New Energy and CCUS Thermal Power Synergistic Peaking Cost Model and Apportionment Optimization Strategy

• Published in: IEEE access Vol. 13; pp. 132501 - 132513
• Main Authors: Song, Yihui, Wang, Gang, Li, Hanlin, Sun, Yunpeng, Jin, Hongyang, Su, Tianyang, Wu, Xueqing, Zhang, Qiang
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bi-level game optimization; Carbon capture and storage; carbon capture optimization; Carbon dioxide; Carbon sequestration; CCUS-new energy synergy; Correlation coefficients; Costs; Economic impact; Emissions; Energy storage; Environmental protection; Industrial plant emissions; Kernel; Multiagent systems; nucleolus allocation; Optimization; peaking cost dynamics; Power plants; Power system dynamics; Regulation; Resource management; Strategy; Thermal power plants; Thermoelectricity; Wind power generation
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3585989

Driven by the global carbon neutral strategy, the large-scale application of Carbon Capture, Utilization and Storage (CCUS) technology has significantly weakened the peaking margin of thermal power units, and the traditional peaking cost allocation model fails to take into account the inequitable distribution of costs faced by the carbon capture power plants participating in peaking. This study proposes a peaking cost quantification method based on multidimensional characterization. In terms of temporal characteristics, the scenario substitution method is used to quantify the participation of different peaking entities in peaking; in terms of spatial characteristics, the initiative constraints of unit peaking are taken into account to ensure that each peaking entity actively participates in peaking and profits from it. Considering the impact of carbon capture power plants' participation in peak shifting on carbon emission reduction and carbon cost, we constructed a dynamic peak shifting model for multiple subjects. We innovatively construct a two-layer architecture of "spatio-temporal two-dimensional quantization and dynamic game sharing", and adopt the improved kernel method to establish a coalition reorganization trigger mechanism to solve the unfair sharing of peaking costs when carbon capture power plants participate in peaking. Taking the improved IEEE34 node as an example, the results show that the established cost calculation model can accurately calculate the peak shaving cost of each unit in different scenarios. The total cost of thermal power peak shaving using CCUS is 6.5% lower than that of conventional thermal power units, which significantly verifies the effectiveness of CCUS technology in reducing the economic burden of peak shaving. When the carbon price is 150-250 yuan/ton, the carbon emission is reduced by 40%-50%, which highlights the dual advantages of the model in deep emission reduction and carbon price co-optimization, and the cost increase is <inline-formula> <tex-math notation="LaTeX">\le 5 </tex-math></inline-formula>%, which proves the feasibility of achieving large-scale carbon emission reduction under the premise of strictly controlling the economic cost. On the basis of completing energy conservation and emission reduction, the peak regulation task is completed at the minimum cost, and the unity of economy and environmental protection is achieved. Compared with the Shapley value method, the correlation coefficient of the contribution of the kernel method is increased to 0.91, which indicates that the accuracy of the proposed method for the evaluation of multi-agent contribution is significantly improved, and the fairness of the peak-shifting cost-sharing model of the kernel method is significantly improved, which provides a reliable solution for solving the problem of cost allocation imbalance between traditional power supply and CCUS units.