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Using Probabilistic Forecasts in Stochastic Optimization

 
: Groß, A.; Lenders, A.; Zech, T.; Wittwer, C.; Diehl, M.

:
Postprint urn:nbn:de:0011-n-6145933 (214 KByte PDF)
MD5 Fingerprint: 0402f6644dfafa834233c0a2b6227878
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Created on: 16.2.2021


Institute of Electrical and Electronics Engineers -IEEE-:
International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2020 : August 18-21, 2020, Liège, Belgium : conference proceedings
Piscataway, NJ: IEEE, 2020
ISBN: 978-1-72812-822-1
ISBN: 978-1-72812-823-8
6 pp.
International Conference on Probabilistic Methods Applied to Power Systems (PMAPS) <16, 2020, Online>
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
0324054A; ALPRO
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
0324125B; eco4wind
Bundesministerium fur Wirtschaft und Energie BMWi (Deutschland)
0324166B; DyConPV
English
Conference Paper, Electronic Publication
Fraunhofer ISE ()
Leistungselektronik; Netze und Intelligente Systeme; probabilistic forecast; PV battery systems; stochastic optimization; intelligentes Netz

Abstract
In the coming years, the energy system will be transformed from central carbon-based power plants to decentralized renewable generation. Due to the dependency of these systems on external influences such as the weather, forecast uncertainties pose a problem. In this paper, we will compare different methods that mitigate the impact of these forecast uncertainties. Our results suggest that estimating these uncertainties and modeling them for optimization can increase the benefit for the individual system.

: http://publica.fraunhofer.de/documents/N-614593.html