基于温控负荷电价响应的配用电交互影响分析

doi:10.3969/j.issn.1000-7229.2018.02.017

电力建设 ›› 2018, Vol. 39 ›› Issue (2): 130-.doi: 10.3969/j.issn.1000-7229.2018.02.017

• 电力经济研究 • 上一篇

基于温控负荷电价响应的配用电交互影响分析

吴磊1，于建成2，李思维3，胡庆娥4，唐佳4，王丹4

（1.国网天津市电力公司电力科学研究院，天津市 300384；2.国网天津市电力公司，天津市 300010；
3.国网信息通信产业集团公司北京中电飞华通信股份有限公司，北京市 100070；

4.天津大学智能电网教育部重点实验室，天津市 300072）

出版日期:2018-02-01
作者简介:吴磊（1985），男，硕士，工程师，主要研究方向为智能电网；于建成（1977），男，博士，高级工程师，主要研究方向为智能电网；李思维（1988），男，工程师，主要从事电动汽车充放电设施建设、智能用电小区、智能园区、智能楼宇、能效管理等技术领域的方案设计、科研开发和推广应用工作；胡庆娥（1993），女，硕士研究生，主要研究方向为综合需求响应；唐佳（1992），男，硕士研究生，主要研究方向为主动配电网、需求侧响应；王丹（1981），男，博士，副教授，主要研究方向为综合能源系统分析、智能配用电技术、需求侧管理。
基金资助:
基金项目：国家电网公司科技项目（SGTJDK00DWJS1700034）

Interaction Effect of Distribution Network and Users Based on Thermostat Controlled Load Responding to Price Signals

WU Lei1，YU Jiancheng2，LI Siwei3，HU Qinge4，TANG Jia4，WANG Dan4

（1.Electric Power Research Institute of State Grid Tianjin Electric Power Company, Tianjin 300384, China；
2.State Grid Tianjin Electric Power Company, Tianjin 300010, China；3.Beijing Fibrlink Communications Co., Ltd., State Grid Information & Telecommunication Group, Beijing 100070, China；4.Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China）

Online:2018-02-01
Supported by:

摘要/Abstract

摘要： 摘要：售电市场开放条件下，实施基于电价的需求响应（demand response，DR）有利于充分调动用户的积极性，使其主动参与到负荷调控中去，实现电力系统的削峰填谷，保证其安全经济运行。文章以居民空调为研究对象，首先建立了居民空调负荷的数学模型，分析了其单体和聚合负荷的响应特性；在此基础上，引入基于实时电价（real-time pricing，RTP）的线性化居民空调交互式需求响应控制策略，实现对单台空调的控制。为了量化交互式需求响应策略对用户侧和电网侧的影响，提出了温度越限时间百分比、高峰时段负荷削减比、高峰时段馈线注入有功功率削减比、高峰时段网损削减比这4个指标。仿真结果表明，在不影响空调用户舒适度的前提下，基于实时电价的线性化居民空调交互式需求响应策略能有效实现削峰的作用，减少馈线有功功率注入和系统网络损耗。

关键词: , 关键词：售电市场, 居民空调模型, 实时电价（RTP）, 交互式需求响应, 削峰填谷 ,

Abstract: ABSTRACT： Under the condition of opening the electricity market, the implementation of price based demand response （DR） is conducive to fully mobilizing the enthusiasm of users, making them participate in load regulation and achieving the goal of peak load cutting in power system, maintaining its safe and economic operation. This paper takes the residential air conditioning model as the research object and establishes the mathematical model for the residential air conditioning load. Then the response characteristics of the monomer and the polymerization load are analyzed. On this basis, the linearized interactive demand response strategy of residential air conditioning based on real-time pricing （RTP） is applied to realize the active control of the load. Four indexes including the percentage of the temperature over upper limit time, the load reduction ratio during the peak period, the total active power reduction ratio and the network loss reduction ratio during the peak period are developed to quantify the impact of the strategy on the demand side and the power supply side. The simulation results show that without affecting the comfort of air conditioning users, the linearized interactive demand response strategy of residential air conditioning based on RTP can effectively achieve the role of peak cutting, reducing the feeder active power injection and system power loss.

Key words: KEYWORDS： electricity market, residential air conditioning model, real-time pricing （RTP）, interactive demand response, peak load cutting and valley filling

中图分类号:

TM 73

吴磊，于建成，李思维，胡庆娥，唐佳，王丹. 基于温控负荷电价响应的配用电交互影响分析 [J]. 电力建设, 2018, 39(2): 130-.

WU Lei，YU Jiancheng，LI Siwei，HU Qinge，TANG Jia，WANG Dan. Interaction Effect of Distribution Network and Users Based on Thermostat Controlled Load Responding to Price Signals [J]. Electric Power Construction, 2018, 39(2): 130-.

参考文献

［1］QI Y, WANG D, WANG X, et al. Frequency control ancillary service provided by efficient power plants integrated in queuing-controlled domestic water heaters［J］. Energies, 2017, 10（4）: 559.

［2］王锡凡, 肖云鹏, 王秀丽. 新形势下电力系统供需互动问题研究及分析［J］. 中国电机工程学报, 2014, 34（29）:5018-5028.

WANG Xifan, XIAO Yunpeng, WANG Xiuli. Study and analysis on supply-demand interaction of power systems under new circumstances［J］. Proceedings of the CSEE, 2014, 34 （29）: 5018-5028.

