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Our company is prospecting suitable lands for solar power plant using algorithms and geospatial data. This article will guide you through recent work I've done find optimal locations that align with visibility constraints.
I hope you find this peek into our solutions insightful!
At first I thought it was just another solar shade analysis tool (which uses GIS to determine how much shading a site will get from nearby hills & buildings), but nope, this is actually doing the opposite: analyzing whether other people can see your solar plant. That's pretty cool, especially since (as far as I know) it's not a common criterion in the US.
Is it a common case in Europe (or France in particular?) where this is a big consideration, e.g. "I don't want your solar panels uglyfying my landscape"?
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On another note... I wish there were an open-source all-in-one solar analysis tool, something that combines this algorithm with stuff like Project Sunroof (rooftop shade analysis: https://sunroof.withgoogle.com/) and array siting/layout (https://www.opensolar.com/). I've thought about doing that for a long time now, but it's a lot of work, and I don't have all the skills necessary...
Such a tool could be useful to businesses, individuals, and governments all over, and would build on existing open-source tooling (like GDAL, mentioned in the article, but also frontend mapping libs and things like PostGIS).
When I was working on my tool for off grid pv hot water (www.pvh2o.com), I was was able to get insolation data for USA from NREL NSRDB. They claim they have data for the whole planet in the DB but I find many spots outside USA do not seem to work.
Where did you get insolation data for Europe?
Is that part of your model/long term plans or are you skipping it for some reason?
We could easily find many many large expanses of land, and given the margins on farming, many in my local area are renting / selling / building solar farms on the corners or harder-to-reach areas of their fields. Since those areas tend to have trees around anyway .... But none of that is really a viewshed problem unless you've modelled trees.
Now in hillier areas or mountainous regions this gets more compliated.
Lots of NIMBYs that “did their own research” to conclude that solar is actually bad for the environment or uneconomical or whatever. But the real reason for their rage is that they see it as a physical manifestation of the spread of “progressive ideas” and for that reason alone it must be stopped
Of course he didn't mean that we should cover all the highways; it just says we could afford to build and maintain them all and have that much land area, so building an equivalent area of solar would be both cheaper and feasible.
That was over a decade ago; solar is both cheaper and more efficient. IIRC something small like a hundred km2 or so of desert would do the trick. We spend much more of that growing corn that we wastefully turn into fuel.
By that logic of ignoring constraints, nuclear takes 0 land area compared to solar and could generate more power than solar panels we’d ever produce.
FWIW there are a lot of deserts and plains that can be used for year round generation.
Does this work address a specious, disingenuous argument that is being put forth by NIMBYs to block solar installation, so does it address a real pain point?
I agree we should be able to build enough solar but does this work address a real bottleneck or a fake problem presented as real by people with ulterior motives?
For marketing purposes I generate viewsheds around each of our ~500 towers, so we can get an idea which suburbs to market to.
At the time of sale, my system will calculate the line of site from the access point on the tower to the customer rooftop to determine the height of the pole (is any) needed to get service.
Like the OP, we re-sampled (gdalwarp, raster2pgsql) some of the 15cm lidar data to ~1m to get it down to a manageable size (7TB) and run it on a single bare-metal PostgGIS instance (500GB ram, 64 cores)
Radio waves at 5GHz are quite 'fat' so we need to allow for that on LOS calculations as per [0].
The GIS magic mostly sits in PostGIS and we use a number of data sets to solve problems: * Shuttle Radar Topography Mission - Digital Elevation Model and Digital Surface Model, 30m grid [1] * Building footprints for all of Australia [2] * National Roads [3] * property boundaries (cadastre) [4] * All Australian addresses [5] * Australian suburbs [6]
For the front end we use a VueJS app (quasar.dev) using DeckGL on Google Maps to visualise the LOS path. Back end is Rust (axum/sqlx).
GIS is a very interesting are to work in - if I had more fun they might start charging me admission to come to work!
[0] https://s.campbellsci.com/documents/au/technical-papers/line... [1] https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/me... [2] https://github.com/microsoft/AustraliaBuildingFootprints [3] https://ecat.ga.gov.au/geonetwork/srv/eng/catalog.search#/me... [4] https://www.data.qld.gov.au/dataset/cadastral-data-queenslan... [5] https://www.industry.gov.au/publications/geocoded-national-a... [6] https://www.abs.gov.au/statistics/standards/australian-stati...