WorkingPaper, ponencias invitadas y contribuciones en congresos no publicadas http://hdl.handle.net/11531/4153 2026-04-07T20:46:33Z 2026-04-07T20:46:33Z Auer, Felix Clemens Alexander Gaugl, Robert Klatzer, Thomas Tejada Arango, Diego Alejandro Wogrin, Sonja http://hdl.handle.net/11531/109469 2026-04-07T17:18:22Z

Uncovering Hidden Biases in Hydropower: Why Detailed Inflow Data is Crucial for Energy System Optimization Models Auer, Felix Clemens Alexander; Gaugl, Robert; Klatzer, Thomas; Tejada Arango, Diego Alejandro; Wogrin, Sonja ; Hydropower plays a critical role in balancing highly renewable power systems, yet its representation in energy system optimization models is often constrained by limited availability of high-resolution inflow data. In practice, aggregated inflow time series are frequently used to reduce data requirements, potentially introducing hidden modeling biases. This paper analyzes the impact of inflow aggregation on generation expansion and operation decisions in energy system optimization models. Using an adapted NREL-118 bus test system and the open-source Low-carbon Expansion Generation Optimization (LEGO) model, we compare planning outcomes based on hourly inflow data against daily, weekly, monthly, and yearly aggregations. By computing expost regret with respect to hourly inflow operation, we quantify the cost and investment distortions caused by aggregation.Our results show that inflow aggregation leads to substantial cost increases and misallocation of generation capacity investments once hydropower becomes a significant share of the energy mix, as coarse aggregations systematically underestimate inflow variability. These findings demonstrate that high-resolution hydropower inflow data is essential for robust energy system planning and highlight the need for openly available, standardized inflow time series to support planning of future energy systems.

Rodríguez Pérez, Néstor Matanza Domingo, Javier Sigrist, Lukas Rueda Torres, José Luis López López, Gregorio http://hdl.handle.net/11531/109468 2026-04-07T17:18:14Z

MaDIoT 3.0: Análisis de Ataques a la Demanda y Generación Distribuida en un Sistema Eléctrico Rodríguez Pérez, Néstor; Matanza Domingo, Javier; Sigrist, Lukas; Rueda Torres, José Luis; López López, Gregorio Este artíıculo analiza el impacto y la eficacia de los ataques MaDIoT 3.0 en el modelo PST-16, un sistema europeosimplificado. Estos ataques, de carácter novedoso, permiten comprometer de forma simultánea dispositivos IoT de alta potencia en la demanda y recursos de generación distribuida, como inversores fotovoltaicos. Los resultados muestran que la incorporación de generación solar fotovoltaica distribuida reduce tanto la tasa de éxito como el impacto de los ataques MaDIoT centrados en la alteración de la demanda, en comparación con un sistema sin dicha generación. En los ataques MaDIoT 3.0, la manipulación de la demanda resulta más determinante para el éxito global del ataque que la generación distribuida. Asimismo, se evidencia que la escalabilidad y la replicabilidad local de dispositivos IoT vulnerables de alta potencia son factores más críticos que su despliegue en áreas geográficas extensas.;

Alfaya Sánchez, David Calvo Pascual, Luis Ángel Cazorla García, Pedro-José Rodrigo Hitos, Javier Srinivasan, Anitha http://hdl.handle.net/11531/109467 2026-04-07T17:17:32Z

Branches of markoff m-triples with two k-ibonacci components Alfaya Sánchez, David; Calvo Pascual, Luis Ángel; Cazorla García, Pedro-José; Rodrigo Hitos, Javier; Srinivasan, Anitha We study infinite paths of Markoff m-triples, that is, solutions to the generalised Markoff equationx2 + y2 + z2 = 3xyz + m,with m > 0, with at least two k-Fibonacci components. First, we obtain a complete classification of Markoff m-triples whose last two entries are k-Fibonacci numbers and that are not roots of any Markoff trees. [...]; We study infinite paths of Markoff m-triples, that is, solutions to the generalised Markoff equationx2 + y2 + z2 = 3xyz + m,with m > 0, with at least two k-Fibonacci components. First, we obtain a complete classification of Markoff m-triples whose last two entries are k-Fibonacci numbers and that are not roots of any Markoff trees. [...]

Gómez González, Juan Luis Cantizano González, Alexis Ayala Santamaría, Pablo http://hdl.handle.net/11531/109388 2026-03-27T05:23:01Z

Operational Wildfire Ensembles for Electric Grid Assets: Mapping Burn Probability and Exposure Gómez González, Juan Luis; Cantizano González, Alexis; Ayala Santamaría, Pablo ; Wildfires are increasingly affecting electric power systems through two coupled pathways: ignitions initiated by grid assets (e.g., faults, conductor–vegetation contact, arcing) and damage or operational disruption when fires reach overhead transmission corridors, substations, and supporting structures. Utilities, therefore, need quantitative, spatially explicit methods that turn fire-behavior uncertainty into actionable indicators at the asset level.This conference paper presents an operational workflow for building wildfire ensembles and deriving burn probability and time-to-impact metrics for power transmission infrastructure. The workflow builds on open-data landscape preparation, combining public forest maps and inventories with LiDAR-derived canopy structure to generate high-resolution fuels and canopy layers. Fire spread is simulated using FlamMap together with a calibrated Cellular Automata (CA) model. Ensemble modelling propagates uncertainty in ignition location, wind regimes, and fuel moisture, so outputs can be expressed as probability and arrival-time ranges rather than single deterministic estimates. The outputs are post-processed into gridded burn-probability and fire-arrival-time maps, and then aggregated within corridor buffers to produce segment-level exposure indices for planning and operations. In this contribution, we focus on the methodology and its data requirements; a full case-study validation and quantitative performance assessment are the subject of ongoing work.