Power Systems
Work description
The increasing integration of distributed energy resources (DER) into power systems introduces significant challenges in assessing Hosting Capacity and quantifying network flexibility, particularly at the transmission–distribution interface. In this context, the proposed work aims to develop probabilistic and data-driven methodologies to efficiently and accurately evaluate the impact of DER penetration on system operation. The main activities to be carried out include: - Conduct a state-of-the-art review on Hosting Capacity assessment methodologies, probabilistic simulation, and machine learning applied to power systems; - Develop probabilistic scenarios considering renewable generation, load, storage, and component failures; Implement a simulation engine based on Multi-Level Monte Carlo (MLMC) for hosting capacity assessment; - Develop and integrate Physics-Informed Machine Learning (PIML) models to improve computational efficiency while preserving physical consistency; - Evaluate system performance indicators, including energy not served, overloads, and curtailment; - Validate the developed methods using realistic transmission network models and benchmark distribution systems; - Prepare technical reports and contribute to scientific publications.
Minimum profile required
- Basic knowledge of power systems;- Basic knowledge of optimization and/or probabilistic methods;- Programming skills (Python, MATLAB or equivalent);- Fluency in English (written and spoken);
Preference factors
- Knowledge of power systems and renewable energy integration; - Experience in modelling and simulation of power systems; - Knowledge of probabilistic or stochastic methods; - Programming skills (Python, MATLAB or similar); - Previous experience in scientific research activities;
Application Period
Since 07 May 2026 to 21 Jun 2026
Centre
Power and Energy Systems