基于多主体三层博弈的区域综合能源系统低碳运行策略

doi:10.11930/j.issn.1004-9649.202411078

中国电力 ›› 2025, Vol. 58 ›› Issue (8): 69-83.DOI: 10.11930/j.issn.1004-9649.202411078

• 交直流配电系统灵活资源规划运行及动态控制 • 上一篇下一篇

基于多主体三层博弈的区域综合能源系统低碳运行策略

王辉¹^,²(), 夏玉琦¹(), 李欣¹^,²(), 董宇成¹, 周子澜¹

1. 三峡大学电气与新能源学院，湖北宜昌　443002
2. 智慧能源技术湖北省工程研究中心（三峡大学），湖北宜昌　443002

收稿日期:2024-11-22 发布日期:2025-08-26 出版日期:2025-08-28
作者简介:
王辉（1969），男，教授，从事新能源微电网运行优化与控制、电能质量检测与谐波治理研究，E-mail：wanghui@ctgu.edu.cn
夏玉琦（2000），女，硕士研究生，通信作者，从事综合能源系统运行与规划研究，E-mail：19855996686@163.com
李欣（1986），男，副教授，从事新型人工智能算法在大规模电力系统中的应用研究，E-mail： ctgulx@ctgu.edu.cn
基金资助:
国家自然科学基金资助项目（基于集成-深度学习的高比例新能源大规模电力系统动态安全评估研究，52107107）。

Research on Low-carbon Operation Strategies for Regional Integrated Energy Systems Based on Multi-agent Three-level Game

WANG Hui¹^,²(), XIA Yuqi¹(), LI Xin¹^,²(), DONG Yucheng¹, ZHOU Zilan¹

1. College of Electrical and New Energy, China Three Gorges University, Yichang 443002, China
2. Hubei Provincial Engineering Research Center of Intelligent Energy Technology, China Three Gorges University, Yichang 443002, China

Received:2024-11-22 Online:2025-08-26 Published:2025-08-28
Supported by:
This work is supported by National Natural Science Foundation of China (Research on Dynamic Security Assessment of High Proportion New Energy Large Scale Power Systems Based on Integration Deep Learning, No.52107107).

摘要/Abstract

摘要：

为解决区域综合能源系统中多主体利益冲突、用户侧分布式储能投资成本高昂、容量利用不均以及碳排放量较高等问题，提出一种基于云储能服务商-综合能源系统运行商（integrated energy system operators，IESO）-负荷聚合商（load aggregators，LA）联盟三层博弈的区域综合能源系统低碳运行策略。首先，构建租赁云储能的IESO与LA的能源交易框架。其次，考虑到多个理性主体对盈利最大化的诉求，建立综合能源系统三层博弈模型。第一层为以IESO为主导者、LA联盟为伴随者的主从博弈；第二层为以云储能服务商为供给者、IESO为接收者的主从博弈；第三层是LA联盟成员之间的合作博弈，并采取非对称纳什议价法分配收益。最后，利用二分法、KKT条件结合交替方向乘子法（alternating direction multiplier method，ADMM）对该模型进行求解。仿真结果表明，该策略不仅能够促进系统低碳运行，而且能够满足各主体的经济性需求。

关键词: 非对称纳什议价, 三层博弈模型, 云储能, 负荷聚合商

Abstract:

To address the conflicts of interests among multiple stakeholders in regional integrated energy systems, as well as the issues such as high investment costs, uneven capacity utilization, and significant carbon emissions associated with user-side distributed energy storage, we proposed a low-carbon operation strategy for regional integrated energy systems based on three-level game among cloud energy storage service providers, integrated energy system operator (IESO), and load aggregators (LA). Firstly, an energy trading framework was established between the IESO and LA for leasing cloud energy storage. Secondly, considering the profit maximization demands of multiple rational stakeholders, a three-layer game model for the integrated energy system was established. The first layer is a principal-agent game with IESO as the leader and LA alliance as the follower; the second layer is a master-slave game with cloud energy storage service provider as the supplier and IESO as the receiver; the third layer is a cooperative game among LA alliance members, and the revenue is distributed using the asymmetric Nash bargaining method. Finally, the model was solved using the bisection method, KKT conditions, and the alternating direction multiplier method (ADMM). The simulation results show that the proposed strategy not only promotes the system's low-carbon operation, but also satisfies the economic needs of all stakeholders.

