中国可再生能源的固有时空不确定性
Jianxiao Wang1,2, Liudong Chen3, Zhenfei Tan4
1National Engineering Laboratory for Big Data Analysis and Applications, Peking University, Beijing, 100871, China.
Nature communications
|September 4, 2023
概括
这项研究分析了中国的风能和太阳能预测错误,揭示了大多数错误发生在高利用期. 诸如世代序列差异和峰值比率之类的关键指标解释了这些不确定性.
科学领域:
- 可再生能源系统可再生能源系统
- 统计建模 统计建模
- 能源政策 能源政策
背景情况:
- 可持续能源转型主要依赖于太阳能和风能.
- 现有的研究往往忽略了可再生能源不确定性的统计特征.
- 随机意识的研究需要理解可再生能源发电中固有的随机性.
研究的目的:
- 开发一个统计学习模型来预测风能和太阳能.
- 分析中国的时空分布和预测错误的特征.
- 确定解释可再生能源不确定性的关键指标.
主要方法:
- 开发了一个统计学学习模型,用于功率预测.
- 为30个中国省份生成了一年的每小时预测错误数据集.
- 分析了使用第一阶差异和峰值比率指标的预测误差分布.
主要成果:
- 揭示了风和太阳预测错误中的多样化的时空模式.
- 发现超过60%的风力和50%的太阳能预测错误发生在高利用率下.
- 确定了中国各省的预测错误的持续季节性分布.
结论:
- 可再生能源的预测错误受到利用率和发电特征的重大影响.
- 了解这些不确定性对于可靠的能源整合至关重要.
- 政策建议包括改善激励措施和省际电网调度.
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