可再生能源
电力系统
计算机科学
聚类分析
工程类
可靠性工程
功率(物理)
电气工程
量子力学
机器学习
物理
作者
Qingchun Hou,Jianxiao Wang,Kang Chen
出处
期刊:IEEE Transactions on Power Systems
[Institute of Electrical and Electronics Engineers]
日期:2020-01-01
卷期号:35 (1): 731-741
被引量:96
标识
DOI:10.1109/tpwrs.2019.2929276
摘要
The high penetration of renewable energy will substantially change the power system operation. Traditionally, the annual operation of a power system can be represented by some typical operation modes and acts as the basis for the power-system-related analysis. The introduction of highly penetrated renewable energy will make the power system operation mode highly diversified and variable. These modes may not follow traditional empirical patterns. In this paper, we propose a data-driven method based on high-dimensional power system operation data (including power flow, unit generation, and load demand) to identify the pattern of the operation modes and analyze the impact of high renewable penetration. Specifically, the proposed data-driven method is composed of simulation, preprocessing, clustering, dimension reduction, and visualization with the aim to provide an intuitive understanding of the operation mode variety under high renewable penetration. In addition, several indices are introduced to quantify the space dispersion, time variation, and seasonal consistency of operation modes. A case study on actual Qinghai provincial power system in China validates the effectiveness of the proposed data-driven method and indicates that the dispersion and time variation of operation mode will significantly increase in the beginning and then saturate with the increase in renewable penetration level. The operation mode is also less correlated with seasons in renewable energy dominated power system.
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