摘要
随着高比例光伏接入配电网,愈加复杂的潮流分布给配电网带来反向重过载风险,影响配电网安全稳定运行。为准确评估高比例光伏接入下配电网各电压等级的反向重过载风险大小、明析反向重过载风险来源,首先建立了高比例光伏接入的配电网“台区-馈线-变电站”反向重过载风险评估指标体系;其次,采用基于熵权法和层次分析法的组合赋权法为各指标赋权;最后,对基于湖南某地实际网络拓扑改进的配电网案例进行仿真分析,验证了所提方法的有效性。此外,所提出的方法能有效分析不同分布式光伏接入位置下的配电网反向重过载风险情况,对电力系统风险评估及治理具有参考意义。
The high percentage of photovoltaic(PV)access to the distribution network brings reverse heavy overload risk to the distribution network,which affects the safe and stable operation of the distribution network.In order to accurately evaluate the reverse heavy overload risk of each voltage level in the distribution network with high proportion of PV access and to analyze the sources of reverse heavy overload risk,firstly,a"station-feeder-substation"reverse heavy overload risk assessment index system is established for the distribution network with high proportion of PV access.Secondly,a combined weighting method based on entropy weighting and hierarchical analysis is used to assign weights to each index.Finally,the effectiveness of the proposed method is verified by simulating a distribution network case based on an actual network topology improvement in Hunan.In addition,the proposed method can be adopted to effectively analyze the reverse heavy overload risk situation of distribution network under different distributed PV access locations,which has reference significance for power system risk assessment and management.
作者
于雨彤
王灿
李勇
刘嘉彦
郭钇秀
曹一家
YU Yutong;WANG Can;LI Yong;LIU Jiayan;GUO Yixiu;CAO Yijia(College of Electrical and Information Engineering,Hunan University,Changsha 410082,China;State Grid Hunan Electric Power Company Limited Research Institute,Changsha 410007,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2024年第10期4540-4549,I0004-I0007,共14页
High Voltage Engineering
基金
国家重点研发计划政府间国际科技创新合作重点项目(2022YFE0129300)
国家自然科学基金(U22B200134)
国网湖南省电力有限公司电力科学研究院科技项目(5216A522000B)
长沙市自然科学基金(kq2208028)。
关键词
反向重过载
风险评估
分布式光伏
多电压等级
熵权法
层次分析法
组合赋权法
reverse heavy overload
risk assessment
distributed photovoltaic
multiple voltage levels
entropy weight method
analytic hierarchy process
combinatorial weighting
