Quoted from: https://www.diw.de/en/diw_01.c.599753.en/models.html#ab_600558
dynELMOD is a multi-period investment model of the European electricity sector until 2050. The model optimizes the electricity generation, storage, and network infrastructure investments by minimizing total system cost, given the policy targets constraints. Investments are determined in five or ten-year steps considering the hourly dispatch of existing and new built generation technologies. Interactions between countries through the interconnected transmission network are incorporated by using either a country-sharp power transfer distribution factor matrix (PTDF) based on the actual transmission network or using net transfer capacities (NTCs) based on commercial cross-border transactions.
The model is documented in the DIW Data Documentation 88 (PDF, 4.81 MB) by Clemens Gerbaulet and Casimir Lorenz.
The full source code and data sets can be found in the public repository of dynELMOD.
Recent applications are:
Gerbaulet, C., Kemfert, C., Lorenz, C., von Hirschhausen C. and Oei, P. (2017): Scenarios for decarbonizing the European electricity sector. 14th International Conference on the European Energy Market (EEM), 2017.
Lorenz, C. (2017): Balancing Reserves within a Decarbonized European Electricity System in 2050: From Market Developments to Model Insights. DIW Berlin Discussion Paper 1656 (PDF, 0.98 MB).
Gerbaulet, C., Kunz, F., Lorenz, C., von Hirschhausen C. and Reinhard, B., (2014): Cost-minimal investments into conventional generation capacities under a Europe-wide renewables policy. 11th International Conference on the European Energy Market (EEM), Krakow, 2014.
Kemfert, C., Gerbaulet, C., von Hirschhausen, C. Lorenz, C., Reitz, F. (2015): European Climate Targets Achievable without Nuclear Power. DIW Economic Bulletin 47 / 2015 (PDF, 177.51 KB).
An extension of dynELMOD also covers interactions with the German individual and district heating sector. It contains additional technologies, such as CHP and "heat only" plants, heat storage as well as power-to-heat to cover heating demands. The full source code and a data set are available here (ZIP, 18.08 MB)