Model Experiments and Comparisons Simulating Ways to a Completely Renewable Energy Supply

BMWE sponsored
  • Project no. 150435
  • Duration 07/2015 - 12/2017

Against the background of the shift from an "additive role" of regenerated energies to their dominance in the German energy system, many model-based scenario studies of the energy system have lately been conducted. However, these scenario analyses can hardly be compared due to differing modelling approaches, assumptions and results. In the framework of the project "Model Experiments and Comparisons Simulating Ways to a Completely Renewable Energy Supply" the range of existing know-how is systematically linked. More precisely, different groups of modellers perform experiments using their specific modelling approaches but identical boundary conditions. As a result of the conjoint pre-clarification of the research question, the framework assumptions as well as a common data set and presentation of results, the central barriers which hampered the comparability and quality assurance of scenario results so far can be tackled and resolved.

In particular, the following targets are being pursued:

  • Development of general-purpose-templates for the characterisation of models, scenario analyses and results
  • Analysis of energy transition scenarios which have been calculated using different modelling approaches but identical assumptions and data on future developments
  • Elaboration of robust findings and critical aspects of the scenario analysis: Where do the results of the different modelling approaches diverge? Where do they converge? Why?
  • Establishment and provision (open source) of datasets regarding future developments
  • Sensitivity analyses of scenarios regarding specific disruptions (e.g. abandonment of offshore wind power, strong decline in cost of decentralised storage) which can strongly influence the dissemination of renewable energy sources and which can be accompanied by economic losses, risks as well as system instabilities. As a result, it should be possible to better analyse and anticipate future challenges.