居民用户参与电网调峰激励智慧用能策略研究
CSTR:
作者:
作者单位:

(1.国网电子商务有限公司,北京 100053;2.国网电商科技有限公司,天津 300309;3.长沙理工大学电气与信息工程学院,湖南 长沙 410114)

通讯作者:

马 瑞(1971—),男,博士,教授,主要从事电力系统分析与控制、能源互联网及电力市场的研究;E?mail:marui818@126.com

中图分类号:

TM73

基金项目:

国家自然科学基金(51977012);国网电子商务有限公司科技项目(8200/2021?72001B)


Research on smart energy consumption strategy of residents participating in peak load regulation
Author:
Affiliation:

(1.State Grid Electronic Commerce Co., Ltd., Beijing 100053, China; 2.State Grid Electronic Commerce Technology Co., Ltd., Tianjin 300309, China; 3.School of Electrical & Information Engineering, Changsha University of Science & Technology, Changsha 410114,China)

  • 摘要
  • | |
  • 访问统计
  • |
  • 参考文献 [18]
  • | | | |
  • 文章评论
    摘要:

    以实现居民用户智慧用能为着眼点,提出一种居民用户参与电网调峰激励智慧用能策略。首先,根据家用电器的负荷特性将居民用户用电负荷分为4类,并建立其相应数学模型;然后,综合考虑时间、用电满意度以及电费满意度约束建立居民用户用电成本优化模型;同时,考虑传统分时电价难以调动居民参与电网削峰填谷积极性,根据余弦相似度建立调峰效果评估模型,基于居民调峰贡献度提出市场激励机制;最后,将调峰效果引入夏普利值(Shapley value)分配中,建立居民用户与电网公司的合作博弈模型。仿真结果表明:所提策略能有效降低家庭用电成本,同时促进居民用户积极参与电网调峰。

    Abstract:

    Focusing on enabling smart energy consumption for residential users, a smart energy strategy with incentives for residential users to participate in grid peak shaving is proposed. According to the load characteristics of household appliances, residential loads are classified into four categories, and corresponding mathematical models are established. Considering time constraints, electricity satisfaction constraints, and electricity bill satisfaction constraints, an optimization model for residential electricity consumption cost is established. Given that traditional time-of-use electricity pricing is inadequate in motivating residents to participate in grid peak shaving and valley filling, a peak shaving effect evaluation model is established based on cosine similarity, and a market incentive mechanism is proposed based on residents' contribution to peak shaving. Finally, the peak shaving effect is introduced into the Shapley value allocation to establish a cooperative game model between residential users and grid companies. Simulation results demonstrate that the proposed strategy can effectively reduce household electricity consumption costs while promoting active participation of residential users in grid peak shaving.

