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1.南方电网科学研究院有限责任公司,广东省广州市 510000
2.深圳供电局有限公司,广东省深圳市 518000
3.清华大学深圳国际研究生院,广东省深圳市 518055
Received:10 February 2024,
Revised:2024-05-29,
Published Online:05 September 2024,
Published:25 October 2024
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周保荣,李江南,吕逸帆等.基于改进自组织映射的用户电碳画像构建方法[J].电力系统自动化,2024,48(20):182-190.
ZHOU Baorong,LI Jiangnan,LYU Yifan,et al.Construction Method for User Electricity-Carbon Profile Based on Improved Self-organizing Map[J].Automation of Electric Power Systems,2024,48(20):182-190.
周保荣,李江南,吕逸帆等.基于改进自组织映射的用户电碳画像构建方法[J].电力系统自动化,2024,48(20):182-190. DOI: 10.7500/AEPS20240210001.
ZHOU Baorong,LI Jiangnan,LYU Yifan,et al.Construction Method for User Electricity-Carbon Profile Based on Improved Self-organizing Map[J].Automation of Electric Power Systems,2024,48(20):182-190. DOI: 10.7500/AEPS20240210001.
近年来,“碳达峰∙碳中和”目标的提出促进了能源电力领域的低碳转型。在新型电力系统中,除了发电侧,用户侧也应承担部分碳排放责任。针对用户侧的碳排放责任分摊以及现有用户画像对碳特性的研究缺失,提出了一种基于改进自组织映射(ISOM)的用户电碳画像构建方法。首先,基于节点负荷数据构建潮流模型并进行碳排放流分析;其次,基于碳排放流分析,构建结合减碳潜力的负荷动态调度模型,进而得到多元电碳特征;然后,基于麻雀搜索算法(SSA)和三角拓扑邻域的自组织映射(SOM)对多元电碳特征聚类形成用户电碳画像;最后,在不同调度场景下对电网用户实际负荷数据进行测试,并与现有方法进行对比,实验结果验证了所提方法的有效性和实用性。
In recent years
the proposal of goals of “carbon emission peak and carbon neutrality” has promoted the low-carbon transformation in the field of electric energy. In the new power system
in addition to the power generation side
the user side should also bear part of the responsibility for carbon emissions. To fill the research gap on the allocation of carbon emission responsibilities on the user side and carbon features of existing user profiles
this paper proposes a construction method for the user electricity-carbon profile based on the improved self-organizing map (ISOM). Firstly
a power flow model based on load data is built and the carbon emission flow is analyzed. Secondly
based on the carbon emission flow analysis
the load dynamic dispatching model combining carbon emission reduction potential is constructed
and the multi-dimensional electricity-carbon features are obtained. Then
based on the sparrow search algorithm (SSA) and the self-organizing map (SOM) of triangular-topological neighborhoods
the multi-dimensional electricity-carbon features are clustered to form the user electricity-carbon profile. Finally
actual load data of power grid users are tested in different dispatching scenarios and compared with existing methods. The experimental results verify the effectiveness and practicality of the proposed method.
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