基于负荷分解的居民差异化用电行为特性分析

发布时间：2018-05-07 16:43

  本文选题：多维度 + 聚类分析　； 参考：《湖南大学》2016年硕士论文

【摘要】：随着智能电网的建设,以智能电表为基础的高级测量体系(Advanced Measurement Infrastructure, AMI)能够延伸到普通用户,智能电表采集的海量实时用户用电数据中隐藏着用户的用电行为习惯,针对智能电网建设过程中需要准确掌握居民用电特性的要求,对这些数据进行挖掘,研究用户类型,可以帮助电网了解用户个性化、差异化服务需求,为未来电力需求响应政策的制定提供数据支撑。本文以民用非生产性负荷为研究对象,以环保、节能、经济、安全用电为目标,从时间维度、类属维度、影响维度分别对用户的用电特性进行分析,为需求响应提供有效的数据支撑。本文的主要内容如下：(1)对负荷特性指标进行了分类,并详细介绍了各负荷特性指标的计算方法及其意义,为下文负荷特性的计算和负荷特性曲线的绘制奠定了基础。从电价政策、电力供应能力、需求侧管理措施、气温气候等方面进行了影响地区负荷的主要因素分析,同时介绍了负荷特性分析中常用的分析方法,并通过实例表明该方法在负荷特性精细化分析中存在不足。(2)将电力用户负荷分解为基本负荷和季节性负荷,在利用自适应模糊c均值算法对电力用户基本负荷和夏季降温负荷分别进行分类的基础上,综合考虑日用电量和电力负荷的峰谷特征,基于加权重心典型用户筛选模型完成各类典型用户的筛选,并利用灰色关联度分析法对筛选的结果进行分析比较,结果表明该方法筛选的典型用户是可行的、合理的、有效的。(3)计算各类典型用户的日负荷率、峰期耗电率、平期耗电率、谷期耗电率等负荷特性指标,结合各类典型用户基本负荷曲线和夏季降温负荷曲线,分别分析基本负荷特性和夏季降温负荷特性,并对其进行负荷调控潜力分析,提出了一种新的用户分类方法。在电力用户负荷依据调控潜力的大小重新分类的基础上,从用电类型、负荷特性、错峰潜力3个维度,分析各个用户的用电行为特性,针对不同的用户用电行为特性制定不同的错峰管理政策,辅助需求侧管理的实施。
[Abstract]:With the construction of smart grid, Advanced Measurement Infrastructure (Amis), an advanced measurement system based on smart meter, can be extended to ordinary users. In view of the requirement of accurately mastering the characteristics of household electricity consumption in the process of smart grid construction, mining these data and studying the types of users can help the power grid to understand the personalized and differentiated service requirements of users. To provide data support for future power demand response policy formulation. This paper takes the civilian non-productive load as the research object, taking the environmental protection, energy saving, economy, safety electricity consumption as the goal, from the time dimension, the category dimension, the influence dimension respectively carries on the analysis to the user's electricity use characteristic. Provide effective data support for demand response. The main contents of this paper are as follows: (1) the load characteristic index is classified, and the calculation method and significance of each load characteristic index are introduced in detail, which lays a foundation for the calculation of load characteristic and the drawing of load characteristic curve below. The main factors affecting regional load are analyzed from the aspects of electricity price policy, power supply capacity, demand-side management measures, temperature and climate, etc. At the same time, the analysis methods commonly used in load characteristic analysis are introduced. An example is given to show that the method has shortcomings in the refined analysis of load characteristics. (2) the power user load is decomposed into basic load and seasonal load. Based on the classification of basic load and cooling load in summer by using adaptive fuzzy c-means algorithm, the peak and valley characteristics of daily power consumption and power load are considered synthetically. Based on the typical user screening model of weighted gravity center, the selection of typical users is completed, and the results of screening are analyzed and compared by using grey correlation analysis. The results show that the typical users screened by this method are feasible and reasonable. The daily load rate, peak power consumption rate, average power consumption rate, valley power consumption rate and other load characteristic indexes are calculated effectively. Combined with the basic load curve and summer cooling load curve of various typical users, the basic load curve and the summer cooling load curve are used to calculate the daily load rate, the peak power consumption rate, the average power consumption rate and the valley period power consumption rate. The basic load characteristics and summer cooling load characteristics are analyzed, and the potential of load regulation is analyzed, and a new user classification method is proposed. Based on the classification of power user load according to the potential of regulation and control, this paper analyzes the characteristics of electricity consumption behavior of each user from three dimensions: power type, load characteristic and potential of wrong peak. According to different characteristics of consumer's power consumption behavior, different management policies are made to assist the implementation of demand side management (DSM).
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM714
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本文编号：1857678