A methodology for integrating community acceptance of wind energy into energy system modelling (ESM)

Decarbonisation of the energy system is key to achieving the Paris Agreement goal of limiting global temperature rise to below 2°C which can be achieved by electrified and interconnected systems with a high share of variable renewables.

This transition is shaped by uncertain factors, which include technology innovation, resource availability, and socio-economic variables. Energy system modelling (ESM) has been a key policy tool to study decarbonisation pathways.

ESM provides stakeholders in the energy sector with knowledge-based and systematic methods to reach decisions about which technologies to support.

However, present day ESM mainly integrates techno-economical input parameters, whereas social factors, such as local responses to new installations, are largely neglected.

ESM might therefore produce solutions that are not accepted by communities and as a result, could jeopardize energy transition goals.

The main goal of WINDACCEPT is to develop an innovative and highly interdisciplinary mixed-methods approach to represent community acceptance, stakeholder interests, and well-being variables in ESM.

This new methodology will be developed and applied to the case of Norway, a country with excellent wind resources but high local opposition towards their development.

The methodology will contribute to elucidate the impacts of community barriers and value the costs and benefits of alternative options on the net zero energy system design in Norway and the effects on long term, whole system decarbonisation in an interconnected Europe.

This project will be made possible by combining the applicant’s experience in social acceptance of renewable technologies, and the supervisors and host’s (University of Oslo) expertise in ESM.

The methodological and interdisciplinary training-through-research and unique set of skills gained from WINDACCEPT will position the researcher at the forefront of socio-technical energy transitions modelling.