电力系统自动化2024，Vol.48Issue(1)：119-130,12.DOI:10.7500/AEPS20230115001
基于储能参与的电网连锁跳闸主动防控方法
Active Prevention and Control Method Against Power Grid Cascading Tripping Based on Participation of Energy Storage
摘要
Abstract
The decrease in system inertia and the increase in risk factors for triggering cascading trips in the new power system makes the importance of preventing cascading trips increasingly prominent.Aiming at this problem,a method integrated with energy storage is proposed for suppressing cascading trips in power grid.By using the fast response characteristics of energy storage,the energy storage is put into operation to regulate the power flow in the early stage of power flow transfer,which can quickly suppress the temperature rise of the wires and gain time for the subsequent participation of hydrothermal power units and other slow-responding backup power sources in regulation.Based on dynamic maximum electrical betweenness of the network,risk scanning and demand calculation,the paper proposes a"three-step"allocation decision-making method for energy storage,including constructing a cascading tripping prevention and control plan that reflects network weaknesses and time-varying risks and solving the spatial and temporal selection problem of energy storage resources to suppress cascading tripping.Based on the dynamic thermal characteristics of transmission lines,a calculation method for energy storage power and capacity during the coordinated regulation of energy storage and hydrothermal power is proposed,and the scale of energy storage resources required to prevent cascading tripping is determined.The N-1 and N-2 fault calculation cases are provided for IEEE 30-bus network to verify the effectiveness of the proposed method.关键词
连锁跳闸/快速响应/潮流/继电保护/输电线路/动态热特性/储能Key words
cascading tripping/fast response/power flow/relay protection/transmission line/dynamic thermal characteristic/energy storage引用本文复制引用
余鹏飞,熊小伏,朱继忠,何祥桢,南东亮..基于储能参与的电网连锁跳闸主动防控方法[J].电力系统自动化,2024,48(1):119-130,12.基金项目
国家自然科学基金资助项目(U1866603). This work is supported by National Natural Science Foundation of China(No.U1866603). （U1866603）
