电力系统智能计算的关键技术及应用展望

doi:10.12096/j.2096-4528.pgt.24240

发电技术 ›› 2025, Vol. 46 ›› Issue (3): 421-437.DOI: 10.12096/j.2096-4528.pgt.24240

• AI在新型电力系统中的应用 •

电力系统智能计算的关键技术及应用展望

张俊¹, 蒲天骄², 高文忠¹, 刘友波³, 裴玮⁴, 许沛东¹, 高天露¹, 白昱阳¹

^1.武汉大学电气与自动化学院，湖北省武汉市 430072
^2.中国电力科学研究院有限公司，北京市海淀区 100192
^3.四川大学电气工程学院，四川省成都市 610065
^4.中国科学院电工研究所，北京市海淀区 100080

收稿日期:2024-11-15 修回日期:2025-02-18 出版日期:2025-06-30 发布日期:2025-06-16
作者简介:张俊(1981)，男，博士，教授，主要研究方向为新型电力系统领域中复杂系统建模、人机混合增强智能、人工智能大模型、多源异构数据和知识的融合计算和处理等，jun.zhang.ee@whu.edu.cn。
基金资助:
新一代人工智能国家科技重大专项(2021ZD0112700)

Key Technologies and Application Prospects of Intelligent Computing in Power Systems

Jun ZHANG¹, Tianjiao PU², Wenzhong GAO¹, Youbo LIU³, Wei PEI⁴, Peidong XU¹, Tianlu GAO¹, Yuyang BAI¹

^1.School of Electrical Engineering and Automation, Wuhan University, Wuhan 430072, Hubei Province, China
^2.China Electric Power Research Institute, Haidian District, Beijing 100192, China
^3.College of Electrical Engineering, Sichuan University, Chengdu 610065, Sichuan Province, China
^4.Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100080, China

Received:2024-11-15 Revised:2025-02-18 Published:2025-06-30 Online:2025-06-16
Supported by:
New Generation Artificial Intelligence Major National Science and Technology Project(2021ZD0112700)

摘要/Abstract

摘要：

目的随着我国“双碳”目标所驱动的新型电力系统的建设和应用，以高比例可再生能源和高电力电子化等为特征的电力系统展现出前所未有的复杂动态特性，给电力系统安全稳定运行提出新的挑战。电力系统智能计算利用新一代人工智能技术，尤其是大模型技术、预训练技术等，实现了物理模型、数据驱动模型、知识模型的融合，形成了一种新型的电力系统计算分析方法。为此，综述和展望了电力系统智能计算方法和技术在新型电力系统分析、优化、运行、调度、控制等方面的应用。方法首先，阐述了电力系统智能计算的概念和关键技术；然后，以电力系统时序预测、安全域优化、多能微网协同优化等关键领域的智能化处理为例阐述了其技术路线；最后，展望了其应用前景。结论智能化算法在多个评估指标上优于传统方法，显著提高了电力系统的预测精度和控制效率，验证了智能计算在电力系统计算分析中的应用潜力，为新型电力系统提供了一种有效的智能化技术路线，对于电力系统的智能化发展具有重要意义。

关键词: 智能计算, 新型电力系统, 可再生能源, 大模型, 人工智能(AI), 电网运行控制, 优化调控, 智能评估

Abstract:

Objectives The construction and application of new-type power systems driven by China’s “dual carbon” goals have demonstrated unprecedented complex dynamics characteristics, featuring a high-proportion integration of renewable energy and power electronic devices, posing new challenges to the safe and stable operation of power systems. Intelligent computing in power systems leverages new-generation artificial intelligence technologies—especially large models and pre-training techniques—to achieve the integration of physical models, data-driven models, and knowledge models, thereby developing a novel calculation and analysis method for power systems. This study reviews and discusses the applications of intelligent computing methods and technologies in the analysis, optimization, operation, scheduling, and control of new-type power systems. Methods First, the concept and key technologies of intelligent computing in power systems are introduced. Technical roadmaps are discussed through case studies on intelligent processing in key areas such as time-series prediction, security region optimization, and multi-energy microgrid collaborative optimization. Finally, the prospects of potential applications are explored. Conclusions Intelligent algorithms outperform traditional methods across multiple evaluation indicators, significantly enhancing prediction accuracy and control efficiency. The results validate the potential of intelligent computing in calculation and analysis for power systems, providing an effective intelligent pathway for new-type power systems and holding substantial significance for the development of intelligent power systems.

