基于非均衡纳什谈判的多主体综合能源协同优化

doi:10.11930/j.issn.1004-9649.202502002

中国电力 ›› 2025, Vol. 58 ›› Issue (11): 88-100.DOI: 10.11930/j.issn.1004-9649.202502002

• 高比例新能源区域综合能源系统调度、控制与可靠性研究 • 上一篇下一篇

基于非均衡纳什谈判的多主体综合能源协同优化

卢艺玮¹(), 宋晓通¹(), 张佳惠²^,³, 李华健²^,³, 苏珈², 巨云涛¹(), 贾旭文²

1. 北方工业大学电气与控制工程学院，北京　100144
2. 山西省能源互联网研究院，山西太原　030000
3. 能源互联网山西省重点实验室，山西太原　030000

收稿日期:2025-02-08 修回日期:2025-03-27 发布日期:2025-12-01 出版日期:2025-11-28
作者简介:
卢艺玮（1999），男，硕士研究生，从事综合能源系统及微电网方面研究，E-mail：luyw1999@163.com
宋晓通（1982），男，博士，副教授，从事新型电力系统分析、综合能源系统优化调度、电力设备状态智能监测等研究，E-mail：songxt@ncut.edu.cn
巨云涛（1985），男，通信作者，博士，教授，从事高比例可再生能源系统的分布自律-集中协同的稳定分析与优化调控技术、分布式协同能量管理系统研究，E-mail：juyuntao@ncut.edu.cn
基金资助:
山西省能源互联网研究院研发项目（SXEI2023ZD002）。

Collaborative Optimization of Multi-agent Integrated Energy Based on Asymmetric Nash Bargaining

LU Yiwei¹(), SONG Xiaotong¹(), ZHANG Jiahui²^,³, LI Huajian²^,³, SU Jia², JU Yuntao¹(), JIA Xuwen²

1. College of Electrical and Control Engineering, North China University of Technology, Beijing 100144, China
2. Shanxi Energy Internet Research Institute, Taiyuan 030000, China
3. Energy Internet Key Laboratory of Shanxi Province, Taiyuan 030000, China

Received:2025-02-08 Revised:2025-03-27 Online:2025-12-01 Published:2025-11-28
Supported by:
This work is supported by Shanxi Energy Internet Research Institute (No.SXEI2023ZD002).

摘要/Abstract

摘要：

多主体综合能源系统（integrated energy system，IES）协同运行面临成员间点对点（peer to peer，P2P）交易利益冲突以及隐私数据保护等问题。在阶梯式碳交易机制背景下，提出了基于非均衡纳什谈判的多主体IES协同优化策略。首先，计及碳捕集电厂与掺氢设备等减碳手段，构建多主体IES的电、热、氢能源共享的协同优化模型；其次，基于纳什谈判理论，将协同优化问题等效为多主体IES运营成本最小化和交易支付最大化2个连续子问题，实现合作收益的公平合理分配；最后，采用交替方向乘子法（alternating direction multiplier method，ADMM）求解，确定交易功率和交易价格。算例研究表明，该策略实现了电、热、氢等多元异质能流的协同优化，降低了系统运营成本，实现了成员间利益合理分配。

关键词: 多主体综合能源系统, 交替方向乘子法, 碳捕集电厂, 纳什谈判, 阶梯式碳交易机制

Abstract:

The multi-agent integrated energy system (IES) faces many problems such as conflict of interest in peer-to-peer (P2P) transactions among members and privacy data protection. In the context of the ladder carbon trading mechanism, a collaborative optimization strategy for multi-agent IES based on asymmetric Nash bargaining is proposed. Firstly, a collaborative optimization model for electricity, heat and hydrogen energy sharing among multiple agents in IES is established by introducing such decarbonization measures as carbon capture power plant and hydrogen-blending equipment. Secondly, based on the Nash bargaining theory, the collaborative optimization problem is equivalently decomposed into two sequential subproblems: minimizing the operational costs of multi-agent IES and maximizing transaction payments, thereby achieving a fair and reasonable distribution of cooperative benefits. Finally, the alternating direction multiplier method (ADMM) is employed to solve these problems, determining the trading power and trading prices. The case studies demonstrate that this strategy achieves collaborative optimization of multiple heterogeneous energy flows (including electricity, heat, and hydrogen), reduces system operating costs, and ensures equitable benefit distribution among stakeholders.

Key words: multi-agent integrated energy system, alternating direction multiplier method, carbon capture power plant, Nash bargaining game, ladder carbon trading mechanism

卢艺玮, 宋晓通, 张佳惠, 李华健, 苏珈, 巨云涛, 贾旭文. 基于非均衡纳什谈判的多主体综合能源协同优化[J]. 中国电力, 2025, 58(11): 88-100.

