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.
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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 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
Description: The core of this model is the internal coefficient matrix, which is well-documented and validated. It is referred to as the Perez sky diffuse irradiance model coefficients. Multiple versions of these coefficient sets exist, making it important to distinguish between them.
Usage in Solargis Evaluate: This model is used to simulate GTI Energy systems, providing Time Series of GTI.
Description: This model estimates the relative luminance distribution of the sky dome using sky brightness and clearness parameters. This model is particularly suited for raytracing light simulations, providing a more detailed representation of diffuse shadows on PV modules.
Usage in Solargis Evaluate: 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.
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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 several steps.
Step 1: Direct illumination calculation
Methodology: Each PV cell is covered with multiple sample points. At these points, the algorithm determines whether they are in direct sunlight or shaded. If a cell is partially shaded, a shadow ratio is calculated as a fraction.
Purpose: This step ensures accurate accounting of direct solar radiation on each cell, which is crucial for calculating the overall energy yield.
Step 2: Diffuse radiation calculation
Methodology: A grid of sampling points is placed over the PV module table. At each point, multiple rays are generated in random directions and traced through the 3D simulation scene. The direction of rays at the PV module surface is recorded for angular loss calculations.
Reflection handling: Each intersection of a ray with an object in the scene is treated as a Lambertian reflection, attenuating the ray's power proportionally to the surface's albedo. If a ray hits the sky, its brightness is calculated using the Sky irradiance model.
Termination criterion: The probability of terminating the path tracing increases as the ray's power weakens due to multiple reflections.
Step 3: Post-processing
Denoising: The diffuse radiation values are denoised to eliminate mismatches between substrings of a PV module and modules in a string. This ensures consistent power output calculations.
Resampling and summation: The diffuse radiation is resampled per cell and summed with the direct radiation to obtain the final Global tilted irradiance (GTI) for each cell.
Step 4: Implementation and validation
Applicability: This ray tracing method is implemented for both fixed-mounted PV modules (monofacial or bifacial) and trackers with various tracking strategies.
Validation: The algorithm has been validated against the Bifacial Radiance software by NREL and ground measurements of GTI, ensuring its accuracy and reliability.
For a visual comparison, refer to the image showing the rear GTI spatial distribution from Solargis simulations versus results from the NREL bifacial radiance tool at locations in Saudi Arabia and Finland.
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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.
Application of Soiling Losses
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 proprietary soiling model
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.
Basis of the Solargis soiling model
The Solargis soiling model is based on the Coello and Boyle soiling model, widely used in the solar energy industry. This model uses the following inputs:
Atmospheric particle concentrations: PM2.5 and PM10 concentrations are considered.
PV module configuration: The tilt of the PV modules affects soiling accumulation.
Deposition velocity: This parameter is crucial for estimating mass accumulation and has been parametrized by Solargis based on internal research.
Calculation of transmission loss
The mass accumulation of soiling is used to calculate the transmission loss to GTI. This loss is further adjusted by cleaning events, which can be either natural (from precipitation) or manual. For natural cleaning, precipitation events exceeding a defined threshold are considered.
Processing time depends on the number of segments in the energy system. More segments result in longer estimation computations.
Expression of soiling loss
The transmission loss (TL) can be optionally expressed as a soiling ratio (SR), where SR = 1 - TL. The soiling ratio indicates the percentage of light that passes through the soiled PV modules.
Comparison with other software
Soiling losses are accounted for similarly in other solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Dirt, dust, and soiling | Accounted as an irradiance loss (GTI). |
Solargis Evaluate | Pollution losses | Accounted as an irradiance loss (GTI). |
PVsyst | Soiling loss factor | Accounted as an irradiance loss. |
SAM (NREL) | Soiling | Applies to both the beam and diffuse components of the POA irradiance (page 33). |
SolarFarmer (DNV) | Soiling |
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.
