Solar Farm Design System Optimizing Panel Block Layout
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Solution Overview
Problem
Existing solar farm design systems lack an efficient method to optimize the layout of solar panels and inverters within geographic boundaries while minimizing installation and maintenance costs, leading to suboptimal power output and increased expenses.
Innovation Solution
A solar farm design system that utilizes a library of virtual solar panel block types, each including solar panels, an inverter, and an access road, which are optimized using a design algorithm to fit within geographic features and boundaries, iteratively adjusting dimensions and placement to maximize output power relative to costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If solar panels and inverters are manually arranged within geographic boundaries, then design flexibility is maintained, but optimization efficiency and power output maximization are compromised
Solution Approach 1:
The patent uses virtual solar panel blocks as digital copies that represent physical solar panel arrangements. These virtual blocks are stored in a library and can be repeatedly instantiated and arranged within the geographic boundaries, allowing efficient optimization without manually designing each panel layout from scratch. The virtual blocks capture the essential geometric and electrical characteristics needed for optimization while simplifying the design process.
Solution Approach 2:
The solar farm design is segmented into discrete virtual solar panel blocks, each representing a modular unit with specific dimensions and power ratings. This segmentation allows the optimization algorithm to work with individual blocks rather than continuous complex geometries, improving computational efficiency while maintaining design flexibility through combinatorial arrangement of standardized units.
2Ease of manufacture
If virtual solar panel blocks with predetermined dimensions are used, then layout optimization is simplified, but adaptability to varying geographic features is reduced
Solution Approach 1:
The optimization algorithm dynamically selects and arranges virtual solar panel blocks from the library based on the specific geographic features and boundaries of each site. While individual blocks have predetermined dimensions, their arrangement, orientation, and selection are dynamically adjusted to maximize power output within the given geographic constraints, balancing standardization with site-specific adaptability.
Solution Approach 2:
The system varies parameters such as block orientation, spacing, and arrangement patterns to adapt the standardized virtual blocks to different geographic features. By changing these spatial parameters rather than the fundamental block dimensions, the system maintains ease of implementation while achieving adaptability to diverse geographic conditions.
3Power
If the array is iteratively modified to maximize output power rating, then power generation efficiency is improved, but installation and maintenance costs increase
Solution Approach 1:
The optimization algorithm evaluates multiple parameter configurations including block arrangement, spacing, and orientation to find the optimal balance between power output and cost. By systematically varying these parameters and evaluating trade-offs, the system identifies designs that maximize power rating while keeping installation and maintenance costs within acceptable ranges through standardized block configurations.
Data Source
AI summary
One example includes a method for generating a solar farm design. Geographic map data defines geographic features and boundaries of a geographic region. A solar panel block library that stores virtual solar panel block types is accessed. Each of the virtual solar panel block types corresponds to a design of a respective solar panel block that includes solar panels, an inverter, and an access road, and each virtual solar panel block type includes predetermined dimensions and a predefined output power rating. An array of a virtual solar panel block type to fit within the geographic features and boundaries of the geographic region on the map is generated based on the dimensions of each of the virtual solar panel blocks in the array. The array is iteratively modified to optimize a criterion of the solar farm design, and the design is stored in a memory for subsequent solar farm installation.