The reduction of greenhouse gas emissions in the EU is to be achieved mainly through energy efficiency measures and the expansion of renewable energies. The latter is accompanied by a decentralization of the energy supply as well as increasing fluctuation and uncertainty. This increases the need for flexibility in the energy system. In addition to classic generation-side flexibility options, storage and load shifting measures, sector coupling (e.g. "power-to-gas" or "power-to-heat") is of particular importance in this context. This is also necessary to defossilize the non-electricity sectors and thus to achieve the climate targets that have been set. With coupling, system size and complexity increase, and at the same time interdependencies and interactions between the different elements gain relevance. This brings new challenges for energy system modeling and downstream analysis. To address these challenges, especially in the context of sector coupling, VTT Technical Research Centre Finland has developed the open source energy system modeling framework Backbone. Backbone is a highly adaptable framework for mixed-integer investment and deployment optimization of integrated energy systems. Central components of the framework are the representation of multiple coupled sectors, the integration of numerous technical constraints, reserve products and energy storage, the stochastic consideration of uncertainties as well as a very flexible spatial and temporal resolution.

At the Chair of Energy Systems and Energy Economics at the Ruhr-University Bochum, Backbone is used and specifically developed for various questions of energy infrastructure demand and deployment, market and system integration of renewable energies, and market design and regulation. Current studies analyze e.g. the profitability of different renewable energy technologies in a European comparison or investigate effects of social acceptance on energy system development in Europe. Current and planned further developments of Backbone also focus, among other things, on the integration of so-called "reduced-order models" in order to represent technical details of energy conversion processes (e.g. production of green or blue hydrogen) more realistically, on the multi-objective optimization of energy systems in order to be able to take into account ecological or social aspects in addition to costs, as well as the interaction of climate and energy systems with special consideration of long-term uncertainties. Translated with www.DeepL.com/Translator (free version)