Domestic Daily Load Curve Modeling Based on User Behavior

doi:10.3969/j.issn.1000－7229.2016.10.016

Abstract

Abstract: With the proportion of residential power consumption growing gradually, the residential loads have the increasing influence on the distribution network. The effective modeling of domestic daily load curve is critical for the development of demand side management and smart grid. This paper constructs the model of domestic daily load curve based on user behavior. We construct the electrical models of typical residential loads based on the tested data. And based on the statistic research data, we adopt Markov chain Monte Carlo (MCMC) method to construct the behavioral models of residential loads which introduces probability functions to represent the influence of resident household characteristics such as resident population and household appliances owned. Then, we adopt a bottom-up modeling method to construct the model of domestic daily load curve and the simulation platform with combining electrical model and behavioral model. The proposed model is universal and systematic, whose feasibility and accuracy are validated through the compared analysis between simulations and measurement.

Key words: daily load curve, residential load, user behavior, Markov chain Monte Carlo (MCMC）

CLC Number:

LIN Shunfu, HUANG Nana,ZHAO Lunjia,TANG Bo, LI Dongdong . Domestic Daily Load Curve Modeling Based on User Behavior[J]. Electric Power Construction, 2016, 37(10): 114-.

References

［1］王葵，李建超，潘贞存，等. 居民谐波调查及仿真分析［J］. 电力系统保护与控制，2008，36(22)：88-103.
WANG Kui, LI Jianchao, PAN Zhencun, et al. Harmonic investigation of resident customer and simulation analysis ［J］. Power System Protection and Control, 2008, 36(22): 88-103.
［2］COLLIN A J，TSAGARAKIS G，KIPRAKIS A E，et al. Development of low-voltage load models for the residential load sector［J］. IEEE Transactions on Power Systems，2014，29(5): 2180-2188.
［3］黄海新, 邓丽, 文峰，等. 基于实时电价的用户用电响应行为研究［J］. 电力建设，2016, 37(2)：63-68.
HUANG Haixin, DENG Li, WEN Feng, et al. Customer response behavior based on real-time pricing［J］. Electric Power Construction, 2016, 37(2)：63-68.
［4］和敬涵, 谢毓毓, 叶豪东, 等. 电动汽车充电模式对主动配电网的影响［J］. 电力建设，2015, 36(1): 98-102.
HE Jinghan, XIE Yuyu, YE Haodong, et al. Influence of electric vehicles charging modes on active network distribution ［J］. Electric Power Construction, 2015, 36(1): 98-102.
［5］SWAN L G， UGURSAL V I. Modeling of end-use energy consumption in the residential sector: a review of modeling techniques ［J］. Renewable and Sustainable Energy Reviews, 2009, 13(8): 1819-1835.
［6］CHEN Y，CHU C，TSAO S，et al. Detecting users’ behaviors based on nonintrusive load monitoring technology ［C］//10th IEEE International Conference on Networking，Sensing and Control (ICNSC). Evry: ICNSC，2013：804-809.
［7］DING T，LIANG H，XU W. An analytical method for probabilistic modeling of the steady-state behavior of secondary residential system ［J］. IEEE Transactions on Smart Grid，2016, PP(99): 1-10.
［8］CHUAN L，UKIL A. Modeling and validation of electrical load profiling in residential buildings in Singapore ［J］. IEEE Transactions on Power Systems，2015，30(5): 2800-2809.
［9］JARDINI J A，TAHAN C M V，GOUVEA M R，et al. Daily load profiles for residential，commercial and industrial low voltage consumers［J］. IEEE Transactions on Power Delivery, 2000，15(1)：375-380.
［10］MARSZAL-POMIANOWSKA A, HEISELBERG P, LARSEN O K. Household electricity demand profiles -A high-resolution load model to facilitate modelling of energy flexible buildings ［J］. Energy，2016, 103: 487-501.
