期刊文献+

居民用户智能用电建模及优化仿真分析 被引量:33

Intelligent Electricity Consumption Modeling and Optimal Simulations for Residential Users
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摘要 随着居民用户智能家电普及率的不断提高,实现智能用电优化已成为电力需求侧管理的重要研究内容。通过对居民用户智能家电的用电起始时间、用电时长、用电时段数的设计,并结合智能家电的用电特征及电价机制,给出了一种以用电费用最小为目标的智能用电优化方法。该方法同时考虑了用电起始时间、结束时间和允许的最大中断次数等约束条件。通过算例中电价机制及智能家电约束对用电安排影响的仿真分析,验证了该方法可有效减少用电费用及降低居民用户用电负荷峰值;同时探讨了在实时电价机制及更灵活的家电中断约束下,智能用电优化效果更为显著。 With the trend of increasing smart appliances used in homes,their electricity consumption scheduling is becoming an important research area of the demand side management.By dividing a day into slots,representing the starting time,the operation length and electricity consumption with these slots while referring to electricity consumption characteristics and the pricing mechanism,an intelligent electricity consumption optimization approach is proposed to minimize the electricity consumption fee.The approach considers such constraints as the allowed earliest starting instant,the latest ending instant and maximum interruption times at the same time.Simulation results show that the method proposed is effective in reducing the electricity consumption fee and the load peak,especially the more appreciable reduction in the spot price mechanism and with flexible interruption times of smart appliances.
出处 《电力系统自动化》 EI CSCD 北大核心 2016年第3期46-51,共6页 Automation of Electric Power Systems
基金 国家电网公司科技项目(SGTYHT/14-JS-188)~~
关键词 居民用户 智能用电 智能家电 电价机制 用电安排 residential users intelligent electricity consumption smart appliance pricing mechanism electricity consumption scheduling
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  • 1苗新,张恺,田世明,李建歧,殷树刚,赵子岩.支撑智能电网的信息通信体系[J].电网技术,2009,33(17):8-13. 被引量:138
  • 2何永秀,王冰,熊威,张婷,刘洋洋.基于模糊综合评价的居民智能用电行为分析与互动机制设计[J].电网技术,2012,36(10):247-252. 被引量:72
  • 3谢少军,肖华锋,罗运虎.直流楼宇技术初议[J].电工技术学报,2012,27(1):107-113. 被引量:27
  • 4LEEJ H, KIM H J, PARK G L, et al. Energy consumption scheduler for demand response systems in the smart grid[J]. Journal of Information Science and Engineering, 2012, 28 (5) : 955-969.
  • 5CHOI C S, LEE J I, LEE I W. Complex home energy management system architecture and implementation for green home with built-in photovoltaic and motorized blinders[C]// International Conference on ICT Convergence (ICTC), October 30 November 2, 2012, Hangzhou, China: 295-296.
  • 6HAN J S, CHOI C S, PARK W K, et al. Green home energy management system through comparison of energy usage between the same kinds of home appliances[C]// 2011 IEEE 15th International Symposium on Consumer Electrics, June 14 17, 2011, Singapore: 4p.
  • 7LI Jian, CHUNG J Y, XIAO Jin, et al. On the design and implementation of a home energy management system[C]// 2011 6th International Symposium on Wireless and Pervasive Computing (ISWPC), February 23-25, 2011, Hong Kong, China.
  • 8PIPATTANASOMPORN M, KUZLU M, RAHMAN S. An algorithm for intelligent home energy management and demand response analysis[J]. IEEE Trans on Power Systems, 2012, 3(4) : 2166-2173.
  • 9NGUYEN D T, LE L B. Joint optimization of electric vehicle and home energy scheduling considering user comfort preference [J]. IEEE Trans on Smart Grid, 2014, 5(1): 188-199.
  • 10ZHAO Z, LEE W C, SHIN Y, et al. An optimal power scheduling method for demand response in home energy management system[J]. IEEE Trans on Smart Grid, 2013, 4(3) : 1391-1400.

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