PV energy yield simulation

In this document

We will explain the importance and key concepts of the solar energy yield simulation and present the Solargis approach.

Solar energy yield simulation

Solar energy yield simulation is a key to estimating the performance of photovoltaic (PV) systems. It includes optical and electrical models to estimate how much electricity a solar photovoltaic system can generate at a specific location. Accurate simulations help stakeholders make informed decisions regarding system design, investment, and operational strategies.

Key concepts

PV simulator is used to generate solar power production data. Different use cases require varying levels of accuracy and speed. For instance, bankable energy yield simulations provide full evaluation resulting in comprehensive detailed results. The prefeasibility studies in early stages of a PV project development benefit from quick, indicative simulations with high level results. The PV design optimization efforts focus on small segments of the power plants with high level of detail at high accuracy. PV power simulation for solar power forecasting and monitoring applications necessitates fast simulations with reasonable accuracy. Tailored simulation approaches are essential to meet needs of different services for a PV project.

The yield simulation process involves three main steps: input data, energy modelling, and outputs. The input datainclude solar, meteorological, and environmental parameters, parameters describing the geography of a site, and PV system configuration. Optical part of the model focuses on modelling of Global Tilted Irradiance (GTI), known also as Plane of Array irradiance. To model irradiance received by PV modules direct and diffuse components of Global Horizontal Irradiance (GHI) are used. Electrical part focuses on conversion of irradiance-to-electricity, and simulation of energy conversion at the level of PV modules, strings, inverters, transformers up to the point of connection to the grid, resulting in the photovoltaic power output (PVOUT). In electricity modelling, air temperature (TEMP), ground surface albedo (ALB) and wind speed (WS) are used. The outputs include time series of the electrical output at different levels of the PV system. Aggregated statistics is computed assuming modelling uncertainty for the needs of financial modelling. In this stage, typically, values at P50 and P90 probabilities of exceedance are considered, under various scenarios.

A a standard, two types of input data products are used in a PV simulation: (1) Time Series (TS) and (2) Typical Meteorological Year (TMY). Various solar irradiance calculation techniques, including solar radiation splitting model, ray tracing, isotropic/anisotropic sky model, and view factor model. The choice of models, accuracy and granularity of the input data and its processing significantly affect the accuracy of the results. It is important to quantify uncertainties involved in the simulation steps.

While solar radiation is a fuel for solar power plants, local geography factors influence performance efficiency and risks of increased system performance degradation and damage. The factors involved are terrain, land cover, humidity, temperature, wind, ground albedo, precipitation (rainfall), snow, pollution, dust and soiling and others.

Understanding the factors influencing energy yield—from input data to modeling techniques—solar professionals can improve the system design and optimize performance for sustainable return on investment with reduced uncertainty and risks.

Best practices in use of input data and energy yield simulation

Use Reliable Data: Use site-specific high resolution data products covering long history. Choose service of trustable suppliers, long track record of delivery for global projects, and offering world-class customer support. Consider suppliers of data and software solutions with long record of research and development of the underlying models, systematically adjusting to the industry needs.
Select Appropriate Datasets: Choose between TMY and TS datasets based on project requirements; TMY is suitable for initial assessments in early stage of project development or fast evaluation of multiple design choices, and TS is the only choice for the bankable detailed analysis in due diligence process.
Uncertainty: Understand the uncertainty inherent in the input data and simulation models and evaluate its impact and project risks.
Monitor Performance: Track actual energy output against simulations to identify discrepancies and optimize system performance through maintenance adjustments.

The Solargis approach

At Solargis, we focus on high-quality algorithms and models in our PV simulation chain, to ensure accurate energy yield assessments tailored to specific use cases. We utilize different simulation approaches, including those for site selection and prefeasibility, project development and due diligence, performance monitoring and solar power forecasting.

By systematic monitoring quality of the input data and modeling steps, we maintain high quality of our simulations to provide reliable insights for solar energy projects. We understand and quantify uncertainties to effectively assess their impact on project outcomes and risks, ensuring we exceed industry standards for accuracy and reliability.

Application	Key features
Prospect app	Simple, very fast simulation for preliminary assessment
Evaluate app	A new generation GTI and PV simulator used for simulations of extensive PV energy systems in complex geographies. The simulator uses ray tracing, considering high level of detail, including far and near shading of the PV module field.
Consultancy	Ultimate solution simulator for special projects and customized needs.
Monitor, Forecast, Analyst, web services	More empirical and fast simulator used in services where fast and repetitive computation at good accuracy is important
Solarmaps, map services	Simple simulator for fast computation based on raster data

Prospect application simulation

This fast empirical PV simulator, used in the Prospect application, and Global Solar Atlas, runs on a statistically aggregated data, and provides energy yield estimates for a specific location assuming simple rectangular photovoltaic design.

It utilizes a view factor model to simulate shading effects between panels, assuming regularity in their arrangement. The simulator calculates both GTI and PVOUT, allowing users to quickly assess potential energy yields across various scenarios.

Evaluate application simulations

Solargis Evaluate web application, specifically the PV energy system simulator, employs our latest and most advanced simulation methods. It supports two types of energy systems, each using a distinct simulation approach:

GTI simulator for theoretical calculation within GTI energy systems
PV simulator for photovoltaic (PV) energy systems.

GTI simulator

The GTI simulator is designed as a fast and simple tool for calculating Global Tilt Irradiance (GTI) without involving electrical simulations. It focuses exclusively on horizontal and inclined planes of PV module, lacking support for terrain variations, and is limited to simple layouts without bi-facial PV module capabilities.

