摘要
极端自然灾害对配电网的影响具有强随机性,给配电网灾后快速恢复带来巨大挑战。为此,提出了一种考虑元件故障位置不确定性的两阶段配电网负荷恢复方法。在灾前决策阶段,综合考虑系统的交通状况和灾后恢复过程,制定与元件抢修配合的预调度方案。为了应对元件故障位置的不确定性,将预调度过程构建为3层鲁棒优化模型,并针对3层混合整数线性规划模型求解困难的问题,设计了嵌套列和约束生成算法,实现模型的精确求解。在灾后恢复阶段,计及分布式发电机和储能等资源和网络重构,构建混合整数线性规划模型,对重要负荷进行快速恢复。所提方法在改进的IEEE 33节点配电系统中得到了验证,算例结果表明其充分考虑了故障位置的不确定性,可以显著提升灾后恢复的效率,降低配电网负荷损失。
The impact of extreme natural disasters on distribution network has a strong randomness,which poses a huge challenge to the rapid restoration of distribution networks after disasters.Therefore,a two-stage load restoration method for distribution network considering the uncertainty of component fault location is proposed.In the pre-disaster decision-making stage,the traffic and the post-disaster restoration process are considered comprehensively,and a pre-scheduling scheme is developed to cooperate with the component repair.To deal with the uncertainty of component fault location,the pre-scheduling process is formulated as a tri-level robust optimization model.Considering the difficulty of solving a tri-level mixed integer linear programming model,a nested column-and-constraint generation algorithm is designed to achieve the optimal solution.In the post-disaster restoration stage,a mixed integer linear programming model considering distributed generators,energy storage devices and network reconfiguration is formulated to quickly restore critical loads.The proposed method has been validated in an improved IEEE 33-node test distribution system.The results show that the proposed method fully considers the uncertainty of fault location,and can significantly improve the efficiency of load restoration and reduce the loss of load.
作者
祝昊
付炜
谢海鹏
高建龙
别朝红
ZHU Hao;FU Wei;XIE Haipeng;GAO Jianlong;BIE Zhaohong(State Key Laboratory of Electrical Insulation and Power Equipment,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《电力自动化设备》
EI
CSCD
北大核心
2024年第6期161-168,共8页
Electric Power Automation Equipment
基金
国家重点研发计划项目(2021YFB2401300)
陕西省自然科学基础研究计划资助项目(2023⁃JC⁃QN⁃0480)。
关键词
配电网弹性
负荷恢复
维修队
故障位置不确定性
鲁棒优化
distribution system resilience
load restoration
repair crew
uncertainty of fault location
robust optimization
