Scholarlink

<p>Gridded meteorological datasets—reanalyses and climate simulations—are increasingly central to renewable energy yield assessments, offering complete, physically consistent atmospheric variables required to transform resource data into power output. This review delivers the first holistic synthesis of methodologies and uncertainties across the full modeling chain for both wind and solar energy, from resource characterization to energy yield and bankability metrics. It emphasizes the rising use of long-term, spatially resolved data to improve site selection, financial planning (P50/P90), and climate risk assessment. The review addresses critical modeling steps—vertical extrapolation, air density correction, irradiance decomposition—and quantifies sources of uncertainty inherent in each. By integrating historical and climate-future perspectives, it defines best practices for robust, transparent, and reproducible energy yield assessments.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Assessing renewable wind and solar energy yield with gridded climate datasets

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月6日

查看全文
  • Philippe Drobinski

摘要

Gridded meteorological datasets—reanalyses and climate simulations—are increasingly central to renewable energy yield assessments, offering complete, physically consistent atmospheric variables required to transform resource data into power output. This review delivers the first holistic synthesis of methodologies and uncertainties across the full modeling chain for both wind and solar energy, from resource characterization to energy yield and bankability metrics. It emphasizes the rising use of long-term, spatially resolved data to improve site selection, financial planning (P50/P90), and climate risk assessment. The review addresses critical modeling steps—vertical extrapolation, air density correction, irradiance decomposition—and quantifies sources of uncertainty inherent in each. By integrating historical and climate-future perspectives, it defines best practices for robust, transparent, and reproducible energy yield assessments.