arXiv subjects

Thomas Klatzer

Publications and source records attributed to Thomas Klatzer.

7 recordsLinked to original sources

arXiv2026

Machine Learning for Exact Time Series Aggregation in Generation Expansion Planning with Energy Storage

This paper investigates a generation expansion planning (GEP) problem encompassing renewable, thermal, and storage technologies while simultaneously optimizing market participation, operational expenditures, and capital investment. To alleviate the computational burden of the GEP model, we propose a novel iterative time series aggregation (TSA) method that constructs a temporally aggregated counterpart of the original full-scale GEP model. Unlike traditional TSA methods, which are purely heuristic, our method enables the assessment of the optimality gap between the aggregated and full-scale models. Moreover, by leveraging machine learning-based estimates of the GEP model marginal costs, the algorithm guides TSA to construct an aggregated model that preserves the active constraints of its full-scale counterpart, which has been shown to yield exact temporal aggregation. Numerical results show that incorporating estimated marginal costs as clustering features substantially improves the quality of temporal aggregation compared with traditional TSA methods that rely solely on input data analysis.

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arXiv2026

QGas: Interactive Gas Infrastructure Toolkit

Gas infrastructure datasets are essential inputs for energy system planning to support strategic decision-making toward decarbonization. However, relevant data are typically scattered across heterogeneous sources, including geospatial datasets, image-based infrastructure plans, and tabular data, making it complex, time-consuming, and error-prone to create topology-consistent network representations with existing tools.This paper presents QGas, an interactive toolkit for visualizing, creating, and collaboratively extending georeferenced gas infrastructure datasets. QGas integrates GIS-based geometry editing with topology-preserving graph operations in a unified web-based environment, enabling users to digitize infrastructure plans, edit network elements, manage attributes, and perform topology-consistent modifications while maintaining a georeferenced representation of the system. The toolkit is implemented using a modular architecture based on Python, JavaScript, and the Leaflet mapping library. An illustrative example demonstrates its application in extending a natural gas dataset to include hydrogen and CO2 infrastructure, highlighting QGas's capability to support the preparation of consistent multi-carrier gas infrastructure datasets for energy system planning.

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arXiv2026

Simplification Ad Absurdum? Revisiting Gas Flow Modeling for Integrated Energy System Planning

This paper analyzes the implications of simplified pipeline gas flow models for integrated energy system planning. A case study of an integrated power-hydrogen expansion planning problem shows that simplifying pressure-flow relationships and gas dynamics can lead to expansion plans that incur substantial regret when evaluated under a more realistic dynamic gas flow model -- due to suboptimal system expansion, operation, and non-supplied hydrogen. Numerical experiments show that planning under the highly simplified transport and transport-linepack models -- commonly used in expansion studies -- can result in regret exceeding several thousand percent and yield expansion plans that lack robustness across demand levels. Planning under steady-state conditions partially mitigates these effects, but still leaves significant cost-reduction potential untapped compared to dynamic planning due to neglected linepack flexibility. Developing efficient solution algorithms for the dynamic model is a promising direction for future research.

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arXiv2026

Mapping Austria's Natural Gas and Hydrogen Infrastructure Plans

This paper presents a comprehensive, spatially disaggregated dataset of Austria's natural gas and hydrogen infrastructure towards 2040. The dataset covers the complete gas transmission and distribution networks down to the medium-pressure level and integrates hydrogen expansion plans from the Austrian Gas Grid Management. Transmission infrastructure is reconstructed from ENTSOG maps, converted into a topologically consistent graph representation, and enriched with technical attributes through automated spatial matching with open-source datasets such as OpenStreetMap and Global Energy Monitor. Distribution networks and infrastructure modifications are implemented using QGas, a newly developed GIS-based tool for graph-based infrastructure manipulation. To enable forward-looking energy system analyses, the dataset explicitly represents the stage-wise transition from natural gas to hydrogen infrastructure within a single dataset. Repurposed and newly constructed hydrogen pipelines are integrated within a unified network topology using node splitting and time dependent connector elements, enabling consistent modeling of parallel natural gas and hydrogen operation over time. The resulting dataset provides a detailed representation of Austria's gas and hydrogen infrastructure, including 586 natural gas pipeline segments (5000 km), 113 repurposed segments (1250 km), and 39 newly constructed hydrogen segments (820 km), connecting 720 nodes. Moreover, it includes a comprehensive set of gas demands, biogas production facilities, storage units, electrolyzers, and compressor elements, making it directly applicable for energy system optimization models.

