基于动态碳排放强度的电碳市场耦合建模方法及市场优化机制分析

doi:10.11930/j.issn.1004-9649.202409061

中国电力 ›› 2025, Vol. 58 ›› Issue (4): 31-43.DOI: 10.11930/j.issn.1004-9649.202409061

• 电-碳协同下分布式能源系统运营关键技术 • 上一篇下一篇

基于动态碳排放强度的电碳市场耦合建模方法及市场优化机制分析

赵彤¹(), 李雪松¹(), 周浩¹, 丁羽¹, 杨斌¹, 王文涛²(), 王鹏²()

1. 国网江苏省电力有限公司，江苏南京　210000
2. 华北电力大学电气与电子工程学院，北京　102206

收稿日期:2024-09-18 录用日期:2024-12-17 发布日期:2025-04-23 出版日期:2025-04-28
作者简介:
赵彤（1970），男，高级工程师，从事电力市场运行管理、电力交易研究，E-mail：zhaotong@js.sgcc.com.cn
李雪松（1981），男，硕士，高级工程师，从事电力市场运行管理、电力交易研究，E-mail：xuesongli@js.sgcc.com.cn
王文涛（2000），男，通信作者，博士研究生，从事电碳市场协同优化研究，E-mail：winter.ncepu@gmail.com
王鹏（1973），男，博士，教授，博士生导师，从事能源电力经济、中国能源战略研究，E-mail：wangpeng@ncepu.edu.cn
基金资助:
国网江苏省电力有限公司科技项目（J2023046）。

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

ZHAO Tong¹(), LI Xuesong¹(), ZHOU Hao¹, DING Yu¹, YANG Bin¹, WANG Wentao²(), WANG Peng²()

1. State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210000, China
2. School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China

Received:2024-09-18 Accepted:2024-12-17 Online:2025-04-23 Published:2025-04-28
Supported by:
This work is supported by Technological projects of State Grid Jiangsu Electric Power Co., Ltd. (No.J2023046).

摘要/Abstract

摘要：

由于碳市场与电力市场交易的时序差异，电力市场与碳市场耦合模型的建模和仿真面临准确度不足的问题。通过对实际工况的分析发现，火电机组碳排放强度随机组负荷率而波动，为电碳市场实时耦合和精确化建模提供了研究基础。为此，建立了考虑差异化动态碳排放特性的多发电企业博弈的电力市场和碳市场双层均衡仿真模型，其中上层为发电企业决策模型，下层为电力市场和碳市场交易模型，并提出了一种结合其他行业互动的低碳优化机制，以进一步增强电力市场的协同效应。为求解该模型，提出了一种基于马尔科夫决策迭代的协同优化算法。基于IEEE 39节点的算例分析表明，动态碳排放强度模型能够体现电碳市场的实时耦合，在电碳市场中采用低碳优化机制使系统进一步降低5.42%的碳排放量，使其他行业经济流入电力行业4.79万元，提高了电力市场的经济效益和环境效益。

关键词: 电力市场, 碳市场, 碳排放强度, 双层模型, 马尔科夫决策, 纳什均衡

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

赵彤, 李雪松, 周浩, 丁羽, 杨斌, 王文涛, 王鹏. 基于动态碳排放强度的电碳市场耦合建模方法及市场优化机制分析[J]. 中国电力, 2025, 58(4): 31-43.

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.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202409061

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

图/表 12

图 1 电碳市场交易过程

Fig.1 Electricity and carbon market trading process

图 2 模型框架

Fig.2 Model framework

图 3 火电机组动态碳排放特性

Fig.3 Dynamic carbon emission intensity characteristics of thermal units

图 4 MDIOCA算法流程

Fig.4 Process of MDIOCA

表 1 各机组的碳排放强度参数

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

图 5 不同情景下电力市场中标功率及价格

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

图 6 各情景出清电价

Fig.6 The market clearing prices under different scenarios

表 2 不同情景下的收入与支出

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

图 7 不同情景下碳市场中标量及价格

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

图 8 纳什均衡度

Fig.8 The Nash equilibrium degree

图 9 碳配额交易情况

Fig.9 Carbon emissions trading status

图 10 电碳市场能流

Fig.10 Electric carbon market energy flow

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基于动态碳排放强度的电碳市场耦合建模方法及市场优化机制分析

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

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基于动态碳排放强度的电碳市场耦合建模方法及市场优化机制分析

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

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