Optimizing trading decisions of wind power plants with hybrid energy storage systems using backwards approximate dynamic programming

GND
1220737003
ORCID
0000-0002-8464-7906
LSF
58344
Zugehörige Organisation
Mercator School of Management, University of Duisburg-Essen, Lotharstraße 65, 47057 Duisburg, Germany
Finnah, Benedikt;
GND
14226119X
ORCID
0000-0002-5699-1320
LSF
57737
Zugehörige Organisation
Mercator School of Management, University of Duisburg-Essen, Lotharstraße 65, 47057 Duisburg, Germany
Gönsch, Jochen

On most modern energy markets, electricity is traded in advance and a power producer has to commit to deliver a certain amount of electricity some time before the actual delivery. This is especially difficult for power producers with renewable energy sources that are stochastic (like wind and solar). Thus, short-term electricity storages like batteries are used to increase flexibility. By contrast, long-term storages allow to exploit price fluctuations over time, but have a comparably bad efficiency over short periods of time.

In this paper, we consider the decision problem of a power producer who sells electricity from wind turbines on the continuous intraday market and possesses two storage devices: a battery and a hydrogen based storage system. The problem is solved with a backwards approximate dynamic programming algorithm with optimal computing budget allocation. Numerical results show the algorithm's high solution quality. Furthermore, tests on real-world data demonstrate the value of using both storage types and investigate the effect of the storage parameters on profit.

Zitieren

Zitierform:
Zitierform konnte nicht geladen werden.

Rechte

Nutzung und Vervielfältigung:
Dieses Werk kann unter einer
CC BY-NC-ND 4.0 LogoCreative Commons Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 Lizenz (CC BY-NC-ND 4.0)
genutzt werden.