论文标题
捕获可再生产生的随机性和互相关的代表性场景
Representative Scenarios to Capture Renewable Generation Stochasticity and Cross-Correlations
论文作者
论文摘要
为电力系统计划生成代表性的方案，其中可再生能源和负载之间可再生生成和互相关的随机性是充分捕获的，这是一个具有挑战性的问题。场景产生的传统方法通常无法产生各种场景，包括季节性（经常发生）和非典型（极端）天数。本文提出了一种有条不紊的方法来产生代表性方案。它还提出了新指标，这些指标与从应用程序的角度评估生成的方案更相关。当应用于电力公司的历史数据时，提出的方法导致场景包括季节性和非典型日子的良好结合。结果还证明了所提出的群集验证指标的相关性。最后，本文为确定给定应用程序的最佳场景数量提供了权衡。
Generating representative scenarios for power system planning in which the stochasticity of renewable generation and cross-correlations between renewables and load are fully captured, is a challenging problem. Traditional methods for scenario generation often fail to generate diverse scenarios that include both seasonal (frequently occurring) and atypical (extreme) days required for planning purposes. This paper presents a methodical approach to generate representative scenarios. It also proposes new metrics that are more relevant for evaluating the generated scenarios from an applications perspective. When applied to historical data from a power utility, the proposed approach resulted in scenarios that included a good mix of seasonal and atypical days. The results also demonstrated pertinence of the proposed cluster validation metrics. Finally, the paper presents a trade-off for determining optimal number of scenarios for a given application.
