@article{HenningsPetkovicStreubel, author = {Hennings, Felix and Petkovic, Milena and Streubel, Tom}, title = {On the Numerical Treatment of Interlaced Target Values - Modeling, Optimization and Simulation of Regulating Valves in Gas Networks}, series = {Optimization and Engineering}, journal = {Optimization and Engineering}, publisher = {Springer Nature}, doi = {10.1007/s11081-023-09812-0}, abstract = {Due to the current and foreseeable shifts towards carbon dioxide neutral energy production, which will likely result in balancing fluctuating renewable energy generation by transforming power-to-gas-to-power as well as building a large-scale hydrogen transport infrastructure, the trading and transport operations of gas will become more dynamic, volatile, and hence also less predictable. Therefore, computer-aided support in terms of rapid simulation and control optimization will further broaden its importance for gas network dispatching. In this paper, we aim to contribute and openly publish two new mathematical models for regulators, also referred to as control valves, which together with compressors make up the most complex and involved types of active elements in gas network infrastructures. They provide direct control over gas networks but are in turn controlled via target values, also known as set-point values, themselves. Our models incorporate up to six dynamical target values to define desired transient states for the elements' local vicinity within the network. That is, each pair of every two target values defines a bounding box for the inlet pressure, outlet pressure as well as the passing mass flow of gas. In the proposed models, those target values are prioritized differently and are constantly in competition with each other, which can only be resolved dynamically at run-time of either a simulation or optimization process. Besides careful derivation, we compare simulation and optimization results with predictions of the widely adopted commercial simulation tool SIMONE, serving as our substitute for actual real-world transport operations.}, language = {en} } @article{LePetkovic, author = {Le, Thi Thai and Petkovic, Milena}, title = {Predicting Fluid Interface Instability in Energy Systems for Sustainable Energy Transition}, series = {Operations Research Proceedings}, journal = {Operations Research Proceedings}, 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{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} } @article{ZakiyevaPetkovic, author = {Zakiyeva, Nazgul and Petkovic, Milena}, title = {High-dimensional high-frequency time series prediction with a mixed integer optimisation method}, series = {Operations Research Proceedings}, journal = {Operations Research Proceedings}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-93068}, abstract = {We study a functional autoregressive model for high-frequency time series. We approach the estimation of the proposed model using a Mixed Integer Optimisation method. The proposed model captures serial dependence in the functional time series by including high-dimensional curves. We illustrate our methodology on large-scale natural gas network data. Our model provides more accurate day-ahead hourly out-of-sample forecast of the gas in and out-flows compared to alternative prediction models.}, language = {en} } @misc{ZakiyevaPetkovic, author = {Zakiyeva, Nazgul and Petkovic, Milena}, title = {High-dimensional high-frequency time series prediction with a mixed integer optimisation method}, series = {Operations Research Proceedings}, journal = {Operations Research Proceedings}, issn = {1438-0064}, url = {http://nbn-resolving.de/urn:nbn:de:0297-zib-93114}, 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} }