Optimization Method for Coordinated Operation of Computing Power Network and Electric Power Network Under Multi-source Uncertainty Risk Constraints

MA Ding; YE Yujian; WU Yizhi; ZHANG Xi; STRBAC Goran

doi:10.7500/AEPS20260120001

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Optimization Method for Coordinated Operation of Computing Power Network and Electric Power Network Under Multi-source Uncertainty Risk Constraints

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

- Optimization Method for Coordinated Operation of Computing Power Network and Electric Power Network Under Multi-source Uncertainty Risk Constraints
- Automation of Electric Power Systems Vol. 50, Issue 17, Pages: 137-152(2026)
- 作者机构：
  
  1.东南大学电气工程学院，江苏省南京市 210096
  2.深圳河套学院科学与工程智能中心，广东省深圳市 518045
  3.中国电力科学研究院有限公司，北京市 100192
  4.伦敦帝国理工学院电气与电子工程系，伦敦 SW72AZ，英国
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(52477083);Scientific Research Project of Shenzhen Loop Area Institute(FP202602)
- DOI：10.7500/AEPS20260120001
  CLC：
- Received：20 January 2026，
  
  Revised：2026-05-28，
  
  Online First：20 July 2026，
  
  Published：10 September 2026
- 稿件说明：
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马丁,叶宇剑,吴奕之等.多源不确定性风险约束下算力网-电力网协同运行优化方法[J].电力系统自动化,2026,50(17):137-152.

MA Ding,YE Yujian,WU Yizhi,et al.Optimization Method for Coordinated Operation of Computing Power Network and Electric Power Network Under Multi-source Uncertainty Risk Constraints[J].Automation of Electric Power Systems,2026,50(17):137-152.
马丁,叶宇剑,吴奕之等.多源不确定性风险约束下算力网-电力网协同运行优化方法[J].电力系统自动化,2026,50(17):137-152. DOI： 10.7500/AEPS20260120001.

MA Ding,YE Yujian,WU Yizhi,et al.Optimization Method for Coordinated Operation of Computing Power Network and Electric Power Network Under Multi-source Uncertainty Risk Constraints[J].Automation of Electric Power Systems,2026,50(17):137-152. DOI： 10.7500/AEPS20260120001.

摘要

针对高耗能数据中心（DC）与高比例新能源协同运行中面临的多源不确定性及算力网拓扑约束难题，提出一种考虑多源不确定性风险约束下算力网-电力网协同运行优化方法。首先，建立精细化DC能耗模型，并基于负载异质性与算力网拓扑构建刻画任务负载时空灵活性的调度模型。其次，构建算电双向协同的双层优化框架：上层以DC运营商利润最大化为目标，利用负载时空迁移响应电价信号；下层以电网综合成本（含碳成本）最小化为目标，基于直流最优潮流协调源荷资源。在此基础上，引入分布鲁棒机会约束处理算力侧负载与电力侧的多源不确定性，并通过线性化技术与数据驱动的节点碳排放约束保证模型的凸性与可求解性。算例分析表明，所提模型能有效引导DC利用时空灵活性规避高电价与高碳排的区域与时段，在缓解电网阻塞、促进新能源消纳的同时，实现DC利润提升与系统低碳经济运行的综合最优。

Abstract

Aiming at the multi-source uncertainties and topology constraints of computing power network in the coordinated operation of energy-intensive data centers (DCs) and high proportion of renewable energy

this paper proposes an optimization method for coordinated operation of computing power network-electric power network under multi-source uncertainty risk constraints. Firstly

a refined DC energy consumption model is established

and a scheduling model that characterizes the spatio-temporal flexibility of task loads is constructed based on load heterogeneity and computing power network topology. Secondly

a bi-level optimization framework for bidirectional computing power-electric power coordination is built: the upper-level aims to maximize the profit of DC operators

and uses the spatio-temporal migration of loads to respond to electricity price signals; the lower-level aims to minimize the comprehensive cost of the power grid (including carbon cost)

and coordinates source-load resources based on DC optimal power flow. On this basis

distributionally robust chance constraints are introduced to deal with the multi-source uncertainties of the load on the computing power side and the electric power side

and linearization techniques and data-driven node carbon emission constraints are adopted to ensure the convexity and solvability of the model. Case studies show that the proposed model can effectively guide DCs to utilize spatio-temporal flexibility to avoid regions and time periods with high electricity prices and high carbon emissions. While alleviating power grid congestion and promoting renewable energy accommodation

it achieves the comprehensive optimum of DC profit improvement and low-carbon economic operation of the system.

关键词

Keywords

references

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