Headline: Integration of socio-technological transition constraints into energy demand and systems models. Deliverable 2.5. Sustainable Energy Transitions Laboratory (SENTINEL) project

The decarbonisation of the European energy system is a large-scale transformation, which demands not only for a techno-economic feasibility analysis, but also for an assessment of the social and political feasibility and environmental impacts. However, most energy models are not able to fully represent the social and political developments and dynamics of the energy transition, such as preferences, acceptance and behavioural changes of citizens and decision-makers. To address this shortcoming, we developed QTDIAN (Quantification of socio-Technological DIffusion and sociAl constraiNts) − a toolbox of qualitative and quantitative descriptions of socio-technical and political aspects of the energy transition. In this deliverable, we present and discuss the linking of QTDIAN with the energy demand models DESSTINEE, HEB and DREEM, and the energy system model Euro-Calliope. The purpose of linking the models is to integrate the outputs from QTDIAN into the energy models to allow for an empirically based and thus more realistic analysis of energy system trajectories, with a higher relevance for informing pending policy decisions. The central question we address is: How can the social storylines and quantifications from QTDIAN be transferred into energy demand and systems models? We show several ways how QTDIAN’s quantified variables allow for a direct application of the storylines into the modelling process of Euro-Calliope, DESSTINEE, HEB and DREEM. The qualitative storylines ensure that modellers do not create technically feasible energy systems that are outside the realms of social or political realities. In addition, the quantitative data can be used to improve the accuracy and especially the policy relevance of the modelling results by providing specific estimates for social and political variables and constraints. However, not all aspects of QTDIAN could be integrated because not all aspects of the storylines could be quantified, and the models to which QTDIAN links in this deliverable are not able to capitalise on all QTDIAN outputs. We identified further requirements for data, including different temporal and spatial scales. We conclude that the linking of QTDIAN with energy demand and energy systems models is a promising approach to better represent socio-political drivers and barriers for technology changes and climate change mitigation measures. We will run the models with the integrated linkage with QTDIAN to evaluate the outcomes and added value of the linking in the context of SENTINEL case studies (WP7).