文章摘要
陈中,李云倩,冷钊莹,等.典型家用大功率负载精细化建模及能量管理策略[J].电力系统自动化,2018,42(22):135-143. DOI: 10.7500/AEPS20180411008.
CHEN Zhong,LI Yunqian,LENG Zhaoying, et al.Refined Modeling and Energy Management Strategy of Typical Household High-power Loads[J].Automation of Electric Power Systems,2018,42(22):135-143. DOI: 10.7500/AEPS20180411008.
典型家用大功率负载精细化建模及能量管理策略
Refined Modeling and Energy Management Strategy of Typical Household High-power Loads
DOI:10.7500/AEPS20180411008
关键词: 精细化建模  家庭能量管理系统  空调负荷  电热水器  电动汽车
KeyWords: refined modeling  home energy management system  air conditioning load  electric water heater  electric vehicles
上网日期:2018-10-15
基金项目:国家重点研发计划资助项目(2016YFB0101803);国家电网公司科技项目(SGTYHT/17-JS-199)
作者单位E-mail
陈中 东南大学电气工程学院, 江苏省南京市 210096  
李云倩 东南大学电气工程学院, 江苏省南京市 210096 yunqian_lo@163.com 
冷钊莹 东南大学电气工程学院, 江苏省南京市 210096  
陆广香 东南大学电气工程学院, 江苏省南京市 210096  
摘要:
      基于负载本体物理模型和表达用户主观使用意愿的控制模型,提出了三种居民侧典型大功率可控负载(暖通空调系统、电热水器和电动汽车充电负荷)的精细化建模方法。首先,对自定义家庭算例进行仿真,详细分析了各项技术参数的精细化定义及非典型用电过程对负载用电效果的影响。然后,建立了以优化用电成本为目标的能量管理策略优化模型。该研究得到了用户个性化用电环境及主观用电理念能够反映至精细化模型并显著影响智能化家庭能量管理系统负载优化控制策略的结论。
Abstract:
      Based on the load physical models and the refined control models which reflect users' subjective willingness, a precise modeling method for three typical high-power controllable loads is proposed on residential side including heating, ventilating and air conditioning(HVAC)systems, electric water heaters and electric vehicles. Firstly, the simulation is implemented in an example of a customer home. The influences of the refined definition of various relevant technical parameters and the typical power consumption process on electricity consumption of different loads are analyzed in details. Then, the home energy management strategy optimization model which aims at optimizing electricity cost is established. It is concluded that the individualized power consumption environment and subjective electricity consumption concept from users could be incorporated and demonstrated by the refined models of loads and significantly impact the load optimization control strategies of intelligent home energy management system.
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