电力用户行为模型:基本概念与研究框架
|
摘要
在高比例可再生能源并网和电力市场改革不断推进的背景下,通过广泛的用户互动为电力系统提供了灵活性、实现个性化的用户服务,成为未来智能电网发展的必然要求。随着智能电表等信息获取手段的不断普及,电力系统"物理-信息-社会"深度耦合的特征日益突出,对电力用户的分析面临手段上的突破,以物理模型与数据模型相结合的综合分析成为重要趋势。本文提出电力用户行为模型的概念,借鉴社会学对于用户行为的解读,从行为主体、行为环境、行为手段、行为结果和行为效用五个方面进行剖析,然后进一步延拓为集群行为和预见行为;在此基础上对电力用户行为模型的内涵和外延进行分析,阐述用户行为模型的研究意义;最后对电力用户行为建模的理论研究框架进行了分析。
With the increasing integration of renewable energy and the advancement of the electric power market, broad interaction between consumers and systems, which is an effective way to provide flexibility to the power system and realize personalized consumer service, become an inevitable requirement of the development of future smart grid. Meanwhile, information acquisition devices such as smart meters are gaining popularity. The "cyber-physical-social" deep coupling characteristic of the power system becomes more prominent. Breakthroughs are needed to analyze the electrical consumer, where, combining physical-driven and data-driven approaches is an important trend. This paper decomposes consumer behavior into five basic aspects from the sociological perspective: behavior subject, behavior environment, behavior means, behavior result, and behavior utility. On this basis, the concept of consumer behavior model is proposed. Finally, the research framework for electrical consumer behavior model is analyzed.
With the increasing integration of renewable energy and the advancement of the electric power market, broad interaction between consumers and systems, which is an effective way to provide flexibility to the power system and realize personalized consumer service, become an inevitable requirement of the development of future smart grid. Meanwhile, information acquisition devices such as smart meters are gaining popularity. The "cyber-physical-social" deep coupling characteristic of the power system becomes more prominent. Breakthroughs are needed to analyze the electrical consumer, where, combining physical-driven and data-driven approaches is an important trend. This paper decomposes consumer behavior into five basic aspects from the sociological perspective: behavior subject, behavior environment, behavior means, behavior result, and behavior utility. On this basis, the concept of consumer behavior model is proposed. Finally, the research framework for electrical consumer behavior model is analyzed.
引文
[1]康重庆,姚良忠.高比例可再生能源电力系统的关键科学问题与理论研究框架[J].电力系统自动化,2017,41(9):2-11.Kang Chongqing,Yao Liangzhong.Key scientific issues and theoretical research framework for power systems with high proportion of renewable energy[J].Automation of Electric Power System,2017,41(9):2-11.
[2]陈启鑫,刘敦楠,林今,等.能源互联网的商业模式与市场机制(一)[J].电网技术,2015,39(11):3050-3056.Chen Qixin,Liu Dunnan,Lin Jin,et al.Business models and market mechanisms of energy internet(I)[J].Power System Technology,2015,39(11):3050-3056.
[3]Wang Yi,Chen Qixin,Kang Chongqing,et al.Load profiling and its application to demand response:a review[J].Tsinghua Science and Technology,2015,20(2):117-129.
[4]丁一,惠红勋,林振智,等.面向电力需求侧主动响应的商业模式及市场框架设计[J].电力系统自动化,2017,41(14):2-9.Ding Yi,Hui Hongxun,Lin Zhenzhi,et al.Design of business model and market framework oriented to active demand response of power demand side[J].Automation of Electric Power System,2017,41(14):2-9.
[5]Xue Yusheng,Yu Xingguo.Beyond smart gridcyber-physical-social system in energy future[J]Proceedings of the IEEE,2017,105(12):2290-2292.
[6]郭庆来,辛蜀骏,孙宏斌,等.电力系统信息物理融合建模与综合安全评估:驱动力与研究构想[J].中国电机工程学报,2016,36(6):1481-1489.Guo Qinglai,Xin Shujun,Sun Hongbin,et al.Power system cyber-physical modeling and security assessment:motivation and ideas[J].Proceedings of the CSEE,2016,36(6):1481-1489.
[7]屈星,李欣然,宋军英,等.考虑配电网调压的综合负荷模型[J].电工技术学报,2018,33(4):759-770.Qu Xing,Li Xinran,Song Junying,et al.Composite load model considering voltage regulation of distribution network[J].Transactions on China Electrotechnical Society,2018,33(4):759-770.
[8]Hong Tao,Fan Shu.Probabilistic electric load forecasting:a tutorial review[J].International Journal of Forecasting,2016,32(3):914-938.
