Comillas Pontifical University

Cross-border electricity exchanges are crucial for operating and planning highly renewable power systems. Many studies reduce spatial granularity to keep models tractable and prescribe cross-border exchanges exogenously, often by reusing historical import/export time series. Such assumptions become inconsistent as renewable pen etration changes the magnitude and timing of flows. The core contribution of this work is a machine-learning (ML) surrogate framework that maps nodal time-series data (e.g., hourly demand and renewable generation) to synthetic interconnector-level flow time series. The resulting flows serve as fixed boundary conditions in reduced power system optimization models (PSOMs). This addresses studies focused on a subset of an interconnected system, for which repeatedly solving the full model across multiple climate years or scenarios is computationally expensive. We demonstrate the framework on a pan-European single-node-per-country DC optimal power flow model. We benchmark k-nearest neighbors (KNN), ridge regression, random forests, Extra Trees, gradient boost ing, XGBoost, and feedforward neural-network (SQU) surrogates. As an additional contribution, we introduce a novel custom neural-network loss that softly penalizes physically implausible flow patterns. This soft penalty is not a guarantee of full power-flow feasibility. Across benchmarks, the SQU variants provide the most consis tent out-of-sample and downstream optimization performance while substantially outperforming scaled historical profiles. The ML-generated flows closely reproduce country-level results of the full European model while greatly reducing computation time (up to similar to 500 & times;). The proposed framework enables scenario-consistent reduced PSOM studies across climate years after a single full-model run, eliminating repeated pan-European simulations and supporting more robust climate-year analyses under fixed system assumptions.

A precise estimation of the Rate of Change of Frequency (RoCoF) is crucial for secure power system operation. In fact, RoCoF is strictly related to the amount of the available physical and/or virtual inertia of the system and the severity of the active power unbalance following a disturbance. For this reason, it is widely exploited in different protection systems, e.g., Anti-Islanding, Under Frequency Load Shedding (UFLS) and wide-area protection systems. The new paradigm of modern power systems, with a low-inertia and converter-based generation assets, is increasing the transient severity, making the frequency and the RoCoF estimation more complex and less precise for the actual devices. This work addresses this issue by proposing a numerically robust approach based on concepts inherited from differential geometry and fluid mechanics. The proposed approach is then tested with high-sampling real experimental measurements and used to develop a faster control logic for a RoCoF-based UFLS control scheme. The proposed approach provides information to protections regarding the nature of the contingency which can be used to improve its response.

Guaranteeing sufficient damping of inter-area oscillations is a critical requirement for Transmission System Operators (TSOs). The increasing penetration of power electronics-interfaced devices (PEIDs) introduces significant variability in operating conditions, complicating the design of robust Power Oscillation Damping (POD) controllers. This paper presents a novel framework for assessing POD robustness using symbolic computation of eigenvalue sensitivities (ES). The method provides analytical expressions of ES as explicit functions of system variables for different POD inputs and outputs, enabling systematic evaluation of signal selection across a wide range of operating points. The approach is developed on a Two-Machine System, and the results are applied to the Two-Area Four-Generator System.

Competition among entrepreneurial funding platforms has made it more important than ever to attract and retain investors to ensure the sustainability of entrepreneurial ecosystems. In this context, investor recommendations are a key mechanism to generate trust in crowdlending platforms. This paper investigates the existence of necessary and sufficient conditions that explain investor recommendations of crowdlending platforms and explores how the motivations behind investor behavior are linked to investor recommendations of these platforms. Necessary condition analysis (NCA) and qualitative comparative analysis (QCA) were performed using data on 83 investors. Of the intrinsic/self-oriented motivations considered in the analysis, only satisfaction emerged as an individual in-degree necessary condition from the NCA. The analysis also confirmed the synergistic effects of intrinsic/self-oriented and extrinsic/other-oriented motivations as sufficient conditions for explaining investor behavior. Nonetheless, such behavior can still be explained exclusively by intrinsic/self-oriented conditions. The study enriches the knowledge of how investor motivations explain the sustainability of platforms that provide alternative forms of financing for entrepreneurs.

PurposeThis original research paper aims to explore how English-medium instructors in internationalized higher education settings navigate challenges and affordances regarding internationalization, student expectations and research demands, and how these dynamics shape their multilingual and multicultural (MLMC) identities.Design/methodology/approachThis phenomenological study explores the MLMC identities of 16 English-medium instruction (EMI) faculty members at a Spanish university, using semistructured interviews and a conceptual framework examining knowledge, skills and abilities; values, beliefs and attitudes; awareness; behaviors; and emotions.FindingsFindings reveal a positive orientation toward MLMC identity and EMI, minimal monolingual bias and a strong acknowledgment of the need to adapt teaching methods, despite an emphasis on content delivery. Awareness of the negative impacts of "Englishization" on linguistic diversity and equity is limited. This study positions EMI faculty as potential agents of change but calls for stronger institutional support.Originality/valueTeacher identity has been extensively researched, yet much of the focus has remained on language teacher identity or on isolated aspects of EMI faculty identity, such as classroom practices and teaching beliefs, rather than adopting a more holistic perspective. This study offers in-depth insights from a single institution, with implications that are relevant for broader EMI contexts.