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The model implements the full simulation chain—from the downscaling of meteorological inputs to the calculation of irradiance on the tilted PV module surface and its subsequent conversion into a capacity factor. It incorporates major physical loss mechanisms (optical, thermal, and electrical), ensuring a realistic representation of PV system performance under varying environmental conditions.

Input Data and Pre-processing

The following meteorological and technical data serve as model inputs:

• Gridded surface solar radiation downwards (GHI)

• Gridded 2 m temperature (TA)

• PV system characteristics (tilt, azimuth), derived from private plant metadata and generalised rules

Temporal Downscaling

To better represent sub-hourly variability in solar irradiance, the original gridded data is downscaled to 15-minute intervals before recomputing the PV output. This approach avoids artefacts that may occur when relying solely on hourly averages.

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irradiance values. Air temperature (TA​) is also interpolated linearly to 15-minute intervals. This temporal refinement enables a more accurate reconstruction of irradiance variability within each hour. Once the PV conversion is completed at the 15-minute scale, the results are averaged back to hourly values.

Methodology

The model implements a complete physical simulation chain—from irradiance transposition to loss modelling and power output conversion—ensuring realistic system performance across geographies and climates.

Irradiance Decomposition and Transposition

To compute the irradiance on the tilted surface of the PV modules (GTI – Global Tilted Irradiance), the incoming solar radiation is decomposed into direct (beam), diffuse, and reflected components.

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R_r = \rho \times \Bigl(1-\cos\frac{\beta}{2}\Bigr)

Modelling of PV Efficiency and Losses

Several conversion steps transform the plane-of-array (POA) irradiance into AC power output, taking into account system-level losses.

Optical Losses

The Martin-Ruiz model (2001, 2013) estimates reflection losses based on the incidence angle:

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is a surface-dependent angular loss coefficient.

Conversion Efficiency at 25°C

The PV module conversion efficiency under standard conditions (25°C) is calculated using a fourth-order polynomial (Beyer et al., 2004):

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with parameters calibrated using French and German plant data listed in Table 2.1 below.

Table 2.1: Parameters used in the estimation of the PV generation. 

Thermal Losses

The module temperature 

T_{PV}

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The model assumes that ground-based installations experience greater ventilation, leading to lower PV module temperatures and thus reduced thermal losses, as outlined by Skoplaki et al. (2008). This empirical adjustment improves the realism of the estimated thermal behaviour of utility-scale PV systems, which are typically mounted on open structures allowing natural airflow.

Inverter Losses

AC power output 

P_{PV,AC}

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b_3 = -0.002217

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Representation of Global Tilt and Orientation

To define representative values for panel tilt and azimuth at the global scale, metadata from hundreds of utility-scale PV installations in Germany and France was analysed. The spatial distribution of these tilt angles, each associated with plants over 1 MWp, is shown in Figure 2.1. The observed angles were compared to theoretical optimum values, defined as the tilt that maximises annual incident radiation on the module surface.

The optimal tilt was computed by simulating PV output over a 5-year period (2015–2020) using ERA5 irradiance data. For each grid cell, multiple tilt configurations were tested, and the angle yielding the maximum annual GTI (Global Tilted Irradiance) was selected. The resulting global distribution of optimal tilts is shown in Figure 2.2.

A comparison between actual and optimal tilts revealed that many installations operate with a tilt around 75% of the theoretical optimum. This trend is visualised in Figure 2.3, which shows the relationship between actual and optimal tilt values. The deviation from the optimum is likely explained by practical trade-offs, such as minimising inter-row shading or reducing installation costs.

To formalise this pattern, the ratio between actual tilt and optimal tilt was analysed alongside the azimuth angle. As shown in Figure 2.4, the tilt ratio follows a normal distribution centred around 0.75, while the azimuth angles are tightly clustered around 0°, indicating a strong preference for south-facing modules. Based on these findings, the global model adopts a south-facing orientation and assumes a tilt equal to 75% of the optimal value for each ERA5 grid cell.

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Figure 2.1: Geographic distribution of tilt angles for over 300 utility-scale PV plants (>1 MWp) located in France and Germany.
Colour scale indicates tilt angle (°), highlighting spatial variation as a function of latitude and regional deployment practices.

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Figure 2.2: The map shows the optimal fixed tilt (in degrees) computed for each ERA5 grid cell based on the 2015–2020 average.
This value is derived by simulating energy output across varying tilts and selecting the configuration with the highest annual GTI (Global Tilted Irradiance).

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) used in the global modelling approach.

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).
Top-left: Joint 2D histogram of azimuth angle and tilt ratio.
Top-right: Histogram of tilt ratio with fitted normal distribution N(0.75, 0.17).
Bottom: Histogram of azimuth angles fitted to N(0, 7.96), confirming a strong concentration of south-facing modules.

Output Data

The final output consists of:

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These capacity factors reflect local climate, system losses, and realistic tilt/azimuth configurations, offering consistent comparability across regions and timescales.

References

For the references, please refer to the References section in the Product User Guide.

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