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Differentiated Real-time Pricing Strategy for Maximizing Social Welfare Based on Blockchain Technology

Author

Listed:
  • Junxiang Li

    (University of Shanghai for Science and Technology)

  • Xuan Liu

    (Zaozhuang University)

  • Ru Wang

    (University of Shanghai for Science and Technology)

  • Deqiang Qu

    (Henan University of Science and Technology)

  • Xi Wang

    (University of Shanghai for Science and Technology)

Abstract

With the continuous construction of new power systems, optimizing the market power structure through the formulation of reasonable electricity pricing mechanisms is an important way to achieve the low-carbon goal, so this paper proposes a differentiated real-time pricing strategy to maximize social welfare supported by blockchain technology. The traceability and non-tamperable characteristics of blockchain quantify the impact of blockchain on market electricity differentiation and give demand-side users the right to choose electricity. This paper uses the utility function to characterize the user’s preference and utility for different electricity sources, sets up a tiered supply cost mechanism on the supply side, and further constructs a differentiated real-time pricing model based on blockchain to maximize social welfare, and uses duality theory and shadow price to solve it. The simulation results show that during a day’s electricity consumption cycle, user’s electricity demand has increased by 7%, traditional thermal power supply has decreased by 56.72%, and the proportion of renewable energy supply has increased by 37.86%. Therefore, the differentiated real-time pricing strategy can guide users to use electricity more accurately in a market manner, adjust the dispatch order of electricity more finely, optimize the power structure, and promote the low-carbon development of the power field.

Suggested Citation

  • Junxiang Li & Xuan Liu & Ru Wang & Deqiang Qu & Xi Wang, 2025. "Differentiated Real-time Pricing Strategy for Maximizing Social Welfare Based on Blockchain Technology," Computational Economics, Springer;Society for Computational Economics, vol. 66(3), pages 1851-1875, September.
    • Handle: RePEc:kap:compec:v:66:y:2025:i:3:d:10.1007_s10614-024-10756-5
    DOI: 10.1007/s10614-024-10756-5
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    References listed on IDEAS

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    1. Guo, Shi-man & Feng, Tian-tian, 2024. "Blockchain-based smart trading mechanism for renewable energy power consumption vouchers and green certificates: Platform design and simulation," Applied Energy, Elsevier, vol. 369(C).
    2. Zhang, Li & Gao, Yan & Zhu, Hongbo & Tao, Li, 2022. "Bi-level stochastic real-time pricing model in multi-energy generation system: A reinforcement learning approach," Energy, Elsevier, vol. 239(PA).
    3. Yuan, Guanxiu & Gao, Yan & Ye, Bei, 2021. "Optimal dispatching strategy and real-time pricing for multi-regional integrated energy systems based on demand response," Renewable Energy, Elsevier, vol. 179(C), pages 1424-1446.
    4. Nikzad, Mehdi & Samimi, Abouzar, 2021. "Integration of designing price-based demand response models into a stochastic bi-level scheduling of multiple energy carrier microgrids considering energy storage systems," Applied Energy, Elsevier, vol. 282(PA).
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