[9]张鹏,李春燕,张谦.基于需求响应调度容量上报策略博弈的电网多代理系统调度模式[J].电工技术学报,2017,32(19):170-179.Zhang Peng,Li Chunyan,Zhang Qian.A power system dispatch model based on game-theoretic multi-agent system demand response scheduling capacity reporting[J].Transactions on China Electrotechnical Society,2017,32(19):170-179.
[10]赵波,汪湘晋,张雪松,等.考虑需求侧响应及不确定性的微电网双层优化配置方法[J].电工技术学报,2018,33(14):3284-3295.Zhao Bo,Wang Xiangjin,Zhang Xuesong,et al.Two-layer method of microgrid optimal sizing considering demand-side response and uncertainties[J].Transactions on China Electrotechnical Society,201833(14):3284-3295.
[11]孙毅,顾玮,李彬,等.面向售电侧改革的用户分层聚类与套餐推荐方法[J].电网技术,2018,42(2):447-454.Sun Yi,Gu Wei,Li Bin,et al.Electricity-retailmarket-reform oriented recommendation method of user’s hierarchical clustering and pricing packages[J].Power System Technology,2018,42(2):447-454.
[12]Wang Yi,Chen Qixin,Kang Chongqing,et al.Clustering of electricity consumption behavior dynamics toward big data applications[J].IEEETransactions on Smart Grid,2016,7(5):2437-2447.
[13]Wang Yi,Chen Qixin,Hong Tao,et al.Review of smart meter data analytics:applications,methodologies,and challenges[J].IEEE Transactions on Smart Grid,2018,DOI:10.119/TSG.2018.2818167.
[14]Akouemo Hermine N,Povinelli Richard J.Data improving in time series using arx and ann models[J].IEEE Transactions on Power Systems,2017,32(5):3352-3359.
[15]赵天辉,王建学,马龙涛,等.基于非参数回归分析的工业负荷异常值识别与修正方法[J].电力系统自动化,2017,41(18):53-59.Zhao Tianhui,Wang Jianxue,Ma Longtao,et al.Outlier detection and correction method for industrial loads based on nonparametric regression analysis[J].Automation of Electric Power System,2017,41(18):53-59.
[16]赵文清,沈哲吉,李刚.基于深度学习的用户异常用电模式检测[J].电力自动化设备,2018,38(9):34-38.Zhao Wenqing,Shen Zheji,Li Gang.Anomaly detection for power consumption pattern based on deep learning[J].Electric Power Automation Equipment,2018,38(9):34-38.
[17]Júnior Leandro Aparecido Passos,Ramos Caio César Oba,Rodrigues Douglas,et al.Unsupervised nontechnical losses identification through optimum-path forest[J].Electric Power Systems Research,2016,140:413-423.
[18]Granell Ramon,Axon Colin J,Wallom David C.Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles[J].IEEE Transactions on Power Systems,2015,30(6):3217-3224.
[19]Benítez Ignacio,Quijano Alfredo,Díez José-Luis,et al.Dynamic clustering segmentation applied to load profiles of energy consumption from spanish custommers[J].International Journal of Electrical Power&Energy Systems,2014,55:437-448.
[20]Wang Pu,Liu Bidong,Hong Tao.Electric load forecasting with recency effect:a big data approach[J].International Journal of Forecasting,2016,32(3):585-597.
[21]蒋玮,黄丽丽,祁晖,等.基于分布式图计算的台区负荷预测技术研究[J].中国电机工程学报,201838(12):3419-3430.Jiang Wei,Huang Lili,Qi Hui,et al.Research on load forecasting technology of transformer areas based on distributed graph computing[J].Proceedings of the CSEE,2018,38(12):3419-3430.
[22]Chitsaz Hamed,Shaker Hamid,Zareipour Hamidreza et al.Short-term electricity load forecasting of buildings in microgrids[J].Energy and Buildings2015,99:50-60.
[23]Mocanu Elena,Nguyen Phuong H,Gibescu Madeleine,et al.Deep learning for estimating building energy consumption[J].Sustainable Energy,Grids and Networks,2016,6:91-99.
[24]Xie Jingrui,Hong Tao,Laing Thomas,et al.On normality assumption in residual simulation for probabilistic load forecasting[J].IEEE Transactions on Smart Grid,2017,8(3):1046-1053.
[25]Liu Bidong,Nowotarski Jakub,Hong Tao,et al.Probabilistic load forecasting via quantile regression averaging on sister forecasts[J].IEEE Transactions on Smart Grid,2017,8(2):730-737.
