Application Potential and Development Prospects of AI Large Models in Energy System Planning

FANG Yujuan; WU Zheng; QIU Hui; ZHOU Yifan; YANG Wenjin; LIU Yunchuan; NI Jin; DING Tianrong; WANG Zhenqian; PAN Yicheng

doi:10.19805/j.cnki.jcspe.2025.250687

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Application Potential and Development Prospects of AI Large Models in Energy System Planning

更新时间：2026-10-10

- Application Potential and Development Prospects of AI Large Models in Energy System Planning
- Chinese Journal of Power Engineering Vol. 45, Issue 12, Pages: 2207-2218(2025)
- 作者机构：
  
  1. 清华大学低碳能源实验室,北京,100084
  2. 清华大学碳中和研究院,北京,100084
  3. 清华大学能源与动力工程系,北京,100084
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.250687
  CLC：
- Online First：16 December 2025，
  
  Published：16 December 2025
- 稿件说明：
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方宇娟,吴铮,仇慧,周一帆,杨汶瑾,刘云川,倪金,丁田荣,王振乾,潘弈成. AI大模型在能源系统规划的应用潜力及发展展望动力工程学报, 2025, 45(12): 2207-2218 https://doi.

org/10.19805/j.cnki.jcspe.2025.250687
方宇娟,吴铮,仇慧,周一帆,杨汶瑾,刘云川,倪金,丁田荣,王振乾,潘弈成. AI大模型在能源系统规划的应用潜力及发展展望动力工程学报, 2025, 45(12): 2207-2218 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.250687.

org/10.19805/j.cnki.jcspe.2025.250687 DOI：

摘要

能源系统转型规划面临研究对象规模大、关联性复杂导致的数据处理难、高维求解难等挑战

传统技术难以应对

AI大模型等人工智能技术依托强大的计算和推理能力可为解决该问题提供关键支撑。然而

AI大模型在能源系统规划这一特定交叉学科中的应用探索仍处于初步阶段

相关研究成果目前尚显薄弱。因此

通过探讨AI大模型技术在能源系统规划领域的应用潜力及发展前景

提出从理论到工程实践的系统性研究与应用框架

提炼得出规划对象、目标及指标确立、数据研究、需求预测、规划方法设计和结果审定与调整等5项能源系统规划主要任务

以及相同任务中需选用的不同方法和尺度。此外

研究了AI大模型与能源系统规划间的耦合关系

划分大模型技术种类分析其可发挥作用以适应规划研究

并分析了AI大模型在能源系统规划中的研究及应用现状、挑战和空白。本研究可推动AI大模型以重构能源系统规划的认知框架与决策模式

有效助力我国双碳目标下能源结构的转型升级与配置优化。

Abstract

Energy system transition planning face challenges such as difficulties in data processing and high-dimensional solution

which are caused by the large scale of research objects and complex correlations. Traditional technologies are difficult to cope with these challenges

while artificial intelligence (AI) technologies such as AI large models could provide key support for solving this problem by virtue of their strong computing and reasoning capabilities. However

the exploration of applying AI large models in the specific interdisciplinary field of energy system planning is still in its initial stage

and the relevant research results remain relatively weak at present. Therefore

the application potential and development prospects of AI large model technology in the field of energy system planning were discussed

a systematic research and application framework from theory to engineering practice were proposed

and five main tasks of energy system planning were extracted

including planning objects

goals and indicators establishment

data research

demand forecasting

planning method design

and result approval and adjustment

as well as different methods and scales to be selected in the same task. In addition

the coupling relationship between AI large models and energy system planning was studied

the types of large model technologies were divided and their potential roles to adapt to planning research were analyzed. The research and application status

challenges

and gaps of AI large models in energy system planning were analyzed

and the possible research directions and content prospects were discussed. This study can promote the use of AI large models to reconstruct the cognitive framework and decision-making mode of energy system planning

and effectively assist in the transformation

upgrading

and configuration optimization of China's energy structure under the dual carbon goals.

关键词

Keywords

references

王心玉, 李金航, 陈衡, 等. 同时参与绿证交易-碳交易的区域多能互补电力系统优化调度[J]. 动力工程学报, 2025, 45(2): 315-324. WANG Xinyu, LI Jinhang, CHEN Heng, et al. Optimization scheduling of regional multi-energy complementary power system participating simultaneously in green certificate trading-carbon trading[J]. Journal of Chinese Society of Power Engineering, 2025, 45(2): 315-324.

