Method and system for optimizing the configuration of a solar power system
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional solar power system configuration methods are often inefficient due to limited designer knowledge and sensitivity to shading, leading to suboptimal performance and increased costs in installation.
Innovation Solution
A computer-implemented optimization engine determines the optimal configuration of solar modules by projecting spans onto a target surface, populating them with solar modules, and aligning them to maximize performance, using cloud-based and client-side optimization engines to select module types and placements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual configuration by designers is used, then flexibility in design is maintained, but configuration accuracy and optimization are insufficient due to limited designer knowledge
Solution Approach 1:
The patent replaces manual designer configuration with an automated computer-based optimization engine that uses algorithms to determine optimal solar panel placement. This substitution eliminates reliance on designer expertise while achieving superior configuration accuracy through computational optimization.
Solution Approach 2:
The optimization engine performs self-directed configuration analysis by automatically evaluating multiple design scenarios and selecting the optimal arrangement without human intervention. The system serves itself by using embedded algorithms to make configuration decisions based on target surface geometry and performance criteria.
2Productivity
If traditional manual configuration methods are used, then installation can proceed, but time and cost increase due to the complexity and sensitivity of solar power system performance
Solution Approach 1:
The patent performs configuration optimization in advance before physical installation by using the optimization engine to determine the optimal solar panel arrangement on the target surface. This preliminary computational analysis eliminates time-consuming trial-and-error adjustments during installation, directly improving installation speed while reducing configuration time through automated algorithms.
3Reliability
If solar panels are installed without optimized configuration, then installation is simpler, but performance decreases dramatically due to shading sensitivity
Solution Approach 1:
The patent replaces subjective designer judgment with an objective optimization engine that systematically evaluates configuration options based on performance metrics. This substitution ensures reliable, shading-optimized configurations by using computational algorithms to identify arrangements that maximize energy capture while minimizing shading effects.
Solution Approach 2:
The optimization engine incorporates feedback mechanisms by evaluating configuration performance through simulated shading analysis and energy production calculations. The system iteratively refines configurations based on performance feedback, ensuring optimal reliability while managing configuration complexity through automated evaluation.
Data Source
AI summary
An optimization engine determines an optimal configuration for a solar power system projected onto a target surface. The optimization engine identifies an alignment axis that passes through a vertex of a boundary associated with the target surface and then constructs horizontal or vertical spans that represent contiguous areas where solar modules may be placed. The optimization engine populates each span with solar modules and aligns the solar modules within adjacent spans to one another. The optimization engine then generates a performance estimate for a collection of populated spans. By generating different spans with different solar module types and orientations, the optimization engine is configured to identify an optimal solar power system configuration.