Hello Everyone, my name is Evan Sarkisian, and I am the marketing director for Tigo Energy. This presentation is a myth-buster. We will be quantifying the sources of mismatch in solar arrays to try to up-end the illusion that all panels operate uniformly at standard test conditions and will age at exactly the same rate for the life of the system. Normally, my presentation covers all the benefits of module-level optimization [next slide]
But today I’m just going to focus on this one [next slide]
Increasing energy harvest. And I’m actually going to focus on why centralized systems lose power from different types of mismatch. But let’s start with a quick introduction of our technology [next slide]
In traditional arrays, the string current is defined by the weakest module. When one panel underperforms, the whole string is affected similar to Christmas lights. There are a few ways to solve this problem, but impedance matching is the technology that sets Tigo Energyapart. It is the most efficient way to mitigate mismatch, because it only operates when necessary. Traditional solutions boost or transform power constantly, even when they are performing perfectly. Smart modules powered by Tigo Energy log less working hours per kWh and use a fraction of the part count of alternative approaches.
Tigo Energy is a critical part of the world’s first TUV certified smart module, which embeds all the benefits of panel level optimizers and micro inverters directly into the module. We’ve partnered with leading module manufacturers to make the installation of intelligent panels easier than ever before.
There are now millions of optimizers in the field reporting data at the module-level. We are measuring every 2-seconds with an accuracy better than 1%. The industry has never had more data to answer the question of why is there so much mismatch between panels in “perfect” arrays. Here we see a young system experiencing a voltage variance of 15%. We’ll be looking at all the major sources of mismatch starting with differences that are inherent in the panels [next slide]
If modules receive the same irradiance you would still see variance in performance just because of the manufacturing process. PV cells are crystalline structures, like snow flakes, and no two solar cells are ever identical. Impurities in the air, residue buildup in tooling, and thermal drift all contribute to variance in modules. Manufacturers are continuously working to improve their binning range, and you can see in the table here that the average range is well above 5%. But beyond the stated numbers we need to consider the accuracy of the flash testing done to get these ranges. These are typically accurate to plus or minus one percent, further increasing the actual range of mismatch in module performance. In addition to module variance out of the factory, there is also variance in how fast modules degrade [next slide]
Module degradation is well understood, what is less commonly known is the variable nature of degradation. Not all modules will age at the same rate, and this leads to increased mismatch from burn-in as well as over the long-term. NREL completed a study of module degradation rates and found a wide difference in degradation between modules. A significant number degraded between 0 and 1 percent per year, but a 1 percent variation per year will lead to a 5 percent difference in power production throughout the entire array by year 5. A smaller, but not trivial, amount of modules showed significant degradation rates ranging from 1 to 4 percent per year. Within 5 to 10 years these modules will be underperforming their peers by as much as 20 to 40 percent. All the variance I’ve described so far is inherent to the module, and would be an issue even if you were designing in a lab. But since no solar array will experience laboratory conditions, let’s take a look at some environmental factors that impact mismatch on real world installations. [next slide]
Here we see the difference in irradiance a site will see just from clouds passing over head. This data set was collected by NREL and it gave us our first look into second level data of the effect of clouds. NREL used 17 different light meters and the annual average deviation was 15.4 percent, this is just from clouds. That’s a 5-8 percent energy loss due to variable light levels. On top of that, modules will get dirty affecting production beyond irradiance differences. [next slide]
Dust buildup alone can reduce standard module performance by 30 watts, and if part of the array is closer to trees or roadways the effects of soiling will be felt unevenly throughout the array. A proximity to birds adds a harder to predict, but equally impactful, source of mismatch to arrays. And since soiling effects require a cleaning visit to fix, they are often left unattended for 3 to 6 months at a time. This means that some modules will operate for an extended time at a reduced efficiency, creating a hotspot of increased resistive losses, which will accelerate degradation for that module. Based on historical data, most developers use 1 to 4 percent mismatch loss due to soiling.
Thermal mismatch affects module performance relative to the temperature coefficient of the module. This is typically modeled at 1 percent loss for every 2 degrees of temperature increase. Within Tigo Energy’s fleet we’ve measured an average variance of 4 to 7 degrees celsius, with 20 degree spreads commonly found throughout the fleet. This equates to an average mismatch loss of 2 to 4 percent. We’ve had this validated with a presentation by First Solar that mentioned a 10 percent average module temperature difference throughout their ground mount arrays. So far all the variance I’ve described has assumed that 100% of the modules will work perfectly for the life of the system. Solar modules are highly reliable products, but they are not immune to failure. [next slide]
Half a percent of modules have a failed bypass diode the day they are installed. Failed diodes are almost impossible to detect with traditional measuring devices since a load must be drawn for the issue to manifest. Which means, one third of a module’s power can be entirely missing for the life of a system. Working bypass diodes can operate at temperatures 2 times ambient, leading to high stress operating conditions. And as I am about to show you, aging systems continue to suffer from accumulated wear and tear
This is a case study involving a 9 year old retrofit system in Germany. Module-level optimizers were installed to diagnose and isolate system issues. Immediately after installing, the system experienced a 25% increase in production just from module-level power point tracking. With module-level monitoring the system owner quickly identified 10 modules producing at 90% relative to neighboring modules, 12 modules at 50%, and 76 modules producing at 20%. In addition, 2 entire strings were experiencing open-circuit faults. The point being, solar systems are exposed to extreme environments and aging is inevitable. These things are in the sun all day, and temperature cycle from hot to cold each night. Rubber, polymer, glass and circuit connections are all exposed to thermal expansion and humid conditions. That covers all the main sources of mismatch, now I’d like to show some real-world data [next slide]
Here is the standard deviation range for over 10,000 systems using Tigo Energy module-level monitoring, with hundreds of commercial sites included. This represents over 60 gigawatt hours of production. We’re reading power, current and voltage every two seconds with accuracy greater than 1 percent. Across the entire range we see an average standard deviation of 11.3 percent. But these numbers are a little skewed. Tigo Energy is a growing company. We doubled our installations last year, which means one half of our systems are less than a year old. Let’s look at an older data set. I just found this next study, and the information is eye-opening. [next slide]
Arizona State University completed a study of 1900 modules using Arizona Public Services’ STAR research facility. These modules were cleaned and an IV curve trace was run to measure degradation. They found a degradation rate well above the 0.5% degradation rate used by many models, but more importantly, they found a significant mismatch in the rates of degradation.
Thank you all for listening. I hope you are now mismatch experts that can challenge anyone that hides behind a spec sheet and argues that mismatch isn’t an issue. Please contact us if you’re interested in learning more about some of the other optimizer values I didn’t cover, and feel free to raise your hand now if you have any questions.