Heterogeneous graph-enhanced approach for demand response potential modeling: Mining load flexibility from user micro-behavioral patterns
Shan Liu,
Jie Yan,
Yamin Yan,
Haoran Zhang,
Shuang Han and
Yongqian Liu
Applied Energy, 2025, vol. 399, issue C, No S0306261925011547
Abstract:
Flexible resource scheduling on the electricity side is an important means to ensure the full consumption of renewable energy and the safe and stable operation of the power system. It is of great significance for the construction and development of new power systems. In the context of increasingly complex residential electricity consumption behavior, accurate load simulation and flexible load mining are important foundations for achieving demand response. Therefore, this article proposes a mining method for regional flexible loads that considers the micro electricity consumption patterns of power users. Firstly, through a double-layer clustering analysis based on multi-class fluctuation characteristics, accurately depict the electricity consumption patterns of power users. Secondly, considering the varying sensitivities of different households to environmental changes, a fine-grained electricity data simulation method is proposed that integrates heterogeneous graphs of user electricity consumption relationships to obtain refined energy consumption data. Finally, a flexible load mining method based on mixture density neural network is proposed, which mines regional flexible loads by combining the proportion of regional electricity consumption patterns. Taking the residential load in central China as an example, the proposed method achieves an overall simulation accuracy of 92.9 % and 90.5 % for residential load simulation on weekdays and holidays, respectively, which is higher than the simulation accuracy of traditional models; At the same time, this method successfully achieved load flexibility mining of regional residents on typical workdays and holidays, providing reliable technical support for flexible resource scheduling in the power system.
Keywords: Load flexibility; Micro-electricity users; Analysis of electricity consumption patterns; Heterogeneous diagram; Mixture density neural network (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261925011547
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:appene:v:399:y:2025:i:c:s0306261925011547
Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic
DOI: 10.1016/j.apenergy.2025.126424
Access Statistics for this article
Applied Energy is currently edited by J. Yan
More articles in Applied Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().