    参考文献
    [1] 向悦萍,杨健维,臧天磊,等.计及电能质量的电力市场多主体博弈模型[J].电网技术,2020,44(9):3383-3394. XIANG Yueping,YANG Jianwei,ZANG Tianlei,et al. Multi-agent game model in electricity market considering power quality[J]. Power System Technology,2020,44 (9):3383-3394.
    [2] 陈岑,武传涛,林湘宁,等.计及上下游市场的园区综合能源商购售能策略[J].电工技术学报,2022,37(1):220-231. CHEN Cen,WU Chuantao,LIN Xiangning,et al.Purchase and sale strategies of park integrated energy suppliers in wholesale and retail markets[J].Transactions of China Electrotechnical Society,2022,37(1):220-231.
    [3] 孙毅,贾孟扬,陆俊,等.计及用户需求响应的智能用电互动潜力分析[J].电力科学与技术学报2016,31(4):43-50. SUN Yi,JIA Mengyang,LU Jun,,et al..Analysis of the potential of intelligent electricity interaction taking into account the response of user needs[J].Journal of Electric Power Science and Technology,2016,31(4):43-50.
    [4] 李振坤,黄滢,李谅,等.计及需求侧响应的主动配电网多时间尺度优化调度[J].电力建设,2023,44(3):36-48. LI Zhenkun,HUANG Ying,LI Liang,et al.Multi-time scale optimal dispatching of active distribution network considering demand-side response[J].Electric Power Construction,2023,44(3):36-48.
    [5] 史林军,史江峰,杨启航,等.基于分时电价的家庭智能用电设备的运行优化[J].电力系统保护与控制,2018,46(24):88-95. SHI Linjun,SHI Jiangfeng,YANG Qihang,,et al.Optimization of the operation of household smart electrical equipment based on time-of-use electricity price[J].Power System Protection and Control,2018,46(24):88-95.
    [6] 王永明,陈宇星,殷自力,等.基于大数据分析的电力用户行为画像构建方法研究[J].高压电器,2022,58(10):173-179. WANG Yongming,CHEN Yuxing,YIN Zili,et al.Research on construction method of power user behavior portrait based on big data analysis[J].High Voltage Apparatus,2022,58(10):173-179.
    [7] 赵会茹,王学杰,李兵抗,等.考虑能量共享的多社区光储系统分布鲁棒优化调度[J].电力系统自动化,2022,46(9):21-31. ZHAO Huiru,WANG Xuejie,LI Bingkang,et al.Distributionally robust optimal dispatch for multi-community photovoltaic and energy storage system considering energy sharing[J].Automation of Electric Power Systems,2022,46(9):21-31.
    [8] 朱超婷,杨玲君,崔一铂,等.考虑需求响应用户参与度的主动配电网优化调度[J].电测与仪表,2023,60(4):99-105. ZHU Chaoting,YANG Lingjun,CUI Yibo,et al.Optimal scheduling of active distribution network considering user participation in demand response[J].Electrical Measurement & Instrumentation,2023,60(4):99-105.
    [9] 孙毅,贾孟扬,陆俊,等.需求侧管理中面向居民用电的互动化评价模型[J].电力系统自动化,2017,41(13):62-69. SUN Yi,JIA Mengyang,LU Jun,et al.Interactive evaluation model for residential power consumption in DSM[J].Automation of Electric Power Systems,2017,41 (13):62-69.
    [10] HUANG Y,ZHANG J,MO Y,et al.A hybrid optimization approach for residential energy management[J].IEEE Access,2020,8:225201-225209.
    [11] YU Y,WANG B,WANG Z,et al.Wrapper feature selection based multiple logistic regression model for determinants analysis of residential electricity consumption[C]//Asian Conference on Energy,Power and Transportation Electrification (ACEPT),Singapore,2017.
    [12] ZHANG Y,MENG K,KONG W,et al.Bayesian hybrid collaborative filtering-based residential electricity plan recommender system[J].IEEE Transactions on Industrial Informatics,2019,15:4731-4741.
    [13] VIVEKANANTHAN C,MISHRA Y,LEDWICH G,et al.Demand response for residential appliances via customer reward scheme[J].IEEE Transactions on Smart Grid,2017,5(2):809-820.
    [14] 韩峰,曾成碧,苗虹.计及EV与可再生能源的家庭微电网能源管理系统[J].电力科学与技术学报,2021,36(1):79-86. HAN Feng,ZENG Chengbi,MIAO Hong.A home microgrid energy management system that takes into account EVs and renewable energy[J].Journal of Electric Power Science and Technology,2021,36(1):79-86.
    [15] 白东壮,田世明,邹毅豪,等.基于FDA的居民用户空调用电行为分类分析方法[J].智慧电力,2022,50(3):44-49+71. BAI Dongzhuang,TIAN Shiming,ZOU Yihao,et al.Classification analysis method of residential air conditioning electricity consumption behavior based on functional data analysis model[J].Smart Power,2022,50(3):44-49+71.
    [16] 朱明辉,权琛,朱超,等.耦合聚类基线及舒适度的居民负荷需求响应评估方法[J].电网与清洁能源,2023,39(5):99-105+112. ZHU Minghui,QUAN Chen,ZHU Chao,et al.An evaluation method of residents’load demand response based on coupling clustering baseline and comfort degree[J].Power System and Clean Energy,2023,39(5):99-105+112.
    [17] HERATH P,VENAYAGAMOORTHY G K.Scalable residential demand response management[J].IEEE Access,2021,9:159133-159145.
    [18] 傅质馨,李紫嫣,朱俊澎,等.面向多用户的多时间尺度电力套餐与家庭能量优化策略[J].电力系统保护与控制,2022,50(11):21-31. FU Zhixin,LI Ziyan,ZHU Junpeng,et al.Multi-user multi-timescale power packages and home energy optimization strategies[J].Power System Protection and Control,2022,50(11):21-31.
    相似文献
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

王静智,赵 磊,邓方明,等.居民用户参与电网调峰激励智慧用能策略研究[J].电力科学与技术学报,2024,39(4):121-127.
WANG Jinzhi, ZHAO Lei, DENG Fangming, et al. Research on smart energy consumption strategy of residents participating in peak load regulation[J]. Journal of Electric Power Science and Technology,2024,39(4):121-127.

复制
分享
文章指标
  • 点击次数:105
  • 下载次数: 812
  • HTML阅读次数: 0
  • 引用次数: 0
历史
  • 在线发布日期: 2024-09-10
文章二维码