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arXiv2025

Connecting Representative Periods in Energy System Optimization Models using Markov Transition Matrices

Time series aggregation reduces the computational complexity of large-scale energy system optimization models, but maintaining chronological continuity between the resulting representative periods (RPs) remains a key challenge, as transitions between RPs are typically lost. This causes inaccuracies in storage behavior, unit commitment, and other time-linked aspects of the model. We propose a novel method that uses the Transition Matrix between RPs to link them via probabilistic transitions and expected values. In contrast to existing Transition Matrix approaches that add variables and constraints to reconstruct inter-period chronology (e.g., for seasonal storage), our method reformulates the existing intra-RP constraints at the period boundaries without introducing any additional variables or constraints. It also handles constraints that connect multiple time steps and can be adapted to binary variables. We demonstrate the benefits on an illustrative case study and validate them on the updated IEEE Reliability Test System (RTS-GMLC). The improvement over the state of the art depends on the structure of the Transition Matrix, which can be inspected a priori at no additional data cost. When it is near-diagonal, the established cyclic connection already performs well, whereas for less diagonal matrices the Markov Transition reduces the median operational deviation by up to 80% (from about 32% to 6%). These gains come at practically no extra cost, as the mean computational effort stays below 2% of the full-model runtime, at most 0.9 percentage points more than the cyclic connection.

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arXiv2025

Towards Exact Temporal Aggregation of Time-Coupled Energy Storage Models via Active Constraint Set Identification and Machine Learning

Time series aggregation (TSA) aims to construct temporally aggregated optimization models that accurately represent the output space of their full-scale counterparts while using a significantly reduced temporal dimensionality. This paper presents a theoretical approach that achieves exact temporal aggregation of full-scale power system models -- even in the presence of energy storage time-coupling constraints -- by leveraging active constraint sets and dual information. This advances the state of the art beyond existing TSA methods, which typically cannot guarantee solution accuracy or rely on iterative procedures to determine the required number of representative periods. To bridge the gap between this theoretical analysis and practical application, we employ machine learning, i.e., classification and clustering, to inform TSA in models that co-schedule variable renewable energy sources and energy storage. Numerical results show substantially improved computational performance relative to the full-scale model, while maintaining a favorable trade-off between solution accuracy and complexity.

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arXiv2023

Enhancing Time Series Aggregation For Power System Optimization Models: Incorporating Network and Ramping Constraints

Power system optimization models are large mathematical models used by researchers and policymakers that pose tractability issues when representing real-world systems. Several aggregation techniques have been proposed to address these computational challenges and it remains a relevant topic in power systems research. In this paper, we extend a recently developed Basis-Oriented time series aggregation approach used for power system optimization models that aggregates time steps within their Simplex basis. This has proven to be an exact aggregation for simple economic dispatch problems. We extend this methodology to include network and ramping constraints; for the latter (and to handle temporal linking), we develop a heuristic algorithm that finds an exact partition of the input data, which is then aggregated. Our numerical results, for a simple 3-Bus system, indicate that: with network constraints only, we can achieve a computational reduction by a factor of 1747 (measured in the number of variables of the optimization model), and of 12 with ramping constraints. Moreover, our findings indicate that with temporal linking constraints, aggregations of variable length must be employed to obtain an exact result (the same objective function value in the aggregated model) while maintaining the computational tractability, this implies that the duration of the aggregations does not necessarily correspond to commonly used lengths like days or weeks. Finally, our results support previous research concerning the importance of extreme periods on model results.

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