Mechanism of Angular Losses
Incidence angle dependency: At an incidence angle of 0 degrees from the surface normal vector, there is no angular loss. However, as the incidence angle increases towards 90 degrees, the loss increases, reaching a complete (100%) loss at 90 degrees.
Factors influencing accuracy: The accuracy of angular reflectivity loss calculations depends on the cleanliness and specific properties of the module surface, such as antireflection coatings and texture.
Model used in Solargis Evaluate
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. The coefficient affects the shape of the angular loss curve, ensuring that the model accurately accounts for the surface characteristics of the PV modules.
Comparison with other software
Angular reflection losses are accounted for similarly in other solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Angular reflectivity | Modeled by the same model as in Solargis Evaluate (Martin & Ruiz). |
Solargis Evaluate | Angular reflectivity | Angular reflection losses model by Martin & Ruiz. |
PVsyst | IAM factor on global | Custom IAM, ASHRAE, Fresnel or Fresnel with anti-reflective coating, depending on user settings. |
SAM (NREL) | Reflection (IAM) | According to IEC 61853 model: standard glass or glass with anti-reflective coating (page 63). |
SolarFarmer (DNV) | Incidence Angle Modifier | ASHRAE, CIEMAT, Fresnel normal glass, Fresnel anti-reflective coated glass, Custom IAM, depending on settings. |
Spectral correction
Spectral correction is an essential step in the simulation process, as it adjusts for the differences in spectral response between silicon and cadmium telluride (CdTe) PV modules. Solargis Evaluate uses the Lee & Panchula model for this purpose, which is also known as the First Solar spectral correction model.
Factors Influencing Spectral Correction
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.
Both air mass and precipitable water content influence the spectral distribution of sunlight reaching the Earth's surface, which in turn affects the spectral response and output of PV modules.
Comparison with other software
Spectral correction is also implemented in other solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Spectral correction | Modeled by the same model as in Evaluate (Lee & Panchula). |
Solargis Evaluate | Spectral correction | First Solar spectral correction (Lee & Panchula) |
PVsyst | Spectral correction | CREST, SANDIA, or First Solar spectral correction model, according to user selection. |
SAM (NREL) | N/A | According to IEC 61853 model: SANDIA effective air mass (page 52). |
SolarFarmer (DNV) | Spectral | First Solar spectral correction (Lee & Panchula). |
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.
Model parameters
The Single diode model requires five key parameters to describe the current-voltage (IV) curves of PV cells. These parameters are typically defined 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.
Calculation process
GTI and cell temperature: The GTI is the output of the optical simulation, while the cell temperature is determined using the model proposed by Duffie and Beckman. Solargis has modified this model to account for module efficiency dependency on module temperature.
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IV curve generation: The five model parameters, GTI, and cell temperature are used to generate the IV curves for each PV cell. The Lambert W function by Darko Veberic is utilized to simulate the diode part of the cell model.
Combination of cell IV curves: The IV curves of individual cells are summed up into submodule IV curves. These are then combined with the characteristics of bypass diodes (modeled as ideal P-N junction diodes) to form a single PV module. The resulting IV curves of the PV modules are summed to string characteristics and connected to DC cabling.
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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.
Comparison with other software
The Single Diode Model is widely used in solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Conversion to DC | Single diode model using De Soto's “Five parameter” model to derive PV module characteristics. |
Solargis Evaluate | Single diode model | Single diode model using De Soto's “Five parameter” model to derive PV module characteristics. Temperature model with steady-state NOCT and wind speed considered. |
PVsyst | PV conversion | Single diode model described by Beckman. The temperature model is an energy balance model with air circulation and wind velocity consideration. |
SAM (NREL) | N/A | Simple efficiency module model, CEC performance model, IEC 68153 single diode model, Sandia PV array performance model (page 49), according to selection. |
SolarFarmer (DNV) | Modeling Modeling correction Temperature | Modelling – PVsyst single diode model. Temperature – Faiman model (including wind effect). |
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.
