Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset

Ana Carolina do Amaral Burghi, Tobias Hirsch and Robert Pitz-Paal
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Ana Carolina do Amaral Burghi: Institute of Solar Research, German Aerospace Center (DLR), Wankelstrasse 5, 70563 Stuttgart, Germany
Tobias Hirsch: Institute of Solar Research, German Aerospace Center (DLR), Wankelstrasse 5, 70563 Stuttgart, Germany
Robert Pitz-Paal: Institute of Solar Research, German Aerospace Center (DLR), Linder Höhe, 51147 Cologne, Germany

Energies, 2020, vol. 13, issue 3, 1-25

Abstract: Weather forecast uncertainty is a key element for energy market volatility. By intelligently considering uncertainties on the schedule development, renewable energy systems with storage could improve dispatching accuracy, and therefore, effectively participate in electricity wholesale markets. Deterministic forecasts have been traditionally used to support dispatch planning, representing reduced or no uncertainty information about the future weather. Aiming at better representing the uncertainties involved, probabilistic forecasts have been developed to increase forecasting accuracy. For the dispatch planning, this can highly influence the development of a more precise schedule. This work extends a dispatch planning method to the use of probabilistic weather forecasts. The underlying method used a schedule optimizer coupled to a post-processing machine learning algorithm. This machine learning algorithm was adapted to include probabilistic forecasts, considering their additional information on uncertainties. This post-processing applied a calibration of the planned schedule considering the knowledge about uncertainties obtained from similar past situations. Simulations performed with a concentrated solar power plant model following the proposed strategy demonstrated promising financial improvement and relevant potential in dealing with uncertainties. Results especially show that information included in probabilistic forecasts can increase financial revenues up to 15% (in comparison to a persistence solar driven approach) if processed in a suitable way.

Keywords: renewable systems; storage; dispatch; optimization; machine learning; probabilistic forecasts (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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