Schinke-Nendza (2021) - Forecasting Congestions in Interconnected Power Systems.pdf (456.93 kB)
Download file

Forecasting Congestions in Interconnected Power Systems with High Shares of Renewable Energy: A Probabilistic Approach using Copulas

Download (456.93 kB)
posted on 2021-05-28, 09:05 authored by Aiko Schinke-NendzaAiko Schinke-Nendza, Christoph Weber
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.


Federal Ministry of Education and Research (BMBF), Grant No. 05M18PGA


Email Address of Submitting Author

ORCID of Submitting Author

Submitting Author's Institution

House of Energy Markets and Finance, University of Duisburg-Essen

Submitting Author's Country

  • Germany