Analysis of Spatial and Temporal Variation Character of Climate Risks of Wind and Solar Resources in China

doi:10.11930/j.issn.1004-9649.202211009

Abstract

Abstract: The proportion of renewable energy (represented by wind and solar energy) in the future energy structure of China should be substantially increased to achieve the goals of carbon peak and carbon neutrality. However, wind and solar power generation is largely affected by local climate conditions, with intermittent and volatile characteristics. In recent years, extreme weather and climate events have become more frequent and stronger and have shown concurrency, which seriously restricts the effective utilization of wind and solar resources. Therefore, the research on climate risks of wind and solar resources is of great theoretical and practical significance for improving the utilization of wind and solar resources and reducing the risks of renewable energy. This study quantitatively analyzes the spatial and temporal characteristics of scarce wind and light scenarios in China on the basis of a high-resolution database of wind and solar resources from 2007 to 2014. The analysis is based on aspects such as the degree, range, duration, and frequency of inefficient wind and solar output. The results show that the current distribution of scarce wind and light scenarios demonstrates prominent seasonal and regional differences. The climate risk of solar and wind resources mainly occurs in autumn and winter. The frequent scarce wind and light scenarios mainly occur in Xinjiang, Sichuan, and the middle reaches of the Yangtze River. This study could provide a scientific basis for future site selection of wind and solar power stations and regional renewable energy planning in China.

Key words: wind energy, solar energy, climate risk of solar and wind resources, spatial and temporal distribution, China

SUN Jingbo, WANG Yang, YANG Xiaofan, LU Zheng, HE Yuan, CHAO Qingchen. Analysis of Spatial and Temporal Variation Character of Climate Risks of Wind and Solar Resources in China[J]. Electric Power, 2023, 56(5): 1-10.

