综合智慧能源2026，Vol.48Issue(8)：44-56,13.DOI:10.3969/j.issn.2097-0706.2026.08.004
考虑算力弹性伸缩的智算中心与电网协同优化运行关键技术综述
Review of key technologies for coordinated optimal operation of intelligent computing centers and power grids considering elastic scaling of computing power
摘要
Abstract
Intelligent computing centers,serving as high-power-density and highly fluctuating critical loads in new power systems,pose challenges to coordinated operation.To address these issues,key technologies for coordinated optimal operation of intelligent computing centers and power grids considering elastic scaling of computing power are systematically reviewed along the cross-layer coupling chain of"task—resource—energy consumption—power grid".Computing tasks are classified into three types:offline training,online inference,and scientific computing.The differentiated elasticity characteristics of temporal shiftability for offline training and spatial migratability in online inference are analyzed.Key technologies,including physical energy consumption modeling,spatiotemporal load prediction,heterogeneous computing resource pooling and scheduling,dynamic voltage and frequency scaling(DVFS),checkpoint-based training resumption,and coordinated energy storage control,are reviewed.In addition,the linkage mechanisms through which intelligent computing centers participate in demand response and electricity markets as generalized virtual power plants are summarized.Furthermore,computing-electricity coordination strategies are modeled as a Markov decision process based on deep reinforcement learning,and a cloud-edge-end coordinated linkage mechanism incorporating the temporal strategy of"computing follows energy sources"and the spatial strategy of"computing follows electricity"is designed.The results indicate that training tasks can release temporal elasticity through task reordering,power adjustment,and checkpoint-based training resumption,serving as the principal regulation resource for peak shaving and valley filling.Inference tasks can release spatial elasticity through request routing and cross-regional migration,but exhibit low elasticity due to service-level agreement(SLA)constraints.Scientific computing tasks can be executed during off-peak periods within specified time windows.Equipment frequency regulation and energy storage can provide supplementary rapid power response,although the overall regulation capability is constrained by multiple factors,such as communication bandwidth,thermal safety,reserve capacity,and equipment degradation.Engineering practices in China and abroad,including Google's carbon-aware computing scheduling,Microsoft's grid-friendly energy storage,the electricity-computing coordination virtual power plant in Zhangjiakou,northern Hebei,and the integrated"source-grid-load-storage-computing"project in Zhongwei,Ningxia,have preliminarily verified the technical feasibility of computing load participating in renewable energy consumption,peak shaving and valley filling,and ancillary services.However,most of these practices remain at the pilot or demonstration stage,and large-scale application still faces bottlenecks such as inconsistent electricity-computing data interfaces,difficulty in credibly quantifying elasticity boundaries,insufficient coupling of cross-domain security constraints,incomplete value transmission mechanisms across multiple markets,and the lack of a unified flexibility trading framework.Future efforts should establish a unified multi-timescale model covering tasks,equipment,intelligent computing centers,and power grids.Hardware degradation models and computing interruption costs should be incorporated into scheduling algorithms to balance the benefits of power grid demand response against physical damage to computing assets,and computing elasticity assessment,baseline verification,and electricity-computing-carbon coordinated mechanisms should be improved,thereby supporting integrated"source-grid-load-storage-computing"operation.关键词
智算中心/算力弹性伸缩/协同优化/源网荷储算/需求响应/虚拟电厂/算电协同Key words
intelligent computing center/elastic scaling of computing power/coordinated optimization/source-grid-load-storage-computing/demand response/virtual power plant/computing-electricity coordination分类
能源科技引用本文复制引用
李彬,翟振杨,祁泽源,李淑静,陈宋宋,徐靖..考虑算力弹性伸缩的智算中心与电网协同优化运行关键技术综述[J].综合智慧能源,2026,48(8):44-56,13.基金项目
国家电网有限公司科技项目(52060026007C-256-ZN) （52060026007C-256-ZN）
智能电网国家科技重大专项(2026ZD0811203)Science and Technology Project of State Grid Corporation of China(52060026007C-256-ZN) （2026ZD0811203）
National Science and Technology Major Project on Smart Grid(2026ZD0811203) （2026ZD0811203）
