Key technologies and trends for computing-power synergistic development under the new power system

doi:10.12267/j.issn.2096-5931.2026.07.001

Abstract

Abstract:

Under the new power system construction and the rapid development of the new generation of Artificial Intelligence (AI), the continuous growth of computing demand has placed high requirements on the stability, reliability and green electricity ratio, creating a structural contradiction with the new power system dominated by wind and solar energy, making computing-power synergistic development imperative. This paper studies the connotation, development situation, key technologies and challenges of computing-power synergy. It first expounds the basic concepts and connotation of computing-power synergistic development and reviews its development situation. Then, it systematically examines key technical paths from five aspects, namely, computing load forecasting, highly reliable power supply, green power direct supply, spatiotemporal scheduling and market trading. Finally, it discusses the challenges and future development trends of computing-power synergy. The research results aim to provide a certain theoretical basis and application guidance for the development of computing-power synergy, and serve as a reference for relevant practitioners.

Key words: new power system, new-generation AI, computing-power synergy, spatiotemporal scheduling

CLC Number:

SHI Xin, WANG Yanhe, GAO Feng, ZHANG Di. Key technologies and trends for computing-power synergistic development under the new power system[J]. Information and Communications Technology and Policy, 2026, 52(7): 2-10.

Add to citation manager EndNote|Ris|BibTeX

URL:

http://ictp.caict.ac.cn/EN/10.12267/j.issn.2096-5931.2026.07.001

http://ictp.caict.ac.cn/EN/Y2026/V52/I7/2

Figures/Tables 6

References 17

[1]	邵子豪, 王志浩, 周晓茂, 等. 算电协同研究综述:架构、关键技术与展望[J]. 通信学报, 2025, 46(10):287-308.
[2]	李洁, 王月, 姜凌菲. 算力电力协同发展内涵与关键举措研究[J]. 信息通信技术与政策, 2025, 51(2):2-8. doi: 10.12267/j.issn.2096-5931.2025.02.001
[3]	Radovanovic A, Koningstein R, Schneider I, et al. Carbon-aware computing for datacenters[J]. IEEE Transactions on Power Systems, 2021, 38(2):1270-1280. doi: 10.1109/TPWRS.2022.3173250 URL
[4]	Kez D A, Foley A M, Morrow D J. Analysis of fast frequency response allocations in power systems with high system non-synchronous penetrations[J]. IEEE Transactions on Industry Applications, 2022, 58(3): 3087-3101. doi: 10.1109/TIA.2022.3160997 URL
[5]	Melnatami C C. AI data centers and U.S. grids: a 2026-2030 regional forecast[C]// 2026 IEEE Green Technologies Conference (GreenTech). Boulder, USA, 2026: 1-6.
[6]	Colangelo P, Coskun A K, Megrue J, et al. AI data centers as grid-interactive assets[J]. Nature Energy, 2026(11):254-261.
[7]	Zhang D, Chen K, Li W, et al. A load forecasting method for data centers based on MHSA-GRU[C]// 2026 11th Asia Conference on Power and Electrical Engineering (ACPEE). Macao, 2026:931-936.
[8]	Mughees M, Li Y, Chen Y, et al. Short-term load forecasting for AI-data center[PP/OL]. arXiv(2025-03-10)[2026-06-14].https//arxiv.org/abs/2503.07756.
[9]	Wang H, Chao J. ENNP: an enhanced neural networks-based power consumption prediction algorithm to deep learning-based workloads on AI-enabled data centers[J]. Cluster Computing, 2026, 29: 36. doi: 10.1007/s10586-025-05840-w
[10]	Tripathi S, Priyadarshni P, Misra R, et al. Multilayer multivariate forecasting network for precise resource utilization prediction in edge data centers[J]. Future Generation Computer Systems, 2025, 166:107692. doi: 10.1016/j.future.2024.107692 URL
[11]	Liu X. Neural network-based prediction method for data center resource utilization[C]// 2024 International Seminar on Artificial Intelligence, Computer Technology and Control Engineering (ACTCE). IEEE, 2024: 452-456.
[12]	Xia S, Yang J, Ruan X, et al. ALAS: attention mechanism-based and load-aware active scheduling for server resource optimization in complex scenarios[J]. Displays, 2026, 93:103438. doi: 10.1016/j.displa.2026.103438 URL
[13]	Jiang H, Qu B, Zhu J, et al. HyperLoad: a cross-modality enhanced large language model-based framework for green data center cooling load prediction[C]//Association for the Advancement of Artificial Intelligence.Proceedings of the AAAI Conference on Artificial Intelligence. Washington, DC, USA: AAAI Press, 2026:480-488.
[14]	Huang S, Dong J, Peng Z, et al. Resource load prediction of cloud computing based on STL-DeepAR-HW composite model[J]. Computer Applications and Software, 2025, 42(8): 367-373.
[15]	GB/T 45418—2025 配电网通用技术导则[S].
[16]	中国信息通信研究院. 算力电力协同发展研究报告(2025年)[R], 2025.
[17]	郑颖. 这份《意见》带出盎然春色[J]. 中国石油石化, 2025(8): 44-45.