[26]Kavousian Amir,Rajagopal Ram,Fischer Martin.Determinants of residential electricity consumption:using smart meter data to examine the effect of climate,building characteristics,appliance stock,and occupants’behavior[J].Energy,2013,55:184-194.
[27]McLoughlin Fintan,Duffy Aidan,Conlon Micheal.Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables:an irish case study[J].Energy and Buildings,2012,48:240-248.
[28]王冬,王拓,王旗,等.一种面向需求响应资源的模糊聚类算法[J].中国电机工程学报,2018,38(14):4056-4063.Wang Dong,Wang Tuo,Wang Qi,et al.A fuzzy C-means clustering algorithm for demand-side response resource[J].Proceedings of the CSEE,2018,38(14):4056-4063.
[29]Dyson Mark E,Borgeson Samuel D,Tabone Michealangelo D,et al.Using smart meter data to estimate demand response potential,with application to solar energy integration[J].Energy Policy,2014,73:607-619.
[30]Mahmoudi-Kohan N,Moghaddam Moghaddam P,Sheikh-El-Eslami M.An annual framework for clustering-based pricing for an electricity retailer[J].Electric Power Systems Research,2010,80(9):1042-1048.
[31]Zhang Yi,Chen Weiwei,Xu Rui,et al.A clusterbased method for calculating baselines for residential loads[J].IEEE Transactions on Smart Grid,2016,7(5):2368-2377.
[32]Nature Research.Behavioural and social sciences at nature research[Z/OL].https://socialsciences.nature.com/.
[33]张静,石丽雯,郭伟,等.面向医疗供应链的新零售平台用户体验研究[J].包装工程,2019,40(4):42-51.Zhang Jing,Shi Liwen,Guo Wei,et al.User experience of new retail platform for medical supply chain[J].Packaging Engineering,2019,40(4):42-51.
[34]邓奕军.图书馆用户行为模型的动态更新方法研究与实现[J].图书情报导刊,2018,3(11):27-31.Deng Yijun.Research and implementation of dynamic updating method for library’s user behavior model[J].Journal of Library and Information Science2018,3(11):27-31.
[35]理查德·格里格.心理学与生活[M].16版.北京:人民邮电出版社,2006.
[36]Wang Yi,Chen Qixin,Gan Dahua,et al.Deep learning-based socio-demographic information identification from smart meter data[J].IEEE Transactions on Smart Grid,2019,10(3):2593-2602.
[37]Mcloughlin Finta,Duffy Aidan,Conlon Micheal.Aclustering approach to domestic electricity load profile characterisation using smart,metering data[J].Applied Energy,2015,141:190-199.
[38]Zoha Ahmed,Gluhak Alexander,Imran Muhammad Ali,et al.Non-intrusive load monitoring approaches for disaggregated energy sensing:a survey[J].Sensors,2012,12(12):16838-16866.
[39]Wang Yi,Chen Qixin,Kang Chongqing,et al.Sparse and redundant representation-based smart meter data compression and pattern extraction[J].IEEE Transactions on Power Systems,2017,32(3):2142-2151.
[40]Teeraratkul Thanchanok,O’Neill Daniel,Lall Sanjay Shape-based approach to household electric load curve clustering and prediction[J].IEEE Transactions on Smart Grid,2018,9(5):5196-5206.
[41]Wang Yi,Gan Dahua,Sun Mingyang,et al.Probabilistic individual load forecasting using pinball loss guided LSTM[J].Applied Energy,2019,235:10-20.
[42]Albert Adrian,Rajagopal Ram.Smart meter driven segmentation:what your consumption says about you[J].IEEE Transactions on Power Systems,201328(4):4019-4030.
[2]陈启鑫,刘敦楠,林今,等.能源互联网的商业模式与市场机制(一)[J].电网技术,2015,39(11):3050-3056.Chen Qixin,Liu Dunnan,Lin Jin,et al.Business models and market mechanisms of energy internet(I)[J].Power System Technology,2015,39(11):3050-3056.
[3]Wang Yi,Chen Qixin,Kang Chongqing,et al.Load profiling and its application to demand response:a review[J].Tsinghua Science and Technology,2015,20(2):117-129.
[4]丁一,惠红勋,林振智,等.面向电力需求侧主动响应的商业模式及市场框架设计[J].电力系统自动化,2017,41(14):2-9.Ding Yi,Hui Hongxun,Lin Zhenzhi,et al.Design of business model and market framework oriented to active demand response of power demand side[J].Automation of Electric Power System,2017,41(14):2-9.
[5]Xue Yusheng,Yu Xingguo.Beyond smart gridcyber-physical-social system in energy future[J]Proceedings of the IEEE,2017,105(12):2290-2292.
