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Lookup NU author(s): Professor Jingxin DongORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
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.
Author(s): Wang Y, Li J, Gao Y, Xu H, Dong JX, Qu D, Liu Q
Publication type: Article
Publication status: Published
Journal: Energy Economics
Year: 2025
Volume: 152
Print publication date: 01/12/2025
Online publication date: 15/10/2025
Acceptance date: 09/10/2025
Date deposited: 16/10/2025
ISSN (print): 0140-9883
ISSN (electronic): 1873-6181
Publisher: Elsevier
URL: https://doi.org/10.1016/j.eneco.2025.108983
DOI: 10.1016/j.eneco.2025.108983
ePrints DOI: 10.57711/3a2b-7n49
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