Efforts to address the contemporary climate and energy-related challenges towards a green, inclusive, and fair transition by 2050 require the empowerment and engagement of citizens and other societal actors, as has been duly acknowledged within the recent European Union’s strategic and legislative frameworks. Citizens are anticipated to expand their role as self-consumers and contributors within energy communities, actively shaping alterations in the energy landscape, impacting both demand and supply.
As a result, in recent years the concept of “energy citizenship” has emerged and gained considerable attention due to its potential to bridge the gap between energy transition policies and social participation, by placing emphasis on the importance of participatory and democratic processes in decision-making and underlining the need for a more inclusive and equitable energy system.
In this context, the European Commission-funded Horizon 2020 “ENCLUDE” project aims at operationalizing the concept of energy citizenship and understand the multi-scale relationship between its various forms and decarbonization pathways across diverse contexts, with a view to provide appropriate decision-support through the application of appropriate modeling frameworks.
Building on the work done previously, in this deliverable we expand and employ different modeling frameworks of the ENCLUDE modeling ensemble to quantify the decarbonization potential of strategic energy citizen clusters.
More specifically, in past deliverables, and through the use of machine learning algorithms, clusters of energy citizens have been identified utilizing data on energy consumption and carbon emissions for specific Member States. This resulted in the identification of commonalities within the utilized dataset and a subsequent categorization of citizens in distinct groupings. The citizen clusters were then analyzed based on sociodemographic characteristics and cognition-related attributes in order to produce a set of citizen profiles that can be used to draw narratives for energy scenario development.
Leveraging this work, clustering and profiling results were then integrated into energy system modeling and simulation tools, by translating the clusters’ characteristics and energy behaviors into inputs for the ENCLUDE modeling ensemble. This integration allowed to quantify the decarbonization potential of different citizen clusters and demonstrate which clusters are more crucial and responsive towards accelerating decarbonization efforts. Targeting these clusters with tailored policies and interventions can significantly enhance the overall effectiveness and efficiency of decarbonization initiatives.
Overall, we provide modeling results for the following two (2) cases:
- Adoption of rooftop solar photovoltaic (PV) systems in the housing sector for different energy citizen clusters in Greece and the Netherlands.
- Power sector capacity buildout based on electricity consumption patterns of different energy citizen clusters in the housing and mobility sectors in Greece.