Backbone
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)

BackboneTools:
A python toolbox to support the usage of the energy system modelling framework Backbone:
BackboneTools

This toolbox aims to support the use of the energy system modelling framework Backbone
(https://gitlab.vtt.fi/backbone/backbone/) by providing python tools.
It includes conversion of input and output .gdx files to pandas dataframes and vice versa, automated plotting and some execution routines like automated scenario analysis.

Multi-Objective Inverse Optimisation (MOIn) method for turning model input parameters into outputs:
Git repository

Multi-objective inverse optimisation (MOIn) is an optimisation approach to turn traditional input parameters of energy system models (ESM) into optimisation variables and, hence, outputs.
Thereby, we invert the ESM formulation by turning a technology input parameter into a decision variable and carrying out a multi-objective optimisation between the technology-level cost and the system-level total cost.
Here, system-level refers to an energy system, e.g. a national power grid or a residential quarter, with multiple energy supply, storage and transport technologies as well as energy demand.
This is especially relevant for including technologies with low technology readiness level (TRL) into ESMs as techno-economic parameters are not yet known or subject to high uncertainty in early development stages.

It requires expert knowledge to use the energy system models we develop and employ. To make their insights accessible to ordinary consumers and to support them in their decarbonisation decisions, we have developed three tools that enable the use of two energy system models and the methodology of multi-criteria decision analysis.

https://ee-chair-apps.streamlit.app/

The first tool enables the use of a model to optimise household energy consumption by investing in a PV system, a battery storage unit and a heat pump. Taking into account individual load time series and assumptions regarding costs, end-user prices and subsidies, the model optimises the sizing of the three technologies and indicates the degree of self-sufficiency achieved.

The second tool visualises the results of a multi-objective optimisation that minimises the costs of heating requirements whilst taking into account various heating technologies, and simultaneously considers thermal comfort and the resulting CO₂ emissions. It enables the analysis of trade-offs between the three variables.

The third tool is based on methods of “multi-criteria decision analysis” (MCDA) and helps users decide whether to buy a new car or a new heating system. Taking personal preferences and multiple criteria into account, the tool evaluates various options and visualises the results.