New Prospect map layers

  • Updated on Feb 14, 2025
  • Published on Jan 31, 2025
  • 10 minute(s) read
  • MK

In this document

We will introduce new map layers added to the Prospect application.

New maps in Prospect

New map layers have been added to Solargis Prospect. These layers provide important information on factors that can impact the performance, long-term durability, and potential damage of photovoltaic (PV) systems. PV project engineers, financial planners, and decision-makers will find these layers essential for making informed decisions about their projects.

In total, eight new layers have been added:

  • Long-term solar resource variability (GHI VAR long)

  • Short-term solar resource variability (GHI VAR short)

  • Highest expected operating temperature (THEO)

  • Ultraviolet radiation A (UVA)

  • Ultraviolet radiation B (UVB)

  • Daily module temperature amplitude higher than 50 degrees Celsius (TMOD AMP50)

  • Wind gust p99 (WG P99)

  • Corrosion degradation rate (CORR)

Long-term solar resource variability (GHI VAR long)

Long-term variability of Global horizontal irradiation (GHI) describes the cycles of solar resource year-by-year and over decades. Solargis Prospect now includes a global map and data detailing interannual solar resource variability.

Definition: GHI VAR long in Solargis Prospect is characterized using the standard deviation of the yearly time series of GHI, expressed as a relative value (percentage).

Firstly, Solargis Time Series data, specifically the GHI parameter, is aggregated into yearly time series for each point in a grid that covers the entire world. The standard deviation is then calculated from the aggregated yearly time series. This standard deviation is used as the metric of the long-term variability of solar resource.

Developers and investors need to take into account the uncertainty related to the long-term variability of solar resource. The yearly changes in solar resource cause variability in the power output of a PV power plant, which in turn affects financial planning.

A higher value indicates greater interannual variability, signifying a larger dispersion of yearly summaries in relation to the long-term average (e.g., values span a wider range). The map representation allows comparing the stability of solar resource between locations or regions.

Short-term solar resource variability (GHI VAR short)

Short-term (or intra-day) variability is connected to the actual state of the atmosphere and clouds, and reflects the solar resource intermittency caused by clouds during the day. The parameter mapped in this layer is Global horizontal irradiation.

Definition: GHI VAR short can be characterized by multiple metrics. Here, we selected the yearly average count of GHI ramps exceeding the 300 W/m2 threshold, analyzed from Solargis GHI Time Series.

GHI VAR short is based on analysis of the sub-hourly time series of GHI from the Solargis database. It is based on harmonized 10-minute or 15-minute time series grid data of GHI representing years 2019 to 2023 (era of modern meteorological satellites available globally), with a nominal spatial resolution of 4 km (specifically 2 arcmin). The harmonized time series is further processed in the global grid as follows:

  • Calculation of solar noon for each day of the year.

  • Sub-setting Solargis GHI Time Series to ±3 hours from the solar noon.

  • Calculation of differences in the consecutive time slots.

  • Counting the ramps and calculation of other statistics from the filtered dataset.

  • Merging of the data  into a single seamless global dataset and map composition.

More detailed information is available in our contribution at EU PVSEC 2024 in Vienna.

Sudden changes in solar resource availability are caused by forming and moving clouds. Specifically, moving scattered (intermittent) clouds tend to cause frequent changes in solar irradiance. Solar resource intermittency is a challenge for the operation of PV power plants and the balancing of grids.

GHI VAR short map is valuable as an indicator for sizing and management of PV systems and short-term energy storage or other compensation mechanisms, assessment of performance of PV power plants (e.g., indication for clipping losses), as a siting factor for new solar parks, and ultimately a key resource for grid management.

Highest expected operating temperature (THEO)

The highest expected operating temperature helps to understand the upper limits of temperature tolerance in PV systems. This understanding allows a comprehensive assessment of potential dangers and the implementation of appropriate mitigation measures.

Definition: The highest expected operating temperature is calculated using a methodology similar to that for the lowest expected operating temperature (TLEO), defined in the IEC 62738 standard. It is calculated as the average of the highest recorded air temperatures over 20 years at the site.

To calculate THEO, Solargis uses the ERA5-Land climate reanalysis data (from ECMWF and Copernicus services), with a grid resolution of 0.1° (~11 km) disaggregated to a 1 km grid. The highest annual air temperature at 2 meters height is calculated for each of the years between 2001 and 2020, and then averaged to obtain the final value of THEO. The air temperature data is validated against measurements from over 5,900 weather stations worldwide. The validation is outlined in our blog post about TLEO.

Extreme heat negatively affects the performance of PV systems, leading to reduced efficiency, shortened lifespans, and more frequent occurrence of failures, which may potentially be catastrophic. Several key components in PV systems are impacted:

  1. High temperatures can cause accelerated degradation in PV modules, reducing power output and potentially necessitating early replacement. While thermal runaway is less likely than with batteries, prolonged heat can cause performance issues.

  2. Inverters are vulnerable to overheating if their cooling is insufficient. If not properly managed, overheating can lead to failures that disrupt the entire PV system.

  3. Batteries, especially lithium-ion types, can experience thermal runaway in extreme heat conditions, leading to fires or explosions.

Ultraviolet A and B (UVA and UVB)

Higher UV radiation from the Sun can accelerate the degradation of PV modules and other PV components, reducing their efficiency. It damages materials, creating free radicals that cause cracking and discoloration. Over time, it can also weaken encapsulation materials, leading to delamination and moisture damage.

Definition: The UVA and UVB maps and data in Solargis Prospect represent the average yearly summary of radiation within the respective spectrum.

