全球能源互联网2025，Vol.8Issue(3)：347-356,10.DOI:10.19705/j.cnki.issn2096-5125.2025.03.010
面向管理应用的塔式光热发电资源分析系统研究
Research on Management-oriented Resource Analysis System for Tower Solar Thermal Power Generation:Based on Meteorological Satellite Retrieval Data
摘要
Abstract
Currently,the majority of solar thermal resource assessments focus on localized solar energy potential and power plant-level development design.However,challenges remain in conducting large-scale,regional evaluations of solar thermal power generation capacity on a global scale.This paper proposes an algorithm for solar thermal resource assessment based on global solar resource data retrieved from meteorological satellites.The algorithm integrates boundary conditions affecting solar thermal resource evaluation,including land cover,protected areas,water systems,and transportation infrastructure,as well as the practical constraints of limited scalability for individual solar thermal power plants under current technological conditions.By adopting tower-based solar thermal power generation technology and fixing the capacity of individual plants,the method calculates the number of fixed-capacity plants that can be installed within the evaluated area's available land.This approach yields the technically exploitable potential of solar thermal power generation for macro-site selection.The assessment results indicate a global technically exploitable solar thermal power generation potential of approximately 197.5 billion kilowatts,with an annual electricity generation of 705 trillion kilowatt-hours when combined with 8 hours of thermal storage.The evaluated installed capacity per plant demonstrates low error margins compared to existing projects,offering reliable support for desktop analysis in macro-site selection for solar thermal resource development.关键词
反演数据/光热发电/资源评估/装机密度Key words
inversion data/thermal power generation/resource assessment/installed density分类
动力与电气工程引用本文复制引用
李鹏,郑润祺..面向管理应用的塔式光热发电资源分析系统研究[J].全球能源互联网,2025,8(3):347-356,10.基金项目
国家自然科学基金专项项目(72243004). National Natural Science Foundation of China(72243004). （72243004）