[6]郭庆来,辛蜀骏,孙宏斌,等.电力系统信息物理融合建模与综合安全评估:驱动力与研究构想[J].中国电机工程学报,2016,36(6):1481-1489.Guo Qinglai,Xin Shujun,Sun Hongbin,et al.Power system cyber-physical modeling and security assessment:motivation and ideas[J].Proceedings of the CSEE,2016,36(6):1481-1489.
[7]屈星,李欣然,宋军英,等.考虑配电网调压的综合负荷模型[J].电工技术学报,2018,33(4):759-770.Qu Xing,Li Xinran,Song Junying,et al.Composite load model considering voltage regulation of distribution network[J].Transactions on China Electrotechnical Society,2018,33(4):759-770.
[8]Hong Tao,Fan Shu.Probabilistic electric load forecasting:a tutorial review[J].International Journal of Forecasting,2016,32(3):914-938.
[9]张鹏,李春燕,张谦.基于需求响应调度容量上报策略博弈的电网多代理系统调度模式[J].电工技术学报,2017,32(19):170-179.Zhang Peng,Li Chunyan,Zhang Qian.A power system dispatch model based on game-theoretic multi-agent system demand response scheduling capacity reporting[J].Transactions on China Electrotechnical Society,2017,32(19):170-179.
[10]赵波,汪湘晋,张雪松,等.考虑需求侧响应及不确定性的微电网双层优化配置方法[J].电工技术学报,2018,33(14):3284-3295.Zhao Bo,Wang Xiangjin,Zhang Xuesong,et al.Two-layer method of microgrid optimal sizing considering demand-side response and uncertainties[J].Transactions on China Electrotechnical Society,201833(14):3284-3295.
[11]孙毅,顾玮,李彬,等.面向售电侧改革的用户分层聚类与套餐推荐方法[J].电网技术,2018,42(2):447-454.Sun Yi,Gu Wei,Li Bin,et al.Electricity-retailmarket-reform oriented recommendation method of user’s hierarchical clustering and pricing packages[J].Power System Technology,2018,42(2):447-454.
[12]Wang Yi,Chen Qixin,Kang Chongqing,et al.Clustering of electricity consumption behavior dynamics toward big data applications[J].IEEETransactions on Smart Grid,2016,7(5):2437-2447.
[13]Wang Yi,Chen Qixin,Hong Tao,et al.Review of smart meter data analytics:applications,methodologies,and challenges[J].IEEE Transactions on Smart Grid,2018,DOI:10.119/TSG.2018.2818167.
[14]Akouemo Hermine N,Povinelli Richard J.Data improving in time series using arx and ann models[J].IEEE Transactions on Power Systems,2017,32(5):3352-3359.
[15]赵天辉,王建学,马龙涛,等.基于非参数回归分析的工业负荷异常值识别与修正方法[J].电力系统自动化,2017,41(18):53-59.Zhao Tianhui,Wang Jianxue,Ma Longtao,et al.Outlier detection and correction method for industrial loads based on nonparametric regression analysis[J].Automation of Electric Power System,2017,41(18):53-59.
[16]赵文清,沈哲吉,李刚.基于深度学习的用户异常用电模式检测[J].电力自动化设备,2018,38(9):34-38.Zhao Wenqing,Shen Zheji,Li Gang.Anomaly detection for power consumption pattern based on deep learning[J].Electric Power Automation Equipment,2018,38(9):34-38.
[17]Júnior Leandro Aparecido Passos,Ramos Caio César Oba,Rodrigues Douglas,et al.Unsupervised nontechnical losses identification through optimum-path forest[J].Electric Power Systems Research,2016,140:413-423.
[18]Granell Ramon,Axon Colin J,Wallom David C.Impacts of raw data temporal resolution using selected clustering methods on residential electricity load profiles[J].IEEE Transactions on Power Systems,2015,30(6):3217-3224.
[19]Benítez Ignacio,Quijano Alfredo,Díez José-Luis,et al.Dynamic clustering segmentation applied to load profiles of energy consumption from spanish custommers[J].International Journal of Electrical Power&Energy Systems,2014,55:437-448.
[20]Wang Pu,Liu Bidong,Hong Tao.Electric load forecasting with recency effect:a big data approach[J].International Journal of Forecasting,2016,32(3):585-597.
[21]蒋玮,黄丽丽,祁晖,等.基于分布式图计算的台区负荷预测技术研究[J].中国电机工程学报,201838(12):3419-3430.Jiang Wei,Huang Lili,Qi Hui,et al.Research on load forecasting technology of transformer areas based on distributed graph computing[J].Proceedings of the CSEE,2018,38(12):3419-3430.
