In this document
We will provide a detailed explanation of our most advanced energy yield simulation, used in Solargis Evaluate. This simulation estimates the energy yield of a PV energy system based on the location's solar, meteorological, and environmental conditions.
Overview
Our PV simulator, developed for Solargis Evaluate, leverages advanced technologies such as the Perez anisotropic and all-weather sky models to provide unparalleled accuracy in estimating the energy yield of PV systems. By integrating these models with high-resolution Solargis Time Series (TS) or Typical Meteorological Year (TMY) data, this simulation offers a comprehensive analysis that surpasses current market standards. The simulation is designed to account for a wide range of factors, including sun geometry calculated using the PSA model, terrain and horizon data, and meteorological conditions like dust and precipitation. For a better understanding of the concept, processes, and methods involved, we have segregated the information into three main sections:
The simulation inputs: This section will cover both technical and site parameters. Technical parameters include the configuration of the PV power plant, such as module and inverter specifications provided by the PV Components Catalog (PVCC), while site parameters encompass sun geometry, terrain, and solar radiation inputs like Global horizontal irradiation (GHI) and Direct normal irradiation (DNI) from Solargis data sources. These inputs are crucial for setting up the simulation environment.
Optical simulation: This part will delve into the optical aspects of the simulation, utilizing sky irradiance models like the Perez anisotropic model and the all-weather sky model. These models are used to calculate the Global tilted irradiance (GTI) on PV modules.
Electrical simulation: This section will explain how the electrical configuration of the PV system is simulated, including energy losses in the system and how they are accounted for in the final energy yield calculation. The electrical simulation ensures that all components, from PV modules to grid connections, are accurately modeled.
The overall simulation chain and sequence of processing steps are illustrated in the Evaluate simulation diagram.
The simulation inputs
The simulation inputs in Solargis Evaluate are categorized into two main types: technical parameters and site parameters. Each plays a crucial role in accurately modeling the PV system's performance.
Technical parameters
Technical parameters define the configuration and components of the PV power plant. These include:
System layout
Shading objects
PV module and inverter specifications
System layout
The configuration of the PV power plant is fully specified using the Energy system designer, part of the Solargis Evaluate application. The output includes both physical and electrical layouts:
Physical layout: Details such as segment parameters (plots of land), support structures, PV module configurations, inverter and transformer locations, and infrastructure like access roads and fences.
Electrical layout: Connection of PV modules into strings, inverters with MPPT inputs, transformers, and grid connections. Energy losses in each section are also specified.
Shading objects
All elements of the PV power plant configuration are considered in shading calculations. Users can also specify additional shading objects like fences, tree lines, or buildings by drawing them in the Energy system designer or importing them via KML files.
PV module & inverter specifications
These are provided by the PV components catalog (PVCC), which offers verified specifications in the required format. The PVCC ensures high-quality inputs, leading to consistent and reliable simulation results. More information about the PVCC can be found in the Solargis knowledge base.
Site parameters
Site parameters encompass environmental and meteorological conditions that affect the PV system's performance.
Calculated using the PSA model from geographical coordinates and date-time information.
Provided as Solargis Time Series or TMY data with a standard time resolution of 15 minutes.
Terrain: Available from multiple sources with different spatial resolutions (30 m or 90 m). Terrain is considered as a shading object but can be optionally disabled.
Horizon: Provided by Solargis with a resolution of 7.5 degrees horizontally and 0.01 degrees vertically. Each segment has its own horizon calculated.
Ground albedo: Monthly average values are provided, and users can adjust these or set a single yearly value. Albedo affects all ground within the project.
Object albedo: Specified for shading objects and torque tubes in tracker mounting systems. Users can adjust these values, including setting monthly variations.
Include dust, precipitation, precipitable water, atmospheric pressure, air temperature, wind speed, and snow. These factors are crucial for accurately simulating real-world conditions.
