Probabilistic behavioral aggregation: A case study on the Nordic power grid

Abstract

This study applies the Probabilistic Behavioral Tuning (ProBeTune) framework to transient power grid simulations to address challenges posed by increasing grid complexity. ProBeTune offers a probabilistic approach to model aggregation, using a behavioral distance measure to quantify and minimize discrepancies between a full-scale system and a simplified model. We demonstrate the effectiveness of ProBeTune on the Nordic5 (N5) test case, a model representing the Nordic power grid with complex nodal dynamics and a high share of RESs. We substantially reduce the complexity of the dynamics by tuning the system to align with a reduced swing-equation model. We confirm the validity of the swing equation with tailored controllers and parameter distributions for capturing the essential dynamics of the Nordic region. This reduction could allow interconnected systems like the Central European power grid to treat the Nordic grid as a single dynamic actor, facilitating more manageable stability assessments. The findings lay the groundwork for future research on applying ProBeTune to microgrids and other complex sub-systems, aiming to enhance scalability and accuracy in power grid modeling amidst rising complexity.