Lowest expected operating temperature (TLEO) & Solargis approach

Extreme cold conditions can lead to increased voltages in photovoltaic (PV) modules, potentially causing system overload.

Learn about the critical importance of understanding the lowest expected operating temperature parameter (or TLEO in Solargis language) for optimal PV array sizing.

In the article, we'll discuss:

• Why consider low temperatures in your PV projects
• Definition of TLEO (according to IEC 62738) and technical background
• Our process for mapping global TLEO data, drawing from both climate models and meteorological observations
• Where to find TLEO in Solargis Prospect
• How we validate the TLEO data layer
• Notes on standardization

Whether you're an investor, designer, operator, or any other professional in the solar industry, this article will provide a clear understanding of how to think about PV arrays in cold conditions. This can lead to reduced system failures, longer operational lifespan, and fewer maintenance headaches.

Why consider situations when PV plants experience low temperatures?

A critical aspect that often gets overlooked within PV projects? Performance of PV systems in extremely cold conditions.

The lowest temperature typically occurs shortly before or during the sunrise. The combination of the extremely low ambient temperature of PV modules and abundant solar radiation during sunrise can trigger a sudden rise in the voltage in the PV string.

When certain critical levels are exceeded, it has the potential to lead to system overload.

The lowest expected operating temperature is central to this concern. Accurately determining TLEO is essential for the safe and efficient operation of PV arrays.

The technical background

According to best practices in sizing of PV arrays (introduced among others in the IEC 62548 and IEC 62738 standards), the maximum number of PV modules in the string is determined by the maximum voltage rating of the components such as the modules and inverters.

The open circuit voltage (Voc) for a PV string is calculated based on the module's Voc rating and the lowest expected operating temperature (TLEO), as per industry standards.

TLEO is defined in the IEC 62738 [1] standard as the mean of the lowest annual values of air temperature. If enough data is available, it can be limited to the hours of sunlight and a low irradiance threshold.

The calculated maximum PV string length is often the most cost-effective option because it reduces losses in the cables and the overall cable length. In cases where the calculated maximum voltage is slightly above the maximum system rating, further analysis may be necessary to assess the risk of overvoltage.

How we create the global TLEO data layer at Solargis

The best option to obtain air temperature for a given location is through continuous long-term measurements, using high-accuracy, calibrated, and well-maintained temperature sensors, mounted according to WMO standards [2].

Unfortunately, this method is restricted to locations where long-term meteorological observations are conducted, normally as part of national meteorological services or other observation networks.

On a global level, the only alternative is to derive historical air temperature data from available climate models.

In this project, the development of the TLEO global map begins with the processing of ERA5 climate reanalysis data (operated by ECMWF and Copernicus services [3]), provided with a native resolution of 0.25° (nominally 28 km). Here, for each year the absolute minimum air temperature at 2 meters height is derived from a 1-hourly time series, spanning the period of 2001-2020. The resulting TLEO value is then averaged from 20 yearly values.

To overcome the coarse spatial resolution of ERA5 data (particularly in mountains with significant vertical terrain dissection and coastal areas), we introduce a spatial disaggregation based on calculated lapse-rate corrections, derived from terrain elevation (Fig X2) and air-temperature data from various height levels.

The result is a fine-resolution data layer (~1 km pixel size) with TLEO values available globally for any location in the world (see Fig 1 and 2).

Find TLEO maps and data in Solargis Prospect

Starting from September 2023, we made available the lowest expected operation temperature (TLEO) data in Solargis Prospect.

With the TLEO variable, the calculation of the maximum voltage fluctuations and PV string sizing decisions are more accurate, ensuring the safe and efficient operation of the PV system under diverse weather conditions.

Fig 1. High-resolution TLEO map, long term average, period 2001 – 2020

Fig 2. High-resolution TLEO map, long term average, period 2001 – 2020. Focus on the regions with high TLEO gradients: Pacific coast in South America (left), Central Europe (middle) and South Asia (right)

Validation of the TLEO layer

The final TLEO data layer is validated with time-series data measured at more than 8500 stations worldwide and made available in the Integrated Surface Database (ISD) by the National Oceanic and Atmospheric Administration (NOAA) [4] for years 2001-2021.

