It's always good to collect a lot of examples as you look to map out a system. For that you want to have generative tools that suggest the kinds of examples that you want to look for.
The power outage maps I have been collecting started with the generative system of making a blank entry for each of 50 states, and looking to collect one in each state. I've done similar exercises where the generator is the first letter of the alphabet collecting examples A-Z. You are looking for breadth in your view, and something that constrains the search so that you don't spend too much time in one place before going on.
It helps to have some kind of rudimentary ranking algorithm when wrapping up your search, so that you can look for gaps into which another example will fit. The utility map effort looks like it's going to generate a simple checklist for each map, so that I can give each one a score on completeness (0 = no map, add 1 for county by county stats, add 1 for city by city stats, add 1 for zoom to the outage, add 1 for systemwide counts etc).
Put the things you collect into categories that have names, so that you can start working with abstractions instead of concrete instances. The list of categories becomes another place to generate a bounded set of additional elements.
Once you have all of these you can start to think about plausible things that might be in the system that you haven't found yet. My piece on "unknowledge management" (the phrase is from Tom Munnecke) where the task becomes looking for names for things that don't exist yet but that might plausibly exist given the system that you have described. This means that you end up with something peculiar to search for, and either it doesn't exist yet, it can't exist (and your models are off), or you find it.
So the iterative process looks like
describe a search space
rank along common attributes
categorize into abstractions
synthesize the undiscovered instances
and that seems to be as good a plan as any for survey of the field.