基于非均衡纳什谈判的多主体综合能源协同优化

卢艺玮; 宋晓通; 张佳惠; 李华健; 苏珈; 巨云涛; 贾旭文

doi:10.11930/j.issn.1004-9649.202502002

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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

高比例新能源区域综合能源系统调度、控制与可靠性研究 | 更新时间：2025-12-20

- 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
- 中国电力 2025年58卷第11期页码：88-100
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
  
  山西省能源互联网研究院研发项目（SXEI2023ZD002）。
- DOI：10.11930/j.issn.1004-9649.202502002
  中图分类号：
- 网络出版：2025-12-01，
  
  纸质出版：2025-12-01
- 稿件说明：
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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, et al. Collaborative Optimization of Multi-agent Integrated Energy Based on Asymmetric Nash Bargaining[J]. 2025, 58(11): 88-100.
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. DOI： 10.11930/j.issn.1004-9649.202502002.

LU Yiwei, SONG Xiaotong, ZHANG Jiahui, et al. Collaborative Optimization of Multi-agent Integrated Energy Based on Asymmetric Nash Bargaining[J]. 2025, 58(11): 88-100. DOI： 10.11930/j.issn.1004-9649.202502002.

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。

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.

关键词

Keywords

references

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