Herausforderungen der künftigen Energieversorgung

Schellong, Wolfgang

doi:10.1007/978-3-662-49463-9_1

Cited by 2 publications

(2 citation statements)

References 1 publication

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“…As defined by the Intergovernmental Panel on Climate Change (IPCC), energy systems comprise the “production, conversion, delivery, and use of energy.” 1 Each of these components is subject to major changes and highly uncertain developments 2,3 . Energy system models (ESMs) are used to depict energy system components and their behavior to simulate this transition in a laboratory‐like setting.…”

Section: Introductionmentioning

confidence: 99%

“…1 Each of these components is subject to major changes and highly uncertain developments. 2,3 Energy system models (ESMs) are used to depict energy system components and their behavior to simulate this transition in a laboratorylike setting. In the current energy transition, energy supply, demand, and policies are changing at an unprecedented rate.…”

Section: Introductionmentioning

confidence: 99%

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The potential of deep learning to reduce complexity in energy system modeling

Köhnen

Priesmann

Nolting

et al. 2021

Intl J of Energy Research

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Summary In order to cope with increasing complexity in energy systems due to rapid changes and uncertain future developments, the evaluation of multiple scenarios is essential for sound scientific system analyses. Hence, efficient modeling approaches and complexity reductions are urgently required. However, there is a lack of scientific analyses going beyond the scope of traditional energy system modeling. For this reason, we investigate the potential of metamodels to reduce the complexity of energy system modeling. In our explorative study, we investigate their potential and limits for applications in the fields of electricity dispatch and design optimization for heating systems. We first select a suitable metamodeling approach by conducting pre‐tests on a small scale. Based on this, we selected artificial neural networks due to their good performance compared to other approaches and the multiple possibilities of network topologies and hyperparameter settings. As for the dispatch model, we show that a high accuracy of price replication can be achieved while substantially reducing the runtimes per investigated scenario (from 2 hours on average down to less than 30 seconds). With the design optimization model, we find double‐edged results: while we also achieve a substantial reduction of runtime in this case (from ~0.8 hours to less than 30 seconds), the simultaneous forecasting of several interdependent variables proved to be problematic and the accuracy of the metamodel shows to be insufficient in many cases. Overall, we demonstrate that metamodeling is a suitable approach to complemement traditional energy system modeling rather than to replace them: the loss of traceability in (black‐box) metamodels indicates the importance of hybrid solutions that combine fundamental models with metamodels.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The potential of deep learning to reduce complexity in energy system modeling

Köhnen

Priesmann

Nolting

et al. 2021

Intl J of Energy Research

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Effects of Load Forecast Deviation on the Specification of Energy Storage Systems

Emde,

Märkle,

Kratzer

et al. 2023

Designs

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The liberalization of the German energy market has created opportunities for end-consumers, including industrial companies, to actively participate in the electricity market. By making their energy loads more flexible, consumers can generate additional income and thus save money. Energy storage systems can be utilized to achieve the required flexibility by temporarily storing excess electrical energy in the form of heat, cold, or electricity for later use. This publication focuses on how the dimensionality of energy storage is influenced by load forecasting. The results show that inaccuracies in load forecasting lead to a direct over-dimensioning and thus, a deterioration of the economics of energy storage technologies. Using two scenario cases, it shows on the one hand how important good forecasts are and on the other hand that buffers must be included in the conceptual design in order to be able to compensate for forecast errors.

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Herausforderungen der künftigen Energieversorgung

Cited by 2 publications

References 1 publication

The potential of deep learning to reduce complexity in energy system modeling

The potential of deep learning to reduce complexity in energy system modeling

Effects of Load Forecast Deviation on the Specification of Energy Storage Systems

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