面向有功潮流最优问题的配电网集群划分策略
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TM73

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国家自然科学基金资助项目(51977128);国家电网有限公司科技项目(SGSH0000SCJS2100533)


Distribution network cluster division strategy for active power flow optimization problem
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    摘要:

    新能源大规模馈入配电网的背景下,集群划分是配电网实现海量数据分析和设备调控的重要手段,但当前集群划分研究存在划分结果不合理、划分算法准确度低的问题。针对上述问题,文中阐述了分布式电源高渗透的配电网集群划分时应考量的因素,设计了规模限制指标;对遗传算法迭代过程进行观察记录,分析遗传算法全局寻优能力受限的原因,并利用遗传过程中个体趋同的特征对算法进行机理改进。经过仿真实验验证,可知文中所提出的规模限制指标能够有效规避不合理分区,避免人为筛选结果的弊端;所提出的改进遗传算法较大程度地提高了计算的准确度,并降低了迭代次数。但由于遗传算法缺少收敛判据,降低迭代次数暂时不能减少单次实验耗时。综上,文中所提策略能有效提高配电网集群划分结果的准确度及配电网分区控制的效率。

    Abstract:

    Cluster division can effectively solve the problem of massive data analysis and a large number of equipment regulation caused by large-scale access of new energy to the distribution network. However,existing research on cluster partitioning algorithms exhibits low accuracy and may yield unreasonable outcomes. In order to solve the above problems,factors that should be considered in the cluster division strategy of distribution network are described when a large number of distributed power sources are connected,and the scale limit index is designed accordingly. By studying the process of genetic algorithm,the reason why genetic algorithm shows poor global optimization ability is found out,and then the algorithm is enhanced. Simulation results demonstrate that the proposed scale limit index successfully avoids unreasonable partitioning outcomes. The proposed improved genetic algorithm greatly improves the accuracy of the algorithm. Because the genetic algorithm has no convergence criterion,the reduction of the number of iterations can not directly reduce the experiment time. In summary,the research effectively improve the accuracy of genetic algorithm and enhance the efficiency of cluster division in distributed network.

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李宗强,赵耀,王云,纪坤华.面向有功潮流最优问题的配电网集群划分策略[J].电力工程技术,2024,43(3):151-160

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  • 收稿日期:2023-11-23
  • 最后修改日期:2024-01-21
  • 录用日期:2023-07-24
  • 在线发布日期: 2024-05-23
  • 出版日期: 2024-05-28
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