Key words: asymmetric Nash bargaining, three-level game model, cloud energy storage, load aggregator

中图分类号:

TM73

王辉, 夏玉琦, 李欣, 董宇成, 周子澜. 基于多主体三层博弈的区域综合能源系统低碳运行策略[J]. 中国电力, 2025, 58(8): 69-83.

WANG Hui, XIA Yuqi, LI Xin, DONG Yucheng, ZHOU Zilan. Research on Low-carbon Operation Strategies for Regional Integrated Energy Systems Based on Multi-agent Three-level Game[J]. Electric Power, 2025, 58(8): 69-83.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202411078

https://www.electricpower.com.cn/CN/Y2025/V58/I8/69

图/表 17

图 1 系统框架

Fig.1 System framework

图 2 三层博弈框架

Fig.2 Three-level game operation framework

图 3 总体流程

Fig.3 Overall flow

表 1 云储能服务商从电网购/售电价

Table 1 The electricity purchase and sale prices for cloud energy storage service providers from/to the grid

时间	购电价/(元·(kW·h)^–1)	售电价/(元·(kW·h)^–1)
00:00—07:00	0.43	0.30
23:00—24:00	0.43	0.30
07:00—10:00	0.79	0.60
15:00—18:00
21:00—23:00
10:00—15:00	1.21	1.02
18:00—21:00	1.21	1.02

图 4 3个楼宇LA的新能源和负荷曲线

Fig.4 The renewable energy and load curves of three buildings designated as LA.

图 5 IESO给LA制定电价

Fig.5 The electricity prices set by the IESO for LA

图 6 IESO给LA制定热价

Fig.6 The heat prices set by IESO for LA

图 7 云储能服务商给IESO定价

Fig.7 The prices set by cloud energy storage service provider for IESO

图 8 云储能服务商充放电决策

Fig.8 The charging and discharging decision-making by cloud energy storage service providers

图 9 各LA间交易电价

Fig.9 The electricity transaction prices among various LAs

图 10 LA1电能优化调度结果

Fig.10 The optimal power dispatch results for LA1

图 11 LA1热能优化调度结果

Fig.11 The optimal thermal energy dispatch results for LA1

表 2 3种情景下的各主体的运行成本

Table 2 The operational costs of stakeholders in three scenarios

情景	成本/元
情景	云储能服务商	IESO	LA	LA联盟
1	0	50454.7	LA1：4429.9	13560.2
			LA2：4969.6
			LA3：4160.7
2	0	50241.6	LA1：8991.7	13150.5
			LA2：2046.8
			LA3：2112.0
3	4240.9	40209.8	LA1：9002.1	13112.7
			LA2：2059.0
			LA3：2051.6

表 3 不同情景下上级能源交互成本、碳交易成本和碳排放量

Table 3 Superior energy interaction costs, carbon trading costs and carbon emissions under different scenarios

情景	上级能源交互成本/元	碳交易成本/元	碳排放量/kg
2	11579.04	2558.96	5729.85
3	7811.95	709.20	4832.58

图 12 不同情景下新型CHP机组出力、新能源消纳量和可再生能源利用率

Fig.12 Output of advanced CHP units, accommodation capacity of renewable energy, and utilization rate of renewable energy under different scenarios

图 13 不同方法下各LA运行成本对比

Fig.13 Comparison of LA operating costs with different methods

表 4 不同议价利益分配方法的结果对比

Table 4 Comparison of different bargaining benefits distribution methods

议价方式	LA	议价因子	议价成本/元	收益提升/元
标准议价方式	1	1.00	–6.9	207.6
	2	1.00	–3054.0	205.8
	3	1.00	3060.9	206.6
非对称议价方式	1	3.48	52.2	266.7
	2	0.63	–3167.1	92.7
	3	3.32	3114.9	260.6

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基于多主体三层博弈的区域综合能源系统低碳运行策略

Research on Low-carbon Operation Strategies for Regional Integrated Energy Systems Based on Multi-agent Three-level Game

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基于多主体三层博弈的区域综合能源系统低碳运行策略

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