基于用户行为预测的分布式光伏智能社区需求响应策略

doi:10.6040/j.issn.1672-3961.0.2022.177

山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (5): 24-34.doi: 10.6040/j.issn.1672-3961.0.2022.177

基于用户行为预测的分布式光伏智能社区需求响应策略

刘振¹,孙媛媛^1*,李亚辉¹,许庆燊¹,于涛²,庞延庆³

1.山东大学电气工程学院, 山东济南 250061;2.济南市规划设计研究院, 山东济南 250000;3.山东电工时代能源科技有限公司, 山东济南 250022

发布日期:2022-10-20
作者简介:刘振(1998— ),男,山东青岛人,硕士研究生,主要研究方向为源网荷储协同规划. E-mail:914173263@qq.com. *通信作者简介:孙媛媛(1981— ),女,山东淄博人,教授,博士生导师,主要研究方向为电能质量与直流电力系统等. E-mail:sunyy@sdu.edu.cn
基金资助:
国家自然科学基金面上资助项目(51977123)

Demand response strategy for distributed photovoltaic smart community based on model prediction of user behavior analysis

LIU Zhen¹, SUN Yuanyuan^1*, LI Yahui¹, XU Qingshen¹, YU Tao², PANG Yanqing³

1. School of Electrical Engineering, Shandong University, Jinan 250061, Shandong, China;
2. Jinan City Planning and Design Institute, Jinan 250000, Shandong, China;
3. Shandong Electric Times Energy Technology Co., Ltd., Jinan 250022, Shandong, China

Published:2022-10-20

摘要/Abstract

摘要： 为解决分布式能源接入场景下用户行为不确定性与需求计划的冲突问题,建立一种基于用户行为预测的分布式光伏智能社区响应策略,通过分布式光伏与需求负荷资源的协调配合,协调用户行为的不确定性与需求计划的制定。建立需求资源的数学模型,采用广义回归神经网络和概率神经网络预测用户行为,形成预前调度措施。基于建立的模型与措施形成光伏社区需求响应整体策略,提出用户自主响应算法以提升用户舒适度。基于分时电价、光照辐射、负荷参数等信息,构建智能家居控制系统并进行仿真分析,预测模型的准确性达96.33%,具有较强的适用性,验证了需求响应策略的有效性,解决了用户行为与需求调度的配合问题,有效降低用户用电成本。

关键词: 智能家居, 需求响应, 分布式光伏接入, 用户行为预测, 不确定性分析

中图分类号:

TM73

刘振,孙媛媛,李亚辉,许庆燊,于涛,庞延庆. 基于用户行为预测的分布式光伏智能社区需求响应策略[J]. 山东大学学报 (工学版), 2022, 52(5): 24-34.

LIU Zhen, SUN Yuanyuan, LI Yahui, XU Qingshen, YU Tao, PANG Yanqing. Demand response strategy for distributed photovoltaic smart community based on model prediction of user behavior analysis[J]. Journal of Shandong University(Engineering Science), 2022, 52(5): 24-34.

