loading page

Forecasting Congestions in Interconnected Power Systems with High Shares of Renewable Energy: A Probabilistic Approach using Copulas
  • Aiko Schinke-Nendza ,
  • Christoph Weber
Aiko Schinke-Nendza
House of Energy Markets and Finance

Corresponding Author:[email protected]

Author Profile
Christoph Weber
Author Profile


This paper proposes a novel approach to forecast congestions in high-voltage grids with high shares of distributed photovoltaic (PV) infeed. The approach is based on a physical PV model using intra-day numerical weather prediction (NWP) input data. Subsequently, probabilistic forecasts are generated based on Kernel density estimators (KDE) and Copula, describing the multivariate spatial dependencies for the marginal distributions of forecasting and approximation errors. Finally, a probabilistic power flow (PPF) using a linearized AC version is proposed, combining the benefits of high accuracy with high computational performance. To assess and quantify the overall advantages of this approach, a case study is carried out for an existing power system.