Don't Miss Tree Shadows! Leveraging 3D Models Created with LRTK for PVsyst Simulations

Shadow Analysis for Solar PV Plants and Its Importance

In the design of solar PV plants, it is critically important to accurately assess the impact of shadows from surrounding trees, buildings, and terrain. Shadows on PV panels not only reduce power generation but can also cause additional losses (mismatch losses) by disrupting the output balance of strings (groups of series-connected panels).

Especially in locations like Japan where forests and buildings are nearby, generation losses caused by shadows from adjacent woods or nearby hills cannot be ignored. If the influence of surrounding shadows is overlooked during simulation, actual generation may fall far short of projections, potentially compromising business plans. Therefore, shadow analysis requires meticulous consideration that "does not miss tree shadows."

Challenges in Conventional Shadow Analysis

There have been several challenges in conventional shadow analysis for solar power. First, collecting information about the objects that cause shadows is not easy. Accurately determining the height and position of trees or buildings near a plant traditionally required on-site surveying with instruments or estimates from design drawings and topographic maps. These methods are labor-intensive and may fail to cover all obstacles. For example, some small trees or off-site structures may not be reflected in drawings and can be overlooked.

Second, even if data can be obtained, the effort required to incorporate it into simulations is a challenge. Inputting shading factors into simulation software such as PVsyst generally requires 3D modeling or specifying coordinates. Manually modeling nearby objects in 3D by designers is tedious, and oversimplification leads to loss of accuracy. As a result, conventional shadow analysis often ended up "considering only distant mountain shadows from simplified horizon data and overlooking nearby tree shadows" or "examining inter-row shading between panels but not the influence of surrounding structures." To solve these issues, a new approach was needed to digitize shading causes with high accuracy and comprehensive coverage and reliably import that data into simulations.

Detailed Shadow Simulation with PVsyst

PVsyst, a widely used global solar PV simulation software, provides powerful functions to evaluate shading impacts in detail. PVsyst allows construction of a plant's 3D scene (layout) and can calculate how shadows fall on panel receiving surfaces at each time along the sun’s trajectory. Therefore, with accurate 3D models as input, quantitative calculation of losses from surrounding structures and trees is possible. In detailed settings, PVsyst can not only compute the fraction of irradiance blocked but also perform electrical mismatch calculations that consider imbalances in string voltage and current caused by shading. This enables evaluation of additional losses resulting from shifts in each string’s maximum power point due to shading, yielding simulations closer to real generation behavior.

However, to fully utilize PVsyst’s detailed shading analysis features, equally high-accuracy input data are indispensable. If panel layouts, terrain data, and the positions and dimensions of surrounding obstacles (trees and buildings) are not accurate, simulation results will diverge from reality. In other words, "simulation accuracy depends on the accuracy of input data." So how can we acquire such high-precision data? One answer is the use of 3D point cloud data obtained via LRTK, described next.

Advantages of High-Precision Point Cloud Surveying with LRTK

LRTK is a surveying solution centered on a compact RTK-GNSS receiver that attaches to a smartphone, enabling anyone to easily perform high-precision 3D surveying (point cloud measurement). RTK-GNSS is a technique that combines satellite positioning with correction information to achieve centimeter-level accuracy, and attaching LRTK to a smartphone allows continuous centimeter-precision positioning of the phone. Meanwhile, recent smartphones (e.g., LiDAR-equipped models) can 3D-scan environments several meters away. By combining a smartphone’s 3D scanning capability with high-precision positioning, LRTK’s major feature is the ability to generate wide-area point clouds accurately without distortion. Tasks that previously required specialized laser scanners or drone surveys can be completed by one person in a short time with just a smartphone, greatly reducing field workload.

The specific advantages of point cloud surveying with LRTK include the following:

• High accuracy (centimeter-level): Point clouds obtained with RTK-GNSS are tagged with geographic coordinates (absolute coordinates), and the positional error of each point is typically within a few centimeters. This allows accurate capture of the measured trees’ and terrain’s heights and positions in a real-world coordinate system, dramatically improving the accuracy of simulation models.

• Comprehensiveness and level of detail: Point cloud data capture the site’s topography and structures down to fine details. Features that are difficult to measure manually—such as the spread of tree branches and foliage or complex surface undulations—can be digitized, reducing the chance of overlooking obstacles. Think of it as "digitizing the entire site," allowing you to inspect dimensions of any object later in the office.

• Immediacy and efficiency: The simplicity of using only a smartphone and a small device means surveying can start immediately when needed. Measurement itself completes quickly, and point clouds are generated in real time on the smartphone screen. You can measure necessary dimensions on the spot (e.g., tree height or distances), enabling part of the analysis to be done simultaneously with data collection and allowing for speedy field decisions.

• Low cost: Dedicated 3D laser scanners and surveying drones are very expensive (costing as much as a luxury car), and the equipment is large and cumbersome to transport and install. In contrast, an LRTK device and a compatible smartphone can be introduced at a fraction of the cost of dedicated equipment. If you can use an existing smartphone, the cost advantage is even greater. Reducing the frequency of outsourcing as-built surveys or earthwork volume calculations to external surveying firms can further cut costs. This makes adoption accessible even for small and medium-sized enterprises, enabling a "one-person, one-phone" surveying setup.

