面向连锁故障风险评估的可再生能源场景聚类技术
A Study on Renewable Energy Scenario Clustering Technology for Power System Cascading Failure Assessment
李小娣 1柴斌 2毛春翔 2郭祥阳3
作者信息
- 1. 银川能源学院,宁夏 银川 750100
- 2. 国网宁夏电力有限公司超高压公司,宁夏 银川 750001
- 3. 三峡大学电气与新能源学院,湖北 宜昌 443000
- 折叠
摘要
随着大规模可再生能源的集成,电力系统连锁故障风险评估和追踪的计算时间显著增加.为了解决传统方法直接基于可再生能源数据进行场景聚类可能导致评估误差的问题,该文提出了一种新的多场景风险导向聚类算法,该算法特别考虑了可再生能源的特性.在该算法中,首先采用枚举法计算不同场景的连锁故障风险,然后根据每个场景的连锁故障风险,利用模糊C-means聚类方法对场景进行聚类,使同一场景中场景的相似度最大化,并保留对连锁故障风险贡献较大的高风险场景.基于这些聚类场景,进行系统连锁故障风险评估.通过对IEEE RTS-24 系统的案例研究,验证了所提方法的准确性和效率.
Abstract
With the integration of large-scale renewable energy sources,the computation time for risk assessment and tracking of cascading failures in power systems increases significantly.To address the issue that traditional methods may lead to evaluation errors by directly clustering scenarios based on renewable energy data,this paper proposes a new multi-scenario risk-oriented clustering algorithm that specifically considers the characteristics of renewable energy sources.In this algorithm,the cascading failure risk of different scenarios is first calculated using the enumeration method,and then,based on the cascading failure risk of each scenario,the fuzzy C-means clustering method is used to cluster the scenarios,maximizing the similarity within the same scenario and retaining high-risk scenarios that contribute significantly to cascading failure risk.Based on these clustered scenarios,the system's cascading failure risk assessment is conducted.Through a case study of the IEEE RTS-24 system,the accuracy and efficiency of the proposed method are verified.
关键词
级联评估/模糊C聚类/可再生能源场景Key words
cascading assessment/fuzzy C-means clustering/renewable energy scenario引用本文复制引用
出版年
2024
NETL
NSTL
万方数据