Extraction of typical operating scenarios of new power system based on deep time series aggregation

IF 8.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhaoyang Qu, Zhenming Zhang, Nan Qu, Yuguang Zhou, Yang Li, Tao Jiang, Min Li, Chao Long
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Abstract

Extracting typical operational scenarios is essential for making flexible decisions in the dispatch of a new power system. A novel deep time series aggregation scheme (DTSAs) is proposed to generate typical operational scenarios, considering the large amount of historical operational snapshot data. Specifically, DTSAs analyse the intrinsic mechanisms of different scheduling operational scenario switching to mathematically represent typical operational scenarios. A Gramian angular summation field-based operational scenario image encoder was designed to convert operational scenario sequences into high-dimensional spaces. This enables DTSAs to fully capture the spatiotemporal characteristics of new power systems using deep feature iterative aggregation models. The encoder also facilitates the generation of typical operational scenarios that conform to historical data distributions while ensuring the integrity of grid operational snapshots. Case studies demonstrate that the proposed method extracted new fine-grained power system dispatch schemes and outperformed the latest high-dimensional feature-screening methods. In addition, experiments with different new energy access ratios were conducted to verify the robustness of the proposed method. DTSAs enable dispatchers to master the operation experience of the power system in advance, and actively respond to the dynamic changes of the operation scenarios under the high access rate of new energy.

Abstract Image

基于深度时间序列聚合的新型电力系统典型运行场景提取
提取典型运行情景对于制定灵活的新电力系统调度决策至关重要。针对大量历史操作快照数据,提出了一种新的深度时间序列聚合方案(DTSAs)来生成典型操作场景。具体而言,dtsa分析了不同调度操作场景切换的内在机制,以数学方式表示典型的操作场景。设计了一种基于格拉曼角和场的作战场景图像编码器,将作战场景序列转换为高维空间。这使得dtas能够使用深度特征迭代聚合模型充分捕捉新电力系统的时空特征。编码器还有助于生成符合历史数据分布的典型操作场景,同时确保网格操作快照的完整性。实例研究表明,该方法提取了新的细粒度电力系统调度方案,优于最新的高维特征筛选方法。此外,通过不同新能源获取比的实验验证了所提方法的鲁棒性。dtsa使调度员能够提前掌握电力系统的运行经验,积极应对新能源高接入率下运行场景的动态变化。
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来源期刊
CAAI Transactions on Intelligence Technology
CAAI Transactions on Intelligence Technology COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
11.00
自引率
3.90%
发文量
134
审稿时长
35 weeks
期刊介绍: CAAI Transactions on Intelligence Technology is a leading venue for original research on the theoretical and experimental aspects of artificial intelligence technology. We are a fully open access journal co-published by the Institution of Engineering and Technology (IET) and the Chinese Association for Artificial Intelligence (CAAI) providing research which is openly accessible to read and share worldwide.
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