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Process monitoring and adjustment method with application to real-time electricity and internal carbon pricing models under reputation theory

Author

Listed:
  • Wang, Ying
  • Li, Junxiang
  • Gao, Yan
  • Xu, Honglei
  • Dong, Jingxin
  • Qu, Deqiang
  • Liu, Qi

Abstract

During low-carbon and market-oriented electricity reforms, power companies seek lower carbon targets to constrain their internal power supplies. Internal carbon trading within a power company can increase its competitive advantage in external carbon trading. Real-time electricity and internal carbon pricing strategies are effective ways to improve energy efficiency and tackle reforms’ challenges. Traditional objective of profit maximization no longer suffices for internal pricing due to unclear low-carbon benefits. Besides, fluctuations of users’ power consumption and internal suppliers’ generation strategies result in failure to meet initial carbon reduction goal. Therefore, the power company needs new pricing mechanisms to guide users and internal suppliers to optimize consumption and generation strategies in a collaborative manner, respectively. In this paper, we construct a nonlinear reputation benefit function to characterize the effect of low-carbon power generation. And then internal carbon pricing problem is incorporated into a day-ahead social welfare maximization model. With further monitoring carbon emission and power consumption, an intra-day pricing model with process monitoring is offered to adjust day-ahead prices via quadratic automated process control strategies. Using the dual gradient algorithm and automated process control theory, the day-ahead distributed pricing and the intra-day monitoring algorithms are designed. The simulation results demonstrate that the proposed models and algorithms not only can realize the balance of power supply and demand, but also contribute to internal power suppliers to collaborate in allocating resources and reduce carbon emission. Social welfare increases by 34.91%. The welfare of the power supply company and that of users rise by 68.60% and 28.59%, respectively. Total carbon emission decreases by 27.15%. In particular, the proposed application improves the power company’s competitiveness in the electricity market.

Suggested Citation

  • Wang, Ying & Li, Junxiang & Gao, Yan & Xu, Honglei & Dong, Jingxin & Qu, Deqiang & Liu, Qi, 2025. "Process monitoring and adjustment method with application to real-time electricity and internal carbon pricing models under reputation theory," Energy Economics, Elsevier, vol. 152(C).
    • Handle: RePEc:eee:eneeco:v:152:y:2025:i:c:s0140988325008138
    DOI: 10.1016/j.eneco.2025.108983
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    References listed on IDEAS

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    1. Bento, Nuno & Gianfrate, Gianfranco, 2020. "Determinants of internal carbon pricing," Energy Policy, Elsevier, vol. 143(C).
    2. Liu, Haiying & Liu, Zexiao & Zhang, Chunhong & Li, Tianyu, 2023. "Transformational insurance and green credit incentive policies as financial mechanisms for green energy transitions and low-carbon economic development," Energy Economics, Elsevier, vol. 126(C).
    3. Wang, Tonghe & Guo, Jian & Ai, Songpu & Cao, Junwei, 2021. "RBT: A distributed reputation system for blockchain-based peer-to-peer energy trading with fairness consideration," Applied Energy, Elsevier, vol. 295(C).
    4. Dai, Yeming & Yu, Weijie & Leng, Mingming, 2024. "A hybrid ensemble optimized BiGRU method for short-term photovoltaic generation forecasting," Energy, Elsevier, vol. 299(C).
    5. Trinks, Arjan & Mulder, Machiel & Scholtens, Bert, 2022. "External carbon costs and internal carbon pricing," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    6. Li, Yuanyuan & Li, Junxiang & He, Jianjia & Zhang, Shuyuan, 2021. "The real-time pricing optimization model of smart grid based on the utility function of the logistic function," Energy, Elsevier, vol. 224(C).
    7. Wang, Juan & Zheng, Junjun & Yu, Liukai & Goh, Mark & Tang, Yunying & Huang, Yongchao, 2023. "Distributed Reputation-Distance iterative auction system for Peer-To-Peer power trading," Applied Energy, Elsevier, vol. 345(C).
    8. Shamsuzzaman, Mohammad & Shamsuzzoha, Ahm & Maged, Ahmed & Haridy, Salah & Bashir, Hamdi & Karim, Azharul, 2021. "Effective monitoring of carbon emissions from industrial sector using statistical process control," Applied Energy, Elsevier, vol. 300(C).
    9. Balasubramanian, C. & Lal Raja Singh, R., 2024. "IOT based energy management in smart grid under price based demand response based on hybrid FHO-RERNN approach," Applied Energy, Elsevier, vol. 361(C).
    10. Paschalidou, Eleftheria G. & Thomaidis, Nikolaos S., 2025. "Risk factors in the formulation of day-ahead electricity prices: Evidence from the Spanish case," Energy Economics, Elsevier, vol. 142(C).
    11. Lv, Fei & Wu, Qiong & Ren, Hongbo & Zhou, Weisheng & Li, Qifen, 2024. "On the design and analysis of long-term low-carbon roadmaps: A review and evaluation of available energy-economy-environment models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    12. Cao, GangCheng & Fang, Debin & Wang, Pengyu, 2021. "The impacts of social learning on a real-time pricing scheme in the electricity market," Applied Energy, Elsevier, vol. 291(C).
    13. 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).
    14. Ri Piao & Deok-Joo Lee & Taegu Kim, 2020. "Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach," Energies, MDPI, vol. 13(22), pages 1-19, November.
    15. Qu, Deqiang & Li, Junxiang & Ma, Xiaojia, 2024. "Distributed real-time pricing of smart grid considering individual differences," Omega, Elsevier, vol. 127(C).
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    Keywords

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    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • D25 - Microeconomics - - Production and Organizations - - - Intertemporal Firm Choice: Investment, Capacity, and Financing
    • D47 - Microeconomics - - Market Structure, Pricing, and Design - - - Market Design

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