The overall bias, calculated as the difference between the modeled and measured TLEO, is 1.2°C, standard deviation of 2.4°C. The most extreme values (presented here as percentile values P5 and P95) drop below -2.6°C and exceed 5.1°C, respectively.

In general, the results meet the purpose of the PV array sizing in most of the world. However, regions with microclimate anomalies were identified and potential recurring problems were found in the modeled dataset (Fig 3).

As expected, the ERA5 model resolution is not detailed enough for some mountainous regions worldwide. The majority of meteorological stations are located in valleys where the ERA5 representation of elevation is typically higher than the actual elevation of the sites, resulting in underestimated temperature values. The lapse-rate correction helps to some extent to address these discrepancies, but the magnitude of the correction is often not sufficient – e.g. Central Alps (Fig 3A), Scandinavian Mountains (Fig 3C), Tibet, Himalayas, West Iran, Southeast Turkey, etc.

Another recurring issue is that ERA5 underestimates the continental influence, particularly along the coastal areas (e.g. Southeast Australia in Fig 3B).

The model overestimates the influence of sea/ocean, which can lead to inaccurate temperature predictions. For example, cold air falling from the mountains towards water bodies can have a significant impact on the temperature of mountainous islands, but this effect is not always well-captured by models with coarse resolution (e.g. Corsica island in Fig 3A).

Another problem identified was the temperature inversion in cold valleys. Inversions happen when a layer of cold air gets trapped in the valley, sealed off by a layer of warm air above. The geography of the area prevents the cold air from flowing out of the valley, and the surrounding mountains inhibit winds from easily clearing it out and bringing in new air.

Fig. 3: Selected areas with significant differences between calculated TLEO and TLEO derived from NOAA ISD meteorological stations

Further notes on standardization

As already described above, TLEO is defined in the IEC 62738 standard as the mean of the lowest annual values of air temperature.

On the one hand, averaging filters out incidental extremes. On the other hand, the averaging introduces an error associated with estimating absolute TLEO for the entire period of 2001-2020 and the TLEO calculation as a mean as prescribed in the IEC standard.

Fig 4 illustrates the comparison of the error distribution introduced by monthly lapse rate simplification against the distribution of standard deviations of annual TLEO and the distribution of the absolute difference between record high and record low annual TLEO. A total of 30,000 points were evaluated globally by comparing the TLEO calculated using a full time-series approach against raster calculations using a random distribution of sites.

Fig 4: Comparison of distribution in differences in TLEO calculation method (time-series vs raster), standard deviation of annual TLEO and the distribution of the absolute difference between record high and record low annual TLEO

The relatively high value of the standard deviation of annual TLEO and the high difference between the record low and record high annual TLEO is an important issue to be considered in sizing PV power plants based on the average TLEO map alone.

It must be also noted that the temperature of PV modules facing the open sky can be 5°C lower than the ambient air temperature in some locations.

Analogically, NOAA ISD measurement data was analyzed. Fig 5 shows the difference between absolute TLEO and average TLEO calculated for the period 2010-2019. In many cases the values exceed 5°C, occasionally 10°C.

Fig 5. Analysis of 10 years (period 2010-2019): Difference between the absolute minimum of air temperature and TLEO (the average of absolute annual minimums of air temperature)

References

[1] Zawaydeh, Samer. (2019). IEC 62738:2018 Ground-mounted photovoltaic power plants-Design guidelines and recommendations.
[2] Guide to instruments and methods of observation, World Meteorological Organisation (WMO-No. 8), 2021/2018 edition, ISBN: 978-92-63-10008-5
[3] Hans Hersbach, Bill Bell, Paul Berrisford, et.al. The ERA5 global reanalysis First published: 17 May 2020
[4] Smith, A., N. Lott, and R. Vose, The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 2011, 92, 704–708