  Using cost-optimisation to support energy system design decisions hides from
view many more feasible designs that, while economically comparable to the
least-cost option, may have other practical advantages that stakeholders
prefer. Increasingly, modellers look at Modelling to generate alternatives
(MGA) as a way to go beyond few, optimal designs and provide stakeholders with
broad portfolios of design options to appraise based on their preferences and
knowledge of the problem. In practice, however, generating and exploring all
the feasible system design options is not computationally possible. Modellers
need to choose what design feature to generate diversity around, and yet, they
cannot know what features may matter the most to heterogeneous real-world
stakeholders. Stakeholders' knowledge is thus needed to guide the MGA search
towards the most important trade-offs, but how to incorporate such knowledge in
the MGA workflow is unclear. To address this gap, we propose a
first-of-its-kind human-in-the-loop (HITL) approach that seamlessly and
automatically integrates stakeholder preferences into an MGA workflow. In a
nutshell, we elicit stakeholders' system design preferences via a first
interaction between them and a tentative MGA design space. Hence, we decode
those preferences so that it becomes possible to feed them back to the MGA
algorithm to perform a guided search. This search produces an updated,
human-trained design space with a higher share of designs that align with the
elicited preferences. In a controlled experiment for the case study of the
Portuguese energy system, we demonstrate that our HITL-MGA approach may
facilitate the identification of a consensus solution that balances many
conflicting stakeholder preferences. The approach shows promise for
generalisation and real-world application to accelerate technically and
socially feasible energy transition decisions.
