计算效率高的尾部分布意识到大规模的电力系统超载风险评估
Bendong Tan1, Ketian Ye1, Junbo Zhao2,3
1Department of Electrical and Computer Engineering, University of Connecticut, Storrs, CT, USA.
Nature communications
|January 6, 2026
概括
本研究引入了一种有效的方法来评估可再生能源产生的电力系统过载风险. 该方法显著加快了大型电网的风险分析,确保了准确性.
科学领域:
- 电力系统工程 电力系统工程
- 计算电磁学 计算机电磁学
- 风险分析 风险分析
背景情况:
- 可再生能源 (太阳能,风能) 引入了间歇性和不确定性.
- 这种变化给电力系统带来了严重的过载风险,可能导致级联故障.
- 在大型电力系统中量化这些风险在计算上具有挑战性.
研究的目的:
- 开发一种计算效率高,准确的方法来量化大规模电力系统中的过载风险.
- 为应对间歇性可再生发电和N-k应急情况所带来的挑战.
- 提高风险评估在超载值附近的准确性.
主要方法:
- 一个深核稀疏向量值的高斯过程 (GP) 被开发为一个替代模型.
- 该GP模型包括发电调度,应急情况和不确定的输入 (光伏发电,负载需求).
- 引入了使用电流解决器的自适应性重新采样机制,以纠正代用模型偏差.
主要成果:
- 与21k+公交系统的蒙特卡洛采样相比,拟议的方法可以加快风险评估的22倍.
- 在过载风险量化方面保持了高准确度.
- 该方法在各种分布类型和相关性场景中表现出稳健性.
结论:
- 开发的代用建模方法为电力系统风险评估提供了计算效率高,准确的解决方案.
- 适应性重新采样机制提高了在超载值附近的预测的准确性.
- 该方法对于具有重要的可再生能源集成的大规模电力系统是有效的.
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