Abstract: The development of a new-type energy system necessitates transitioning toward cleaner, lower-carbon, and diversified energy resources. The increasing randomness, intermittency, and volatility of new energy output, coupled with the growing complexity of user-side energy demand, pose greater challenges to the safe and stable operation of the new power system. Demand Response(DR), as an important means to improve system flexibility, economy, security, and sustainability, achieves a coordinated balance between supply and demand by incentivizing users to proactively adjust their energy consumption behavior. However, current demand response practices still suffer from insufficient demand-side resource exploration, low accuracy in response potential assessment, and inadequate utilization of adjustable resources. Especially for high-energy-consuming industrial enterprises, accurately quantifying their demand response potential is of significantly theoretical and engineering application value for formulating response strategies, aggregating flexible resources, assisting market transaction decisions, and optimizing system operation.
To address these issues, this paper proposes a comprehensive demand response potential calculation model for high-energy-consuming industrial enterprises. This model mainly includes four core analytical steps: First, the K-Medoids clustering algorithm is used to cluster the enterprise load curves, accurately representing different energy consumption patterns by identifying typical load characteristics. Second, Discrete Wavelet Transform (DWT) is used to decompose the load data into multiple scales, effectively separating stable loads from adjustable loads, thereby uncovering potential adjustable resources. Third, based on the load step analysis method, the theoretical load reduction potential under ideal response conditions is calculated. Finally, combined with the actual production and operation characteristics and energy constraints of the enterprises, the theoretical load reduction potential is corrected to obtain demand response potential results that better reflect actual operating conditions.
This paper selects historical load data from typical high-energy-consuming enterprises in industries such as calcium carbide, carbon, and ferroalloys for case analysis. Through data mining methods such as cluster analysis, wavelet decomposition, and load step calculation, the accuracy and reliability of the demand response potential assessment results are improved. The actual case verification results show that there are significant differences in the demand response potential of enterprises in different industries. Among them, calcium carbide enterprises demonstrate high responsiveness, with one calcium carbide enterprise’s average demand response potential accounting for 35.67% of its peak load; carbon enterprises have a relatively moderate demand response potential, with the highest reduction potential reaching 19.11%; and ferroalloy enterprises have relatively low response potential, with the highest reduction potential reaching 8.39%.
The research results indicate that enterprises with large peak-to-valley differences, stable load step variations, and low load rates typically possess higher demand-side adjustment potential. Therefore, in the process of demand response resource exploration, priority should be given to high-energy-consuming industries with obvious load step characteristics, fully leveraging their flexible load adjustment capabilities to improve the power system’s supply-demand balance and safe operation.

Key words: demand response, reducible potential, K-Medoids method, DWT decomposition method, TOPSIS evaluation method

摘要： 在新型电力系统建设背景下,需求响应通过合理的激励机制引导用户主动调整用能计划,对于提升电力系统安全可靠性具有重要意义。然而,在实际项目实施过程中,由于对于用户需求响应特性的掌握以及响应潜力的挖掘不足,无法充分利用需求侧资源。为此,本文针对高耗能企业,从负荷数据挖掘与用能模式评估两方面分析用户的用能特性,构建了包含“基于K-Medoids法的负荷曲线聚类—基于DWT的负荷分解—基于负荷台阶的负荷削减理论潜力测算—基于用能模式评估的负荷削减潜力修正”等四个步骤的需求响应潜力测算模型,并以典型高耗能企业为实际案例进行算例分析,验证了本文所提方法的有效性。

关键词: 需求响应, 可削减潜力, K-Medoids法, DWT分解法, TOPSIS评估方法