综合智慧能源2026，Vol.48Issue(8)：69-81,13.DOI:10.3969/j.issn.2097-0706.2026.08.006
人工智能赋能算电协同的关键技术
Key technologies of AI-empowered computing-electricity coordination
摘要
Abstract
With the rapid development of generative AI and foundation models,computing demand and the associated electricity demand continue to grow,making the coordination between computing loads and electricity supply increasingly challenging in terms of time,space,and operational objectives.Considering the differences between computing systems and power systems in data structures,operating cycles,regulation mechanisms,and security boundaries,the overall architecture,operational mechanism,and key technologies of AI-empowered computing-electricity coordination are investigated from a system engineering perspective.A four-layer architecture comprising basic support,technology engine,scenario application,and governance assurance is constructed.With AI-empowered computing-electricity coordination as the core,an operational mechanism is established that integrates state mapping,spatiotemporal supply-demand matching,coupling transmission,multi-objective trade-offs,and bidirectional closed-loop regulation.On this basis,a key technology chain is constructed,covering multi-source sensing and fusion,computing-electricity supply-demand prediction,computing-electricity coupling modeling,coordinated optimization and decision-making,and adaptive control and safe execution.Key technologies,including spatiotemporal feature representation,task-to-power mapping,model-and learning-driven coordinated decision-making,rolling control,and safety constraint checking,are analyzed.Furthermore,major challenges involving cross-domain data fusion,multiscale decision coupling,model generalization,and safety verification are summarized.A case study of Shaanxi Province is conducted based on renewable electricity consumption and the supply of intelligent computing capacity.The results show that the foundation for renewable electricity utilization in Shaanxi Province has been continuously strengthened.Intelligent computing resources exhibit a relatively high proportion of coordinately schedulable capacity and certain spatial disparities,providing practical conditions for temporal coordination between computing loads and renewable energy supply,as well as for cross-node allocation of computing resources.In response to these challenges and regional resource characteristics,the following measures are proposed:improving cross-domain data fusion and trusted sharing,establishing hierarchical rolling coordinated decision-making mechanisms,and developing strategy validation and safe operation mechanisms.This will further enhance computing resource utilization efficiency,renewable electricity consumption,and system operational resilience.关键词
人工智能/算电协同/算力调度/供需时空匹配/协同优化/可再生能源电力消纳Key words
artificial intelligence/computing-electricity coordination/computing resource scheduling/spatiotemporal supply-demand matching/collaborated optimization/renewable electricity consumption分类
能源科技引用本文复制引用
马帅印,牛晨阳,栾龙源,庄严,岳本勇..人工智能赋能算电协同的关键技术[J].综合智慧能源,2026,48(8):69-81,13.基金项目
西安市社会科学规划基金项目(J26JX08) （J26JX08）
咸阳市科技局项目(L2025-ZDYF-RGZN-003) （L2025-ZDYF-RGZN-003）
陕西省社会科学基金项目(2024D054)Xi'an Social Science Planning Fund(J26JX08) （2024D054）
Project of Xianyang Municipal Science and Technology Bureau(L2025-ZDYF-RGZN-003) （L2025-ZDYF-RGZN-003）
Shaanxi Provincial Social Science Fund Project(2024D054) （2024D054）
