Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling
Hierarchical Clustering to Find Representative Operating Periods for Capacity-Expansion Modeling
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DOI：
10.1109/tpwrs.2017.2746379
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发表时间：
2018-05
影响因子：
6.6
通讯作者：
Yixian Liu;R. Sioshansi;A. Conejo
中科院分区：
文献类型：
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作者：
Yixian Liu;R. Sioshansi;A. Conejo
如果电力系统的每一个运行周期都被表示出来，那么电力系统的容量扩展模型通常是难以处理的。这个问题通常可以通过使用具有代表性的操作周期子集来解决。例如，可以通过离散负载持续时间曲线来选择具有代表性的工作时间，该曲线捕获负载水平对系统运行成本的影响。如果系统运行成本取决于负荷以外的参数（例如，可再生资源的可用性），或者存在重要的跨期运行约束（例如，发电机爬坡限制），则这种方法是不合适的。本文提出使用聚类方法选择具有代表性的工作日来克服这些问题。我们提出了两种分层聚类技术，旨在捕获参数的重要统计特征（例如，负载和可再生资源可用性），以选择代表性日期。这包括时间自相关性和不同位置之间的相关性。以德克萨斯州电力系统为例，对这些技术进行了论证。我们表明，我们提出的聚类技术导致的投资决策与使用完整非聚类数据集的投资决策非常匹配。
Power system capacity-expansion models are typically intractable if every operating period is represented. This issue is normally overcome by using a subset of representative operating periods. For instance, representative operating hours can be selected by discretizing the load-duration curve, which captures the effect of load levels on system-operation costs. This approach is inappropriate if system-operating costs depend on parameters other than load (e.g., renewable-resource availability) or if there are important intertemporal operating constraints (e.g., generator-ramping limits). This paper proposes the use of representative operating days, which are selected using clustering, to surmount these issues. We propose two hierarchical clustering techniques, which are designed to capture the important statistical features of the parameters (e.g., load and renewable-resource availability), in selecting representative days. This includes temporal autocorrelations and correlations between different locations. A case study, which is based on the Texan power system, is used to demonstrate the techniques. We show that our proposed clustering techniques result in investment decisions that closely match those made using the full unclustered dataset.