Key words: intelligent computing, new-type power system, renewable energy, large model, artificial intelligence (AI), power grid operation and control, optimal dispatch, intelligent evaluation

中图分类号:

TK 01

张俊, 蒲天骄, 高文忠, 刘友波, 裴玮, 许沛东, 高天露, 白昱阳. 电力系统智能计算的关键技术及应用展望[J]. 发电技术, 2025, 46(3): 421-437.

Jun ZHANG, Tianjiao PU, Wenzhong GAO, Youbo LIU, Wei PEI, Peidong XU, Tianlu GAO, Yuyang BAI. Key Technologies and Application Prospects of Intelligent Computing in Power Systems[J]. Power Generation Technology, 2025, 46(3): 421-437.

图/表 12

图1 电力系统智能计算三大关键要素

Fig. 1 Three key elements of intelligent computing in power systems

图2 新型电力系统四维发展趋势

Fig. 2 Four-dimensional development trends of new-type power systems

图3 智能计算赋能新建风电场的CPTCFS预测模型整体结构示意图

Fig. 3 Overall structure of CPTCFS prediction model for new wind farms empowered by intelligent computing

图4 智能计算赋能高维静态安全域边界拟合优化

Fig. 4 Optimization of high-dimensional static security region boundary fitting empowered by intelligent computing

图5 多能耦合系统优化问题特性

Fig. 5 Characteristics of optimization problems in multi-energy coupling systems

图6 智能计算赋能多能微网协同优化

Fig. 6 Coordinated optimization of multi-energy microgrids empowered by intelligent computing

图7 混合物理信息递归神经网络单元

Fig. 7 Hybrid physics-informed recurrent neural network unit

图8 智能计算赋能暂态功角控制

Fig. 8 Transient power angle control empowered by intelligent computing

图9 基于仿真驱动的图注意力强化学习方法

Fig. 9 Simulation-driven graph attention reinforcement learning method

图10 智能计算赋能配网运行控制

Fig. 10 Operation control of distribution network empowered by intelligent computing

图11 新型电力系统智能体智能量化评估流程

Fig. 11 Intelligent quantitative evaluation process of intelligent agents in new-type power systems

图12 智能计算赋能新型电力系统智能评估

Fig. 12 Intelligent evaluation of new-type power systems empowered by intelligent computing

