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Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten. Using Probabilistic Forecasts in Stochastic Optimization
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Postprint urn:nbn:de:0011-n-6145933 (214 KByte PDF) MD5 Fingerprint: 0402f6644dfafa834233c0a2b6227878 © IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. 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 |
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| 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.