刘康祥, 周家辉, 徐钢, 等. 面向高比例可再生能源消纳的光储氢醇一体化系统规划与调度[J]. 动力工程学报, 2023, 43(11): 1477-1486. LIU Kangxiang, ZHOU Jiahui, XU Gang, et al. Planning and scheduling of integrated system of light hydrogen storage and methanol for high proportion of renewable energy consumption[J]. Journal of Chinese Society of Power Engineering, 2023, 43(11): 1477-1486.

周建国, 吴昭波. 双碳目标下基于Stackelberg和合作博弈的虚拟电厂双层优化调度[J]. 动力工程学报, 2024, 44(10): 1611-1619. ZHOU Jianguo, WU Zhaobo. A two-level optimization scheduling of a virtual power plant based on Stackelberg and cooperative game theory under the dual carbon goals[J]. Journal of Chinese Society of Power Engineering, 2024, 44(10): 1611-1619.

盛稳, 徐世明, 卢官发, 等. 基于KPCA-BiLSTM-GRU的汽轮机主蒸汽流量测量模型[J]. 动力工程学报, 2025, 45(9): 1483-1491, 1510. SHENG Wen, XU Shiming, LU Guanfa, et al. Main steam flowrate measurement model for steam turbines based on KPCA-BiLSTM-GRU[J]. Journal of Chinese Society of Power Engineering, 2025, 45(9): 1483-1491, 1510.

陶超, 郭鑫, 胡柯彦, 等. 以语言为媒介的遥感图像跨时空领域自适应语义分割[J]. 中国图象图形学报, 2025, 30(9): 3153-3170. TAO Chao, GUO Xin, HU Keyan, et al. Language-guided cross-spatiotemporal domain adaptation for remote sensing image semantic segmentation[J]. Journal of Image and Graphics, 2025, 30(9): 3153-3170.

LI Xuan, TIAN Yonglin, YE Peijun, et al. A novel scenarios engineering methodology for foundation models in metaverse[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2023, 53(4): 2148-2159.

李玉峰, 宗国庆, 张东豪. 基于具身大模型的多场景智能巡检机器人系统框架与多机器人协同应用集成研究[J]. 智能计算机与应用, 2025, 15(5): 37-43. LI Yufeng, ZONG Guoqing, ZHANG Donghao. Research on the embodied large model-based multi-scenario intelligent inspection robot system framework and multi-robot collaborative application integration[J]. Intelligent Computer and Applications, 2025, 15(5): 37-43.

黄虎. 基于深度学习的替代模型在油气藏生产优化中的研究与应用[D]. 武汉: 中国地质大学, 2024.

ZAINEL Q M. Leveraging large language models and GAN-augmented data for energy load prediction[J]. Journal of Electrical Systems, 2024, 20(11s): 1926-1940.

任海玉, 刘建平, 王健, 等. 基于大语言模型的智能问答系统研究综述[J]. 计算机工程与应用, 2025, 61(7): 1-24. REN Haiyu, LIU Jianping, WANG Jian, et al. Research on intelligent question answering system based on large language model[J]. Computer Engineering and Applications, 2025, 61(7): 1-24.

孙柏林. 大模型评述[J]. 计算机仿真, 2024, 41(1): 1-7, 24. SUN Bolin. Review of large models[J]. Computer Simulation, 2024, 41(1): 1-7, 24.

刘贞, 张希良, 高虎, 等. 区域可再生能源规划基本框架研究[J]. 中国能源, 2010, 32(2): 38-41. LIU Zhen, ZHANG Xiliang, GAO Hu, et al. Research on basic framework of regional renewable energy planning[J]. Energy of China, 2010, 32(2): 38-41.

PAVL A, FRANCISCO J. Impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids[J]. Energies, 2024, 17: 4501.

GUO Zheng, MA Linwei, LIU Pei, et al. A multi-regional modelling and optimization approach to China's power generation and transmission planning[J]. Energy, 2016, 116: 1348-1359.

CHEN Siyuan, LIU Pei, LI Zheng. Low carbon transition pathway of power sector with high penetration of renewable energy[J]. Renewable and Sustainable Energy Reviews, 2020, 130: 109985.

