  Energy systems models, critical for power sector decision support, incur
non-linear memory and runtime penalties when scaling up under typical
formulations. Even hardware improvements cannot make large models tractable,
requiring omission of detail which affects siting, cost, and emission outputs
to an unknown degree. Recent algorithmic innovations have enabled large scale,
high resolution modeling. Newly tractable, granular systems can be compared
with coarse ones for better understanding of inaccuracies from low resolution.
Here we use a state of the art model to quantify the impact of resolution on
results salient to policymakers and planners, affording confidence in decision
quality. We find more realistic siting in recommendations from high resolution
energy systems models, improving emissions, reliability, and price outcomes.
Errors are generally stronger from low spatial resolution. When models have low
resolution in multiple dimensions, errors are introduced by the coarser of
temporal or spatial resolution. We see no diminishing returns in accuracy for
several key metrics when increasing resolution. We recommend using
computationally efficient techniques to maximize granularity and allocating
resolution without leaving any aspect (spatial, temporal, operational) of
systems unduly coarse.
