首页|基于增强精英保留遗传算法的虚拟微网群动态划分及能量局域自治

基于增强精英保留遗传算法的虚拟微网群动态划分及能量局域自治

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  • 国家科技期刊平台
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互联的多微网系统作为可再生能源利用的有效形式之一,随着"双碳"工作的推进越发受到重视.该文旨在研究基于海量可控设备的优化调节方法,以促进可再生能源充分就地消纳,提高多微网系统运行经济性.然而,对海量可控设备的全局控制将面临"维数灾难"的挑战.现有研究中基于地理分布的分区优化能够实现"降维控制",但可再生能源出力的波动性与负荷在时间上的变化和空间上的迁移,都会导致固定分区的方法难以适用于多微网系统态势变迁下的能量动态管控.针对上述问题,首先建立微电网动态模型;进而提出通过增强精英保留遗传算法(strengthen elitist genetic algorithm,SEGA)将互联多微网场景划分为多个边界可动态调整的虚拟微网群,并进行能量"局域自治"优化;最后基于改进的IEEE-123节点模型进行仿真,结果显示1 h内动态边界虚拟微网群的总运行成本比固定边界虚拟微网群的总运行成本降低了13.6%,且所采用的SEGA的求解时间比传统遗传算法减少了7.09%.
Dynamical Partitioning and Local Energy Autonomy of Virtual Microgrid Groups Based on Strengthen Elitist Genetic Algorithm
As one of the effective forms of renewable energy utilization,the multi-microgrid system has attracted much attention with the advancement of"carbon peaking and carbon Neutrality".The purpose of this paper is to study the optimization method towards massive controllable equipment,so as to promote the local consumption of renewable energy and reduce the operation cost of the multi-microgrid system.However,the global control of massive controllable equipment will face the challenge of"curse of dimensionality".In the existing research,the partitioning based on geographical distribution could achieve such control target via dimensional reduction.However,the fluctuation of renewable energy output,as well as the real-time power change and the migration in space of load will make it difficult for the fixed-boundary partitioning method to be applied in the dynamical energy management under the changing situation of multi-microgrid systems.In view of the above problems,this paper first establishes the system dynamic model for each individual microgrid.Then,the considered multi-microgrid system is divided into multiple virtual microgrid groups whose boundaries can be dynamically adjusted through the strengthen elitist genetic algorithm(SEGA),and the local autonomous energy optimization is implemented.Finally,a modified IEEE-123 node simulation model is built.The simulation results show that for one hour the operation cost of the virtual microgrid groups with dynamic boundary is 13.6%lower than that of the virtual microgrid with fixed boundary,and the time to obtain the solution via SEGA is 7.09%less than that via a conventional genetic algorithm.

multi-microgrid systemvirtual microgrid groupdynamic partitioningstrengthen elitist genetic algorithm(SEGA)

华昊辰、翟家祥、陈星莺、王博、余昆、秦钰超、沈俊、丁一、贺大玮

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河海大学能源与电气学院,江苏省 南京市 210098

剑桥大学应用数学和理论物理系,英国 剑桥 CB3 0WA

北京理工大学机械与车辆学院,北京市 海淀区 100081

浙江大学电气工程学院,浙江省 杭州市 310027

清华四川能源互联网研究院,四川省 成都市 610213

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多微网系统 虚拟微网群 动态分区 增强精英保留遗传算法(SEGA)

国家自然科学基金中央高校基本科研业务费专项中国博士后科学基金中国博士后科学基金

52107089B2002010712021M7000402022T150182

2024

10.13334/j.0258-8013.pcsee.223348

中国电机工程学报
中国电机工程学会

中国电机工程学报

CSTPCD北大核心
影响因子：2.712
ISSN：0258-8013
年,卷(期)：2024.44(12)
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