• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2016, Vol. 37 ›› Issue (11): 64-.doi: 10.3969/j.issn.1000-7229.2016.11.010

• 电力大数据 • 上一篇    下一篇

 基于用电采集数据的需求响应削峰潜力评估方法

 任炳俐1,张振高2,王学军2,李慧2,闫大威3,张沛1   

  1.  1.天津大学电气与自动化工程学院,天津市 300072;2.国网天津市电力公司,
    天津市 300010;3.国网天津市电力公司经济技术研究院,天津市 300171
  • 出版日期:2016-11-01
  • 作者简介:任炳俐(1991),女,硕士研究生,主要研究方向为电力需求响应; 张振高(1972),男,硕士,高级工程师,主要研究方向为电网规划与管理; 王学军(1975),男,硕士,高级工程师,主要研究方向为电网规划; 李慧(1981),女,硕士,高级工程师,主要研究方向为输电网规划; 闫大威(1977),男,硕士,高级工程师,主要研究方向为配电规划和配电自动化; 张沛(1972),男,博士,教授级高级工程师,主要研究方向为电力系统可靠性和风险评估。
  • 基金资助:
     国网天津市电力公司科技项目(SGTYHT/14-JS-188)

 Assessment Method of Demand Response Peak Shaving Potential Based on Metered Load Data 

 REN Bingli1, ZHANG Zhengao2, WANG Xuejun2, LI Hui2, YAN Dawei3, ZHANG Pei1   

  1.  1.School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China;
     2.State Grid Tianjin Electric Power Company, Tianjin 300010, China;
     3.Economic Research Institute of State Grid Tianjin Electric Power Company, Tianjin 300171, China
  • Online:2016-11-01
  • Supported by:
     

摘要:  目前,电网规划按规划年最大负荷场景对电网网架进行规划设计。需求侧响应能达到削减年度尖峰负荷的效果,对电网规划产生影响。文章提出了一种基于用电采集数据的需求响应削峰潜力评估方法。首先,利用统计分析确定峰荷时段。其次,提出利用K-means聚类算法,以日负荷率、日峰谷差率、峰期负荷率、平期负荷率、谷期负荷率5个关键指标,对单一负荷进行降维聚类分析,从而确定适用于评估需求响应能力的用户典型日负荷曲线。在此基础上,综合考虑负荷所在行业的需求响应降负荷率和负荷峰谷差,量化评估负荷的削峰潜力。最后,根据拓扑结构,通过逐层叠加计算总需求响应的潜力及对峰值负荷的总影响。此文提出的方法可以帮助电网规划人员有效量化需求响应对系统峰荷的影响潜力,从而在规划时能考虑需求响应的影响,制定合理的未来电网投资方案。

关键词:  , 需求响应, 日负荷曲线, 削峰, 聚类分析

Abstract:  Currently, grid planning study is typically based on the maximum annual peak load scenario. The demand response can achieve the reduction of annual peak load, which has impact on power grid planning. This paper proposes a new method of assessing peak load reduction due to demand response based on metered load data. Firstly, we use statistic analysis to determine the peak load time frame. Secondly, we carry out the dimension-reducing clustering analysis on single load based on K-means clustering method with five key indicators, daily load rate, peak-valley ratio, peak load rate, normal load rate and valley load rate, and then determine users typical daily load curve suitable for the assessment of demand response ability. On this basis, we quantitatively evaluate the peak load reduction potential with comprehensively considering load-reducing rate and peak-valley difference of demand response in different industry. Finally, according to the topology we calculate the total demand response potential and its impact on peak load by aggregating all of electricity users peak load reduction potentials. The proposed method can effectively quantify the demand response programs impact on peak load reduction, therefore it can consider the impact of demand response in the planning and formulate reasonable future investment scheme of power grid.

Key words:  demand response, daily load curve, peak shaving, clustering analysis

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