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Sonja Wogrin

Publications and source records attributed to Sonja Wogrin.

At least 19 recordsLinked to original sources
arXiv2026

Stochastic Virtual Power Plant Dispatch via Temporally Aggregated Distributed Predictive Control with Performance Guarantees

This paper addresses the energy dispatch of a virtual power plant comprising renewable generation, energy storage, and thermal units under uncertainty in renewable output, energy prices, and energy demand. The nonlinear dynamics and multiple sources of uncertainty render traditional stochastic model predictive control (MPC) computationally intractable as the dispatch horizon, scenario set, and asset portfolio expand. To overcome this limitation, we propose a novel controller that seamlessly integrates MPC with time series aggregation and distributed optimization, simultaneously reducing the temporal, asset, and scenario dimensions of the problem. The resulting controller provides a rigorous performance guarantee through theoretically validated bounds on its approximation error, while leveraging dual information from previous MPC iterations to adaptively optimize the temporal aggregation. Numerical results show that the proposed controller reduces runtime by over 50% relative to traditional stochastic MPC and, crucially, restores tractability where the full-scale dispatch model proves intractable.

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arXiv2026

Scenario Reduction for Two-Stage Stochastic Mixed-Integer Programs

Two-stage stochastic mixed-integer programs are important tools for decision-making under uncertainty. Representing the uncertainty with many scenarios, however, can make them challenging to solve. Scenario reduction addresses this by finding a distribution supported on fewer scenarios that still yields similar optimal first-stage decisions. In this paper, we revisit the classical scenario reduction theory based on distances between probability distributions and the optimal mass transportation problem. The transportation problem's cost function captures scenario similarity and is central to the effectiveness of scenario reduction. We then review and compare various transportation cost functions from the literature and propose a new one. Using the Forward Selection Algorithm, we prove that our proposed cost function selects the best possible scenario from a given sample on the first draw with respect to the relative approximation error. To reduce the computational cost of evaluating this cost function, we further propose a hybrid algorithm with a scenario pre-selection phase. We assess solution quality and computational complexity on the two-stage stochastic unit commitment problem for small 24-bus and large 300-bus case studies. With only around five scenarios, the proposed cost function approximates the full-distribution optimum to within roughly 2.1% and 0.4% error for the small and large cases, respectively. In contrast, prevalent cost functions often need 25 scenarios or more to achieve that solution quality. The hybrid algorithm achieves similar solution quality while reducing wall-clock time by a factor of 18 and work (per Gurobi solver) by a factor of 66 on the large case study.

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arXiv2026

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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arXiv2026

Unlocking the Informational Value of Marginal Costs for Exact Time Series Aggregation in Generation Expansion Planning

This paper addresses the generation expansion planning (GEP) problem, formulated as a mixed-integer linear programming model with intertemporal storage constraints. Being generally NP-hard, the problem's computational complexity grows sharply with the planning horizon and the number of binary variables. While previous research has tackled this challenge using heuristic time series aggregation (TSA) methods, we propose a theoretically grounded marginal-cost-based TSA, designed to construct an aggregated model that preserves the active constraints of its full-scale counterpart, thereby explicitly targeting exact temporal aggregation. This TSA method is embedded within solution algorithms that iteratively refine theoretically validated bounds on the maximum error introduced by the temporal aggregation relative to conventional full-scale optimization, thus offering a formal performance guarantee to the decision-maker. Numerical results highlight the computational advantages of the proposed algorithms, which notably recover tractability whereas full-scale optimization proves intractable.

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arXiv2026

Distributed Stochastic Model Predictive Control with Temporal Aggregation for the Joint Dispatch of Cascaded Hydropower and Renewables

This paper addresses the real-time energy dispatch of a hybrid system comprising cascaded run-of-the-river hydropower plants, wind, and solar photovoltaic units, operated under uncertainty in water inflows and renewable power generation. Traditional scenario-based stochastic model predictive control (MPC) schemes suffer from severe computational limitations due to the high dimensionality induced by both the temporal and scenario dimensions of the dispatch problem, as well as the inherent nonconvexities associated with cascaded hydropower dynamics. To overcome these challenges, we propose a novel control scheme that seamlessly integrates time series aggregation (TSA), distributed optimization, and stochastic MPC. The resulting temporally aggregated distributed stochastic MPC scheme simultaneously reduces the temporal dimension of the dispatch problem via TSA and decomposes it across scenarios through distributed optimization. Our main theoretical result establishes a formal performance guarantee for the proposed controller, enabling a rigorous quantification of its solution accuracy at every MPC iteration. Numerical results based on a real-world case study show the effectiveness of the proposed controller, achieving up to 74% reduction in computational effort relative to the full-scale centralized counterpart when the required solution accuracy is at least 99%, and up to 85% when the accuracy requirement is relaxed to 95%. Notably, the proposed controller not only significantly enhances computational efficiency relative to the traditional full-scale centralized counterpart, but more importantly restores computational tractability, whereas the traditional controller fails to solve the dispatch problem within the prescribed time limit for computing control actions.