Optical simulation
The optical simulation in Solargis Evaluate is the first part of the energy yield calculation process. It involves calculating the global tilted irradiance (GTI) on PV modules, taking into account various factors such as shading, reflection, and soiling effects. This section will delve into the detailed steps and models used in the optical simulation process.
Sky irradiance models
In Solargis Evaluate, satellite-derived Global horizontal irradiation (GHI) and Direct normal irradiation (DNI) variables are used to calculate the distribution of diffuse radiance on the sky vault. This is achieved through the implementation of two sky irradiance models:
Simple Perez anisotropic model: This model is used to simulate GTI Energy systems, providing Time Series of GTI.
Perez all-weather sky model: The model is employed in PV Energy system simulations to calculate GTI and, subsequently, PVOUT.
Solar position calculation
For both models, the solar position is calculated using the PSA model from the geographical coordinates of the site and date-time information. This ensures accurate positioning of the Sun in the simulation environment, which is crucial for calculating GTI and subsequent energy yield estimates.
3D calculation scene
The 3D calculation scene is another important component of the optical simulation in Solargis Evaluate. It is constructed using inputs from the Energy system designer, terrain data, horizon data, and albedo values. The purpose of this scene is to accurately model the surfaces of PV modules and all objects in the area that can cast shadows or reflect solar radiation.
Components of the 3D scene:
PV modules: These are the primary surfaces for which incident solar radiation is calculated.
Objects: Include support structures, inverters, transformers, and any additional shading objects specified by the user, such as fences or buildings.
Terrain: The terrain model is integrated to account for its impact on shading and reflections.
Simulation dynamics
The lighting and shading within the 3D scene are dynamic, depending on the solar position and solar radiation values. These factors vary over time and are recalculated for each time step of the simulation, ensuring that the simulation accurately reflects real-world conditions.
For a visual representation, refer to the example image below. This image illustrates how PV modules, objects, and terrain are integrated into the simulation environment.
.png?sv=2022-11-02&spr=https&st=2025-03-22T08%3A49%3A44Z&se=2025-03-22T09%3A11%3A44Z&sr=c&sp=r&sig=ofF0evyDQTHAdFNT1vYNh28o%2FTHINpwTRtY%2FDJgMxrM%3D)
Figure 1: Illustration of how PV modules, objects, and terrain are integrated into the simulation environment.
Backward raytracing
Backward raytracing, specifically unbiased Monte Carlo path tracing, is a key method used in Solargis Evaluate to calculate the irradiation on PV modules. This process involves the following steps:
Direct illumination calculation: Determines whether sample points on PV cells are in direct sunlight or shaded, calculating a shadow ratio for partially shaded cells.
Diffuse radiation calculation: Generates random rays and traces them through a 3D simulation scene, recording ray direction and treating intersections as Lambertian reflections.
Post-processing: Denoises diffuse radiation values and resamples them per cell, summing them with direct radiation to obtain the final Global tilted irradiance (GTI).
Implementation and validation: Implements the ray tracing method for various PV module types and validates it against Bifacial Radiance software and ground measurements.
Soiling losses
Soiling losses are an essential factor in the energy yield simulation, as they affect the amount of solar radiation that reaches the PV modules. In Solargis Evaluate, these losses are applied to the GTI calculated in the previous steps.
Method of application: Soiling losses are typically applied as average monthly figures or as a single yearly figure. Users have the option to overwrite these values if needed.
Default values: In the initial release of Solargis Evaluate, generic average soiling values are suggested, which are suitable for a wide range of PV projects.
Solargis operates a proprietary soiling model that estimates soiling based on atmospheric pollution at specific locations. This model is expected to be integrated into future releases of Solargis Evaluate.
Processing time depends on the number of segments in the energy system. More segments result in longer estimation computations.
Angular reflection losses
Angular reflection losses occur due to the angle of incidence effects on the surface of PV modules. These losses are significant because they affect the resulting radiation reaching the PV module cell.
