Unified Graph Model and Coordinated Optimization Mechanism for Computing Power-Electric Power Coupling Network

CHEN Zhongxi; ZHANG Peichao; ZHAO Jianli; WANG Qing; YANG Shanglin

doi:10.7500/AEPS20260201002

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Unified Graph Model and Coordinated Optimization Mechanism for Computing Power-Electric Power Coupling Network

Computing Power-Electric Power Coordination | 更新时间：2026-09-01

- Unified Graph Model and Coordinated Optimization Mechanism for Computing Power-Electric Power Coupling Network
- Automation of Electric Power Systems Vol. 50, Issue 17, Pages: 74-84(2026)
- 作者机构：
  
  1.电力传输与功率变换控制教育部重点实验室（上海交通大学），上海市 200240
  2.国网上海市电力公司，上海市 200122
  3.上海市智能电网需求响应重点实验室，上海市 200063
- 作者简介：
- 基金信息：
  
  State Grid Corporation of China(52090D250002)
- DOI：10.7500/AEPS20260201002
  CLC：
- Received：01 February 2026，
  
  Revised：2026-05-25，
  
  Online First：20 July 2026，
  
  Published：10 September 2026
- 稿件说明：
移动端阅览
陈仲禧,张沛超,赵建立等.算力-电力耦合网络的统一图模型及协同优化机制[J].电力系统自动化,2026,50(17):74-84.

CHEN Zhongxi,ZHANG Peichao,ZHAO Jianli,et al.Unified Graph Model and Coordinated Optimization Mechanism for Computing Power-Electric Power Coupling Network[J].Automation of Electric Power Systems,2026,50(17):74-84.
陈仲禧,张沛超,赵建立等.算力-电力耦合网络的统一图模型及协同优化机制[J].电力系统自动化,2026,50(17):74-84. DOI： 10.7500/AEPS20260201002.

CHEN Zhongxi,ZHANG Peichao,ZHAO Jianli,et al.Unified Graph Model and Coordinated Optimization Mechanism for Computing Power-Electric Power Coupling Network[J].Automation of Electric Power Systems,2026,50(17):74-84. DOI： 10.7500/AEPS20260201002.

摘要

算力-电力耦合网络面临模型异构、计算复杂以及市场激励机制等难题。针对这些问题，首先提出算力网络的图建模方法，用于表达算力约束以及算力-电力调节能力的映射关系。在此基础上，提出算力和电力网络的统一图模型表示，推导出具有可扩展性的分布式协同优化方法以及非储能设备的解析解，并支持在算力-电力分界面分割网络以实现隐私计算。基于上述协同优化能力以及纳什谈判方法，提出不依赖节点电价的算力空间迁移激励机制。仿真结果表明，所提分布式优化方法具有线性复杂度，所提算力-电力协同激励机制能够公平分配算力时空迁移带来的合作剩余，实现电网与数据中心利益共赢，并有效提升新能源消纳率、降低系统运行成本与碳排放。

Abstract

Computing power-electric power coupling network faces challenges such as model heterogeneity

computational complexity

and market incentive mechanisms. To address these issues

a graph modeling method for the computing power network is first proposed to express the computing power constraints and the mapping relationship of computing power-electric power regulation capabilities. Based on this

a unified graph model representation for both computing power and electric power networks is proposed

from which a scalable distributed coordinated optimization method and analytical solutions for non-energy-storage devices are derived. This method supports network splitting at the computing power-electric power interface to achieve privacy-preserving computation. Based on this coordinated optimization capability and the Nash bargaining method

an incentive mechanism for the spatial migration of computing power is proposed

which functions independently of nodal electricity prices. Simulation results demonstrate that the proposed distributed optimization method exhibits linear complexity. Furthermore

the proposed computing power-electric power coordinated incentive mechanism can fairly allocate the cooperative surplus generated by spatio-temporal migration

achieving a win-win outcome for both the power grid and data centers. It also effectively enhances the renewable energy accommodation rate while reducing system operation costs and carbon emissions.

关键词

Keywords

references

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