论文标题
在多年需求和天气不确定性下，对电力系统规划的重要性亚采样
Importance subsampling for power system planning under multi-year demand and weather uncertainty
论文作者
论文摘要
本文介绍了基于优化的电力系统计划模型中时间序列减少/聚合的重要性亚采样。最近的研究表明，在气候变异性下可靠地确定最佳电力（投资）策略需要考虑多年的需求和天气数据。但是，在长期模拟长度上解决计划模型通常在计算上是不可行的，并且建立的时间序列减少方法会引起重大错误。重要性亚采样方法可靠地以大大降低计算成本的长期计划模型输出，从而考虑多年样本。关键的创新是对模拟子样本中相关极端事件的系统识别和保存。对生成和传输扩展计划模型的仿真研究说明了该方法在已建立的“代表日”聚类方法上的增强性能。模型，数据和示例代码可作为开源软件提供。
This paper introduces a generalised version of importance subsampling for time series reduction/aggregation in optimisation-based power system planning models. Recent studies indicate that reliably determining optimal electricity (investment) strategy under climate variability requires the consideration of multiple years of demand and weather data. However, solving planning models over long simulation lengths is typically computationally unfeasible, and established time series reduction approaches induce significant errors. The importance subsampling method reliably estimates long-term planning model outputs at greatly reduced computational cost, allowing the consideration of multi-decadal samples. The key innovation is a systematic identification and preservation of relevant extreme events in modeling subsamples. Simulation studies on generation and transmission expansion planning models illustrate the method's enhanced performance over established "representative days" clustering approaches. The models, data and sample code are made available as open-source software.