Solargis Evaluate employs the Martin and Ruiz model to estimate angular reflection losses. This model uses an angular loss coefficient, which is estimated by Solargis based on the properties of the PV module surface, particularly its soiling.
Spectral correction
Spectral correction is an essential step in the simulation process, and Solargis Evaluate uses the Lee & Panchula model for this purpose.
The specific intensity of the spectral responsivity correction depends on two key atmospheric factors:
Air mass: This represents the optical path length of sunlight through the Earth's atmosphere. It increases as the Sun's position moves closer to the horizon, affecting the spectral distribution of sunlight.
Precipitable water content: This refers to the total amount of water vapor present in a column of the atmosphere.
Electrical simulation
Single diode model
The Single diode equivalent circuit model, also known as De Soto's "Five Parameter" model, is used in Solargis Evaluate to simulate the conversion of solar irradiance into electricity within PV cells.
The Single diode model requires five key parameters to describe the current-voltage (IV) curves of PV cells. These parameters are typically acquired at Standard Test Conditions (STC):
Modified ideality factor
Diode saturation current
Light current (photocurrent)
Series resistance
Parallel resistance
These parameters, along with the Global tilted irradiance (GTI) and cell temperature, are used to generate the IV curves for each PV cell.
Advantages of the model
The use of the Single diode model in conjunction with ray tracing allows for detailed analysis of shading conditions at the level of individual PV cells. This enables precise simulation of the electrical performance of PV modules under various environmental conditions.
DC losses
DC losses in the direct current path from PV modules to inverters are a crucial factor in energy yield simulations. These losses occur due to the electrical resistance in the DC combiner boxes and DC cables.
Setting DC cable losses in Solargis Evaluate
In Solargis Evaluate, DC cable losses are set as a percentage value. This percentage represents the total DC electrical loss in the entire DC network at reference conditions, typically Standard Test Conditions (STC).
Default value used in Solargis Evaluate: 2%.
Performance of the inverter
An inverter is an electronic device that converts direct current (DC) generated by PV modules into alternating current (AC). The output can be either one-phase or three-phase voltage. In Solargis Evaluate, the inverter implementation consists of two main stages:
Maximum power point (MPP) calculation: The Maximum Power Point (MPP) calculation utilizes the IV curve at the inverter input, which is derived from the previous simulation steps.
DC/AC conversion model: Solargis Evaluate supports calculations for both one-phase and three-phase PV inverters within AC electrical networks.
Auxiliary losses
Auxiliary losses in a PV power plant are caused by various equipment that consumes energy, including systems for protection, monitoring, heating or cooling (depending on the climate zone), lighting, module tracking, and other energy-consuming devices.
The auxiliary losses can be divided into two categories:
Night losses: These are continuous constant losses measured in watts (night constant losses).
Day losses: Can represent continuous constant losses and proportional losses.
Default values used in Solargis Evaluate:
Parameter | Default value |
|---|---|
Night constant losses | 0.025% of total installed DC power |
Day constant losses | 0.025% of total installed DC power |
Day proportional losses | 5 W/kW |
AC losses
AC losses in a PV power plant occur in the AC cabling and combiner boxes, affecting the transmission of electricity from the inverters to the grid connection point.
Calculation of AC losses
In Solargis Evaluate, required AC cable losses are set as a percentage value. This percentage represents the total AC electrical loss across the entire AC electrical network at reference conditions, typically Standard Test Conditions (STC).
Default values used in Solagis Evaluate:
Parameter | Default value |
|---|---|
Between inverter(s) and inverter transformer (Distribution step-up transformer) | 1% |
Between inverter transformer and power transformer(s) | 0.5% |
Between power transformer(s) and grid connection | 0.05% |
Transformer losses
Transformers are essential devices in PV power plants, used to change the voltage level from the AC side of inverters to the desired voltage level for connection to the utility grid. In Solargis Evaluate, we utilize our proprietary Transformer model, which could be segregated into two sub-models:
Variable losses model: Includes iron (no-load) losses and copper (load) losses.