参考文献 89

1	国家能源局．新型电力系统发展蓝皮书[EB/OL]．[2024-10-12]．．
	National Energy Administration ．Blue book of new power system development[EB/OL]．[2024-10-12]．．
2	蒲天骄，王新迎，赵琦．“科学智能”为电力发展按下快进键[J]．能源评论，2024(7)：66-69．
	PU T J， WANG X Y， ZHAO Q ．“AI for Science(AI4S)” press the fast forward key for power development[J]．Energy Review，2024(7)：66-69．
3	国家发展改革委，国家能源局，国家数据局．加快构建新型电力系统行动方案(2024—2027年)[EB/OL]．(2024-07-25)[2024-11-06]．．
	National Development and Reform Commission，National Energy Administration，National Data Bureau ．Action plan for accelerating the construction of new-type power system (2024-2027)[EB/OL]．(2024-07-25)[2024-11-06]．．
4	YAROTSKY D ．Error bounds for approximations with deep ReLU networks[J]．Neural Networks，2017，94：103-114． doi:10.1016/j.neunet.2017.07.002
5	周孝信，陈树勇，鲁宗相，等．能源转型中我国新一代电力系统的技术特征[J]．中国电机工程学报，2018，38(7)：1893-1904．
	ZHOU X X， CHEN S Y， LU Z X，et al ．Technology features of the new generation power system in China[J]．Proceedings of the CSEE，2018，38(7)：1893-1904．
6	郭剑波，范士雄，蔡忠闽，等．大电网调控人机混合增强智能：概念内涵、应用框架、关键技术以及系统验证[J]．中国电机工程学报，2024，44(17)：6787-6810．
	GUO J B， FAN S X， CAI Z M，et al ．Human-machine hybrid-augmented intelligence for power system dispatching：concept connotation，application framework，key technologies and system verification[J]．Proceedings of the Chinese Society for Electrical Engineering，2024，44(17)：6787-6810．
7	鄂维南．AI助力打造科学研究新范式[J]．中国科学院院刊，2024，39(1)：10-16．
	E W N ．AI helps to establish a new paradigm for scientific research[J]．Bulletin of Chinese Academy of Sciences，2024，39(1)：10-16．
8	WANG H， FU T， DU Y，et al ．Scientific discovery in the age of artificial intelligence[J]．Nature，2023，620(7972)：47-60． doi:10.1038/s41586-023-06221-2
9	BI K， XIE L， ZHANG H，et al ．Accurate medium-range global weather forecasting with 3D neural networks[J]．Nature，2023，619：533-538． doi:10.1038/s41586-023-06185-3
10	KOCHKOV D， YUVAL J， LANGMORE I，et al ．Neural general circulation models for weather and climate[J]．Nature，2024，632：1060-1066． doi:10.1038/s41586-024-07744-y
11	JUMPER J， EVANS R， PRITZEL A，et al ．Highly accurate protein structure prediction with AlphaFold[J]．Nature，2021，596：583-589． doi:10.1038/s41586-021-03819-2
12	THORNTON J M， LASKOWSKI R A， BORKAKOTI N ．AlphaFold heralds a data-driven revolution in biology and medicine[J]．Nature Medicine，2021，27(10)：1666-1669． doi:10.1038/s41591-021-01533-0
13	SZYMANSKI N J， RENDY B， FEI Y，et al ．An autonomous laboratory for the accelerated synthesis of novel materials[J]．Nature，2023，624：86-91． doi:10.1038/s41586-023-06734-w
14	张漫子，黄红华．AI for Science：科学研究新范式[EB/OL]．(2023-05-17)[2024-10-17]．．
	ZHANG M Z， HUANG H H ．AI for Science：a new paradigm for scientific research[EB/OL]．(2023-05-17)[024-10-17]．．
15	张林峰，孙伟杰，李鑫宇，等．AI4S 全球发展观察与展望[R]．北京：2023 科学智能峰会，2023．
	ZHANG L F， SUN W J， LI X Y，et al ．AI for science global outlook 2023 edition[R]．Beijing：AI for Science Congress，2023．
16	新华社．国家电网发布国内首个千亿级多模态电力行业大模型[EB/OL]．(2024-12-19)[2024-12-19]．．
	Xinhua ．State Grid release the first multi-modal power industry model of 100 billion level in China [EB/OL]．(2024-12-19)[2024-12-19]．．
17	中国能源新闻网．全球首款！“大瓦特·驭电”智能仿真大模型将在南方区域应用[EB/OL]．(2024-12-18)[2024-12-18]．．
	China Energy News Network ．The world’s first！“Big Watt·YuDian” intelligent simulation model will be applied in the southern region[EB/OL]．(2024-12-18)[2024-12-18]．．
18	ZHAO W ．A panel discussion on AI for science：the opportunities，challenges and reflections[J]．National Science Review，2024，11(8)：119． doi:10.1093/nsr/nwae119
19	WEINAN E ．AI for Science[J]．Collections，2023，56(10)：118-127．
20	ZHAO H， XU P， GAO T，et al ．CPTCFS：CausalPatchTST incorporated causal feature selection model for short-term wind power forecasting of newly built wind farms[J]．International Journal of Electrical Power & Energy Systems，2024，160：110059． doi:10.1016/j.ijepes.2024.110059
