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住宅小区负荷群用电优化策略研究
引用本文:宋爽,李中伟,刘勇,张啸,郭钰锋. 住宅小区负荷群用电优化策略研究[J]. 电测与仪表, 2021, 58(8): 57-66. DOI: 10.19753/j.issn1001-1390.2021.08.008
作者姓名:宋爽  李中伟  刘勇  张啸  郭钰锋
作者单位:哈尔滨工业大学电气工程及自动化学院,哈尔滨150001
摘    要:针对一般住宅小区存在能耗高、缺少用电优化策略的实际情况,建立了可调整类负荷、不可调整类负荷、温控负荷的数学模型;根据可调整类负荷用电时间灵活的特点,提出了基于蚁群算法的住宅小区可调整类负荷群的用电优化策略;基于MATLAB平台,通过仿真分析了空调、电热水器的工作特性,并根据其工作特性分别提出了改变空调设定室温范围的空调...

关 键 词:住宅小区  智能用电  蚁群算法  温控负荷群  蒙特卡洛
收稿时间:2019-08-20
修稿时间:2019-08-20

Study on optimization strategy of load group power consumption in residential area
Song Shuang,Li Zhongwei,Liu Yong and Zhang Xiao. Study on optimization strategy of load group power consumption in residential area[J]. Electrical Measurement & Instrumentation, 2021, 58(8): 57-66. DOI: 10.19753/j.issn1001-1390.2021.08.008
Authors:Song Shuang  Li Zhongwei  Liu Yong  Zhang Xiao
Affiliation:School of Electrical Engineering Automation,Harbin Institute of Technology,School of Electrical Engineering Automation,Harbin Institute of Technology,School of Electrical Engineering Automation,Harbin Institute of Technology,School of Electrical Engineering Automation,Harbin Institute of Technology
Abstract:Aiming at the actual situation of high energy consumption and lack of power consumption optimization strategy in general residential areas, this paper firstly establishes the mathematical models of adjustable load, non-adjustable load and temperature control load. According to the flexible time of adjustable load, an optimal power consumption strategy based on ant colony algorithm is proposed. The working characteristics of air conditioner and electric water heater are obtained by simulation on MATLAB platform. According to the working characteristics, the optimal strategy of group electricity consumption of air conditioner by changing setting temperature range of air conditioner and the optimal strategy of group electricity consumption of electric water heater by changing the setting value of water temperature and preheating are proposed. At last, the Monte Carlo method is used to obtain various load curves and total load curves before optimization and after optimization. Simulation results show that the strategy proposed in this paper can help users to save electricity costs and reduce peak load and valley filling under the circumstance that it has little impact on users.
Keywords:residenceScommunity, intelligent  electricity consumption, antScolonySalgorithm, temperature  control load  group, Monte  Carlo
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