［11］JAMBAGI A, KRAMER M, CHENG V. Residential electricity demand modelling: Activity based modelling for a model with high time and spatial resolution［C］//2015 3rd International Renewable and Sustainable Energy Conference (IRSEC). Marrakech: IEEE, 2015：1-6.
［12］FISCHER D, HRTL A, WILLE-HAUSSMANN B. Model for electric load profiles with high time resolution for German households［J］. Energy and Buildings, 2015,92:170-179.
［13］FISCHER D, WOLF T, SCHERER J, et al. A stochastic bottom-up model for space heating and domestic hot waterload profiles for German households［J］. Energy and Buildings, 2016,124:120-128.
［14］YOHANIS Y G，MONDOL J D，WRIGHT A，et al. Real-life energy use in the UK: How occupancy and dwelling characteristics affect domestic electricity use ［J］. Energy and Buildings， 2008，40(6): 1053-1059.
［15］CHIOU Y-S. An occupant-driven integrative household system for residential building energy simulation［D］. Pittsburgh，Pennsylvania：Carnegie Mellon University，2009.
［16］SALLES D，JIANG C，XU W，et al. Assessing the collective harmonic impact of modern residential loads—Part I: methodology［J］. IEEE Transactions on Power Delivery，2012，27(4)：1937-1946.
［17］沈勇，杨荥，俞国勤，等. 上海地区居民用电的影响因素和需求预测模［J］. 华东电力，2012，40(10)：1763-1766.
SHEN Yong, YANG Ying, YU Guoqin, et al. Influencing factors and demand forecast model for residential electricity consumption in Shanghai［J］. East China Electric Power, 2012，40(10)：1763-1766.
［18］周晖，钮文洁，纪洪. 各种因素影响下的居民夏季用电状况分析［J］. 华北电力技术, 2003(3)：12-21.
［19］陈晶，张真. 居民生活用电特征与影响机理［J］. 统计研究, 2015, 32(5): 70-75.
CHEN Jing, ZHANG Zhen. Characteristics and impact mechanism of residential electricity consumption［J］. Statistical Research, 2015, 32(5): 70-75.
［20］曹磊，祖蓓. 夏季温湿指数与气象敏感电力负荷的研究［J］. 现代电力, 2011, 28(5): 41-45.
CAO Lei, ZU Bei. Research on summer temperature humidity index and weather sensitive electric load［J］. Modern Electric Power, 2011, 28(5): 41-45.
［21］XU P，HUANG J，SHEN P，et al. Commercial building energy use in six cities in Southern China［J］. Energy Policy，2013，53(1)，76-89.
［22］MILANOVIC J V，YAMASHITA K，MARTNEZ VILLANUEVA S，et al. International industry practice on power system load modeling［J］. IEEE Transactions on Power Systems, 2013，28（3）：3038-3046.
［23］许淑敏. 电力负荷模型结构的研究［D］. 北京: 华北电力大学, 2006.
XU Shumin. The study of power system load model structure ［D］. Beijing: North China Electric Power University, 2006.
［24］石文辉, 别朝红, 王锡凡. 大型电力系统可靠性评估中的马尔可夫链蒙特卡洛方法［J］. 中国电机工程学报，2008, 28(4): 9-15.
SHI Wenhui, BIE Chaohong, WANG Xifan. Applications of Markov chain Monte Carlo in large-scale system reliability evaluation ［J］. Proceedings of the CSEE, 2008, 28(4): 9-15.
［25］王勇, 韩学山, 丁颖，等，基于马尔科夫链的电力系统运行可靠性快速评估［J］. 电网技术, 2013, 37(2)：405-410.
WANG Yong, HAN Xueshan, DING Ying, et al. Markov chain-based rapid assessment on operational reliability of power grid［J］. Power System Technology, 2013, 37(2)：405-410.
［26］BELL M, SWINTON M，ENTCHEV E, et al. Development of micro combined heat and power technology assessment capability at the Canadian centre for housing technology［R］. Ottawa, ON, Canada, Tech. Rep. B-6010, 2003.

[1]	REN Bingli, ZHANG Zhengao, WANG Xuejun, LI Hui, YAN Dawei, ZHANG Pei. Assessment Method of Demand Response Peak Shaving Potential Based on Metered Load Data [J]. Electric Power Construction, 2016, 37(11): 64-.
[2]	SHI Wenbo, WANG Jian, SHENG Tianfu, BIAN Ruien. Prediction Method of Short-Term Load in Holidays Based on Improved Multiple Proportions Smoothing Method [J]. ELECTRIC POWER CONSTRUCTION, 2014, 35(10): 21-25.