If you were tasked with estimating the energy generation from an entire city of PV systems - how would you do it?
A simulation probably jumps into your mind right away. Scale up a model of PV system performance and that must get you close right? Well that's a step in the right direction, and you could do that very accurately if you knew the amount of radiation arriving at the surface of all those PV arrays.
But that's a bit trickier than it sounds! First, where are we going to get an estimate of the available solar radiation at a given location in the city?
The most common answer I get to this is: a pyranometer. And that's a great start - you'd get a measurement of global horizontal irradiance (GHI) at a point location, which is very helpful. But you're left with two major problems:
1. How representative is that pyranometer of the rest of the city's radiation resource? Those clouds are tricky!
2. How do you estimate the amount of radiation arriving on all of those various tilted surfaces around the city?
So, OK, now we need multiple pyranometer sites around the city and at each site we need to tilt and orient them in various directions in order to get a representative sample.
Well, the bad news is that pyranometers cost a few thousand dollars each, need regular cleaning/calibration/maintenance and it's actually pretty difficult to find appropriate sites for them. If you'd like to find out just how difficult it is to install scientific equipment on buildings - be my guest! (Hint: paperwork, approvals, PITAs galore!).
But I think I've got a better idea...
But I think I've got a better idea - what if we used the photovoltaic arrays that are already installed in a given region as our primary input to our city-scale modelling project?
They're pointed in many different directions, there are many of them already reporting data publicly in real-time, someone else has paid for the equipment AND they're representative of the systems we are trying to estimate in the first place. Sounds like a pretty sweet deal to me!
But, they are subject to shading, soiling and wiring inefficiencies, not to mention that they are not really the most scientific form of equipment. Still - they are inherently a type of radiation sensor. And we can probably deal with a lot of those things with some fancy machine-learning algorithms.
So, here is where I introduce our paper:
Estimating Hourly Energy Generation of Distributed Photovoltaic Arrays: a Comparison of Two Methods
J. Tan, N. A. Engerer and F. P. Mills
In it, we compare a two methods for estimating the energy generation of distributed PV arrays.
The first uses pyranometers, radiation models and PV system modelling for the estimation.
The second uses a monitored PV system and my KPV methodology to make the estimation.
I'll let you read the paper to get the details, as that's not the point of blogging (all the boring stuff is for the papers - Ok, I really do actually think that stuff is fun too, #supernerd). But I will let you know that we've found a few interesting things:
1. The pyranometer methods does tend to do a bit better (RMSE 15-20% versus 15-25%)
2. BUT when we start to leverage the prolific availability of the PV systems (there are many more of them out there!), we find the KPV method actually does best! (for distances less than 5km)
3. We actually detected a calibration error in one of the pyranometers using the PV systems - so much for pyranometers being the pinnacle of scientific monitoring!
Overall, I find this result very encouraging. If we can use PV systems as our primary input to our city-wide modelling idea, then we are one step closer to making the estimate we need. And we can do it on the cheap - which is really good for solar!
Now it's time to scale it up, test it on different time scales and handle all those pesky quality control issues. But don't worry, you can count on me to bring you the results soon!
Until then, enjoy my new webpage!