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WP5: Interface between meteorology and energy economics research

Icon for WP5: The sun

WP5 will investigate the impact of incorporating meteorological data (related to WP3) on electricity system analyses at different time scales. For given grid and generation infrastructures, local weather conditions exert strong short-time influences on the dispatch of power plants. This implies challenges (1) for system operators to redispatch capacities and (2) for investors in risks of revenue streams. For the 1st challenge, Nüßler (2012) showed an increasing importance due to enhanced renewable deployment, entailing the threat of inadequate representation in current electricity system models. Research in this field, closely connected to the ongoing research at EWI, will be carried out in co-operation with the electrical engineering unit of Prof. Rehtanz at TU Dortmund on probabilistic load flows and the optimization between dispatch and network extension.1 The 2nd challenge arises due to the market integration efforts for renewable energy sources (RES)2 and its consequences for RES investors as weather uncertainty will directly turn into revenue risk. A quantification of this risk requires a comprehensive understanding of the stochastic interdependencies of individual sites, and the portfolio effects resulting from “optimal” bundles of sites. For this topic, we will build on previous research at EWI by Elberg and Hagspiel (2014), who developed an innovative method, using Copulas for depicting the full interdependencies of wind power. In the long-run energy system models have to take into account that climate will change (related to WP4). Although it is widely recognized that both energy demand and supply are significantly affected by climate changes, long-term scenarios in energy system models usually neglect this aspect. In WP5, we will study how climate change feeds back into the energy system. To this end, we will develop appropriate scenarios regarding possible changes of climate and weather patterns (as analyzed in WP4), and take into account such changed patterns in energy system models of European or smaller scale, based on extensive knowledge and modeling tools for long-term system analyses, as developed and applied e.g. in Jägemann et al. (2013), Hagspiel et al. (2014) or Fürsch et al. (2014).

1 DFG-Project. Ökonomische und elektrotechnische Analyse von Netzausbau versus Redispatch, GZ: HO 5108/2-1, AOBJ: 606163.

2 E.g., “Guidelines on State aid for environmental protection and energy 2014-2020” by the European Commission.

Nüßler, A., 2012: Congestion and Redispatch in Germany. A model-based analysis of the development of redispatch. Disseration. Schriften des Energiewirtschaftlichen Instituts, Bd. 6, 2012.

Elberg, C., S. Hagspiel, 2014: Spatial dependencies of wind power and interrelations with spot price dynamics, accepted subject to minor revision at European Journal of Operational Research (EJOR).

Jägemann, C., M. Fürsch, S. Hagspiel, S. Nagl, 2013: Decarbonizing Europe's power sector by 2050 - Analyzing the economic implications of alternative decarbonization pathways. Energy Economics, 40, 622-636, DOI: 10.1016/j.eneco.2013.08.019.

Hagspiel, S., C. Jägemann, D. Lindenberger, T. Brown, S. Chrevatskiy, E. Tröster, 2014: Cost- Optimal Power System Extension under Flow-Based Market Coupling. Energy, 66, 654-666, DOI: 10.1016/j.energy.2014.01.025.

Fürsch, M., S. Nagl, D. Lindenberger, 2014: Optimization of power plants investments under uncertain renewable energy development paths: A multistage stochastic programming approach. Energy Syst., 5 (1), 85-121, DOI:10.1007/s12667-013-0094-0.


  • Prof. Dr. Felix Höffler (EWI)
  • Andreas Knaut (EWI)
  • Prof. Dr. Stefan Kollet (HPSC TerrSys, Geoverbund ABC/J)
  • PD Dr. Dietmar Lindenberger (EWI)
  • Prof. Dr. Roel Neggers (IGM)
  • Prof. Dr. Yaping Shao (IGM)
  • Philipp Henckes (IGM)
  • Prof. Dr. Clemens Simmer (MIUB)