Cost-Optimal Power System Extension under Flow-Based Market Coupling
By Simeon Hagspiel, Cosima Jägemann, Dietmar Lindenberger, Tom Brown, S. Cherevatskiy, E. Tröster. 2014
140 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering
View Paper: https://www.semanticscholar.org/paper/ea8eff9718001d2a949a49268adcf6d69a65a7bd
How researchers see this paper
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In Electrical and Electronic Engineering
- Tom Brown
- David P. Schlachtberger
- Marta Victoria
- E. Zeyen
- Jonas Horsch
- S. Schramm
- Martin Greiner
- Alexander Kies
Citations in the corpus
- PyPSA: Python for Power System Analysis (2017, methodology): “To keep the optimisation problem linear and therefore computationally fast, x` can be left fixed in each optimisation problem, updated and then the optimisation problem rerun, in up to 5 iterations to ensure convergence, following the methodology of [43].”
- Synergies of sector coupling and transmission reinforcement in a cost-optimised, highly renewable European energy system (2018, background): “HVDC transmission line 400 MWkm 2 40 1 [11]”
- The benefits of cooperation in a highly renewable European electricity network (2017, methodology): “Other classes of studies of the optimal European electricity system model the transmission networks of each country in more spatial detail [8, 16], but because of computational limits, they can only consider a small number of representative weather conditions, which cannot capture the full spatiotem”
- Speed of technological transformations required in Europe to achieve different climate goals (2021, background): “0 40 [57] Battery storage 20 [28] Battery inverter 0.”
- The role of storage technologies throughout the decarbonisation of the sector-coupled European energy system (2019, unknown): “5 20 ≈ 3-4e [25, 40] HVDC lines 400 MWkm 2 40 [42] HVDC converter pair 150 kW 2 40 [42]”
- Buffering Volatility: A Study on the Limits of Germany's Energy Revolution (2017, unknown): “See, for example, Heide et al. (2010, 2011) who, in their forecast model based on European weather data, assume that the store absorbs all variation from overshooting and undershooting spikes alike, while 100% of the power produced and consumed comes from wind and solar energy. In their approach, ex”