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

doi:10.11930/j.issn.1004-9649.202307044

Abstract

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

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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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202307044

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

Figures/Tables 10

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

Fig.2 Schematic diagram of load step effect

Fig.3 The industrial user preference process flowchart

Fig.4 Quantification of user potential by different categories

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

Fig.6 Comparison of comprehensive potential among different industries

Fig.7 Identification of industrial user groups

Fig.8 Six categories of groups represent user potential values

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

Fig.10 Distribution of user clustering results by different industries

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