徐新智, 杜尔顺, 高艺, 等. 电力系统运行模拟与容量规划工具研究与应用综述[J]. 电力系统自动化, 2022, 46(2): 188-199. XU Xinzhi, DU Ershun, GAO Yi, et al. Overview on research and application of power system operation simulation and capacity planning tools[J]. Automation of Electric Power Systems, 2022, 46(2): 188-199.

CONTALDI M, GRACCEVA F, TOSATO G. Evaluation of green-certificates policies using the MARKAL-MACRO-Italy model[J]. Energy Policy, 2007, 35(2): 797-808.

齐天宇, 张希良, 何建坤. 全球能源经济可计算一般均衡模型研究综述[J]. 中国人口资源与环境, 2016, 26(8): 42-48. QI Tianyu, ZHANG Xiliang, HE Jiankun. Review of research on global energy-economic computable general equilibrium models[J]. China Population, Resources and Environment, 2016, 26(8): 42-48.

LI Zhidong. An econometric study on China's economy, energy and environment to the year 2030[J]. Energy Policy, 2003, 31(11): 1137-1150.

吉平, 周孝信, 宋云亭, 等. 区域可再生能源规划模型述评与展望[J]. 电网技术, 2013, 37(8): 2071-2079. JI Ping, ZHOU Xiaoxin, SONG Yunting, et al. Review and prospect of regional renewable energy planning models[J]. Power System Technology, 2013, 37(8): 2071-2079.

HE Yuezhang, LI Zheng, LIU Pei. A co-production system of cement and methanol: unveiling its advancements and potential[J]. Journal of Cleaner Production, 2024, 473: 143523.

罗仕华, 胡维昊, 刘雯, 等. 中国2060碳中和能源系统转型路径研究[J]. 中国科学: 技术科学, 2024, 54(1): 43-64. LUO Shihua, HU Weihao, LIU Wen, et al. Transition pathway for China to achieve carbon neutrality by 2060[J]. Scientia Sinica Technologica, 2024, 54(1): 43-64.

国网能源研究院有限公司. 全球能源分析与展望2021[M]. 北京: 中国电力出版社, 2021.

DE LA PEA L, GUO Rui, CAO Xiaojing, et al. Accelerating the energy transition to achieve carbon neutrality[J]. Resources, Conservation and Recycling, 2022, 177: 105957.

JAVANMARD M E, GHADERI S F. Energy demand forecasting in seven sectors by an optimization model based on machine learning algorithms[J]. Sustainable Cities and Society, 2023, 95: 104623.

LI Tianxiao, LIU Pei, LI Zheng. A multi-period and multi-regional modeling and optimization approach to energy infrastructure planning at a transient stage: a case study of China[J]. Computers & Chemical Engineering, 2020, 133: 106673.

刘强, 王恰, 洪倩倩. "碳中和"情景下能源转型的选择与路径[J]. 中国能源, 2021, 43(4): 19-26. LIU Qiang, WANG Qia, HONG Qianqian. Choice and route of the energy transition under the scenario of carbon neutrality[J]. Energy of China, 2021, 43(4): 19-26.

江训谱, 包哲静, 于淼, 等. 考虑源荷不确定性的园区综合能源系统鲁棒多阶段规划[J]. 电力建设, 2025, 46(4): 99-112. JIANG Xunpu, BAO Zhejing, YU Miao, et al. Robust multi-stage planning of park-level integrated energy system considering source-load uncertainties[J]. Electric Power Construction, 2025, 46(4): 99-112.

SONMEZ M, AKGNGR A P, BEKTA S. Estimating transportation energy demand in Turkey using the artificial bee colony algorithm[J]. Energy, 2017, 122: 301-310.

黄志辉, 纪亮, 尹洁, 等. 中国道路交通二氧化碳排放达峰路径研究[J]. 环境科学研究, 2022, 35(2): 385-393. HUANG Zhihui, JI Liang, YIN Jie, et al. Peak pathway of China's road traffic carbon emissions[J]. Research of Environmental Sciences, 2022, 35(2): 385-393.

中国石油集团经济技术研究院. 世界与中国能源展望[R]. 北京: 中国石油天然气有限公司, 2022.