The worldwide maps of UVA and UVB radiation represent the average annual UVA and UVB radiation calculated over the period ranging from 1994 to 2022. These parameters, though related to solar radiation, are derived from the global meteorological model ERA5 by ECMWF, using Solargis' spectral splitting methodology.

Radiation with a spectral width of 315-400 nm is considered UVA, and a spectral width of 280-315 nm as UVB. The inputs to the model for UVA and UVB radiation are:

  • Broadband UV radiation data available from ECMWF ERA5 model,

  • Total ozone column from ECMWF ERA5 model,

  • Aerosol optical depth AOD from ECMWF MACC-II

PV modules, non-shielded electrical cables, interconnectors, combiner boxes, and other exposed components of PV systems are constantly affected by UV radiation and undergo aging and degradation processes. UV radiation causes a photochemical effect within the polymer structure, which leads to degradation of the material.

The UVA and UVB maps and data provided in Solargis Prospect can be used to compare UV radiation between different geographical locations, assess the risk of premature failure or performance reduction, and adopt appropriate mitigation strategies, e.g., choice of components with higher UV resistance.

Daily module temperature amplitude higher than 50 °C (TMOD AMP50)

The day-night thermal cycling of PV modules causes temperature fluctuations that impact performance and lifespan. Although the occurrence of extreme temperature amplitudes may not be frequent, they can contribute to the accumulation of thermal stress over the operational lifespan of PV systems.

Definition: TMOD AMP50 is calculated as the average number of days in a year with daily PV module temperature amplitude higher than 50 °C.

Firstly, the time series of PV module temperature (TMOD) was calculated in a grid covering the whole world. The inputs for the calculation of PV module temperature are hourly time series of air temperature at 2 meters, and hourly time series of Global horizontal irradiation (GHI), both from the ECMWF ERA5 meteorological model.

A simplified NOCT (Normal operating cell temperature) thermal model was used with a relatively high NOCT value of 48°C and Global horizontal irradiation (GHI) instead of Global tilted irradiation (GTI) to calculate PV module temperature TMOD:

TMOD=Tair+GHI*(NOCT-20)/800

As the largest daily amplitude of module temperature occurs in the hot season (with high Sun elevation and high solar irradiance values), the use of GHI instead of GTI does not significantly impact the results. Although the TMOD model is simplistic, it is relatively accurate, has been validated across a substantial number of PV systems installed worldwide, and is widely employed within the PV industry.

Lastly, the daily amplitude of the TMOD time series was calculated for each day in the dataset. The amplitudes over 50 °C were counted and averaged to obtain an average yearly number.

Thermal cycling is one of the recognized degradation mechanisms for a range of PV components. PV modules specifically must be qualified to withstand a defined limit of thermal cycles according to IEC 61215-1. Knowing the expected number of temperature cycles with a large amplitude at the project site is key to understanding the expected degradation rate of PV modules. This understanding allows the project developers to apply appropriate mitigation strategies. The map in Solargis Prospect additionally allows for a comparison of different regions and enables the consideration of the risk when setting up a PV project.

Wind gust p99 (WG p99)

Wind gusts (WG) impact PV module stability and tracking systems, and their extremes can lead to energy losses and structural risks. Understanding wind patterns is essential for optimizing PV system design, ensuring long-term reliability, and preventing damage.

Definition: WG p99 is the 99th percentile of wind gusts at 10 m height above ground calculated from the ERA5 hourly dataset from the period 2001 to 2020, averaged to provide a yearly number. This percentile corresponds to the top approx. 80 occurrences of wind gusts within a year.

Wind gust is the maximum of the 20-second running average wind speed recorded within an observation cycle (typically 1 hour). It is commonly measured at 10 meters above ground.

To produce the map and data of WG p99 for Solargis Prospect, we used the hourly WG time series from the ECMWF ERA5 meteorological model. From this data, the 99th percentile value was calculated for each year between 2001 and 2020. The yearly values were then averaged to provide a single number for each location in a global grid.

Wind generates mechanical loads on PV structures, affecting durability and performance. Extreme wind speeds can damage PV modules and may induce invisible cracks. Moreover, severe wind gusts can force PV trackers into protective stow positions, thus reducing energy production. Site-specific wind assessments help in selecting appropriate designs of modules, trackers, and supporting structures.

Corrosion degradation rate (CORR)

Corrosion is a degradation effect in PV modules, primarily driven by temperature and humidity. It weakens the electrical connections, increases leakage currents, and reduces power output over time.

Definition: Corrosion degradation rate (e.g., the rate of power output loss at the maximum power point of the PV module) is calculated using the Peck model with inputs from the ECMWF ERA5 meteorological model.

One of the most common models for corrosion is the Peck model, where the degradation rate DR is expressed as:

DR=A*(RHeff)n·exp(-E/(kB*TMOD))

where:

  • A - pre-exponential constant

  • RHeff - effective relative humidity inside the module in [%],

  • n – relative humidity impact parameter

  • E - activation energy of the degradation process in [eV]

  • kB - Boltzmann constant (8.62x10-5 [eV/K])

  • TMOD - module temperature in [K]

This model is used for the calculation of corrosion maps and data in Solargis Prospect. The atmospheric parameters have been obtained from ERA5 reanalysis. The values of A, n, and E provided by Kaaya et al. for the outdoor modules have been used, and the RHeff has been calculated with the model proposed by Koehl at al.

High temperatures and humidity accelerate corrosion, impacting PV module performance and longevity. Corrosion can occur alone or alongside other degradation modes like hotspots, soiling, or glass breakage. It is more prevalent in tropical and coastal regions but tends to worsen over time in any environment. Understanding corrosion risk helps with module selection, maintenance planning, and PV project siting.

Reference documents