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Article

Insights on Germany’s Future Congestion Management from a Multi-Model Approach

1
Chair of Energy Economics, Faculty of Economics and Business Management, Technische Universität Dresden, D-01062 Dresden, Germany
2
Chair of Energy Economics, Karlsruhe Institute of Technology (KIT), D-76187 Karlsruhe, Germany
3
Fraunhofer Institute for Systems and Innovation Research ISI, D-76139 Karlsruhe, Germany
4
ESA2 GmbH, D-01187 Dresden, Germany
*
Author to whom correspondence should be addressed.
Energies 2020, 13(16), 4176; https://doi.org/10.3390/en13164176
Received: 29 June 2020 / Revised: 31 July 2020 / Accepted: 6 August 2020 / Published: 12 August 2020
(This article belongs to the Special Issue Model Coupling and Energy Systems)
In Germany, the political decision to phase out nuclear and coal-fired power as well as delays in the planned grid extension are expected to intensify the current issue of high grid congestion volumes. In this article, we investigate two instruments which may help to cope with these challenges: market splitting and the introduction of a capacity mechanism. For this purpose, we carry out a comprehensive system analysis by jointly applying the demand side models FORECAST and eLOAD, the electricity market model PowerACE and the optimal power flow model ELMOD. While a German market splitting has a positive short-term impact on the congestion volumes, we find the optimal zonal delimination determined for 2020 to become outdated by 2035 resulting in new grid bottlenecks. Yet, readjusting the zonal configuration would lower the ability of the market split to provide regional investment incentives. Introducing a capacity mechanism with a congestion indicator allows allocating new power plants in regions with higher electricity demand. Consequently, we find the required congestion management to be substantially reduced in this setting. However, given the large amount of design parameters, any capacity mechanism needs to be carefully planned before its introduction to avoid new inefficiences on the market side. View Full-Text
Keywords: congestion management; market splitting; capacity mechanism; model coupling; demand-side modeling; agent-based modeling; optimal power flow congestion management; market splitting; capacity mechanism; model coupling; demand-side modeling; agent-based modeling; optimal power flow
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MDPI and ACS Style

Hladik, D.; Fraunholz, C.; Kühnbach, M.; Manz, P.; Kunze, R. Insights on Germany’s Future Congestion Management from a Multi-Model Approach. Energies 2020, 13, 4176. https://doi.org/10.3390/en13164176

AMA Style

Hladik D, Fraunholz C, Kühnbach M, Manz P, Kunze R. Insights on Germany’s Future Congestion Management from a Multi-Model Approach. Energies. 2020; 13(16):4176. https://doi.org/10.3390/en13164176

Chicago/Turabian Style

Hladik, Dirk, Christoph Fraunholz, Matthias Kühnbach, Pia Manz, and Robert Kunze. 2020. "Insights on Germany’s Future Congestion Management from a Multi-Model Approach" Energies 13, no. 16: 4176. https://doi.org/10.3390/en13164176

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