论文标题
时间序列聚合中的极端事件：最佳住宅能源供应系统的案例研究
Extreme events in time series aggregation: A case study for optimal residential energy supply systems
论文作者
论文摘要
为了说明挥发性可再生能源供应，能源系统优化问题需要高度分辨率。许多模型都使用时间序列聚类来查找代表性期，以减少时间序列输入数据的量，并使优化问题计算。但是，聚类方法消除了峰值和其他极端事件，这对于实现强大的系统设计很重要。我们提出了一个一般决策框架，将极端事件包括在一组代表时期中。我们介绍了一种基于优化问题本身的松弛变量来找到极端周期的方法。我们的方法通过文献中的其他极端周期纳入方法评估和基准，用于设计和操作优化问题：住宅能源供应系统。我们的方法可确保对住宅能源供应系统的完整输入数据的可行性，尽管在减少的数据集上进行了设计优化。 我们表明，使用极端周期作为代表时期的一部分，将优化结果的准确性提高了3％至75％以上，具体取决于系统限制，而仅与聚类的结果相比，可以降低系统成本并增强系统的可靠性。
To account for volatile renewable energy supply, energy systems optimization problems require high temporal resolution. Many models use time-series clustering to find representative periods to reduce the amount of time-series input data and make the optimization problem computationally tractable. However, clustering methods remove peaks and other extreme events, which are important to achieve robust system designs. We present a general decision framework to include extreme events in a set of representative periods. We introduce a method to find extreme periods based on the slack variables of the optimization problem itself. Our method is evaluated and benchmarked with other extreme period inclusion methods from the literature for a design and operations optimization problem: a residential energy supply system. Our method ensures feasibility over the full input data of the residential energy supply system although the design optimization is performed on the reduced data set. We show that using extreme periods as part of representative periods improves the accuracy of the optimization results by 3% to more than 75% depending on system constraints compared to results with clustering only, and thus reduces system cost and enhances system reliability.
