基于负荷台阶的工业需求响应用户优选方法

doi:10.11930/j.issn.1004-9649.202307044

中国电力 ›› 2024, Vol. 57 ›› Issue (1): 18-29.DOI: 10.11930/j.issn.1004-9649.202307044

基于负荷台阶的工业需求响应用户优选方法

苏湘波(), 吕睿可(), 郭鸿业(), 陈启鑫()

清华大学电机工程与应用电子技术系，北京　100084

收稿日期:2023-07-12 接受日期:2023-11-23 出版日期:2024-01-28 发布日期:2024-01-23
作者简介:苏湘波（2000—），男，硕士研究生，从事智慧用电、需求响应研究，E-mail：sxb22@mails.tsinghua.edu.cn
吕睿可（1999—），男，博士研究生，从事电力市场、需求侧管理研究，E-mail：lrk21@mails.tsinghua.edu.cn
郭鸿业（1993—），男，通信作者，博士，助理研究员，从事电力市场、智慧用电研究，E-mail：hyguo@tsinghua.edu.cn
陈启鑫（1982—），男，博士，教授，博士生导师，从事电力市场、低碳电力技术、能源互联网、电力系统运行研究，E-mail：qxchen@tsinghua.edu.cn
基金资助:
国家自然科学基金青年基金资助项目（52107102），国家电网有限公司科技项目（5108-202218280A-2-378-XG）。

A Method for Optimal Selection of High-Capacity Industrial Users for Demand Response Based on Load Step Data Processing Mode

Xiangbo SU(), Ruike LYU(), Hongye GUO(), Qixin CHEN()

Department of Electrical Engineering, Tsinghua University, Beijing 100084, China

Received:2023-07-12 Accepted:2023-11-23 Online:2024-01-28 Published:2024-01-23
Supported by:
This work is supported by National Natural Science Foundation of China (No.52107102), the Science and Technology Project of SGCC (No.5108-202218280A-2-378-XG).

摘要/Abstract

摘要：

在未来高比例新能源渗透下，供需平衡不确定性逐步增加，需求响应是通过挖掘用户侧灵活性资源保障系统电力电量平衡的重要手段。在电力部门进行需求响应工作时，需要使用历史数据来初步评估负荷响应潜力，以便选择潜力高的用户并展开动员工作。面向表征工业用户用能特点的负荷台阶，对其进行了定义并给出了数学表达，进而提出了基于负荷台阶的工业需求响应用户优选方法。首先，构建了基于负荷台阶的工业用户多时间尺度需求响应潜力指标体系；然后，构建了需求响应用户优选模型，实现对不同用户响应潜力的初评估，并利用k-means算法和近邻传播算法进行群体划分，在不同时间尺度对用户进行优选；最后，基于水泥、造纸等4个行业的多个工业用户实际负荷数据进行算例分析，呈现了所提方法下工业需求响应的用户优选结果。

关键词: 工业需求响应, 负荷台阶效应, 潜力评估, 用户优选

Abstract:

In the context of future high penetration of new energy, the uncertainty of supply-demand balance gradually increases. Demand response is an important means of ensuring the balance of power and electricity in the system by tapping into user-side flexible resources. When power sector works on demand response, historical data is needed for an initial assessment of load response potential, so as to select the users with high potential and initiate mobilization efforts. This article focuses on defining and providing a mathematical expression for load step that represents the energy consumption characteristics of industrial users. And then a user selection method for industrial demand response based on load step is proposed. Firstly, an index system for the potential of industrial users' demand response across multiple time scales based on load step is proposed. And then, a user selection model is established to conduct an initial evaluation of different users' response potential, and the k-means algorithm and the nearest neighbor propagation algorithm are used to divide groups, allowing for user selection across different time scales. Finally, a case study is presented based on actual load data from several industrial users in industries such as cement and paper, illustrating the user selection results for industrial demand response using the proposed method.

Key words: industrial demand response, load step effect, potential assessment, user preference

苏湘波, 吕睿可, 郭鸿业, 陈启鑫. 基于负荷台阶的工业需求响应用户优选方法[J]. 中国电力, 2024, 57(1): 18-29.

Xiangbo SU, Ruike LYU, Hongye GUO, Qixin CHEN. A Method for Optimal Selection of High-Capacity Industrial Users for Demand Response Based on Load Step Data Processing Mode[J]. Electric Power, 2024, 57(1): 18-29.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202307044

https://www.electricpower.com.cn/CN/Y2024/V57/I1/18

图/表 10

图 1 日前15天负荷差分值分布

Fig.1 Distribution of load difference values for the previous 15 days

图 2 负荷台阶示意

Fig.2 Schematic diagram of load step effect

图 3 工业用户优选流程

Fig.3 The industrial user preference process flowchart

图 4 不同类别用户潜力量化

Fig.4 Quantification of user potential by different categories

图 5 高、低潜力代表的用户负荷曲线

Fig.5 Load curves of users with high and low potential

图 6 不同行业综合潜力对比

Fig.6 Comparison of comprehensive potential among different industries

图 7 工业用户群体识别

Fig.7 Identification of industrial user groups

图 8 6类群体代表用户潜力值

Fig.8 Six categories of groups represent user potential values

图 9 6类群体代表用户15天的负荷曲线

Fig.9 Load profile of groups represent user of six categories for fifteen days

图 10 不同行业用户聚类结果分布

Fig.10 Distribution of user clustering results by different industries

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基于负荷台阶的工业需求响应用户优选方法

A Method for Optimal Selection of High-Capacity Industrial Users for Demand Response Based on Load Step Data Processing Mode

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