Solar PV seems to be the current darling of the renewable energy world. But how much “resource” is really out there? How much should cities rely on the development of local solar resources to meet their climate and energy goals? What trade-offs should urban cities make between desirable things like tree canopy and maximizing solar energy resources? GIS tools and new data resources can help begin to answer that question.
Counties and states are beginning to produce LiDAR data more regularly, which provides the building block information needed to analyze solar resources on buildings and elsewhere (see my previous post for a brief intro to LiDAR, or see here). Minnesota happens to have LiDAR for the whole state, and Minneapolis has a climate action goal that references local renewable development, so I’ll focus there.
So how much solar electric potential does Minneapolis have? Enough to supply 773,000 megawatt-hours (MWHs) each year, at the upper bound. That would mean covering every piece of rooftop with good sun exposure and appropriate pitch (southeast to southwest facing or flat) with the best modern PV panels. It would also mean solar installations on 68,351 structures, consisting of over 2.3 million individual panels.
773,000 MWhs would represent about 18% of Minneapolis’ total annual electricity consumption (based on 2010 figures). It would be the equivalent of reducing 392,684 metric tons of CO2 (also based on 2010 figures), which is equal to the emissions from the energy usage of almost 36,000 average American homes each year.
There are some limitations to this calculation, and some additional interesting findings, but first a brief description of how I came up with these numbers.
I briefly covered how to calculate solar potential in a previous post, and the process for this analysis was similar. I was able to get my hands on the solar insolation raster for the whole city thanks to the excellent work of some students in Dr. Elizabeth Wilson’s capstone class at the University of Minnesota’s Humphrey School. Solar insolation represents a measure of the total energy from the sun reaching any particular point (each square meter in this case) on a building, tree, earth, etc. To calculate this, ArcGIS has a complex tool called Solar Radiation Analysis. It takes in to account things like how trees shade buildings, and how the sun moves across the sky at different times of year based on the latitude of a particular point on earth. It spits out a measure of solar energy hitting that location over the course of a year, measured in watt-hours per square meter. This gives you a good idea of where exactly on each building a suitable spot might be for a solar PV system.
LiDAR data can also be used to calculate the slope of roofs, another important piece of information to understand solar potential. This allows a user to pick out areas of flat or south-facing roofs.
Finally, Minneapolis supplies building footprints, so I knew approximately what was a roof. I confined my analysis to building roofs, assuming we don’t want any of our precious open space filled with solar panels. I also buffered the roof edges, since I’m told OSHA requires some open space between the panels and the roof edge for safety, at least for flat roofs. I also considered 1,000 watts to be the minimum size that would warrant an installer to climb onto a roof.
Combine all this with some assumptions about the space needed for installations on flat and sloped roofs (the students helped with that too) and information on the size and power output of panels, and you get a measurement of the total “good” roof area and associated potential energy production from each roof.
That’s enough how-to, here are more interesting findings.
The 100 buildings (0.14 percent of the total building with solar) with the largest solar potential would provide 14 percent of the total production, or over 109,000 MWhs annually. The 1,000 buildings (1.4 percent of the total buildings with solar) with the largest solar potential would provide 43 percent of the total production, or over 333,000 MWhs annually. Targeting these structures for further analysis and possibly incentives would probably make sense to achieve the largest economies of scale for installation costs.
The 100 highest-potential buildings are geographically concentrated in roughly three areas: the northeast industrial area – roughly north and east of the U of M campus, the Lake Street/Greenway Corridor, and extending from the North Loop along the river into north and northeast Minneapolis. Unsurprisingly, these are areas that still have many large, flat-roofed warehouse and industrial buildings. If Minneapolis wants to maximize its solar resource, we may want to think about the trade-offs in redeveloping these areas or developing high density near them that may shade existing rooftops.
Commercial, industrial and single-family residential structures (based on parcel data) each account for almost exactly 23% of the total roof-top solar potential in the city. The next largest potential was among apartment properties at 9%, and duplexes at 7%. While the top three were evenly split potential-wise, single-family residences with good solar potential included over 46,000 structures, while commercial and industrial together was about 4,300. See economies of scale note above.
The fact that 46,000 residential structures have good solar potential means that lots of homeowners, even in leafy Minneapolis, could be empowered to go solar. This would be a more powerful political constituency than a small number of commercial property owners. Obviously some would face the trade-off between more trees and their benefits and electricity from solar.
Suburban areas are much more likely to approach energy production equal to energy usage. With its high density commercial core, Minneapolis uses a lot more energy than it can produce on its roofs. Residential structures are also smaller and more shaded than many suburban areas. This isn’t necessarily a bad thing, as density brings many other environmental benefits, like the ability to use transit cost-effectively.
Xcel Energy limits the size of solar installations they allow to be connected to their system. An interconnected solar PV system cannot be designed to produce more than 120 percent of the customer’s total usage from the previous year. Many homes in Minneapolis, and possibly low-energy warehouse buildings, could accommodate systems larger than that. This analysis limited system size only based on roof/sun conditions, and not electricity usage in the structure since that wasn’t known. In some cases, this means this analysis over-represents solar potential.
This analysis includes no information on roof age or structural integrity. Some flat-roofed buildings aren’t structurally able to accommodate solar without expensive retrofits. Residential structures may need to have old roofs replaced before putting on a solar energy system (which are typically designed to last 20 years). Some structures, like parking ramps and stadiums, would require additional structural supports to be added before a solar energy system could be added. These factors could all further limit solar potential on Minneapolis buildings.
There was a geometry problem I couldn’t solve in GIS. While I could calculate the size of a roof area that got good sun and had the correct slope, I couldn’t quickly figure out how many solar panels of a certain shape (defined length and width) fit in that area. I only used total square footage divided by the square footage of a standard solar panel. Internet forums are filled with many people better at GIS than I discussing this problem (but not providing me with easy solutions). If anyone reading this wants to take a crack at it, let me know in the comments.
Originally posted at netdensity.net.
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