Tool Chain for Deriving Consistent Storage Model Parameters for Optimization Models

Kristin Wode (), Tom Strube, Eva Schischke, Markus Hadam, Sarah Pabst and Annedore Mittreiter
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Kristin Wode: Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany
Tom Strube: Fraunhofer Institute for Software and Systems Engineering ISST, Emil-Figge-Straße 91, 44227 Dortmund, Germany
Eva Schischke: Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany
Markus Hadam: Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany
Sarah Pabst: Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany
Annedore Mittreiter: Fraunhofer Institute for Environmental, Safety and Energy Technology UMSICHT, Osterfelder Straße 3, 46047 Oberhausen, Germany

Energies, 2023, vol. 16, issue 3, 1-22

Abstract: Since existing energy system models often represent storage behavior in a simplified way, in this work, a tool chain for deriving consistent storage model parameters for optimization models is developed. The aim of our research work is to identify what are non-negligible influences on the the technical characteristics and dynamic behavior of the storage, to quantify the effect of these influences, and represent these effects in the model. This paper describes the developed tool chain and presents its application using an example. The tool chain consists of the steps “parameter screening”, “dynamic simulation”, “regression analysis” and “refining optimization model”. It is investigated which parameters have an influence on the storage system (here pumped hydroelectric energy storage (PHES)), how the storage behavior is modeled, which influencing factors have a measurable effect on the system, and how these findings can be integrated into optimization models. The main finding is that in the case of PHES, the dependency of the charging and discharging efficiency on the power is significant, but no further influencing factor has to be considered for accurate modeling (0.946 ≤ R 2 ≤ 0.988) of the efficiency. It is concluded that the presented toolchain is suitable for other storage technologies as well, including the analysis of aging behavior.

Keywords: energy storage; design of experiments; simulation; regression analysis; optimization; modeling; scientific framework; tool chain (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: 2023
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