Optimizing solar tracker tracking using 3D modeling
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Solution Overview
Problem
Existing solar tracker systems face challenges in optimizing the performance of large-scale PV installations with complex topographies, particularly in optimizing the performance of solar trackers, which are inefficient and costly, and require additional instrumentation and increase operational complexity, limiting their scalability and scalability, especially in large utility-scale PV installations with complex topographies, and optimizing the performance of solar trackers in optimizing the performance of solar trackers in large-scale PV installations with complex topographies, particularly in optimizing the performance of solar trackers in large-scale PV installations with complex topographies, and enhancing early-stage plant design and commissioning without requiring costly real-time sensors or complex smart tracking systems that are costly and real-time sensors or complex smart tracking systems that are costly and complex.
Innovation Solution
A method that uses high-resolution three-dimensional (3D) modeling of PV power plant sites to determine optimized tracking parameters for each solar tracker, simulating shading patterns and adjusting tracker angles based on site-specific conditions, reducing inter-row shading and enhancing energy capture without requiring real-time irradiance sensors or complex smart tracking firmware.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If real-time irradiance sensors and complex smart tracking systems are used to optimize solar tracker performance, then energy capture efficiency is improved, but system cost and operational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating optimal tracking parameters using 3D modeling and shading simulations before the PV plant operates. The system creates a digital twin of the plant geometry and pre-determines backtracking strategies for different times of day and seasons, eliminating the need for real-time sensors and complex control systems during operation.
Solution Approach 2:
The patent uses copying by creating a 3D digital model (digital twin) of the PV plant that replicates the physical geometry, terrain, and module arrangements. This virtual copy is used for shading simulations and optimization calculations, allowing the system to determine optimal tracking parameters without requiring physical sensors in the actual plant.
2Productivity
If uniform tracking strategies are applied across all solar trackers, then system simplicity is maintained, but energy yield is reduced due to inability to account for site-specific conditions
Solution Approach 1:
The patent applies local quality by determining individualized tracking parameters for each solar tracker based on its specific location, orientation, and local shading conditions within the plant. The 3D modeling and ray-tracing simulations calculate optimal backtracking angles and timing for each tracker separately, accounting for variations in terrain, row spacing, and adjacent module configurations, rather than applying a uniform strategy across the entire plant.
3Productivity
If 3D modeling and shading simulation are used to optimize tracking parameters, then inter-row shading is reduced and energy capture is enhanced, but computational complexity and modeling time increase
Solution Approach 1:
The patent performs all 3D modeling, ray-tracing simulations, and optimization calculations during the design and commissioning phase, before the PV plant begins operation. The system pre-calculates shading patterns for different times of day, seasons, and weather conditions, and stores the optimized tracking parameters for direct implementation, eliminating the need for time-consuming computations during operational periods.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances energy yield and improves tracker control by providing a scalable solution for optimizing hundreds or thousands of trackers across large utility-scale PV power plants, offering improved performance over conventional uniform tracking strategies.
Implementation Method 1
simulating the shading patterns for the plurality of PV modules using the 3D model comprises applying a ray-tracing or geometry-aware shading model to the 3D model to compute shading events across the plurality of PV modules
Implementation Method 2
the spatial data comprises LIDAR (Light Detection and Ranging) data including row spacing, racking table coordinates, tracker pile heights, and terrain profile information
Data Source
AI summary
Systems and methods for optimizing power output of a photovoltaic (PV) power plant are disclosed. A method includes obtaining spatial data of the PV power plant and generating a 3D model based on the spatial data. Shading patterns for PV modules are simulated using the 3D model. Optimized tracking parameters for solar trackers are determined based on the simulated shading patterns. The optimized tracking parameters are then applied to the solar trackers. The method may further include actuating the solar trackers according to the optimized tracking parameters to improve energy capture and efficiency of the PV power plant.