  Given the urgent need to devise credible, deep strategies for carbon
neutrality, approaches for `modelling to generate alternatives' (MGA) are
gaining popularity in the energy sector. Yet, MGA faces limitations when
applied to state-of-the-art energy system models: the number of alternatives
that can be generated is virtually infinite; no realistic computational effort
can discover the complete technology and spatial diversity. Here, based on our
own SPORES method, a highly customisable and spatially-explicit advancement of
MGA, we empirically test different search strategies - including some adapted
from other MGA approaches - with the aim of identifying how to minimise
redundant computation. With application to a model of the European power
system, we show that, for a fixed number of generated alternatives, there is a
clear trade-off in making use of the available computational power to unveil
technology versus spatial diversity of system configurations. Moreover, we show
that focussing on technology diversity may fail to identify system
configurations that appeal to real-world stakeholders, such as those in which
capacity is more spread out at the local scale. Based on this evidence that no
feasible alternative can be deemed redundant a priori, we propose to initially
search for options in a way that balances spatial and technology diversity;
this can be achieved by combining the strengths of two different strategies.
The resulting solution space can then be refined based on the feedback of
stakeholders. More generally, we propose the adoption of ad-hoc MGA sensitivity
analyses, targeted at testing a study's central claims, as a computationally
inexpensive standard to improve the quality of energy modelling analyses.