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arXiv2026

Towards time-variant scenario reduction for energy system optimization modeling under uncertainty

Stochastic programming has become a popular tool for supporting decision-making under uncertainty in the long-term planning of energy systems. Existing scenario reduction methods, however, are naive about the long-term temporal nature of scenarios, which limits their efficiency in reducing model size. In this paper, we overcome this inefficiency by proposing a novel time-variant scenario reduction framework that explicitly allows for varying scenario aggregations over time. As a result, scenario probabilities become time-variant, enabling not only the accurate capture of scenario realizations but also their probabilities at the time steps that drive investment decisions. This substantially increases flexibility compared to traditional time-invariant methods, which we demonstrate on a two-stage stochastic generation expansion planning problem with uncertain renewable power production.

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arXiv2026

Modelling the Correlation Structure of Uncertain Input Parameters for Energy System Optimization

Statistical dependence among uncertain input parameters in stochastic energy system optimization models is often ignored, even though this can substantially bias outcomes. To address this gap, we are developing a comprehensive framework for characterizing, modelling, and benchmarking statistical dependence. In this work, we present a copula-based workflow to identify, characterize, and model linear and monotonic correlation structures between input parameters, representing the first development step towards this framework. We demonstrate our workflow using solar generation, day-ahead electricity prices, and electricity demand data in Austria between 2019 and 2025. Our results show substantial linear and monotonic dependence between these variables, and that this dependence is well captured by the copula-based approach. Finally, building on this scalable foundation, we highlight key levers for next steps towards the full framework, including time-dependent and higher-dimensional dependence modelling.

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arXiv2026

Temperature-Aware Heat Pump Modeling for Large-Scale Energy System Optimization

Heat pumps are expected to dominate the heating sector, substantially increasing peak electricity demand. At the same time, building thermal inertia enables operational strategies, providing temporal flexibility in heat pump operation and short-term demand response. However, this dynamic behavior is not yet represented in large-scale energy system optimization models. To address this gap, we present an innovative formulation of building thermal inertia. The resulting temperature variable is integrated into a novel conic temperature-aware heat pump efficiency formulation, enabling a more precise emulation of smart control strategies. In a case study of the European energy system, we show that the approach captures operational heating flexibility while remaining computationally efficient. The results indicate substantial untapped flexibility potential, enabling up to a 22% reduction in heating-related electricity costs. This potential can be realized through a suitable energy market design that incentivizes coordinated heat pump control, individually or via aggregators.

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arXiv2026

Surrogate Modeling of Interconnector Flows: A Machine Learning Alternative to Full-Scale Power System Simulations with Application to Cross-Border Electricity Exchange

Cross-border electricity exchanges are crucial for operating and planning highly renewable power systems. Many studies reduce spatial granularity to keep the models tractable and prescribe cross-border exchanges exogenously, often by reusing historical import/export time series. Such assumptions become inconsistent as renewable penetration changes the magnitude and timing of flows. This paper proposes a machine-learning (ML) surrogate framework that maps available nodal time series data (e.g., hourly demand and renewable generation) to synthetic, interconnector-level flow time series. The goal is to provide consistent flow profiles that are used as fixed boundary conditions in reduced power system optimization models (PSOMs). To improve downstream feasibility when surrogate flows are imposed in optimization, we further introduce a custom loss for the neural-network surrogate that penalizes physically impossible flow patterns. We demonstrate the framework on a pan-European single-node per country DC optimal power flow setting using the open-source LEGO PSOM with ENTSO-E TYNDP 2024 National Trends assumptions for 2030. We assess two model classes: k-nearest neighbors (KNN) and feedforward neural networks (SQU), using both full and reduced feature sets. The SQU models generalize more robustly than KNN to unseen climate years and substantially improve upon scaled historical benchmarks in terms of predictive accuracy. When imposed as fixed boundary flows in single-node PSOMs, the ML-generated profiles produce outcomes that closely match the results of the full European simulation, while delivering substantial runtime reductions (up to ~500x). These results indicate that ML-based flow surrogates can provide decision-relevant interconnector flows for tractable reduced studies in high-renewable systems.