21	SUN S， LIU Y， LI Q，et al ．Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks[J]．Energy Conversion and Management，2023，283：116916． doi:10.1016/j.enconman.2023.116916
22	JALALI S M J， AHMADIAN S， KHODAYAR M，et al ．An advanced short-term wind power forecasting framework based on the optimized deep neural network models[J]．International Journal of Electrical Power & Energy Systems，2022，141：108143． doi:10.1016/j.ijepes.2022.108143
23	LIN Z， LIU X ．Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network[J]．Energy，2020，201：117693． doi:10.1016/j.energy.2020.117693
24	DUAN J， WANG P， MA W，et al ．Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short-term memory neural network[J]．Energy，2021，214：118980． doi:10.1016/j.energy.2020.118980
25	XIAO J， ZU G， GONG X，et al ．Observation of security region boundary for smart distribution grid[J]．IEEE Transactions on Smart Grid，2015，8(4)：1731-1738．
26	NGUYEN H D， DVIJOTHAM K， YU S，et al ．A framework for robust long-term voltage stability of distribution systems[J]．IEEE Transactions on Smart Grid，2018，10(5)：4827-4837． doi:10.1109/tsg.2018.2869032
27	CHEN S， WEI Z， SUN G，et al ．Convex hull based robust security region for electricity-gas integrated energy systems[J]．IEEE Transactions on Power Systems，2018，99：1-12． doi:10.1109/tpwrs.2018.2888605
28	DING T， BO R， SUN H，et al ．A robust two-level coordinated static voltage security region for centrally integrated wind farms[J]．IEEE Transactions on Smart Grid，2015，7(1)：460-470． doi:10.1109/tsg.2015.2396688
29	CHEN S J， CHEN Q X， XIA Q，et al ．Steady-state security assessment method based on distance to security region boundaries[J]．IET Generation，Transmission & Distribution，2013，7(3)：288-297． doi:10.1049/iet-gtd.2012.0288
30	YU Y， LIU Y， QIN C，et al ．Theory and method of power system integrated security region irrelevant to operation states：an introduction[J]．Engineering，2020，6(7)：754-777． doi:10.1016/j.eng.2019.11.016
31	WU F， KUMAGAI S ．Steady-state security regions of power systems[J]．IEEE Transactions on Circuits and Systems，1982，29(11)：703-711． doi:10.1109/tcs.1982.1085091
32	NGUYEN HUNG D， KRISHNAMURTHY D， KONSTANTIN T ．Constructing convex inner approximations of steady-state security regions[J]．IEEE Transactions on Power Systems，2019，34(1)：257-267． doi:10.1109/tpwrs.2018.2868752
33	LEE D， NGUYEN H D， DVIJOTHAM K，et al ．Convex restriction of power flow feasibility sets[J]．IEEE Transactions on Control of Network Systems，2019，6(3)：1235-1245． doi:10.1109/tcns.2019.2930896
34	LI X， JIANG T， BAI L，et al ．Orbiting optimization model for tracking voltage security region boundary in bulk power grids[J]．CSEE Journal of Power and Energy Systems，2022，8(2)：476-487．
35	LECUN Y， BENGIO Y， HINTON G ．Deep learning[J]．Nature，2015，521：436-444． doi:10.1038/nature14539
36	WU S， ZHENG L， HU W，et al ．Improved deep belief network and model interpretation method for power system transient stability assessment[J]．Journal of Modern Power Systems and Clean Energy，2019，8(1)：27-37． doi:10.35833/mpce.2019.000058
37	ZHU L， HILL D J ．Data/model jointly driven high-quality case generation for power system dynamic stability assessment[J]．IEEE Transactions on Industrial Informatics，2021，18(8)：5055-5066． doi:10.1109/tii.2021.3123823
38	张峥，原帅，时伟光，等．基于深度神经网络的UHVDC输电系统故障诊断[J]．电网与清洁能源，2024，40(7)：88-94．
	ZHANG Z， YUAN S， SHI W G，et al ．Fault diagnosis of UHVDC transmission lines based on deep neural network[J]．Power System and Clean Energy，2024，40(7)：88-94．
39	宋继明，毛继兵，马卫华，等．基于深度神经网络的特高压变压器滤油注油过程故障诊断技术研究[J]．电网与清洁能源，2023，39(12)：95-103．