Components and Calculation
Components: The DC electrical network includes DC combiner boxes (also known as junction boxes) and DC cables. These components are modeled using electrical resistance, which includes the resistivity of contacts in strings, connectors, and cables.
Calculation method: DC losses are calculated as I2R losses, where I is the electrical current flowing through the components, and R is the resistance.
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).
Solargis Evaluate provides a default value of 2% for DC cable losses. Users can adjust this value based on specific project requirements.
Comparison with other software
DC losses are accounted for similarly in other solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Mismatch and cabling in the DC section | DC current path electrical loss at reference (STC) conditions. |
Solargis Evaluate | DC losses | DC current path electrical loss at reference (STC) conditions. |
PVsyst | Ohmic wiring loss | DC wiring losses. |
SAM (NREL) | DC wiring | DC electrical losses factor consisting of several loss types (page 67). |
SolarFarmer (DNV) | DC collectors | DC collection network resistance. |
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:
Process: The Maximum Power Point (MPP) calculation utilizes the IV curve at the inverter input, which is derived from the previous simulation steps. Multiple Maximum Power Point Tracking (MPPT) circuits are simulated to continuously adjust the operating points of PV modules, ensuring maximum power delivery.
Inputs: The inputs for MPP calculation include the P-V characteristics, calculated as P=V×I. If the inverter's power output needs to be limited due to its capabilities or grid restrictions, the input circuits regulate power from the DC array accordingly.
Model used: The Sandia inverter model calculates AC output power from DC input power by representing the inverter with a set of parameters. These parameters are used to determine the efficiency curve under different operating conditions, specifically varying DC input power and voltage levels. Solargis Evaluate supports calculations for both one-phase and three-phase PV inverters within AC electrical networks.
Key features of inverters
Power factor (pf) setting: The power factor (cos(φ)) determines the ratio of active to reactive power at the inverter outputs and at the grid injection point. The calculated apparent power at the inverter output will not exceed the maximum apparent AC power specified in the inverter's datasheet.
Power grid limitation: If required by grid operators, a power limit can be set at the inverter to control the injected electrical active power at the grid injection point.
Night power consumption: An important feature of inverters is their night power consumption, usually specified in watts in the technical datasheet. This represents standby mode consumption when GHI = 0 and DNI = 0.
Electrical power composition
In Solargis Evaluate, electrical power within the AC network consists of both active and reactive power. The resulting complex AC output of the inverter can be expressed as
S=P+i*Q=S*cos(φ)+i*S*sin(φ)
where:
S is the complex value of apparent electrical power,
P is active electrical power,
Q is reactive electrical power,
φ can be calculated as φ=arccos(pf)).
The level of reactive power at the injection point depends on electrical components' parameters and user-defined power factor settings.
Comparison with other software
The performance and modeling of inverters are comparable across various solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Inverters (DC/AC) conversion | SANDIA model for grid-connected PV inverters. |
Solargis Evaluate | Inverter losses: Power limitation and DC/AC conversion | SANDIA model for grid-connected PV inverters, checks of output power limitation, clipping losses, and night power consumption. |
PVsyst | Inverter loss: During operation (efficiency) Over nominal inv. power/voltage Due to max. input current Due to the power/voltage threshold | Single or three efficiency inverter profiles built from maximum, CEC or EU efficiency, considering operating limits, clipping correction, multi-MPPT.
|
SAM (NREL) | Inverter: Power clipping Power consumption Nighttime consumption Efficiency | SANDIA inverter model or Inverter part load curve model, checks of MPPT or power clipping, string voltage check (page 72). |
SolarFarmer (DNV) | Inverter: Min/Max DC voltage Min DC power Max DC current Efficiency Max AC power Overpower shutdown Inverter tare | Inverter models for Maximum and Weighted Efficiencies or CEC Measured Efficiency Curves, identification of operation outside inverter MPPT tracking area.
|
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. This category also includes outdoor lighting, security systems, and facilities for maintenance staff.
