Article - Greenbyte

Introducing: a new fleet of solar signals

Up here in the north, it’s the darkest time of year. While all of us at Greenbyte are anxiously awaiting the return of the sun, we’ve been designing and developing the most powerful version of Bright to date. We’re excited to introduce a new set of solar calculations, unlocking a higher level of accuracy and transparency in the performance of your solar portfolio.

With a wide variety of onsite weather measurement systems at solar sites and minimal standards in the solar industry around how to calculate potential power (also known as expected power), we’ve developed a solution which allows you to select the calculation that best reflects the data available at each site. The inputs for the solar potential power calculations range from common readings (such as irradiance) to less frequently available readings (such as module temperature) and thus the accuracy of the result in the potential power calculation will vary accordingly.

Follow along below to learn about the new signals, the calculations behind them and the world of possibilities they’ve unlocked.

Potential Power Irradiance Only

This signal simply borrows the denominator from Bright’s PR calculation. And, as the name suggests, it is most appropriate when limited inputs are available. All that’s required is the irradiance reading, whether it be from the weather station onsite or satellite data. The irradiance is combined with the site’s capacity to generate a rough estimate of how much power could be generated in the given timeframe. The result will be inevitably high, as losses are not considered, but the approach can serve as a simple starting point.

where Gi is the irradiance value measured during time interval i, and Cp is peak capacity in kWp.

Required metadata: peak capacity

Potential Power Performance Ratio

This signal incorporates the average PR calculated by Bright during the previous hour in an effort to paint a more accurate picture of potential power. For this method, the system will also pull from the total area covered by solar panels in m², the panel yield and the measured irradiance on site [1].

where A is total solar module area (m²), r is solar module yield (%), Gi is the irradiance value measured during time interval i, and PR is the performance ratio, coefficient for losses (calculated in the previous hour).

Required metadata: solar panel yield, total panel area, peak capacity (for the PR calculation).

Potential Power Module Temperature

Here’s where things get exciting! If the site has module temperature sensors, these measurements will be incorporated into the potential power calculation to reflect the effect of the module’s temperature on its productivity. Unfortunately, the hotter your solar panels are, the less productive they are. To account for this in your production estimations, you’ll find a power temperature coefficient (also sometimes referred to as the temperature correction factor) in the datasheet provided by the panel manufacturer. The temperature coefficient is expressed as a percentage decrease in output for every degree (˚C) that the module temperature rises above the Nominal Operating Cell Temperature (NOCT). As you’ll see in the equation below, the NOCT is compared to the actual temperature reading, and the difference multiplied by the temperature coefficient in order to estimate how efficiently the module should be operating given the conditions on site [2].

where Cp the site’s peak capacity (kWp), G is the irradiance value measured during time interval G, y is the module power temperature coefficient (%/˚C), Tm is module temperature (˚C), and T0 is Nominal Operating Cell Temperature (˚C).

Required metadata: peak capacity, NOCT, power temperature coefficient.

Potential Power Ambient Temperature

In this two-step approach, we first estimate the temperature of the modules given the onsite ambient temperature and irradiance [3]. From there the estimated module temperature is treated in the same way the measured module temperature was previously (Potential Power Module Temperature); for each degree of difference from the NOCT, the temperature coefficient is applied to adjust the productivity. Some favor this approach over reading the module temperature as it ensures that any issues resulting in higher than expected operating temperatures would not be masked in the potential power estimation [4], helping to more effectively pinpoint underperformance.

where Cp the site’s peak capacity (kWp), Gi is the irradiance value measured during time interval i, y is the module power temperature coefficient (%/˚C), T0 is Nominal Operating Cell Temperature (˚C), and

Required metadata: peak capacity, NOCT, power temperature coefficient

Others

Lastly, as an FYI, there are a couple of other back-up potential power methods that are available if desired.

  • Potential Power Reference Inverter: uses the power produced by the reference inverter(s) you’ve selected within the given time period. If multiple are selected, an average is taken.
  • Potential Power Highest Inverter: uses the power reading from the best performing inverter on site for the given time period.

Cascading Potential Power

Now, line up your favorites in order! Tell Bright which method to use as the default when all the necessary inputs are available, and which to fall back on if the method(s) before it in line are not possible. Of course, the cascading potential power order will vary from site to site as the weather instruments on site do, so you’re able to create multiple cascading potential power orders as needed and assign them to different devices.

Go to Administrate > Potential Power to create Cascading Potential Power orders; Administrate > Devices to assign your Cascading Potential Power Orders to devices; and Administrate > Availability Contracts to assign an order to a contract.

Lost production

With the potential power signal in place, it opens the door for lost production calculations to take place. It is now possible to calculate lost production due to performance and downtime.

The system will compare the energy exported to the potential power figure. If an Energy Export reading is not available, Power AC will be used as a fallback. To begin, Bright will look at the system’s Time-based System Availability to determine the amount of time the inverters were available or unavailable.

Lost Production (Contractual) will work in the same way, but use Time-based Contractual Availability as the figure for differentiating between available and unavailable.

Production-based Availability

The final piece to fall into place is Production-based Availability; an exciting development for those of you with energy-based contracts.

where Ea is Actual Production and El is Lost Production.

Production-based System Availability will be calculated using Lost Production to Downtime, whereas Production-based Contractual Availability will be calculated with Lost Production (Contractual).

Final touches

  • Degradation factor: if desired, apply a degradation factor to all potential power signals to take the age of the panels into consideration. To activate, fill out the metadata field for commissioning date and degradation factor.
  • Availability Cut-In: Complete the Availability Irradiance Cut-In metadata field to set the level of irradiance needed for the device to be considered available.
  • Performance Mode: set a Performance Irradiance Cut-In to ensure performance related calculations do not occur below a certain irradiance threshold.

References

[1] Hofierka, J. and Kaňuk, J. (2009). Assessment of photovoltaic potential in urban areas using open-source solar radiation tools. Renewable Energy, 34(10): 2206-2214.

[2] Marion, B. and Anderberg, M. (2000). PVWATTS−An Online Performance Calculator for Grid-Connected PV Systems in Proceedings of the ASES Solar 2000 Conference. Madison, WI.

[3] Jakubiec, A. and Reinhart, C. (2012). Towards validated urban photovoltaic potential and solar radiation maps based on LiDAR measurements, GIS data, and hourly DAYSIM simulations. Cambridge, MA: Building Technology Program, Massachusetts Institute of Technology.

[4] Dierauf, T., Growitz, A., Kurtz, S., Cruz, J.L.B., Riley, E. and Hansen C. (2013). Weather-Corrected Performance Ratio. National Renewable Energy Laboratory, NREL/TP5200-57991.