	SONG J M， MAO J B， MA W H，et al ．Research on oil filtration and injection process fault diagnosis for ultrahigh voltage transformers based on deep neural networks[J]．Power System and Clean Energy，2023，39(12)：95-103．
40	申洪涛，李飞，史轮，等．基于气象数据降维与混合深度学习的短期电力负荷预测[J]．电力建设，2024，45(1)：13-21．
	SHEN H T， LI F， SHI L，et al ．Short-term power load forecasting based on reduction of meteorological data dimensionality and hybrid deep learning[J]．Electric Power Construction，2024，45(1)：13-21．
41	HU C， ZHANG J， YUAN H，et al ．Black swan event small-sample transfer learning (BEST-L) and its case study on electrical power prediction in COVID-19[J]．Applied Energy，2022，309：118458． doi:10.1016/j.apenergy.2021.118458
42	HOU Q， ZHANG N， KIRSCHEN D S，et al ．Sparse oblique decision tree for power system security rules extraction and embedding[EB/OL]．(2020-4-20)[2024-12-15]．．org/abs/2004.09579v1．
43	BEHDADNIA T， YASLAN Y， GENC I ．A new method of decision tree based transient stability assessment using hybrid simulation for real-time PMU measurements[J]．IET Generation，Transmission & Distribution，2021，15(4)：678-693． doi:10.1049/gtd2.12051
44	DAI Y， ZHANG J， XU P，et al ．High-dimensional steady-state security region boundary approximation in power systems using feature non-linear converter and improved oblique decision tree[J]．Journal of Modern Power Systems and Clean Energy，2024，12(6)：1786-1797． doi:10.35833/mpce.2024.000188
45	李承周，王宁玲，窦潇潇，等．多能源互补分布式能源系统集成研究综述及展望[J]．中国电机工程学报，2023，43(18)：7127-7150．
	LI C Z， WANG N L， DOU X X，et al ．Review and prospect on the system integration of distributed energy system with the complementation of multiple energy sources[J]．Proceedings of the CSEE，2023，43(18)：7127-7150．
46	DONG J， WANG H， YANG J，et al ．Optimal scheduling framework of electricity-gas-heat integrated energy system based on asynchronous advantage actor-critic algorithm[J]．IEEE Access，2021，9：139685-139696． doi:10.1109/access.2021.3114335
47	CUI F， AN D， XI H ．Integrated energy hub dispatch with a multi-mode CAES-BESS hybrid system：an option-based hierarchical reinforcement learning approach[J]．Applied Energy，2024，374：123950． doi:10.1016/j.apenergy.2024.123950
48	DOLATABADI A， ABDELTAWAB H， MOHAMED Y A R I ．A novel model-free deep reinforcement learning framework for energy management of a PV integrated energy hub[J]．IEEE Transactions on Power Systems，2022，38(5)：4840-4852． doi:10.1109/tpwrs.2022.3212938
49	YANG L， SUN Q， ZHANG N，et al ．Indirect multi-energy transactions of energy internet with deep reinforcement learning approach[J]．IEEE Transactions on Power Systems，2022，37(5)：4067-4077． doi:10.1109/tpwrs.2022.3142969
50	SAYED A R， ZHANG X， WANG G，et al ．Online operational decision-making for integrated electric-gas systems with safe reinforcement learning[J]．IEEE Transactions on Power Systems，2024，39(2)：2893-2906． doi:10.1109/tpwrs.2023.3320172
51	YANG L， SUN Q， ZHANG N，et al ．Optimal energy operation strategy for we-energy of energy internet based on hybrid reinforcement learning with human-in-the-loop[J]．IEEE Transactions on Systems，Man，and Cybernetics：Systems，2020，52(1)： 32-42． doi:10.1109/TSMC.2020.3035406
52	WANG Y， QIU D， SUN X，et al ．Coordinating multi-energy microgrids for integrated energy system resilience：a multi-task learning approach[J]．IEEE Transactions on Sustainable Energy，2023，15(2)：920-937． doi:10.1109/tste.2023.3317133
53	YANG T， ZHAO L， LI W，et al ．Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning[J]．Energy，2021，235：121377． doi:10.1016/j.energy.2021.121377
54	ZHENG L， WU H， GUO S，et al ．Real-time dispatch of an integrated energy system based on multi-stage reinforcement learning with an improved action-choosing strategy[J]．Energy，2023，277：127636． doi:10.1016/j.energy.2023.127636
