LAPSE:2023.34625
Published Article
LAPSE:2023.34625
Community Flexible Load Dispatching Model Based on Herd Mentality
Qi Huang, Aihua Jiang, Yu Zeng, Jianan Xu
April 27, 2023
Abstract
In the context of smart electricity consumption, demand response is an important way to solve the problem of power supply and demand balance. Users participate in grid dispatching to obtain additional benefits, which realises a win-win situation between the grid and users. However, in actual dispatching, community users’ strong willingness to use energy leads to low enthusiasm of users to participate in demand response. Psychological research shows a direct connection between users’ herd mentality (HM) and their decision-making behavior. An optimal dispatching strategy based on user herd mentality is proposed to give full play to the active response-ability of community flexible load to participate in power grid dispatching. Considering that herd mentality is generated by the information interaction between users, by calling on some users to share the experience of successfully participating in demand response in the community information center and using the Nash social welfare function to model herd mentality to explore the impact of the user. The analysis of an example shows that the proposed strategy gives full play to the potential of community flexible loads to participate in demand response. When users have similar electricity consumption behavior, the herd mentality can effectively improve users’ enthusiasm to participate in demand response, and the user response effect meets managers’ expectations.
Keywords
demand response, flexible load, herd mentality, social welfare function, user psychology
Suggested Citation
Huang Q, Jiang A, Zeng Y, Xu J. Community Flexible Load Dispatching Model Based on Herd Mentality. (2023). LAPSE:2023.34625
Author Affiliations
Huang Q: Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Jiang A: Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Zeng Y: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xu J: Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Journal Name
Energies
Volume
15
Issue
13
First Page
4546
Year
2022
Publication Date
2022-06-21
ISSN
1996-1073
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Original Submission
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PII: en15134546, Publication Type: Journal Article
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LAPSE:2023.34625
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https://doi.org/10.3390/en15134546
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