LU Yiwei, SONG Xiaotong, ZHANG Jiahui, LI Huajian, SU Jia, JU Yuntao, JIA Xuwen. Collaborative Optimization of Multi-agent Integrated Energy Based on Asymmetric Nash Bargaining[J]. Electric Power, 2025, 58(11): 88-100.

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链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202502002

https://www.electricpower.com.cn/CN/Y2025/V58/I11/88

图/表 19

图 1 多主体IES基本构架

Fig.1 Framework of multi-agent IES

图 2 非均衡纳什谈判求解流程

Fig.2 Flow for solving asymmetric Nash bargaining

表 1 多主体IES关键设备参数

Table 1 Multi-agent IES key equipment parameters

参数	数值	参数	数值
CHP综合效率	0.92	掺氢比上限	0.20
CHP容量/kW	400	燃煤机组功率上限/kW	650
CHP爬坡功率/kW	50	燃煤机组爬坡功率/kW	200
热电比可调上下限	0.48/2.07	碳捕集基础能耗/kW	100
GB效率	0.80	碳捕集运行能耗系数	268
GB热功率上限/kW	300	储氢罐容量上下限/kW	1500/150
GB爬坡功率/kW	100	初始储氢容量/kW	750
EL转换效率	0.75	单次最大储/释氢功率/kW	500
EL容量/kW	200	储氢/释氢效率	0.85/0.95
EL爬坡功率/kW	50	储热罐功率上下限/kW	270/30
MR转换效率	0.70	初始储热容量/kW	150
MR容量/kW	400	单次最大储/放热功率/kW	75
MR爬坡功率/kW	50	储热/放热效率	0.85/0.85

图 3 各IES的负荷曲线

Fig.3 Load curves of each IES

图 4 各IES新能源出力

Fig.4 New energy output of each IES

图 5 能源价格

Fig.5 Energy prices

图 6 运营成本最小化迭代收敛结果

Fig.6 Iterative convergence of minimized operating costs

图 7 交易支付最大化迭代收敛结果

Fig.7 Iterative convergence of maximized transaction payments

图 8 IES-1电能优化调度结果

Fig.8 Optimal power scheduling results of IES-1

图 9 IES-1热能优化调度结果

Fig.9 Optimal thermal scheduling results of IES-1

图 10 IES-1气能优化调度结果

Fig.10 Optimal gas scheduling results of IES-1

图 11 IES-1氢能优化调度结果

Fig.11 Optimal hydrogen scheduling results of IES-1

图 12 IES间能源交易结果

Fig.12 Energy trading results among IES

图 13 电能交易价格

Fig.13 Electricity trading prices

表 2 纳什谈判下的收益分配情况

Table 2 Benefits distribution under Nash bargaining

议价机制		IES-1	IES-2	IES-3
传统纳什	成员交易收益/元	1 513.9	1 512.9	1 533.9
非均衡纳什	贡献度	0.331	0.306	0.363
非均衡纳什	成员交易收益/元	1 506.4	1 400.9	1 652.8

图 14 IES-1的风光出力及多能负荷不确定性曲线

Fig.14 Uncertainty curves of IES-1

表 3 不确定性背景下的收益分配情况

Table 3 Revenue distribution under uncertainty

场景	贡献度及收益	IES-1	IES-2	IES-3
1	贡献度	0.331	0.306	0.363
1	成员交易收益/元	1 506.4	1 400.9	1 652.8
2	贡献度	0.398	0.261	0.341
2	成员交易收益/元	1 998.4	1 300.2	1 743.8
3	贡献度	0.326	0.310	0.364
3	成员交易收益/元	1 268.9	1 195.2	1 414.9

表 4 运行场景设定

Table 4 Operational scenario setting

场景	IES成员协同情况	碳捕集设备投入情况
4	否	未投入
5	是	未投入
6	是	投入

表 5 不同场景下IES运营成本

Table 5 Operating cost of IES system under different scenarios

场景	IES成员	IES成员运营成本/元	总运营成本/元
4	1	953.5	22 140.7
	2	11 686.8
	3	9 500.4
5	1	709.6	21 397.1
	2	11 460.2
	3	9 227.3
6	1	2 490.5	20 623.9
	2	10 285.9
	3	7 847.6

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基于非均衡纳什谈判的多主体综合能源协同优化

Collaborative Optimization of Multi-agent Integrated Energy Based on Asymmetric Nash Bargaining

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基于非均衡纳什谈判的多主体综合能源协同优化

Collaborative Optimization of Multi-agent Integrated Energy Based on Asymmetric Nash Bargaining

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