[22]Chitsaz Hamed,Shaker Hamid,Zareipour Hamidreza et al.Short-term electricity load forecasting of buildings in microgrids[J].Energy and Buildings2015,99:50-60.
[23]Mocanu Elena,Nguyen Phuong H,Gibescu Madeleine,et al.Deep learning for estimating building energy consumption[J].Sustainable Energy,Grids and Networks,2016,6:91-99.
[24]Xie Jingrui,Hong Tao,Laing Thomas,et al.On normality assumption in residual simulation for probabilistic load forecasting[J].IEEE Transactions on Smart Grid,2017,8(3):1046-1053.
[25]Liu Bidong,Nowotarski Jakub,Hong Tao,et al.Probabilistic load forecasting via quantile regression averaging on sister forecasts[J].IEEE Transactions on Smart Grid,2017,8(2):730-737.
[26]Kavousian Amir,Rajagopal Ram,Fischer Martin.Determinants of residential electricity consumption:using smart meter data to examine the effect of climate,building characteristics,appliance stock,and occupants’behavior[J].Energy,2013,55:184-194.
[27]McLoughlin Fintan,Duffy Aidan,Conlon Micheal.Characterising domestic electricity consumption patterns by dwelling and occupant socio-economic variables:an irish case study[J].Energy and Buildings,2012,48:240-248.
[28]王冬,王拓,王旗,等.一种面向需求响应资源的模糊聚类算法[J].中国电机工程学报,2018,38(14):4056-4063.Wang Dong,Wang Tuo,Wang Qi,et al.A fuzzy C-means clustering algorithm for demand-side response resource[J].Proceedings of the CSEE,2018,38(14):4056-4063.
[29]Dyson Mark E,Borgeson Samuel D,Tabone Michealangelo D,et al.Using smart meter data to estimate demand response potential,with application to solar energy integration[J].Energy Policy,2014,73:607-619.
[30]Mahmoudi-Kohan N,Moghaddam Moghaddam P,Sheikh-El-Eslami M.An annual framework for clustering-based pricing for an electricity retailer[J].Electric Power Systems Research,2010,80(9):1042-1048.
[31]Zhang Yi,Chen Weiwei,Xu Rui,et al.A clusterbased method for calculating baselines for residential loads[J].IEEE Transactions on Smart Grid,2016,7(5):2368-2377.
[32]Nature Research.Behavioural and social sciences at nature research[Z/OL].https://socialsciences.nature.com/.
[33]张静,石丽雯,郭伟,等.面向医疗供应链的新零售平台用户体验研究[J].包装工程,2019,40(4):42-51.Zhang Jing,Shi Liwen,Guo Wei,et al.User experience of new retail platform for medical supply chain[J].Packaging Engineering,2019,40(4):42-51.
[34]邓奕军.图书馆用户行为模型的动态更新方法研究与实现[J].图书情报导刊,2018,3(11):27-31.Deng Yijun.Research and implementation of dynamic updating method for library’s user behavior model[J].Journal of Library and Information Science2018,3(11):27-31.
[35]理查德·格里格.心理学与生活[M].16版.北京:人民邮电出版社,2006.
[36]Wang Yi,Chen Qixin,Gan Dahua,et al.Deep learning-based socio-demographic information identification from smart meter data[J].IEEE Transactions on Smart Grid,2019,10(3):2593-2602.
[37]Mcloughlin Finta,Duffy Aidan,Conlon Micheal.Aclustering approach to domestic electricity load profile characterisation using smart,metering data[J].Applied Energy,2015,141:190-199.
[38]Zoha Ahmed,Gluhak Alexander,Imran Muhammad Ali,et al.Non-intrusive load monitoring approaches for disaggregated energy sensing:a survey[J].Sensors,2012,12(12):16838-16866.
[39]Wang Yi,Chen Qixin,Kang Chongqing,et al.Sparse and redundant representation-based smart meter data compression and pattern extraction[J].IEEE Transactions on Power Systems,2017,32(3):2142-2151.
[40]Teeraratkul Thanchanok,O’Neill Daniel,Lall Sanjay Shape-based approach to household electric load curve clustering and prediction[J].IEEE Transactions on Smart Grid,2018,9(5):5196-5206.
[41]Wang Yi,Gan Dahua,Sun Mingyang,et al.Probabilistic individual load forecasting using pinball loss guided LSTM[J].Applied Energy,2019,235:10-20.
[42]Albert Adrian,Rajagopal Ram.Smart meter driven segmentation:what your consumption says about you[J].IEEE Transactions on Power Systems,201328(4):4019-4030.