GTI energy system simulation inputs

Key input data types to the GTI simulator:

Solar and Meteorological Time Series: Essential are GHI and DNI solar radiation inputs.
Site Conditions: This includes location latitude and longitude.
PV system configuration:
- Mounting details: Select from several PV module mountings.
- Azimuth: Orientation of the GTI system to the cardinal points.
- Spacing: Distance between PV modules.

GTI energy system simulation processing

The processing consists of an optical simulation only, which includes calculations of the light interaction with surface of a PV module:

Isotropic Sky Model: GTI model assumes uniform distribution of solar radiation in the sky dome.
View Factor: Calculates the proportion of the sky visible from a given point on the panel, which enables quick shading assessment.

GTI energy system simulation outputs

The GTI simulator generates several outputs, including:

GTI Theoretical: Provides GTI inoput to the front side of a PV modul ignoring possible shading.

PV simulator

The Solargis in-house developed PV simulator is our most advanced simulator for solar energy yield calculations, utilizing the latest physical methods, suite of high resolution data products. It features an optical simulation based on the Perez All-weather sky model and ray-tracing simulation and incorporates electrical simulations that model the behavior of the system components, such as PV modules, strings, inverters, and transformers. It is capable of handling complex terrain, local objects, and utilizes advanced backward ray tracing (path tracing) calculation techniques to provide output for detailed and accurate photovoltaic performance evaluation.

PV energy system simulation inputs

The PV energy system simulator processes the following inputs:

Solar and Meteorological Time Series and TMY data: Essential for assessing solar radiation and climate conditions.
Site Geographical Conditions: Includes location coordinates, terrain, ground albedo, soiling and snow losses, and horizon.
PV System Configuration:
- PV Configuration from energy system designer: Details about the photovoltaic system setup, e.g., module or table layout.
- Components: Specifications of PV modules, inverters, and transformers.
- Electrical Components Layout: Electrical connections of the components, including losses.
Other Factors: Considerations such as self-consumption, degradation rates, and system unavailability.

PV energy system simulation processing

Optical simulation

Sky Model: We utilize the anisotropic Perez All-Weather model to achieve a smooth gradation between sky components. This approach allows us to enhance the granularity of our results, which is particularly beneficial for our path-tracing method.
Backward Raytracing (Path-tracing): We employ path tracing to accurately model the interaction of light with surfaces. By tracing individual rays as they reflect, refract, and absorb, we can perform a detailed analysis of shading and irradiance on PV modules. This method, while computationally intensive, provides high accuracy, especially in complex shading scenes.
Angular Losses: We calculate angular losses, also known as the incidence angle modifier (IAM), to assess how the angle of incoming light affects irradiation on PV modules.
Backtracking: We implement the backtracking algorithm in configurations with sun trackers to minimize inter-row shading effects. By adjusting the angles of the trackers, we avoid extreme angles to prevent shading between rows, ensuring better overall performance.
Spectral Correction: We apply spectral correction to account for variations in solar spectrum based on air mass and precipitable water. This step enhances our assessments of photovoltaic module performance, ensuring our results are as accurate as possible.

Electrical simulation

Single Diode Model: We utilize the Single Diode Model to generate the current-voltage (IV) curve of a solar cell based on Global Tilt Irradiance (GTI) and temperature. This model enables an understanding of the performance of each cell, considering its specific shading conditions.
IV Curve Summing: After generating the IV curve for a single cell, we sum the IV curves of all cells connected to PV modules, strings and inverters into a single curve. This summation takes into account the characteristics and conditions of each cell and the electrical wiring of the system, providing a comprehensive view of the system's performance.
Inverter Model: We implement the Inverter Model to calculate the alternating current (AC) output power based on direct current (DC) input power. Specifically, we use the SANDIA inverter model, which allows for accurate conversion calculations and performance assessments of photovoltaic systems.
Transformer Model: Our Transformer Model simulates both inverter transformers and power transformers (if applicable). We employ Solargis's proprietary transformer model to ensure accurate representation of transformer behavior within the system.
Grid Model: At the grid connection level, we apply both static limits (set for the entire simulation) and dynamic limits (adjusted per time slot). These limits are distributed across all inverters by setting maximum power thresholds for the Maximum Power Point Tracking (MPPT) algorithm, ensuring optimal performance in real-time conditions.

PV energy system simulation outputs

The PV energy system simulator provides the following outputs:

GTI Outputs: We provide Global Tilt Irradiance (GTI) values per time slot, detailing various stages of losses—from theoretical estimates to spectrally corrected values.
PV Output: The simulation delivers photovoltaic output data, including total yield and specific yield per time slot, reflecting various stages of losses, including electrical losses due to snow and soiling, up to the grid connection.
Uncertainty Analysis: We include an assessment of uncertainty associated with the solar radiation and meteorological parameters, including the one in individual PV simulation steps, helping users understand the reliability of the results.
Analytical Outputs: The Evaluate application offers a range of analytical outputs, including graphs and charts that visualize key performance metrics and trends.
Data Deliverables: Comprehensive reports and data sets are provided, enabling further analysis and integration into other systems or reports.

Validation & data integrity

Importance of validation

Validation is critical to ensure the accuracy and reliability of simulation results. It helps identify discrepancies between the real performance and the model outputs. We provide validation of individual models and computing chains against the theoretical assumptions and other physical models. Solargis performs validation by comparing simulation outputs with real-world measurements from operational PV systems. This involves statistical analysis to assess the accuracy of predictions and refine models used in the simulation. The validation process helps identify the accuracy of individual steps in PV simulation.

Data integrity

To maintain data integrity, Solargis employs rigorous data management practices, including regular quality control of solar and meteorological data, validation checks, and error correction protocols. This ensures that inputs used in the Pv simulation are accurate and up-to-date. For instance, simulation requires consistent time zone information for input data to avoid time shifts that can lead to incorrect results.