Types of auxiliary losses
Auxiliary losses are applied during different times of the day:
Night losses: These are continuous constant losses measured in watts (night constant losses). They are considered only when the power on the AC side of the inverter(s) is equal to or less than zero.
Day losses: These can be categorized into two types:
Continuous constant losses: Measured in watts (day constant losses).
Proportional losses: These are proportional to the inverter output power expressed in watts per kilowatt (W/kW) (day proportional losses). Users can set a threshold for active power at the AC side of the inverter(s) to define when each type of loss should be considered.
Default values:
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 |
Comparison with other software
Auxiliary losses are similarly accounted for in various solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | N/A | N/A |
Solargis Evaluate | Auxiliary losses | Day and night consumption of PV power plant equipment, constant or proportional to generation. |
PVsyst | Auxiliaries (fans, other) | Day and night consumption of energy for managing the system, constant or proportional to generation. |
SAM (NREL) | N/A | N/A |
SolarFarmer (DNV) | N/A | N/A |
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. These losses are modeled using resistance, similar to DC circuits, and do not take into account the inductive or capacitive effects of the cables or combiner boxes.
AC cables: Losses occur on both the low-voltage side and the medium (high, extra high) voltage side, up to the grid connection point with the electricity meter.
Combiner boxes: All combiner boxes in the path of the AC current also contribute to these 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:
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% |
Comparison with other software
AC losses are similarly accounted for in various solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | AC cable losses | AC current path electrical loss at reference (STC) conditions. |
Solargis Evaluate | AC cable losses (low, medium, high voltage) | AC current path electrical loss at reference (STC) conditions, separated for low, medium, and high voltage circuits/connections. |
PVsyst | AC ohmic loss MV line ohmic loss HV line ohmic loss | AC current path electrical loss at reference conditions, related to given reference power (array at STC multiplied by the inverter's efficiency or nominal output of inverters without temperature correction). |
SAM (NREL) | AC wiring Transmission loss
| AC electrical losses factors for AC circuits and transmission line (page 84). |
SolarFarmer (DNV) | AC collectors | AC collection network resistance |
Transformer model
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, two different loss models are implemented for transformers:
Variable losses model
Constant losses model
This model accounts for transformer losses driven by their internal properties, which include:
Iron losses (no-load losses): These losses occur at almost a constant value while the transformer is connected to voltage. Iron losses depend non-linearly on the voltage level and appear negative at night.
Copper losses (load losses): These losses vary based on the transformer's load level and are a variable component of total losses.
Calculation method: Transformer losses are applied at all time steps in the temporal resolution of the simulated time series. The calculation for transformer loss at each time step relies on rated values (e.g., rated apparent power, rated voltage on primary and secondary sides, rated no-load and full-load losses) and actual operational conditions (e.g., electrical voltages and currents).
Default values:
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 |
In this model, transformer losses are represented as a percentage reduction of electrical power at the primary side of the transformer.
Default values:
Standard transformer | 1% of rated apparent power |
High-efficiency transformer | 0.9% of rated apparent power |
Modeling capabilities
Solargis Evaluate enables modeling for:
Inverter transformers: These represent distribution step-up transformers that typically change voltage from low voltage (LV) to medium voltage (MV).
Power transformers: These typically function between medium voltage (MV) and high voltage (HV).
The same modeling approaches are applied for transformers across all voltage levels, from LV to extra high voltage (EHV).