Add to citation manager EndNote|Ris|BibTeX

URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202211009

https://www.electricpower.com.cn/EN/Y2023/V56/I5/1

References

[1] 董昱, 董存, 于若英, 等. 基于线性最优潮流的电力系统新能源承载能力分析[J]. 中国电力, 2022, 55(3): 1–8
DONG Yu, DONG Cun, YU Ruoying, et al. Renewable energy capacity assessment in power system based on linearized OPF[J]. Electric Power, 2022, 55(3): 1–8
[2] 古宸嘉, 王建学, 李清涛, 等. 新能源集中并网下大规模集中式储能规划研究述评[J]. 中国电力, 2022, 55(1): 2–12, 83
GU Chenjia, WANG Jianxue, LI Qingtao, et al. Review on large-scale centralized energy storage planning under centralized grid integration of renewable energy[J]. Electric Power, 2022, 55(1): 2–12, 83
[3] 闫庆友, 魏琪峰. 中国能源高质量发展水平的测度及空间非均衡分析[J]. 华北电力大学学报(社会科学版), 2022(4): 16–25
YAN Qingyou, WEI Qifeng. Measurement on China’s high-quality energy development level and spatial disequilibrium analysis[J]. Journal of North China Electric Power University (Social Sciences), 2022(4): 16–25
[4] 蔡绍宽. 新型电力系统下的储能解决方案探讨[J]. 南方能源建设, 2022, 9(增刊1): 17–23.
CAI Shaokuan. Discussion on energy storage solutions under the new power system[J]. Southern Energy Construction, 2022, 9(S1): 17–23.
[5] 彭光博, 向月, 陈文溆乐, 等. “双碳”目标下电力系统风电装机与投资发展动力学推演及分析[J]. 电力自动化设备, 2022, 42(11): 70–77
PENG Guangbo, XIANG Yue, CHEN Wenxule, et al. Kinetic deduction and analysis of installed capacity and investment development for wind power in power system under “dual carbon” target[J]. Electric Power Automation Equipment, 2022, 42(11): 70–77
[6] 董长贵, 周润民, 李佳颖. 补贴政策对中国光伏装机市场的影响: 基于面板数据回归的实证分析[J]. 资源科学, 2021, 43(6): 1065–1076
DONG Changgui, ZHOU Runmin, LI Jiaying. The effect of feed-in tariff on China’s photovoltaic capacity development: an empirical analysis based on panel data regression[J]. Resources Science, 2021, 43(6): 1065–1076
[7] GHIMIRE L P, KIM Y. An analysis on barriers to renewable energy development in the context of Nepal using AHP[J]. Renewable Energy, 2018, 129: 446–456.
[8] LIU L, WANG Z, WANG Y, et al. Optimizing wind/solar combinations at finer scales to mitigate renewable energy variability in China[J]. Renewable and Sustainable Energy Reviews, 2020, 132: 110151.
[9] 向梦宇, 王深, 吕连宏, 张楠, 白梓函. 基于不同电力需求的中国减污降碳协同增效路径[J/OL]. 环境科学: 1–20[2023-02-06].https://doi.org/10.13227/j.hjkx.202207256.
XIANG Mengyu, WANG Shen, LÜ Lianhong, et al. Synergistic paths of reduce pollution and carbon emissions based on different power demand in China[J/OL]. Environmental Science: 1–20[2023-02-06].https://doi.org/10.13227/j.hjkx.202207256.
[10] 杨策, 孙伟卿, 韩冬. 考虑新能源消纳能力的电力系统灵活性评估方法[J]. 电网技术, 2023, 47(1): 338–349
YANG Ce, SUN Weiqing, HAN Dong. Power system flexibility evaluation considering renewable energy accommodation[J]. Power System Technology, 2023, 47(1): 338–349
[11] 于文搏. 基于MCP的小型风电项目风资源分析[D]. 昆明: 昆明理工大学, 2015.
YU Wenbo. Study of small wind power projects MCP wind resource analysis based on short-term data[D]. Kunming: Kunming University of Science and Technology, 2015.
[12] 朱蓉, 王阳, 向洋, 等. 中国风能资源气候特征和开发潜力研究[J]. 太阳能学报, 2021, 42(6): 409–418
ZHU Rong, WANG Yang, XIANG Yang, et al. Study on climate characteristics and development potential of wind energy resources in China[J]. Acta Energiae Solaris Sinica, 2021, 42(6): 409–418
[13] 李柯, 何凡能, 席建超. 中国陆地风能资源开发潜力区域分析[J]. 资源科学, 2010, 32(9): 1672–1678
LI Ke, HE Fanneng, XI Jianchao. An analysis of utilization potential distribution of wind power in China’s mainland[J]. Resources Science, 2010, 32(9): 1672–1678
[14] 刘淳, 任立清, 李学军, 等. 1990—2019年中国北方沙区太阳能资源评估[J]. 高原气象, 2021, 40(5): 1213–1223
LIU Chun, REN Liqing, LI Xuejun, et al. Evaluation to the solar energy resources in the sandy regions of Northern China from 1990 to 2019[J]. Plateau Meteorology, 2021, 40(5): 1213–1223
[15] 张春桂, 张加春, 彭继达. 福建有云覆盖下地表太阳辐射的卫星遥感监测[J]. 中国农业气象, 2014, 35(1): 109–115
ZHANG Chungui, ZHANG Jiachun, PENG Jida. Remote sensing monitor on cloudy ground solar radiation in Fujian Province[J]. Chinese Journal of Agrometeorology, 2014, 35(1): 109–115
[16] 李净, 王卫东, 张福存. 利用LM-BP神经网络估算西北地区太阳辐射[J]. 干旱区地理, 2015, 38(3): 438–445
LI Jing, WANG Weidong, ZHANG Fucun. Simulation of solar radiation in northwest China based on LM-BP neural network[J]. Arid Land Geography, 2015, 38(3): 438–445
[17] LOSADA CARREÑO I, CRAIG M T, ROSSOL M, et al. Potential impacts of climate change on wind and solar electricity generation in Texas[J]. Climatic Change, 2020, 163(2): 745–766.
[18] PERERA A T D, NIK V M, CHEN D L, et al. Quantifying the impacts of climate change and extreme climate events on energy systems[J]. Nature Energy, 2020, 5(2): 150–159.
[19] 张锐, 房迪. 气候变化背景下的能源安全困境与全球能源治理[J]. 国际石油经济, 2022, 30(5): 1–9
ZHANG Rui, FANG Di. Energy security dilemma and global energy governance under the background of climate change[J]. International Petroleum Economics, 2022, 30(5): 1–9
[20] 姚玉璧, 郑绍忠, 杨扬, 等. 中国太阳能资源评估及其利用效率研究进展与展望[J]. 太阳能学报, 2022, 43(10): 524–535
YAO Yubi, ZHENG Shaozhong, YANG Yang, et al. Progress and prospects on solar energy resource evaluation and utilization efficiency in China[J]. Acta Energiae Solaris Sinica, 2022, 43(10): 524–535
[21] 朱蓉, 徐红, 龚强, 等. 中国风能开发利用的风环境区划[J/OL]. 太阳能学报: 1–14[2023-02-06].https://doi.org/10.19912/j.0254-0096.tynxb.2021-1197.
ZHU Rong, XU Hong, GONG Qiang, et al. Wind environmental regionalization for development and utilization of wind energy in China[J/OL]. Acta Energiae Solaris Sinica: 1–14[2023-02-06].https://doi.org/10.19912/j.0254-0096.tynxb.2021-1197.
[22] 谢国辉, 何永胜, 王卿然. 中国东中部地区风电发展前景分析[J]. 中国电力, 2018, 51(3): 100–104
XIE Guohui, HE Yongsheng, WANG Qingran. Research on development prospect of wind power in eastern and central China[J]. Electric Power, 2018, 51(3): 100–104
[23] 周荣卫, 何晓凤, 朱蓉, 等. 中国近海风能资源开发潜力数值模拟[J]. 资源科学, 2010, 32(8): 1434–1443
ZHOU Rongwei, HE Xiaofeng, ZHU Rong, et al. Numerical simulation of the development potential of wind energy resources over China’s offshore areas[J]. Resources Science, 2010, 32(8): 1434–1443
[24] 梁玉莲, 申彦波, 白龙, 等. 华南地区太阳能资源评估与开发潜力[J]. 应用气象学报, 2017, 28(4): 481–492
LIANG Yulian, SHEN Yanbo, BAI Long, et al. Assessment of solar energy resource and its exploitation potential in South China[J]. Journal of Applied Meteorological Science, 2017, 28(4): 481–492
[25] 孔宏鑫. 新能源风光发电功率预测模型分析[J]. 电子测试, 2022, 36(6): 39–41
KONG Hongxin. Analysis on power prediction model of new energy wind power generation[J]. Electronic Test, 2022, 36(6): 39–41
[26] 邱桂华, 何引生, 邱楠海, 等. 考虑多气象因子累积影响的光伏发电功率预测[J]. 广东电力, 2022, 35(10): 20–28
QIU Guihua, HE Yinsheng, QIU Nanhai, et al. Photovoltaic power generation forecasting considering the cumulative effects of multiple meteorological factors[J]. Guangdong Electric Power, 2022, 35(10): 20–28