A Review of Solar and Wind Energy Resource Projection Based on the Earth System Model

Author:

Chen Guanying1ORCID,Ji Zhenming123ORCID

Affiliation:

1. School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519082, China

2. Key Laboratory of Tropical Atmosphere-Ocean System, Ministry of Education, Zhuhai 519082, China

3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China

Abstract

Many countries around the world are rapidly advancing sustainable development (SD) through the exploitation of clean energy sources such as solar and wind energy, which are becoming the core of the sustainable energy transition. In recent years, the continuous advancement of Earth system models (ESMs) has facilitated numerous studies utilizing them to predict long-term and large-scale meteorological elements, consequently enabling forecasts of wind and solar energy. These forecasts provide critical guidance for formulating national renewable energy policies. Nevertheless, the current literature on ESMs predicting wind and solar energy lacks sufficient integration. Hence, to comprehend the focal points and future research prospects, we conducted this systematic review, employing four academic search tools to comprehensively analyze the relevant literature from the past five years. We summarized the general analytical process and compared the content and conclusions of the literature. The study reveals that future photovoltaic (PV) potential for electricity generation may increase in certain regions but decrease in others, while the global potential for concentrated solar power (CSP) may diminish, influenced by diverse factors and displaying significant regional disparities. In addition, wind resource trends vary in different regions, and forecasts exhibit considerable uncertainty. Therefore, many studies have corrected wind speeds prior to predicting wind energy. Subsequent research endeavors should concentrate on optimizing ESMs, investigating the impacts of technological innovation, and enhancing the prediction and analysis of extreme weather events.

Funder

National Natural Science Foundation of China

Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory

Publisher

MDPI AG

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