As described above, LRTK enables easy acquisition of high-precision, high-density point cloud data, making the digitalization of surrounding environments necessary for shadow analysis at solar PV sites dramatically easier.

Workflow for Converting LRTK Point Clouds to 3D Models and Importing into PVsyst

How exactly can data acquired with LRTK be utilized in PVsyst simulations? The general workflow is as follows:

• On-site point cloud acquisition: Bring a smartphone with LRTK to the planned site and surrounding areas and perform 3D scans while walking the entire plant area. Scan objects that could cause shading—such as trees near the site boundary, existing buildings, and changes in ground elevation—thoroughly. Point clouds acquired with LRTK can be immediately checked on the smartphone to ensure no omissions.

• Point cloud processing and 3D modeling: From the acquired point clouds, create a 3D model in a format suitable for simulation. Remove unnecessary noise points and, if needed, convert point clouds of trees and buildings into polygon meshes. For extensive terrain, you may extract only ground surface points to generate a digital terrain model (DTM). Using LRTK cloud services, you can upload point clouds and perform 3D viewing and basic editing in a browser. Once the 3D model data of the surrounding environment (terrain, trees, and structures) are ready, export them in a format that PVsyst can read (for example, Collada format (.dae)).

• Importing the model into PVsyst: In the PVsyst project, open the Near Shadings 3D scene editor and import the 3D model you created. Thanks to LRTK’s absolute coordinates, the model’s scale and placement should match reality, though you can adjust positioning in PVsyst if necessary. Also set the PV panel layout within the site (either place arrays inside PVsyst or import a layout created in external CAD). With panel layouts and surrounding environmental objects combined in a single 3D scene, you have virtually recreated the real plant.

• Run shadow simulation and analyze results: After completing the 3D scene, run the generation simulation in PVsyst. The software calculates the sun’s position for each hour of a year (8,760 hours) and determines the fraction of each panel that is partially or fully shaded. It then computes energy forecasts considering both irradiance losses and electrical losses due to shading. Simulation outputs include annual and monthly generation, system efficiency (PR), and, notably, the losses due to shading reported in the output. For example, PVsyst might report "Near shading loss: annual △△ kWh (▲▲%)", quantitatively showing how much generation is lost due to trees or structures. These results form the basis for the next optimization steps in design.

Design Optimization Examples Based on Simulation Results

Once you obtain precise shading simulation results, you can use them to optimize plant design. Because simulations reveal "when," "where," and "how much" shading occurs, you can clearly prioritize and plan countermeasures. Below is an example of design improvements informed by shading analysis.

Example: In a certain utility-scale solar project, a roughly 15 m tall forest stood just outside the eastern boundary of the site. From point clouds obtained with LRTK, the team accurately modeled the trees’ heights and positions in 3D and ran PVsyst simulations. The results showed that nearly half of the eastern arrays were covered by tree shadows during morning hours. This caused reduced irradiance and mismatch losses in the affected strings, resulting in annual generation losses on the order of several percent. The forest’s influence had been underestimated during initial design, but detailed simulation data prompted consideration of countermeasures.

The design team first tested physical measures, such as rearranging several rows of panels near the site boundary. Specifically, they reduced the number of panels in the most heavily shaded area and redistributed them to less shaded areas, thereby lowering the number of panels that would be completely shaded in the morning. This layout change improved the forecasted annual generation. As a technical measure, they also considered installing power optimizers on some strings that unavoidably experienced shading to reduce localized mismatch losses. Simulation of the optimizer’s effect showed that the output gap between shaded and unshaded strings was mitigated, and generation losses were recovered to some extent. All of these optimization studies were possible only because of the detailed data obtained with LRTK. The result was an arrangement and equipment configuration that minimized shading impacts and produced a more reliable annual generation forecast for the entire plant.

As this example demonstrates, quantitative data from precise shading analysis provide strong evidence to support design decisions. Judgments that used to rely on experience or intuition—such as estimating how much shading to expect—can now be made rationally based on PVsyst’s numerical results. For instance, you can determine that "removing a particular tree will likely increase annual generation by ○○ kWh" or that "optimizing panel layout can mitigate monthly PR reductions by ▲▲%," enabling design optimizations that consider investment return.

Conclusion: High-Precision Shading Analysis Anyone Can Do with Smartphone-Only LRTK Surveys

In shadow analysis for solar PV plants, accurately simulating the impacts of surrounding trees, structures, and terrain without overlooking them has become an indispensable process. Importing high-precision 3D models created with LRTK into PVsyst enables simulations that faithfully reproduce the real environment and dramatically improve generation forecast accuracy. This allows identification of potential risks and losses during the planning stage and implementation of appropriate design changes and countermeasures.

Notably, this advanced analysis workflow can be completed with only a smartphone. The advent of LRTK has made 3D surveying—once requiring specialists or expensive equipment—accessible to field engineers themselves. By simply walking around a site with a smartphone, you can acquire high-precision point cloud data and then feed it into simulation software. LRTK, a simple surveying tool anyone can use, is bringing a new wave of DX (digital transformation) to solar PV design processes.

Going forward, the combination of smartphone surveying with LRTK and PVsyst simulation is likely to become the new standard in solar plant design. With meticulous simulations that "do not miss tree shadows," you can robustly support the reliability and profitability of generation projects. Armed with high-precision 3D models, we encourage you to apply these approaches to achieve optimal design and operation in your projects.