Comparison with other software
Transformer performance and loss modeling are comparable across various solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Transformer losses | Transformer efficiency modeled by simple losses |
Solargis Evaluate | Transformer losses (LV/MV, MV/HV) | Variable or constant losses model for LV/MV (named inverter transformer) or MV/HV transformers (named power transformer), considering copper and iron losses. |
PVsyst | Medium voltage transformer loss High voltage transformer loss | Transformer losses model considering copper and iron losses with possible night disconnection. |
SAM (NREL) | Transformer loss
| Transformer model for distribution or substation transformers with load and no-load loss, considering power factor of 1 (page 84). |
SolarFarmer (DNV) | Transformer | Transformer model with considering of load and no-load loss. |
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
Weather-incurred events (snow)
Internal reasons: These include maintenance and failures of components, leading to Internal unavailability losses. This type of loss is set as a percentage reduction of produced electrical power.
External reasons: These losses arise from shutdowns or power limitations caused by factors outside the control of the PV power plant, resulting in external unavailability losses. A power grid limit is set, leading to a reduction in injected electrical active power at the energy delivery point.
Both internal and external unavailability losses apply to selected time steps in the temporal resolution of the simulated time series within internal calculations.
Default values:
Parameter | Default value |
|---|---|
Internal unavailability losses | 0.5% |
External unavailability losses | 0% |
A special case of system unavailability losses occurs due to snow events. Even a thin layer of snow can block nearly all irradiation, rendering DC generation insufficient for inverter operation. In such cases, the PV system is considered unavailable due to snow on the PV modules, as the non-production is not caused by a lack of radiation or technical unavailability events. This type of loss is applied as monthly losses.
Default values:
Parameter | Default value |
|---|---|
Snow losses | 0% |
The implementation of technical losses is based on IEC Technical Specification 61724-3, which outlines energy evaluation methods for photovoltaic systems.
Comparison with other software
The treatment of system unavailability losses is comparable across various solar simulation software:
Technical events
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Technical availability | Including technical operational issues caused by PV power plant outages and grid unavailability. |
Solargis Evaluate | Unavailability losses: Internal External | PV power production losses due to: Internal issues – operational, malfunctions, service works on the PV power plant External issues – distribution grid unavailability. |
PVsyst | System unavailability
| Unavailability of the system due to system failures or maintenance stops. |
SAM (NREL) | AC availability and curtailment
| Operating losses are imposed on the system by factors other than the solar resource and system’s design, such as forced, scheduled, and unplanned outages or other factors that reduce the system’s AC power output (page 84). |
SolarFarmer (DNV) | Grid availability | The Grid availability effect quantifies the amount of energy lost because the electrical grid to which the PV plant is connected is not available to accept power. |
Weather-incurred events
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Losses due to snow | Monthly effect of snow cover on PV modules |
Solargis Evaluate | Snow losses | Reduction of energy generation due to snow blocking the surface of PV modules, the behavior of PV system is like PV system unavailability. |
PVsyst | Soiling loss factor
| Considered monthly as a part of the soiling loss factor. |
SAM (NREL) | DC snow losses
| Calculates a loss caused by the snow that applies to the subarray’s gross DC power output (page 67). |
SolarFarmer (DNV) | Soiling | Included in the soiling parameter (monthly) |
Long-term degradation
The performance of PV modules and other components decreases over time, and 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.
PV simulations on historical time series are performed without considering degradation losses. Instead, degradation losses are applied after the PV simulation to illustrate their impact on electricity production over a 25-year lifespan of the PV system. Solargis Evaluate provides insights into the effects of long-term degradation on:
PVOUT specific: Measured in kWh/kWp
PVOUT total: Measured in GWh
Performance ratio (PR)
Comparison with other software
The treatment of long-term degradation is comparable across various solar simulation software:
Software | Parameter name | Notes |
|---|---|---|
Solargis Prospect | Long-term degradation | Similar approach as in Solargis Evaluate |
Solargis Evaluate | Long-term degradation | User-defined options for first-year and annual degradation losses |
PVsyst | Degradation factor | User-defined settings for module degradation |
SAM (NREL) | Degradation | Includes provisions for long-term performance decline |
SolarFarmer (DNV) | Degradation | Accounts for long-term performance impacts |
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.
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.