海洋捕食者算法在含分布式电源配电网无功优化中的应用

Application of the Marine Predators Algorithm to Reactive Power Optimization in Distribution Networks with Distributed Generation

中文关键词:分布式电源  无功优化  日投切限制  熵权法  多目标海洋捕食者算法

keywords:distributed generation  reactive power optimization  daily cutting limit  entropy weight  multi-objective marine predator algorithm

中文摘要:针对新能源出力的不确定性与负荷波动可能导致并网后电压越限，对电力系统稳定运行造成严重影响的问题，构建了含分布式电源的配电网动态无功优化数学模型，考虑分布式电源接入位置及变压器变比的日投切次数，以系统有功损耗和电压偏差作为目标函数，并采用熵权法对多目标帕累托（Pareto）解集进行决策，选取合适的折中解。通过改进的多目标海洋捕食者算法对 IEEE 33 节点和 IEEE 69 节点算例进行仿真分析。仿真结果表明，所提模型与方法能够满足系统运行规划需求，提升运行的经济性与稳定性；相较于其他多目标智能算法，所提算法具有更强的寻优能力，可获得更前沿、更广泛的 Pareto 解集。

Abstract:Given the rapid development trend in new energy sources，the inherent uncertainty of their output and load fluctuations may trigger voltage overruns upon grid connection，severely impacting power system stability.This paper develops a mathematical model for the dynamic reactive power optimization of distribution networks integrating distributed generation.The model explicitly considers the connection location of distributed generation sources and the daily switching limits for transformer taps，taking system active power loss and voltage deviation as objective functions.The entropy weight method facilitates decision-making within the multi-objective Pareto solution set，enabling the selection of an optimal compromise solution.Simulation analysis is conducted on the IEEE 33-bus and IEEE 69-bus test cases using an improved multi-objective marine predators algorithm.The results demonstrate that the proposed model and method effectively satisfy system operation planning requirements while enhancing operational economy and stability.Furthermore，compared to alternative multi-objective intelligent algorithms，the proposed algorithm demonstrates superior optimization capabilities，yielding a more frontier and extensive Pareto solution set.