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Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior

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  • Xiaofeng Liu

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Qi Wang

    (School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China)

  • Wenting Wang

    (School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China)

Abstract

Demand response (DR) has been recognized as a powerful tool to relieve energy imbalance in the smart grid. Most previous works have ignored the irrational behavior of energy consumers in DR project implementation. Accordingly, in this paper, we focus on solving two questions during the execution of DR. Firstly, considering the bounded rationality of residential users, a population dynamic model is proposed to describe the decision behavior on whether to participate in the DR project, and then the evolutionary process of consumers participating in DR is analyzed. Secondly, for the DR participants, they have to compete dispatching amounts for maximal profit in a day-ahead bidding market, hence, a non-cooperative game model is proposed to describe the competition behavior, and the uniqueness of the Nash equilibrium is analyzed with mathematical proof. Then, the distributed algorithm is designed to search the evolutionary result and the Nash equilibrium. Finally, a case study is performed to show the effectiveness of the formulated models.

Suggested Citation

  • Xiaofeng Liu & Qi Wang & Wenting Wang, 2019. "Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior," Energies, MDPI, vol. 12(19), pages 1-19, September.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:19:p:3727-:d:272076
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    References listed on IDEAS

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    4. Gyamfi, Samuel & Krumdieck, Susan, 2011. "Price, environment and security: Exploring multi-modal motivation in voluntary residential peak demand response," Energy Policy, Elsevier, vol. 39(5), pages 2993-3004, May.
    5. Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.
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    Cited by:

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    2. Perri, Cecilia & Giglio, Carlo & Corvello, Vincenzo, 2020. "Smart users for smart technologies: Investigating the intention to adopt smart energy consumption behaviors," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    3. Boyuan Wei & Geert Deconinck, 2019. "Distributed Optimization in Low Voltage Distribution Networks via Broadcast Signals †," Energies, MDPI, vol. 13(1), pages 1-18, December.
    4. Pedro Faria & Zita Vale, 2023. "Demand Response in Smart Grids," Energies, MDPI, vol. 16(2), pages 1-3, January.
    5. Md Mamun Ur Rashid & Fabrizio Granelli & Md. Alamgir Hossain & Md. Shafiul Alam & Fahad Saleh Al-Ismail & Ashish Kumar Karmaker & Md. Mijanur Rahaman, 2020. "Development of Home Energy Management Scheme for a Smart Grid Community," Energies, MDPI, vol. 13(17), pages 1-24, August.

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