陈荣, 张希良, 何建坤, 等. 基于MESSAGE模型的省级可再生能源规划方法[J]. 清华大学学报(自然科学版), 2008, 48(9): 1525-1528. CHEN Rong, ZHANG Xiliang, HE Jiankun, et al. Provincial level renewable energy planning based on the MESSAGE model[J]. Journal of Tsinghua University (Science & Technology), 2008, 48(9): 1525-1528.

清华大学气候变化与可持续发展研究院. 中国2035年及中长期低碳发展战略2023[M]. 北京: 清华大学出版社, 2023.

黄韧. "双碳"目标下北京市能源转型重点领域及路径研究[D]. 北京: 华北电力大学, 2021.

钟海旺, 张宁, 杜尔顺, 等. 新型电力系统中的规划运营与电力市场: 研究进展与科研实践[J]. 中国电机工程学报, 2024, 44(18): 7084-7103. ZHONG Haiwang, ZHANG Ning, DU Ershun, et al. Planning, operation and market of new power system: research progress and practice[J]. Proceedings of the CSEE, 2024, 44(18): 7084-7103.

牛皓玮, 刘达, 陈广娟. 双碳及能源安全背景下电煤供需策略演化博弈研究[J]. 智慧电力, 2022, 50(9): 8-15. NIU Haowei, LIU Da, CHEN Guangjuan. Evolutionary game of thermal coal supply and demand strategy under carbon peak, carbon neutrality and energy security[J]. Smart Power, 2022, 50(9): 8-15.

HOLTTINEN H, TUHKANEN S. The effect of wind power on CO 2 abatement in the Nordic countries[J ] . Energy Policy, 2004, 32(14): 1639-1652.

别朝红, 王旭, 胡源. 能源互联网规划研究综述及展望[J]. 中国电机工程学报, 2017, 37(22): 6445-6462. BIE Zhaohong, WANG Xu, HU Yuan. Review and prospect of planning of energy internet[J]. Proceedings of the CSEE, 2017, 37(22): 6445-6462.

PRASAD R D, BANSAL R C, RATURI A. Multi-faceted energy planning: a review[J]. Renewable and Sustainable Energy Reviews, 2014, 38: 686-699.

GRYS M, CASCIANO R, PIENIAZEK I. MSR56 comparison of AI-enhanced tools for automating scientific literature reviews[J]. Value in Health, 2025, 28(6): S284.

JOHNSON B, BIRD C, FORD D, et al. Make your tools sparkle with trust: the PICSE framework for trust in software tools[C]//Proceedings of 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering in Practice. Melbourne, Australia: IEEE, 2023: 409-419.

BURKE C M. AI-assisted exam variant generation: a human-in-the-loop framework for automatic item creation[J]. Education Sciences, 2025, 15(8): 1029.

ONCIUL R, TATARU C I, DUMITRU A V, et al. Artificial intelligence and neuroscience: transformative synergies in brain research and clinical applications[J]. Journal of Clinical Medicine, 2025, 14(2): 550.

AKILA K, GOPINATHAN R, ARUNKUMAR J, et al. The role of artificial intelligence in modern healthcare: advances, challenges, and future prospects[J]. European Journal of Cardiovascular Medicine, 2025, 15(4): 615-624.

ZHANG Kunpeng, ZHOU Feng, WU Lan, et al. Semantic understanding and prompt engineering for large-scale traffic data imputation[J]. Information Fusion, 2024, 102: 102038.

LI Yihao, EL HABIB DAHO M, CONZE P H, et al. A review of deep learning-based information fusion techniques for multimodal medical image classification[J]. Computers in Biology and Medicine, 2024, 177: 108635.

WU Haixu, XU Jiehui, WANG Jianmin, et al. Autoformer: decomposition transformers with auto-correlation for long-term series forecasting[J].Advances in Neural Information Processing Systems, 2021, 34: 22419-22430.

WANG Chong, LI Xiang, SHI Yan, et al. Load forecasting method based on CNN and extended LSTM[J]. Energy Reports, 2024, 12: 2452-2461.

ALSAIGH R, MEHMOOD R, KATIB I. AI explainability and governance in smart energy systems: a review[J]. Frontiers in Energy Research, 2023, 11: 1071291.

UKOBA K, OLATUNJI K O, ADEOYE E, et al. Optimizing renewable energy systems through artificial intelligence: review and future prospects[J]. Energy & Environment, 2024, 35(7): 3833-3879.

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