基于实时电价的用户用电响应行为研究

doi:10.3969/j.issn.1000-7229.2016.02.009

电力建设 ›› 2016, Vol. 37 ›› Issue (2): 63-68.doi: 10.3969/j.issn.1000-7229.2016.02.009

基于实时电价的用户用电响应行为研究

黄海新1 ,2，邓丽1，文峰1，王飞3

1.沈阳理工大学，沈阳市 110159； 2.中国科学院沈阳自动化研究所，沈阳市 110016；3. 国电科学技术研究院，沈阳市 110001

出版日期:2016-02-01
作者简介:黄海新（1973 ），女，博士，副教授，研究方向为智能电网、机器学习、聚类技术；邓丽（1991），女，研究生，研究方向为智能电网、机器学习；文峰（1977），男，博士，副教授，研究方向为智能交通、数据挖掘；王飞（1980），男，工程师，研究方向为智能电网、电网安全。
基金资助:
国家自然科学基金项目（61233007）

Customer Responsive Behavior Based on Real-Time Pricing

HUANG Haixin1 2, DENG Li1, WEN Feng1, WANG Fei3

1. Shenyang Ligong University, Shenyang 110159, China； 2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China； 3. Guodian Science and Technology Research Institute, Shenyang 110001, China

Online:2016-02-01
Supported by:
Project supported by National Natural science foundation of China(61233007

摘要/Abstract

摘要：

随着智能电网的发展，电网开放性不断增强，需求响应（demand response, DR）策略提出，并广泛应用于电力市场的运营模式中。各国相继推出需求响应的实时电价（real-time pricing, RTP）策略，来提高电网的有效性与电力市场的可靠性。合理地分析实时电价下用户的用电响应行为，对制定更高效的实时电价机制，实施需求响应策略具有重要意义。因此，基于用户的需求价格弹性（price elasticity of electricity demand, PED）模型，通过回归模型学习需求价格弹性，模拟用户响应行为。实验表明，学习获得的用户价格弹性可以很好地实现用户响应行为的拟合，较传统的调查问卷方式获得固定的用户价格弹性，回归模型克服时间与空间的变化问题，更高效地实现用户响应行为的学习，为实时电价提供决策支持。

关键词: 需求响应（DR）, 实时电价（RTP）, 需求价格弹性（PED）, 回归模型

Abstract:

With the development of smart grid and the deregulation of electricity market, the demand response (DR) has been widely used in the operating mode of power market. Many countries have introduced the realtime pricing (RTP) strategies of DR to improve the efficiency of power system and the ability of power market. The careful analysis of the users response to the RTP is significant for the implementation of DR strategies. Therefore, we propose a regression model to learn the price elasticity of electricity demand (PED) based on the PED model and simulate the responsive behaviors of users. Experimental results show that comparing with the content PED obtained by tradition questionnaire, the learned PED can effectively meet the demand, and the regression model can overcome the problems both in time and space and achieve the analysis of users responsive behaviors efficiently, which can provide decision support for the realtime pricing strategy.

Key words: demand response （DR）, real-time pricing （RTP）, price elasticity of electricity demand （PED）, regression model

中图分类号:

TU 47

黄海新，邓丽，文峰，王飞. 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, WANG Fei. Customer Responsive Behavior Based on Real-Time Pricing[J]. Electric Power Construction, 2016, 37(2): 63-68.

