arXiv subjects

Robert Gaugl

Publications and source records attributed to Robert Gaugl.

3 recordsLinked to original sources

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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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

Implementing Dynamic Power Feed-In Limitations of Photovoltaic Systems in Distribution Grids for Generation Expansion Planning

The rapid growth of photovoltaic (PV) systems in Austria's medium- and low-voltage grids has intensified challenges in grid access, with technical limits increasingly leading to restrictions on full feed-in power. This issue has sparked discussions about limiting PV feed-in power and the implications for both generated and curtailed PV energy. At the same time, expanding PV capacity remains critical to achieving future climate targets. However, there is a lack of robust methodologies of quantify the impact of PV feed-in limitations when implemented in an optimization model. This impact affects both the curtailed energy and the increase in maximum PV installation capacity and total energy production. To address this gap, we have developed a mathematical formulation of dynamic PV feed-in limitations and integrated it into an optimization model. This approach enables a comprehensive evaluation of its effects on PV integration potential and energy curtailment, validated through case studies on four representative real-world Austrian medium- and low-voltage grids. We analyzed maximum PV expansion, energy generation, and curtailment under feed-in constraints. The results highlight the potential for integrating up to 32% additional PV systems within existing infrastructure while keeping PV curtailment relatively low, i.e. at 2%. We provide actionable insights for grid operators and policymakers aiming to balance renewable energy expansion with grid reliability.

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