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

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

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posted on 28.05.2021, 09:05 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.

Funding

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

History

Email Address of Submitting Author

aiko.schinke-nendza@uni-due.de

ORCID of Submitting Author

https://orcid.org/0000-0002-7914-1281

Submitting Author's Institution

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

Submitting Author's Country

Germany