参考文献

［1］牟龙华,朱国锋,朱吉然.基于智能电网的智能用户端设计［J］.电力系统保护与控制,2010,38（21）:53-56.
MU Longhua, ZHU Guofeng, ZHU Jiran. Design of intelligent terminal based on smart grid［J］. Power System Protection and Control, 2010,38（21）: 53-56.
［2］熊雄, 杨仁刚, 叶林,等. 电力需求侧大规模储能系统经济性评估［J］. 电工技术学报, 2013, 28（9）: 224-230.
XIONG Xiong, YANG Rengang, YE Lin, et al. Economic evaluation of large-scale energy storage allocation in power demand side［J］. Transactions of China Electrotechnical Sosiety, 2013, 28（9）: 224-230.
［3］孙近文, 万云飞, 郑培文, 等. 基于需求侧管理的电动汽车有序充放电策略［J］. 电工技术学报, 2014, 29（8）: 64-69.
SUN Jinwen, WAN Yunfei, ZHENG Peiwen, et al. Coordinated charging and discharging strategy for electric vehicles based on demand side management［J］. Transactions of China Electrotechnical Society, 2014, 29（8）: 64-69.
［4］US Department of Energy. Benefits of demand response in electricity markets and recommendations for achieving them: a report to the United States congress pursuant to section 1252 of the energy policy act of 2005 ［EB/OL］. http://energy.gov/oe/downloads/benefits-demand-response-electricity-markets-and-recommendations-achieving-them-report.2006-02/2015-03-29.
［5］SAMADI, PEDRAM. Optimal real-time pricing algorithm based on utility maximization for smart grid［C］// Smart Grid Communications, 2010 First IEEE International Conference on IEEE, 2010:415-420.
［6］KIRSCHEN D S, STRBAC G, CUMPERAYOT P, et al. Factoring the elasticity of demand in electricity prices［J］. Power Systems IEEE Transactions on, 2000, 15（2）: 612-617.
［7］ALBADI M H, EL-SAADANY E F. A summary of demand response in electricity markets［J］. Electric Power Systems Research, 2008, 78（11）: 1989-1996.
［8］AALAMI H A, MOGHADDAM M P, YOUSEFI G R. Demand response modeling considering interruptible/curtailable loads and capacity market programs［J］. Applied Energy, 2010, 87（1）: 243-250.
［9］MOGHADDAM M P, ABDOLLAHI A, RASHIDINEJAD M. Flexible demand response programs modeling in competitive electricity markets［J］. Applied Energy, 2011, 88（9）: 3257-3269.
［10］EVENS S K C, KRKKINEN S. Pricing models and mechanisms for the promotion of demand side integration［J］. VTT Technical Research Centre of Finland, 2009（9）: 2-58.
［11］何永秀,戴爱英,罗涛,等.智能电网条件下的两阶段电力需求预测模型研究［J］.电力系统保护与控制, 2010, 38（21）: 167-172.
HE Yongxiu, DAI Aiying, LUO Tao, et al. A two-stage electricity demand forecasting model in the smart grid［J］. Power System Protection and Control, 2010, 38（21）: 167-172.
［12］LI Zuyi. Fundamentals of Power System Economics ［Book Review］［J］. IEEE Power&Energy Magazine, 2006, 4（4）:76-78.
［13］KIRSCHEN D S. Demand-side view of electricity markets［J］. Power Systems IEEE Transactions on, 2003, 18（2）: 520-527.
［14］AALAMI H, Yousefi G R, MOGHADAM M P. Demand response model considering EDRP and TOU programs［C］//Transmission and Distribution Conference and Exposition, IEEE/PESIEEE, 2008:1 - 6.
［15］SCHWEPPE F C, CARAMANIS M C, TABORS R D, et al. Spot pricing of electricity［M］. Boston: Kluwer，1989.
［16］牛朋超,康积涛,李爱武,等.智能电网开启电网运行新形式［J］.电力系统保护与控制,2010,38（19）: 240-244.
NIU Pengchao, KANG Jitao, LI Aiwu, et al. New operation form of power network started by smart grid［J］. Power System Protection and Control, 2010, 38（19）: 240-244.
［17］周伏秋,王娟.我国电力需求侧管理工作面临的形势及建议［J］.电力需求侧管理,2015,02:1-4.
ZHOU Fuqiu, WANG Juan. Situations and suggestions of national DSM work［J］.Power demand side management,2015（2）:1-4.

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基于实时电价的用户用电响应行为研究

Customer Responsive Behavior Based on Real-Time Pricing

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