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Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design

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  • Ming, Hao
  • Meng, Jing
  • Gao, Ciwei
  • Song, Meng
  • Chen, Tao
  • Choi, Dae-Hyun

Abstract

Coupon Incentive-based Demand Response (CIDR) is a novel type of demand response mechanism targeting at small residential end-consumers, and has advantages over traditional Demand Response (DR) programs in its adaptability to most electricity retail plans, voluntary and without penalty. CIDR’s three-layer mechanism was modeled with Stackelberg Game in the authors’ previous studies, and its Nash Equilibrium is usually obtained by iterative solution. However, there is limited study on the efficiency and social welfare of the traditional CIDR mechanism. This paper provides the following contributions: (1) Introducing the response function of consumers and Load Serving Entities (LSEs) considering bounded rationality, and modeling the demand response resources as Virtual Power Plants (VPPs) to find the analytical solution; (2) By comparing the response function with centralized dispatch, this paper reveals the social welfare loss under the Nash Equilibrium of current CIDR mechanism; (3) An Extra Incentive Term (EIT) awarded to the LSE was designed to maximize the social welfare, and its form and parameters are determined under different conditions such as multiple DRs and LSEs, as well as congested power system. The case study shows that EIT has successfully helped to improve the social welfare as well as increased the retail saving and suppressed Locational Marginal Prices in most scenarios.

Suggested Citation

  • Ming, Hao & Meng, Jing & Gao, Ciwei & Song, Meng & Chen, Tao & Choi, Dae-Hyun, 2023. "Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design," Applied Energy, Elsevier, vol. 331(C).
    • Handle: RePEc:eee:appene:v:331:y:2023:i:c:s0306261922015744
    DOI: 10.1016/j.apenergy.2022.120317
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    References listed on IDEAS

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    1. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
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    1. Hussain, Shahid & Irshad, Reyazur Rashid & Pallonetto, Fabiano & Hussain, Ihtisham & Hussain, Zakir & Tahir, Muhammad & Abimannan, Satheesh & Shukla, Saurabh & Yousif, Adil & Kim, Yun-Su & El-Sayed, H, 2023. "Hybrid coordination scheme based on fuzzy inference mechanism for residential charging of electric vehicles," Applied Energy, Elsevier, vol. 352(C).
    2. Kong, Xiangyu & Wang, Zhengtao & Liu, Chao & Zhang, Delong & Gao, Hongchao, 2023. "Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants," Applied Energy, Elsevier, vol. 334(C).
    3. Mei, Shufan & Tan, Qinliang & Liu, Yuan & Trivedi, Anupam & Srinivasan, Dipti, 2023. "Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction," Energy, Elsevier, vol. 284(C).
    4. Wang, Zhenyi & Zhang, Hongcai, 2024. "Customer baseline load estimation for virtual power plants in demand response: An attention mechanism-based generative adversarial networks approach," Applied Energy, Elsevier, vol. 357(C).
    5. Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).

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