［3］张晓萱, 薛松, 杨素,等. 售电侧市场放开国际经验及其启示［J］. 电力系统自动化, 2016, 40（9）:1-8.

ZHANG Xiaoxuan, XUE Song, YANG Su, et al. International experience and lessonsin power sales side market liberalization ［J］. Automation of Electric Power Systems, 2016, 40（9）: 1-8.

［4］U S Department of Energy. Benefits of demand response in electricity markets and recommendations for achieving them: A report to the United State Congress pursuant to section 1252 of the energy policy act of 2005［R/OL］.（2008-02-21）［2017-08-21］.http://www.oe.energy,gov/DocumentMedia/congress_1252d.pdf.

［5］Federal Energy Regulatory Commission. Assessment of demand response and advanced metering: 2006 staff report［R/OL］.（2008-08-21）［2017-08-21］.http://www.ferc.gov/legal/staff-reports/demand-reaponse.pdf.

［6］Federal Energy Regulatory Commission. Assessment of demand response and advanced metering:2007 staff report［R/OL］.（2008-09-13）［2017-08-21］.http://www.ferc.gov/legal/staff-reports/09-07-demand-reaponse.pdf.

［7］赵岩, 李博嵩, 蒋传文. 售电侧开放条件下我国需求侧资源参与电力市场的运营机制建议［J］. 电力建设, 2016, 37（3）:112-116.

ZHAO Yan, LI Bosong, JIANG Chuanwen. Operation mechanism suggestions of demand-side resources in electricity market under retail power market deregulation in China［J］. Electric Power Construction, 2016, 37 （3）: 112-116.

［8］张钦, 王锡凡, 王建学,等. 电力市场下需求响应研究综述［J］. 电力系统自动化, 2008, 32（3）:97-106.

ZHANG Qin, WANG Xifan, WANG Jianxue, et al. Survey of demand response research in deregulated electricity markets［J］. Automation of Electric Power Systems, 2008, 32（3）: 97-106.

［9］MCKENNA K, KEANE A. Residential load modeling of price-based demand response for network impact studies［J］. IEEE Transactions on Smart Grid, 2015, 7（5）: 2285-2294.

［10］邝国军, 李少娟. 计及可中断负荷与用户电价响应的实时电价研究［J］. 广西电力, 2013, 36（1）:7-11.

KUANG Guojun, LI Shaojuan. Study on spot price considering interruptible load and customer price response［J］. Guangxi Electric Power, 2013, 36（1）: 7-11.

［11］LU N, CHASSIN D P. A state-queueing model of thermostatically controlled appliances［J］. IEEE Transactions on Power Systems, 2004, 19（3）: 1666-1673.

［12］赵慧颖, 刘广一, 贾宏杰,等. 基于精细化模型的需求侧响应策略分析［J］. 电力系统保护与控制, 2014, 42（1）:62-69.

ZHAO Huiying, LIU Guangyi, JIA Hongjie, et al. Analysis of demand response program based on refined models ［J］. Power System Protection and Control, 2014, 42（1）: 62-69.

［13］MOHSENIAN-RAD A H, LEON-GARCIA A. Optimal residential load control with price prediction in real-time electricity pricing environments［J］. IEEE Transactions on Smart Grid, 2010, 1（2）: 120-133.

［14］OLDEWURTEL F, ULBIG A, PARISIO A, et al. Reducing peak electricity demand in building climate control using real-time pricing and model predictive control［C］//2010 49th IEEE Conference on Decision and Control （CDC）. Atlanta, GA, USA: IEEE, 2010: 1927-1932.

［15］KATIPAMULA S, LU N. Evaluation of residential HVAC control strategies for demand response programs［J］. Transactions-American Society of Heating Refrigerating and Air Conditioning Engineers, 2006, 112（1）: 535.

［16］GridLAB-D project［EB/OL］.（2017-08-18）［2017-08-21］. http://sourceforge.net/p/gridlab-d/ files/?source=navbar.

［17］BROEER T. Analysis of smart grid and demand response technologies for renewable energy integration: Operational and environmental challenges［D］. Canada: University of Victoria, 2015.

［18］范孟华, 王丹, 张家安,等. 电热泵负荷等值热力学建模及控制策略评估［J］. 电力系统及其自动化学报, 2016, 28（4）:31-37.

FAN Menghua, WANG Dan, ZHANG Jiaan, et al. Modeling method and control strategy evaluation for electric heat pump［J］. Proceedings of the CSU-EPSA, 2016, 28（4）: 31-37.

［19］SUBBARAO K, FULLER J C, KALSI K, et al. Transactive control and coordination of distributed assets for ancillary services［R］. Richland, WA: Pacific Northwest National Laboratory （PNNL）, 2013.

［20］王珂, 姚建国, 姚良忠,等. 电力柔性负荷调度研究综述［J］. 电力系统自动化, 2014, 38（20）:127-135.

WANG Ke, YAO Jianguo, YAO Liangzhong, et al. Survey of research on flexible loads scheduling technologies［J］. Automation of Electric Power Systems, 2014, 38（20）: 127-135.

［21］PIPATTANASOMPORN M, KUZLU M, RAHMAN S. An algorithm for intelligent home energy management and demand response analysis［J］. IEEE Transactions on Smart Grid, 2012, 3（4）: 2166-2173.

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基于温控负荷电价响应的配用电交互影响分析

Interaction Effect of Distribution Network and Users Based on Thermostat Controlled Load Responding to Price Signals

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