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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

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

Voltage-Aware Grid Aggregation: Expanding the European High-Voltage Network

Energy system optimization models are indispensable for planning the European energy transition. Yet their applicability is constrained by the fundamental trade-off between spatial detail and computational tractability. Modelers often tackle this by spatially aggregating electricity networks. Existing methods, however, neglect differences in voltage levels, reducing them to a single level and thereby overlooking the critical role of transformers in expansion planning. Therefore, we propose a novel voltage-aware network partitioning and aggregation methodology that preserves individual voltage levels and transformers. We demonstrate the effectiveness of this approach and compare it against a voltage-unaware grid aggregation by solving a network expansion problem for a European case study using PyPSA. Our findings show that the proposed methodology preserves up to 70% of the transformer expansion costs in the aggregated model compared to the full grid model, thereby significantly improving the accuracy of investment decisions for transformers in the aggregated grid.

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arXiv2026

NPAP: Network Partitioning and Aggregation Package for Python

NPAP (Network Partitioning and Aggregation Package) is an open-source Python library for reducing the spatial complexity of network graphs. Built on NetworkX, it provides an accessible standalone package designed to be readily integrated with other software and frameworks. Instead of treating the spatial reduction process as a single action, NPAP explicitly splits it into two distinct steps: partitioning, which assigns vertices (nodes) to groups (clusters), and aggregation, which reduces the network based on a given assignment. NPAP's strategy pattern architecture allows users to employ and register custom partitioning and aggregation strategies seamlessly without modifying the core code. Currently, NPAP provides 13 different partitioning strategies and two pre-defined aggregation profiles. Although initially developed with a focus on power systems, its architecture is general-purpose and applicable to any network graph.

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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

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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arXiv2026

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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arXiv2025

Scenario Reduction for the Two-Stage Stochastic Unit Commitment Problem

The two-stage stochastic unit commitment problem has become an important tool to support decision-making under uncertainty in power systems. Representing the uncertainty by a large number of scenarios guarantees accurate results but challenges the solution process. One way to overcome this is by using scenario reduction methods, which aim at finding a distribution supported on fewer scenarios, but leading to similar optimal first-stage decisions. In this paper, we recap the classical scenario reduction theory based on the distance of probability distributions and the optimal mass transportation problem. We then review and compare various formulations of the underlying cost function of the latter used in the literature. Using the Forward Selection Algorithm, we show that a specific formulation of the cost function can be proven to select the best possible scenario from a given sample on the first draw with respect to the Relative Approximation Error. We demonstrate this result and compare the quality of the approximation as well as the computational performance of the different cost functions using a modified version of the IEEE RTS 24-Bus System. In many cases, we find that the optimal solution of the two-stage stochastic unit commitment problem with 200 scenarios can be approximated with around 2% scenarios when using this cost function.

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arXiv2025

What Are We Clustering For? Establishing Performance Guarantees for Time Series Aggregation in Generation Expansion Planning

Generation expansion planning (GEP) is a prominent example of capacity expansion problems in operations research. Being generally NP-hard, GEP optimization models can become intractable when nonconvex dynamics, time-coupling constraints, and complex asset interactions are involved. Time series aggregation (TSA) tackles this by reducing temporal complexity via input data clustering. However, existing TSA methods either focus solely on preserving the statistical features of the input data, yielding heuristics without guarantees on the aggregated model's accuracy, or provide error bounds limited to linear models, neglecting time-coupling constraints and applying only to specific clustering techniques. Moreover, these bounds typically pertain solely to the GEP objective function and do not extend to other stakeholder-specific metrics, such as decision vector partitions. To tackle these issues, we demonstrate that an appropriately constructed aggregated model always provides a lower bound on the optimal objective function value of the full-scale GEP model in both mixed-integer linear and mixed-integer quadratic formulations with time-coupling, independent of the clustering technique employed. Building on this, we propose a performance-guaranteed TSA-based solution algorithm that iteratively refines objective function bounds while generating feasible solutions to the full-scale model at each iteration. We then discuss a comparison with Benders decomposition and demonstrate how the derived bounds can be extended to error estimates for stakeholder-specific metrics. Numerical results show the computational advantages of our method over both full-scale optimization and classical Benders decomposition.

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