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Community Flexible Load Dispatching Model Based on Herd Mentality

Author

Listed:
  • Qi Huang

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

  • Aihua Jiang

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

  • Yu Zeng

    (College of Electrical Engineering, Sichuan University, Chengdu 610065, China)

  • Jianan Xu

    (Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China)

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.

Suggested Citation

  • Qi Huang & Aihua Jiang & Yu Zeng & Jianan Xu, 2022. "Community Flexible Load Dispatching Model Based on Herd Mentality," Energies, MDPI, vol. 15(13), pages 1-18, June.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:13:p:4546-:d:844499
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    References listed on IDEAS

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    1. Lin, Jin & Dong, Jun & Liu, Dongran & Zhang, Yaoyu & Ma, Tongtao, 2022. "From peak shedding to low-carbon transitions: Customer psychological factors in demand response," Energy, Elsevier, vol. 238(PA).
    2. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    3. Miao Miao & Suhua Lou & Yuanxin Zhang & Xing Chen, 2021. "Research on the Optimized Operation of Hybrid Wind and Battery Energy Storage System Based on Peak-Valley Electricity Price," Energies, MDPI, vol. 14(12), pages 1-11, June.
    4. Nilsson, Andreas & Bergstad, Cecilia Jakobsson & Thuvander, Liane & Andersson, David & Andersson, Kristin & Meiling, Pär, 2014. "Effects of continuous feedback on households’ electricity consumption: Potentials and barriers," Applied Energy, Elsevier, vol. 122(C), pages 17-23.
    5. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    6. Andrew Blohm & Jaden Crawford & Steven A. Gabriel, 2021. "Demand Response as a Real-Time, Physical Hedge for Retail Electricity Providers: The Electric Reliability Council of Texas Market Case Study," Energies, MDPI, vol. 14(4), pages 1-16, February.
    7. Wang, Zhaohua & Sun, Yefei & Wang, Bo, 2020. "Policy cognition is more effective than step tariff in promoting electricity saving behaviour of residents," Energy Policy, Elsevier, vol. 139(C).
    8. Nikolas Schöne & Kathrin Greilmeier & Boris Heinz, 2022. "Survey-Based Assessment of the Preferences in Residential Demand Response on the Island of Mayotte," Energies, MDPI, vol. 15(4), pages 1-30, February.
    9. Chen, Chien-fei & Li, Jiaxin & Shuai, Jing & Nelson, Hannah & Walzem, Allen & Cheng, Jinhua, 2021. "Linking social-psychological factors with policy expectation: Using local voices to understand solar PV poverty alleviation in Wuhan, China," Energy Policy, Elsevier, vol. 151(C).
    10. Nikolaos Iliopoulos & Motoharu Onuki & Miguel Esteban, 2021. "Shedding Light on the Factors That Influence Residential Demand Response in Japan," Energies, MDPI, vol. 14(10), pages 1-23, May.
    11. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    12. Mohammad Alipour & Rodney A. Stewart & Oz Sahin, 2021. "Beyond the Diffusion of Residential Solar Photovoltaic Systems at Scale: Allegorising the Battery Energy Storage Adoption Behaviour," Energies, MDPI, vol. 14(16), pages 1-12, August.
    13. Yang, Shu & Zhang, Yanbing & Zhao, Dingtao, 2016. "Who exhibits more energy-saving behavior in direct and indirect ways in china? The role of psychological factors and socio-demographics," Energy Policy, Elsevier, vol. 93(C), pages 196-205.
    14. Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
    15. Hui Hwang Goh & Lian Zong & Dongdong Zhang & Wei Dai & Chee Shen Lim & Tonni Agustiono Kurniawan & Kai Chen Goh, 2022. "Orderly Charging Strategy Based on Optimal Time of Use Price Demand Response of Electric Vehicles in Distribution Network," Energies, MDPI, vol. 15(5), pages 1-25, March.
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    1. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.

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