics, and constraint of balanced supply and demand in the network. The effectiveness of the proposed model is demonstrated through a real-world case study of forecasting demand and supply in a large-scale natural gas transmission network. The results demonstrate that HNR-RB outperforms alternative models for short- and mid-term horizons.}, language = {en} } @incollection{SchwarzLacalandraScheweetal., author = {Schwarz, R. and Lacalandra, F. and Schewe, L. and Bettinelli, A. and Vigo, D. and Bischi, A. and Parriani, T. and Martelli, E. and Vuik, K. and Lenz, R. and Madsen, H. and Blanco, I. and Guericke, D. and Y{\"u}ksel-Erg{\"u}n, I. and Zittel, J.}, title = {Network and Storage}, series = {Mathematical Optimization for Efficient and Robust Energy Networks}, volume = {4}, booktitle = {Mathematical Optimization for Efficient and Robust Energy Networks}, publisher = {Springer}, isbn = {978-3-030-57442-0}, doi = {https://doi.org/10.1007/978-3-030-57442-0_6}, abstract = {Natural gas is considered by many to be the most important energy source for the future. The objectives of energy commodities strategic problems can be mainly related to natural gas and deal with the definition of the "optimal" gas pipelines design which includes a number of related sub problems such as: Gas stations (compression) location and Gas storage locations, as well as compression station design and optimal operation.}, language = {en} } @inproceedings{ZittelClarnerTawfiketal., author = {Zittel, Janina and Clarner, Jan-Patrick and Tawfik, Christine and Dykes, Maxwell and Rivetta, Fabian and Riedm{\"u}ller, Stephanie}, title = {A multi-objective optimization strategy for district heating production portfolio planning.}, series = {Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems - ECOS 2024}, booktitle = {Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems - ECOS 2024}, abstract = {The imperative to decarbonize energy systems has intensified the need for efficient transformations within the heating sector, with a particular focus on district heating networks. This study addresses this challenge by proposing a comprehensive optimization approach evaluated on the district heating network of the M{\"a}rkisches Viertel of Berlin. Our objective is to simultaneously optimize heat production with three targets: minimizing costs, minimizing CO2-emissions, and maximizing heat generation from Combined Heat and Power (CHP) plants for enhanced efficiency. To tackle this optimization problem, we employed a Mixed-Integer Linear Program (MILP) that encompasses the conversion of various fuels into heat and power, integration with relevant markets, and considerations for technical constraints on power plant operation. These constraints include startup and minimum downtime, activation costs, and storage limits. The ultimate goal is to delineate the Pareto front, representing the optimal trade-offs between the three targets. We evaluate variants of the 𝜖-constraint algorithm for their effectiveness in coordinating these objectives, with a simultaneous focus on the quality of the estimated Pareto front and computational efficiency. One algorithm explores solutions on an evenly spaced grid in the objective space, while another dynamically adjusts the grid based on identified solutions. Initial findings highlight the strengths and limitations of each algorithm, providing guidance on algorithm selection depending on desired outcomes and computational constraints. Our study emphasizes that the optimal choice of algorithm hinges on the density and distribution of solutions in the feasible space. Whether solutions are clustered or evenly distributed significantly influences algorithm performance. These insights contribute to a nuanced understanding of algorithm selection for multi-objective multi-energy system optimization, offering valuable guidance for future research and practical applications for planning sustainable district heating networks.}, language = {en} } @inproceedings{MuschnerYuekselErguenGehringetal., author = {Muschner, Christoph and Y{\"u}ksel-Erg{\"u}n, Inci and Gehring, Marie-Claire and Bartoszuk, Karolina and Haas, Sabine and Zittel, Janina}, title = {Sensitivity analysis of the energy transition path in the Berlin-Brandenburg area to uncertainties in operational and investment costs of diverse energy production technologies.}, series = {Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems - ECOS 2024}, booktitle = {Proceedings of the 37th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems - ECOS 2024}, abstract = {The investigation of energy transition paths toward a sustainable and decarbonized future under uncertainty is a critical aspect of contemporary energy planning and policy development. There are numerous methods for analysing uncertainties and sensitivities and many studies on sustainable transformation paths, but there is a lack of combined application to relevant use-cases. In this study, we investigate the sensitivity of energy transition paths to uncertainties in operational and investment costs of power plants in the metropolitan area of Berlin and its rural surroundings. By employing the linear programming energy system model oemof-B3, we extensively focus on the system's energy technologies, such as wind turbines, photovoltaics, hydro and combustion plants, and energy storages. Greenhouse gas reduction and electrification rates per commodity are realized by selected constraints. Our research aims to discern how investments in energy production capacities are influenced by uncertainties of other energy technologies' investment and operational costs in the system. We apply a quantitative approach to investigate such interdependencies of cost variations and their impact on long-term energy planning. Thus, the analysis sheds light on the robustness of energy transition paths in the face of these uncertainties. The region Berlin-Brandenburg serves as a case study and thus reflects on the present space conflicts to meet energy demands in urban and suburban areas and their rural surroundings. An electricity-intensive scenario is selected that assumes a 100 \% reduction in greenhouse gas emissions by 2050. With the results of the case study, we show how our approach enables rural and metropolitan decision-makers to collaborate in achieving sustainable energy. Decision-making in long-term energy planning can be made more robust and flexible by acknowledging the identified sensitivities and enable such regions better to navigate challenges and uncertainties associated with sustainable energy planning.}, language = {en} } @article{HenningsHoppmannBaumZittel, author = {Hennings, Felix and Hoppmann-Baum, Kai and Zittel, Janina}, title = {Optimizing transient gas network control for challenging real-world instances using MIP-based heuristics}, series = {Open Journal of Mathematical Optimization}, volume = {5}, journal = {Open Journal of Mathematical Optimization}, doi = {10.5802/ojmo.29}, language = {en} } @misc{YuekselErguenZittelWangetal., author = {Yueksel-Erguen, Inci and Zittel, Janina and Wang, Ying and Hennings, Felix and Koch, Thorsten}, title = {Lessons learned from gas network data preprocessing}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-78262}, abstract = {The German high-pressure natural gas transport network consists of thousands of interconnected elements spread over more than 120,000 km of pipelines built during the last 100 years. During the last decade, we have spent many person-years to extract consistent data out of the available sources, both public and private. Based on two case studies, we present some of the challenges we encountered. Preparing consistent, high-quality data is surprisingly hard, and the effort necessary can hardly be overestimated. Thus, it is particularly important to decide which strategy regarding data curation to adopt. Which precision of the data is necessary? When is it more efficient to work with data that is just sufficiently correct on average? In the case studies we describe our experiences and the strategies we adopted to deal with the obstacles and to minimize future effort. Finally, we would like to emphasize that well-compiled data sets, publicly available for research purposes, provide the grounds for building innovative algorithmic solutions to the challenges of the future.}, language = {en} }