Constant losses model: Transformer losses are represented as a percentage reduction of electrical power at the primary side of the transformer.
Default values used in Solargis Evaluate:
Variable losses model
Inverter transformer (distribution step-up transformer) | |
Rated no-load losses | 0.15% of rated apparent power |
Rated full-load losses | 1.2% of rated apparent power |
Power transformer | |
Rated no-load losses | 0.08% of rated apparent power |
Rated full-load losses | 0.28% of rated apparent power |
Constant losses model
Standard transformer | 1% of rated apparent power |
High-efficiency transformer | 0.9% of rated apparent power |
System unavailability losses
System unavailability losses quantify the electricity losses incurred due to the shutdown or power output limitation of a PV power plant or its components. These losses can be categorized into two main types:
Technical events: Incurred due to internal reasons (equipment failures or scheduled maintenance work) and external reasons (Grid connection issues).
Weather-incurred events: Caused by the snow coverage of PV modules.
Default values used in Solargis Evaluate:
Parameter | Default value |
|---|---|
Internal unavailability losses | 0.5% |
External unavailability losses | 0% |
Snow losses | 0% |
The implementation of technical losses is based on IEC Technical Specification 61724-3, which outlines energy evaluation methods for photovoltaic systems.
Long-term degradation
The performance of PV modules and other components decreases over time, and long-term degradation serves as a measure of this performance reduction. Typically, PV components experience more rapid degradation in the initial years of their lifespan.
Based on existing in-field experiences from commercial projects, the long-term annual performance degradation for well-manufactured modules may be approximately:
0.8% for the first year
0.5% for subsequent years
This assumption includes initial degradation of the modules.
Solargis Evaluate allows users to set:
Degradation losses for the first year: Users can specify the degradation losses applicable during the first year of operation.
Annual degradation losses for following years: Users can define the annual degradation losses for subsequent years.
The simulation outputs
The result of the simulation is the power output of the PV power plant, referred to as PVOUT. It is quantified both in absolute numbers, and as specific PVOUT, normalized to installed capacity of the power plant.
The simulation results are visualized in numerous charts and tables in the Solargis Evaluate Analysis section. The data are categorized and segregated into different units with a data interpretation guide included for every presented value, making the data easy to understand and interpret.
.png?sv=2022-11-02&spr=https&st=2025-03-22T08%3A49%3A44Z&se=2025-03-22T09%3A11%3A44Z&sr=c&sp=r&sig=ofF0evyDQTHAdFNT1vYNh28o%2FTHINpwTRtY%2FDJgMxrM%3D)
Figure 6: Visualization of PV energy system losses presented in Solargis Evaluate application.
Further reading
A new simplified version of the Perez diffuse irradiance model for tilted surfaces. Richard Perez, Robert Seals, Pierre Ineichen, Ronald Stewart, and David Menicucci.
All-weather model for sky luminance distribution—Preliminary configuration and validation. R. Perez, R. Seals, and J. Michalsky.
Updating the PSA sun position algorithm. Manuel J. Blanco, Kypros Milidonis, and Aristides M. Bonanos.
Comparison of ray tracing rendering technique with ground measurements for improved solar radiation modeling. L. Dvonc, P. Orosi, T. Cebecauer, and B. Schnierer.
Simple Model for Predicting Time Series Soiling of Photovoltaic Panels. M. Coello and L. Boyle.
Calculation of the PV modules angular losses under field conditions by means of an analytical model. Martin N. and Ruiz J.M.
Spectral correction for photovoltaic module performance based on air mass and precipitable water. Lee, M., & Panchula, A.
Improvement and validation of a model for photovoltaic array performance. W. De Soto, S.A. Klein, and W.A. Beckman.
Lambert W function for applications in physics. D.Veberič.