VF-MGA bidirectionally couples MGA and MCDA to generate 691 stakeholder-relevant near-optimal energy configurations and evaluate them via elicited preferences in a university campus decarbonization case study.
Exploring near-optimal energy systems with stakeholders: a novel approach for participatory modelling
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abstract
Involving people in energy systems planning can increase the legitimacy and socio-political feasibility of energy transitions. Participatory research in energy modelling offers the opportunity to engage with stakeholders in a comprehensive way, but is limited by how results can be generated and presented without imposing assumptions and discrete scenarios on the participants. To this end, we present a methodology and a framework, based on near-optimal modelling results, that can incorporate stakeholders in a holistic and engaging way. We confront stakeholders with a continuum of modelling-based energy system designs via an interactive interface allowing them to choose essentially any combination of components that meet the system requirements. Together with information on the implications of different technologies, it is possible to assess how participants prioritise different aspects in energy systems planning while also facilitating learning in an engaging and stimulating way. We showcase the methodology for the remote Arctic settlement of Longyearbyen and illustrate how participants deviate consistently from the cost optimum. At the same time, they manage to balance different priorities such as emissions, costs, and system vulnerability leading to a better understanding of the complexity and intertwined nature of decisions.
fields
econ.GN 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
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Value-focused modelling to generate alternatives -- Coupling multi-criteria decision analysis and optimisation models to support strategic decisions
VF-MGA bidirectionally couples MGA and MCDA to generate 691 stakeholder-relevant near-optimal energy configurations and evaluate them via elicited preferences in a university campus decarbonization case study.