研究目的
To develop and validate analytic geometry methods for efficient calculation of shading and blocking efficiency in heliostat fields, and to propose an intuitive optimization approach using a maximum optical efficiency map.
研究成果
The developed AGM-I and AGM-II methods significantly reduce computational time for shading and blocking efficiency calculations by 32% and 23% compared to Sassi and collision analysis methods, respectively, while maintaining accuracy. The MOEM provides an intuitive guideline for optimizing heliostat field layouts, leading to improved instantaneous and annual optical efficiencies. The case study based on Gemasolar shows that the optimized field achieves higher efficiencies than un-optimized and reference-optimized layouts, demonstrating the effectiveness of the proposed methods for practical CSP plant design.
研究不足
The study is computational and does not account for real-world factors like cost, maintenance, or environmental variations. The methods assume specific heliostat and receiver parameters, and may not be directly applicable to all field layouts without adjustments. Optimization is focused on optical efficiency, not economic factors like levelized cost of electricity.
1：Experimental Design and Method Selection:
The study involves developing two analytic geometry methods (AGM-I and AGM-II) to pre-judge heliostats that may cause shading or blocking, reducing computational time compared to existing methods like Sassi and collision analysis. The methods are based on spatial relationships and geometric calculations.
2：Sample Selection and Data Sources:
A heliostat field based on the Gemasolar plant with 2650 heliostats is used as a case study. Solar insolation data from a typical meteorological year for Seville is sourced from NREL.
3：List of Experimental Equipment and Materials:
Computational tools include a genetic algorithm toolbox developed in Visual Studio Community 2015 for optimization. No physical equipment is mentioned; the study is computational.
4：Experimental Procedures and Operational Workflow:
The procedure includes generating an initial heliostat field, using AGM-I/AGM-II for pre-judgment, calculating optical efficiencies with Sassi method for accuracy, optimizing the field layout using genetic algorithms, and evaluating with MOEM. Steps involve iterative optimization and inverse selection of heliostats.
5：Data Analysis Methods:
Efficiency calculations are performed using models for reflectivity, cosine efficiency, atmospheric attenuation, interception, and shading/blocking. Comparisons are made with reference methods, and results are analyzed for computational time and efficiency improvements.
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