http://hdl.handle.net/11531/4153 WorkingPaper, ponencias invitadas y contribuciones en congresos no publicadas 2026-04-06T09:54:41Z http://hdl.handle.net/11531/109388 Título : Operational Wildfire Ensembles for Electric Grid Assets: Mapping Burn Probability and Exposure Autor : Gómez González, Juan Luis; Cantizano González, Alexis; Ayala Santamaría, Pablo Resumen : ; 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. http://hdl.handle.net/11531/109387 Título : Valuation of Regulatory Risk on Pharmaceutical R&D Autor : Corzo Santamaría, Teresa; Portela González, José; Schwartz, Eduardo Resumen : ; Geopolitical tensions, supply-chain concerns and policy risk have moved to the forefront of the pharmaceutical industry. This paper develops a real options valuation model of drug R&D that captures sequential clinical investment with technical failure, stochastic costs, uncertain cash flows, and optimal abandonment. We incorporate two regulatory shocks: a reduction in effective exclusivity period and a price-control shock that reduces net cash flows. Calibrating to an incremental CNS program, we find that project value at initiation is highly right-skewed: the mean is USD 69.6m but the median is negative, so expected value is driven by rare high-upside outcomes. Regulatory risk mainly compresses this upside. Both reductions in effective exclusivity and price-based interventions substantially weaken investment incentives, even when they occur with moderate probability. Value is strongly convex in exclusivity length, with the final years carrying the highest marginal value. We introduce iso-value maps that summarize how time-based and price-based policies substitute in their impact on project valuation, to clarifythe trade-offs inherent in regulatory design: losing two years of exclusivity is comparable to roughly a 30% cash-flow contraction. Using a standard revenue-to-R&D elasticity, these valuation effects imply a 10% to 25% long-run contraction in investment. The framework provides a transparent mapping from policy design to project value and investment incentives. http://hdl.handle.net/11531/109310 Título : SAMA: A Multi-Agent System for Automating Meta-Analyses Autor : Queipo de Llano Pérez-Gascón, Ignacio; López López, Álvaro Jesús; Lumbreras Sancho, Sara; Collazo Castiñeira, Paula; Sánchez-Izquierdo Alonso, Macarena; Echegoyen Blanco, Ignacio; Garrido Merchán, Eduardo César Resumen : ; http://hdl.handle.net/11531/109309 Título : A Comparative Objective Weighting MCDM-GIS Approach to Renewable Hydrogen Production Site Selection: A Spanish Case Study Autor : Serna Zuluaga, Santiago; Solano, Edna Sofía; Gerres, Timo; Cossent Arín, Rafael Resumen : Deciding on the optimal location for a hydrogen production plant is a complex task that involves multiple factors, including proximity to demand centers, availability of renewable energy resources or existing infrastructure. Identifying suitable sites, therefore, requires integrating diverse geospatial criteria, typically addressed through Geographic Information Systems (GIS) and multi-criteria decision-making (MCDM) frameworks. Existing analyses rely on subjective weighting, which introduces biases and hampers replicability.Objective weighting methods offer a data-driven alternative but have generally been applied to selecting among only a few regional alternatives.This study fills this gap by developing a high-resolution spatial MCDM framework to evaluate thousands of candidate locations across Spain. Increasing spatial resolution from aggregated units (e.g., provinces) to site-level alternatives reveals greater variability in the input indicators, including a wider dispersion of values that is typically masked by spatial averaging. These differences may influence the resulting weights and rankings. To examine this issue, we compare five objective weighting methods using mathematical validation and an empirical comparison with the spatial distribution of existing hydrogen projects.Our results show that all methods are robust and consistently identify the most suitable areas, aligning well with existing project locations. The main differences between methods emerge in intermediate rankings and in their stability to changes in input data, with some methods being more sensitive to constraint definitions, such as the maximum allowable distance to demand centers. These findings highlight that, although objective methods substantially reduce the reliance on expert judgment, they do not eliminate it.Methodological choices materially influence prioritization outcomes, and informed expert judgment remains essential to ensure that methodological choices reflect the objectives and constraints of the analysis.; Deciding on the optimal location for a hydrogen production plant is a complex task that involves multiple factors, including proximity to demand centers, availability of renewable energy resources or existing infrastructure. Identifying suitable sites, therefore, requires integrating diverse geospatial criteria, typically addressed through Geographic Information Systems (GIS) and multi-criteria decision-making (MCDM) frameworks. Existing analyses rely on subjective weighting, which introduces biases and hampers replicability.Objective weighting methods offer a data-driven alternative but have generally been applied to selecting among only a few regional alternatives.This study fills this gap by developing a high-resolution spatial MCDM framework to evaluate thousands of candidate locations across Spain. Increasing spatial resolution from aggregated units (e.g., provinces) to site-level alternatives reveals greater variability in the input indicators, including a wider dispersion of values that is typically masked by spatial averaging. These differences may influence the resulting weights and rankings. To examine this issue, we compare five objective weighting methods using mathematical validation and an empirical comparison with the spatial distribution of existing hydrogen projects.Our results show that all methods are robust and consistently identify the most suitable areas, aligning well with existing project locations. The main differences between methods emerge in intermediate rankings and in their stability to changes in input data, with some methods being more sensitive to constraint definitions, such as the maximum allowable distance to demand centers. These findings highlight that, although objective methods substantially reduce the reliance on expert judgment, they do not eliminate it.Methodological choices materially influence prioritization outcomes, and informed expert judgment remains essential to ensure that methodological choices reflect the objectives and constraints of the analysis.