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基于IRSO算法的含分布式电源配电网重构
引用本文:魏凯,程静. 基于IRSO算法的含分布式电源配电网重构[J]. 科学技术与工程, 2024, 24(3): 1075-1082
作者姓名:魏凯  程静
作者单位:1. 新疆大学电气工程学院;2. 可再生能源发电与并网控制教育部工程研究中心
基金项目:新疆可再生能源发电与并网技术自治区重点实验室开放课题(2020D04048);新疆维吾尔自治区重大科技专项(2022A01001-4);国家重点研发计划项目(2021YFB1506902)
摘    要:在智能电网的背景下,为了减少含分布式电源(distributed generation, DG)配电网的网络损耗,同时提高电网安全性,提出“单条支路安全系数”“最小安全系数”“平均安全系数”三个安全性评价指标,并建立以网损、“平均安全系数”“最小安全系数”为目标的配电网重构数学模型,采用改进鼠群优化算法进行求解。针对传统鼠群算法随机初始种群的迭代次数较多问题,采用有序环网配合启发式规则在初始化阶段生成一个初始解;针对传统算法在运行过程中产生众多无效变异的问题,提出“映射规则”,最大化调用鼠群变异规则;为了避免最后结果未优化到最优,提出“最优个体微调策略”。采用IEEE33节点电力系统和Taipower84节点电力系统进行对比验证分析,证明了该文方法的通用性和有效性。

关 键 词:智能电网  分布式电源  安全系数  网络损耗  配电网重构  改进鼠群算法
收稿时间:2023-03-15
修稿时间:2023-11-06

Distribution Network Reconfiguration with Distributed Generation Based on Improved Rat Swarm optimization
Wei Kai,Cheng Jing. Distribution Network Reconfiguration with Distributed Generation Based on Improved Rat Swarm optimization[J]. Science Technology and Engineering, 2024, 24(3): 1075-1082
Authors:Wei Kai  Cheng Jing
Affiliation:Xinjiang University
Abstract:In the context of the smart grid, efforts are made to reduce network loss in Distributed Generation (DG) distribution networks and enhance grid security. To accomplish this, three security evaluation indexes are proposed: "single branch security coefficient," "minimum security coefficient," and "average security coefficient." Additionally, a mathematical model for the reconfiguration of the distribution network is established, with network loss, "average security coefficient," and "minimum security coefficient" as the objectives. The problem is addressed using an improved rat swarm optimization algorithm. To address the issue of excessive iterations in the random initial population of the traditional rat swarm algorithm, an ordered ring network is utilized in conjunction with heuristic rules to generate an initial solution during the initialization stage. To mitigate the problem of generating numerous invalid variations during the operation process, a "mapping rule" is proposed to maximize the utilization of rat swarm variation rules. Moreover, an "optimal individual fine-tuning strategy" is introduced to ensure that the final result is optimized to the best possible outcome, avoiding suboptimal solutions. Finally, the proposed method is validated through simulations using the IEEE33 node system and Taipower84 node system to assess its generality and effectiveness.
Keywords:smart grid   distributed generation   factor of safety   network loss   distribution network reconfiguration   improved mouse swarm algorithm
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