Schinke-Nendza (2021) - Forecasting Congestions in Interconnected Power Systems.pdf (456.93 kB)
Download fileForecasting Congestions in Interconnected Power Systems with High Shares of Renewable Energy: A Probabilistic Approach using Copulas
preprint
posted on 2021-05-28, 09:05 authored by Aiko Schinke-NendzaAiko Schinke-Nendza, Christoph WeberThis 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.
Funding
Federal Ministry of Education and Research (BMBF), Grant No. 05M18PGA
History
Email Address of Submitting Author
aiko.schinke-nendza@uni-due.deORCID of Submitting Author
https://orcid.org/0000-0002-7914-1281Submitting Author's Institution
House of Energy Markets and Finance, University of Duisburg-EssenSubmitting Author's Country
- Germany