Electricity Carbon Coupled Market Modeling Method and Market Optimization Mechanism Based on Dynamic Carbon Emission Intensity

doi:10.11930/j.issn.1004-9649.202409061

Abstract

Abstract:

Due to the timing differences between carbon market (CM) and electricity market (EM), accurately modeling their coupling is challenging. Analysis of operating data shows that the carbon emission intensity (CEI) of thermal units fluctuates with load rates, providing a foundation for real-time coupling and accurate modeling. This study develops a bi-level equilibrium simulation model with differentiated dynamic CEI in a multi-generator game framework. The upper level models generator decisions, while the lower level models EM and CM transactions. A low-carbon optimization mechanism (LCOM) involving interactions with other industries is proposed to enhance EM synergy. A Markov decision iterative optimal coordination algorithm (MDIOCA) is proposed to solve the model. Case studies based on the IEEE 39-bus system demonstrate that the dynamic CEI model enables real-time coupling of the EM and CM, achieving an additional 5.42% reduction in carbon emissions and facilitating an economic inflow of 47,900 CNY from other industries, thereby improving the economic and environmental performance of the EM.

Key words: electricity market, carbon market, carbon emission intensity, bi-level model, Markov decision, Nash equilibrium

ZHAO Tong, LI Xuesong, ZHOU Hao, DING Yu, YANG Bin, WANG Wentao, WANG Peng. Electricity Carbon Coupled Market Modeling Method and Market Optimization Mechanism Based on Dynamic Carbon Emission Intensity[J]. Electric Power, 2025, 58(4): 31-43.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202409061

https://www.electricpower.com.cn/EN/Y2025/V58/I4/31

Figures/Tables 12

Fig.1 Electricity and carbon market trading process

Fig.2 Model framework

Fig.3 Dynamic carbon emission intensity characteristics of thermal units

Fig.4 Process of MDIOCA

Table 1 Parameters of carbon emission intensity for units

企业编号	机组类型	排放强度线性因子/ ( t·(MW·h)^–2)	排放强度常数项/ ( t·(MW·h)^–1)
1	燃煤	–0.254	0.982
2	燃煤	–0.254	0.982
3	燃煤	–0.311	1.086
4	燃煤	–0.157	0.816
5	燃气	–0.802	0.680
6	燃气	–0.233	0.331

Fig.5 The winning bid power and prices in the electricity market under different scenarios

Fig.6 The market clearing prices under different scenarios

Table 2 Income and expenditure under different scenarios

类型	情景1	情景2	情景3
发电侧收入/万元	153.01	145.42	145.42
用户侧支出/万元	380.03	390.73	390.73

Fig.7 The winning bid power and prices in the carbon market under different scenarios

Fig.8 The Nash equilibrium degree

Fig.9 Carbon emissions trading status

Fig.10 Electric carbon market energy flow

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