PV Plant Cost Optimization via Tracker Pile Length Adjustment
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Solution Overview
Problem
Utility-scale photovoltaic power plants face challenges in optimizing site grading and steel pile design for single-axis trackers, requiring a balance between terrain grading intensity and pile length to minimize costs, while adhering to geometric and mechanical constraints, which is computationally intensive and impractical for manual solutions.
Innovation Solution
A cost-optimization device employing algorithms in computer-readable code, comprising three stages: an objective-state unit for initial cost optimization, an optimum-feasible unit to satisfy geometric constraints, and a grading unit to address non-geometric constraints, using a system mesh and marching algorithm to adjust grading solutions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If terrain grading intensity is increased to smooth topographic irregularities, then alignment of tracker components is improved, but construction cost increases
Solution Approach 1:
The invention changes the parameter of pile length to compensate for terrain irregularities instead of grading the entire terrain. By allowing piles to vary in length within a feasible range, the system achieves proper tracker alignment without extensive terrain grading, thus reducing construction costs while maintaining manufacturing precision.
Solution Approach 2:
The invention performs preliminary grading to create a baseline terrain that satisfies maximum angular deviation constraints, then uses pile length adjustment to handle remaining irregularities. This preliminary action reduces the overall grading intensity needed while ensuring alignment requirements are met.
2Manufacturing precision
If pile length is increased to absorb terrain irregularities, then tracker alignment is maintained, but material cost increases
Solution Approach 1:
The invention optimizes the parameter of pile length to be within a feasible range [min, max] that satisfies both alignment requirements and cost constraints. By carefully selecting pile lengths within this range, the system achieves proper tracker alignment while minimizing steel material usage.
Solution Approach 2:
The invention applies local quality by allowing pile lengths to vary locally based on specific terrain irregularities at each location, rather than using uniform pile lengths throughout. This localized adaptation minimizes total material usage while maintaining alignment precision where needed.
3Reliability
If geometric constraints are strictly enforced, then tracker performance is ensured, but grading complexity increases
Solution Approach 1:
The invention performs preliminary grading to satisfy maximum angular deviation constraints before installing trackers. This preliminary action ensures geometric constraints are met while simplifying the overall grading process by addressing critical constraints first.
Solution Approach 2:
The invention introduces flexibility by allowing pile lengths to dynamically adjust within a feasible range to satisfy geometric constraints. This dynamic approach enables the system to adapt to terrain variations while maintaining constraint compliance, reducing grading complexity.
4Measurement precision
If manual optimization methods are used, then design accuracy can be achieved, but time consumption becomes impractical
Solution Approach 1:
The invention replaces manual mechanical optimization methods with a computational algorithm that automatically calculates optimal pile lengths and grading solutions. This substitution maintains design accuracy while dramatically reducing the time required, making the optimization process practical for real-world applications.
Solution Approach 2:
The invention creates a self-service optimization system where the algorithm automatically iterates through possible solutions, evaluates constraint satisfaction, and identifies the optimal design without human intervention. This self-service approach achieves high design accuracy while eliminating time-consuming manual iterations.
Data Source
AI summary
This invention is embodied in a cost-optimization device for the layout and construction of a utility-scale photovoltaic (“PV”) power plant. The optimization device employs a set of algorithms designed to find the most cost-effective solution under given conditions. The algorithms are written in computer machine readable code and are highly customizable for the specific tracker equipment requirements and owner/builder/maintainer specifications or preferences.The preferred optimization device comprises three principal stages (or “units”) of computing: (1) an objective-state unit, (2) an optimum-feasible unit, and (3) a grading unit. In the first stage, the objective-state unit cost-optimizes site grading by orienting a ruling line between a maximum and a minimum pile reveal length for each tracker in the project (the objective-state solution”). When compared to the existing site topography, the ruling line indicates cost-optimized cut and fill locations.In the second stage, the optimum-feasible unit modifies the objective-state solution to satisfy given geometric constraints of the project. More specifically, the optimum-feasible unit employs a project-wide mesh (“system mesh”) to check whether the objective-state solution complies with the project's geometric restrictions. If not, the optimum-feasible unit applies a marching algorithm to incrementally adjust each ruling line until it finds a solution that minimizes site grading while complying with the project's geometric restrictions (the “optimum-feasible solution”).In the third stage, the grading unit modifies the optimum-feasible solution to satisfy non-geometric constraints of the project (the “final-state solution”).