Construction Method for User Electricity-Carbon Profile Based on Improved Self-organizing Map

被引:0
|
作者
Zhou, Baorong [1 ]
Li, Jiangnan [2 ]
Lyu, Yifan [3 ]
Cai, Xipeng [1 ]
Mao, Tian [1 ]
Xu, Yinliang [3 ]
机构
[1] China Southern Power Grid Electric Power Research Institute Co., Ltd., Guangzhou,510000, China
[2] Shenzhen Power Supply Bureau Co., Ltd., Shenzhen,518000, China
[3] Tsinghua Shenzhen International Graduate School, Shenzhen,518055, China
关键词
D O I
10.7500/AEPS20240210001
中图分类号
学科分类号
摘要
In recent years, the proposal of goals ofcarbon emission peak and carbon neutralityhas 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. © 2024 Automation of Electric Power Systems Press. All rights reserved.
引用
收藏
页码:182 / 190
相关论文
共 50 条
  • [31] Self-organizing map based on block learning
    Ohtsuka, A
    Kamiura, N
    Isokawa, T
    Matsui, N
    IEICE TRANSACTIONS ON FUNDAMENTALS OF ELECTRONICS COMMUNICATIONS AND COMPUTER SCIENCES, 2005, E88A (11) : 3151 - 3160
  • [32] GPU Based Parallelism for Self-Organizing Map
    Gajdos, Petr
    Platos, Jan
    PROCEEDING OF THE THIRD INTERNATIONAL CONFERENCE ON INTELLIGENT HUMAN COMPUTER INTERACTION (IHCI 2011), 2013, 179 : 231 - 242
  • [33] An Improved Electricity-Carbon Economic Dispatch Method for Microgrid Considering Carbon Trading
    Li, Yawei
    Du, Dajun
    Yang, Junlin
    Jiang, Cheng
    2022 41ST CHINESE CONTROL CONFERENCE (CCC), 2022, : 6178 - 6183
  • [34] An improved self-organizing map approach to traveling salesman problem
    Zhu, A
    Yang, SX
    2003 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS, INTELLIGENT SYSTEMS AND SIGNAL PROCESSING, VOLS 1 AND 2, PROCEEDINGS, 2003, : 674 - 679
  • [35] Vector representation of user's view using self-organizing map
    Ae, T
    Yamaguchi, T
    Monden, E
    Kawabata, S
    Kamitani, M
    IMAGE PROCESSING: ALGORITHMS AND SYSTEMS III, 2004, 5298 : 384 - 394
  • [37] Fraud detection using self-organizing map visualizing the user profiles
    Olszewski, Dominik
    KNOWLEDGE-BASED SYSTEMS, 2014, 70 : 324 - 334
  • [38] A Self-Organizing Map-based Method for Multi-Label Classification
    Colombini, Gustavo G.
    de Abreu, Iuri Bonna M.
    Cerri, Ricardo
    2017 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2017, : 4291 - 4298
  • [39] A New Method of Color Map Segmentation Based on the Self-organizing Neural Network
    Xue, Zhenqing
    Jia, Chunpu
    EMERGING INTELLIGENT COMPUTING TECHNOLOGY AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE, 2009, 5755 : 417 - 423
  • [40] Digital Modulation Recognition Method based on Self-Organizing Map Neural Networks
    Xu, Yiqiong
    Ge, Lindong
    Wang, Bo
    2008 4TH INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-31, 2008, : 1755 - 1758