参考文献

[1] 于璇.智能家居互联互通标准跨界落地的新开始[J].电器,2020,1(10):13. YU Xuan.A new start for the cross-border implementation of smart home interconnection standards[J]. Electrical Appliances, 2020, 1(10):13.
[2] 龚诚嘉锐,林顺富,边晓燕, 等.基于多主体主从博弈的负荷聚合商经济优化模型[J].电力系统保护与控制,2022,50(2):30-40. GONGCHENG Jiarui, LIN Shunfu, BIAN Xiaoyan, et al. Economic optimization model of load aggregator based on multi-agent master-slave game[J]. Power System Protection and Control, 2022, 50(2):30-40.
[3] 刘建明.融合通信促进智能用电飞速发展[J].供用电,2014,1(8):14-15. LIU Jianming.Converged communication promotes the rapid development of smart electricity[J]. Power Supply, 2014, 1(8):14-15.
[4] 姚建国,杨胜春,王珂,等.智能电网“源-网-荷”互动运行控制概念及研究框架[J].电力系统自动化,2012,36(21):1-6. YAO Jianguo, YANG Shengchun, WANG Ke, et al. The concept and research framework of “source-grid-load” interactive operation control for smart grids[J].Automation of Electric Power Systems, 2012, 36(21):1-6.
[5] 撖奥洋,邓星,文明浩,等.高渗透率下大电网应对微网接入的策略[J].电力系统自动化,2010,34(1):78-83. AO Aoyang, DENG Xing, WEN Minghao, et al. Strategies for large power grids to respond to microgrid access under a high penetration rate[J]. Automation of Electric Power Systems, 2010, 34(1):78-83.
[6] 曾博,杨雍琦,段金辉,等.新能源电力系统中需求侧响应关键问题及未来研究展望[J].电力系统自动化,2015,39(17):10-18. ZENG Bo, YANG Yongqi, DUAN Jinhui, et al. Key issues and future research prospects of demand-side response in new energy power systems[J]. Automation of Electric Power Systems, 2015, 39(17):10-18.
[7] 郭亦宗,冯斌,岳铂雄,等.负荷聚合商模式下考虑需求响应的超短期负荷预测[J].电力系统自动化,2020,45(1):1-16. GUO Yizeng, FENG Bin, YUE Boxiong, et al.Ultra-short-term load forecasting considering demand response under load aggregator mode[J]. Automation of Electric Power Systems, 2020, 45(1):1-16.
[8] KUZLU M. Score-based intelligent home energy management(HEM)algorithm for demand response applications and impact of HEM operation on customer comfort[J]. Generation Transmission & Distribution Iet, 2015, 9(7):627-635.
[9] 彭文昊,陆俊,冯勇军,等.计及用户参与不确定性的需求响应策略优化方法[J].电网技术,2018,42(5):1588-1594. PENG Wenhao, LU Jun, FENG Yongjun, et al. Demand response strategy optimization method considering user participation uncertainty[J]. Power Grid Technology, 2018, 42(5):1588-1594.
[10] 马汉杰,林霞,胥晓晖,等.基于自适应粒子群算法的智能家居管理系统负荷优化模型[J].山东大学学报(工学版),2017,47(6):57-62. MA Hanjie, LIN Xia, XU Xiaohui, et al. Load optimization model of smart home management system based on adaptive particle swarm algorithm[J]. Journal of Shandong University(Engineering Edition), 2017, 47(6):57-62.
[11] 郑若楠,李志浩,唐雅洁,等.考虑居民用户参与度不确定性的激励型需求响应模型与评估[J].电力系统自动化,2022,46(8):154-162. ZHENG Ruonan, LI Zhihao, TANG Yajie, et al.Incentive demand response model and evaluation considering uncertainty of residential user participation[J]. Automation of Electric Power Systems, 2022, 46(8):154-162.
[12] 米夏,徐晓红,刘小恺.基于波动特性的光伏电站出力时间序列建模方法研究[J].微型电脑应用, 2022, 38(3):120-122. MI Xia, XU Xiaohong, LIU Xiaokai. Research on modeling method of photovoltaic power station output time series based on fluctuation characteristics[J].Microcomputer Application, 2022, 38(3):120-122.
[13] SHAO S, PIPATTANASOMPORN M, RAHMAN S.Development of physical-based demand response-enabled residential load models[J]. IEEE Transactions on Power Systems, 2013, 28(2):607-614.
[14] ZHANG P, ZHOU X, PELLICCIONE P, et al. RBF-MLMR: a multi-label metamorphic relation prediction approach using RBF neural network[J].IEEE Access, 2017, 5:21791-21805.
[15] 张晓东,艾欣,潘玺安. 考虑用户可调度潜力的负荷聚合商优化调度策略[J]. 华北电力大学学报(自然科学版), 2022, 4(8):1-16. ZHANG Xiaodong, AI Xin, PAN Xian. Load aggregator optimal scheduling strategy considering user scheduling potential[J]. Journal of North China Electric Power University(Natural Science Edition), 2022, 4(8):1-16.
[16] 闫坤,沈苏彬.一种基于智能家居的用户行为预测方法[J].计算机技术与发展,2020,30(1):19-24. YAN Kun, SHEN Subin.A user behavior prediction method based on smart home[J]. Computer Technology and Development, 2020, 30(1):19-24.

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

基于用户行为预测的分布式光伏智能社区需求响应策略

Demand response strategy for distributed photovoltaic smart community based on model prediction of user behavior analysis

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

多维度评价

本文评价

推荐阅读 10

[1]	鄢仁武,林剑雄,李培强,吴国耀,匡宇. 考虑碳排放因子与动态重构的主动配电网双层优化策略[J]. 山东大学学报 (工学版), 2025, 55(2): 16-27.
[2]	马汉杰,林霞,胥晓晖,张健,张智晟. 基于自适应粒子群算法的智能家居管理系统负荷优化模型[J]. 山东大学学报(工学版), 2017, 47(6): 57-62.
[3]	马庆,李歧强*. 基于电力需求响应的公共建筑基线负荷预测[J]. 山东大学学报(工学版), 2011, 41(2): 114-118.