国内外太阳能资源评估方法研究现状和展望

doi:10.12006/j.issn.1673-1719.2022.096

摘要/Abstract

摘要：

太阳能被认为是最有希望大规模利用的可再生能源，具有广阔的应用前景。对太阳能资源进行合理且准确的评估是太阳能资源开发利用的第一步，也是关键的一步。然而全球大部分地区的太阳辐射观测资料十分有限，利用其他气象数据对太阳能资源进行评估是目前的主流方法。文中归纳和总结国内外太阳能资源评估的4种主要方法的基本原理、主要技术路线以及最新研究进展，对比分析不同方法的误差范围，系统地讨论各种评估方法的不足，并在此基础上对太阳能资源评估方法在我国未来的发展趋势进行了展望。经验模型和人工智能模型较依赖数据的特征，物理模型中云影响太阳辐射过程的描述存在不准确性，数值天气预报模式中资料同化和物理过程参数化方案的选定较关键。发展多种方法结合的太阳能资源混合评估模型是未来太阳能资源评估的主要发展方向之一。

关键词: 太阳能资源评估, 经验模型, 物理模型, 数值天气预报, 人工智能, 均方根误差

Abstract:

Solar energy is considered as the most promising renewable energy source with extensive application prospects. A reliable and accurate evaluation of solar resources is a primary and essential step before developing and utilizing solar resources. However, in most regions, solar radiation observations are too finite to evaluate solar resources directly. The solar energy resource evalution by other meteorlogical records is available. This paper reviews fundamental theories, the technology roadmap and the advances of four main solar energy resource evaluation methods in domestic and abroad studies. We also investigate errors of each method and discuss their disadvantages. The future development trend of the four methods in China has been prospected. The empirical model and artificial intelligence model highly depend on the characteristics of data. The description of the impacts of the cloud on the solar radiation is inaccurate in the phyical model. The data assimilation and parameterization scheme are critical in the numerical weather forecasting models. Developing hybrid evaluation model with various methods is one of the future directions of solar energy resource assessment.

Key words: Solar energy resource assessment, Empirical model, Physical model, Numerical weather prediction model, Artificial intelligence, Root mean square error

王科, 黄晶. 国内外太阳能资源评估方法研究现状和展望[J]. 气候变化研究进展, 2023, 19(2): 160-172.

WANG Ke, HUANG Jing. Domestic and abroad research status and prospects of solar energy resource evaluation methods[J]. Climate Change Research, 2023, 19(2): 160-172.

图/表 5

表1 全国各省逐小时探测辐射量的气象站点分布个数

Table 1 Number of meteorological stations with hourly radiation records of each province in China

图1 物理模型评估太阳能资源流程图

Fig. 1 Schematic diagram of solar resource evaluation by the physical model

图2 数值天气预报模式中辐射传输方案计算示意图

Fig. 2 Schematic diagram of calculation of the radiative transfer scheme in the numerical weather prediction model

图3 人工神经网络评估太阳能资源流程图

Fig. 3 Schematic diagram of solar resource evaluation by the artificial neural network

表2 4种模型均方根误差的最小值、最大值和变化

Table 2 Minimum, maximum, and changes of root mean square error for four different models MJ/(m2∙d)

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