55	WANG T， ZHAO J， LEUNG H，et al ．A condition knowledge representation and feedback learning framework for dynamic optimization of integrated energy systems[J]．IEEE Transactions on Cybernetics，2024，54(5)：2880-2890． doi:10.1109/tcyb.2023.3234077
56	LI X， CHEN S， ZHANG J，et al ．A physics-informed deep learning paradigm for transient power angle stability assessment[J]．IEEE Journal of Radio Frequency Identification，2022，6：948-952． doi:10.1109/jrfid.2022.3213882
57	李湘，陈思远，张俊，等．基于物理信息嵌入的非固定长度电力系统暂态稳定快速评估[J/OL]．上海交通大学学报，1-15[2024-0913．．
	LI X， CHEN S Y， ZHANG J，et al ．Rapid non-fixed length transient stability assessment of power system based on physics-lnformed neural networks[J/OL]．Journal of Shanghai Jiaotong University，1-15[2024-0913．．
58	GAO J， CHEN S， LI X，et al ．Transient voltage control based on physics-informed reinforcement learning[J]．IEEE Journal of Radio Frequency Identification，2022，6：905-910． doi:10.1109/jrfid.2022.3213895
59	QIU J， YANG H， ZHANG J，et al ．Parameter tuning of new type energy virtual synchronous generator based on physics-informed reinforcement learning[C]//2023 8th International Conference on Power and Renewable Energy (ICPRE)．Shanghai，China：IEEE，2023：888-893． doi:10.1109/icpre59655.2023.10353614
60	张剑，崔明建，何怡刚．结合数据驱动与物理模型的主动配电网双时间尺度电压协调优化控制[J]．电工技术学报，2024，39(5)：1327-1339．
	ZHANG J， CUI M J， HE Y G ．Dual timescales coordinated and optimal voltages control in distribution systems using data-driven and physical optimization[J]．Transactions of China Electrotechnical Society，2024，39(5)：1327-1339．
61	李成翔，杜艳丽，朱益华，等．基于风电机组频率主动支撑的多时间尺度调频控制策略[J]．南方电网技术，2024，18(3)：83-92．
	LI C X， DU Y L， ZHU Y H，et al ．Multi-time scale frequency regulation control strategy based on frequency active support of wind turbines[J]．Southern Power System Technology，2024，18(3)：83-92．
62	车志远，余海涛，庞玉毅，等．基于跟踪微分器的永磁同步电机双时间尺度滑模控制研究[J/OL]．上海交通大学学报，1-23[2024-09-13]．．
	CHE Z Y， YU H T， PANG Y Y，et al ．Research on tracking differentiator-based dual-time- scale sliding mode control for permanent magnet synchronous motors[J/OL]．Journal of Shanghai Jiaotong University，1-23[2024-09-13]．．
63	XU P， DUAN J， ZHANG J，et al ．Active power correction strategies based on deep reinforcement learning—Part I：a simulation-driven solution for robustness[J]．CSEE Journal of Power and Energy Systems，2021，8(4)：1122-1133．
64	XU P， ZHANG J， LU J，et al ．A prior knowledge-embedded reinforcement learning method for real-time active power corrective control in complex power systems[J]．Frontiers in Energy Research，2022，10：1009545． doi:10.3389/fenrg.2022.1009545
65	JIAHAO Y， WENBO M， KE W，et al ．Architecture of intelligent decision embedding knowledge for power grid generation-load look-ahead dispatch based on deep reinforcement learning[C]//2023 IEEE 6th International Electrical and Energy Conference (CIEEC)．Hefei，China：IEEE，2023：2723-2726． doi:10.1109/cieec58067.2023.10166017
66	DU Y， LI F， ZANDI H，et al ．Approximating Nash equilibrium in day-ahead electricity market bidding with multi-agent deep reinforcement learning[J]．Journal of Modern Power Systems and Clean Energy，2021，9(3)：534-544． doi:10.35833/mpce.2020.000502
67	WANG X， ZHONG H， ZHANG G，et al ．Adaptive look-ahead economic dispatch based on deep reinforcement learning[J]．Applied Energy，2024，353：122121． doi:10.1016/j.apenergy.2023.122121
68	BAI Y， CHEN S， ZHANG J，et al ．An adaptive active power rolling dispatch strategy for high proportion of renewable energy based on distributed deep reinforcement learning[J]．Applied Energy，2023，330：120294． doi:10.1016/j.apenergy.2022.120294
69	胡丹尔，彭勇刚，韦巍，等．多时间尺度的配电网深度强化学习无功优化策略[J]．中国电机工程学报，2022，42(14)：5034-5044．
	HU D E， PENG Y G， WEI W，et al ．Multi-timescale deep reinforcement learning for reactive power optimization of distribution network[J]．Proceedings of the CSEE，2022，42(14)：5034-5044．
70	SI R， CHEN S， ZHANG J，et al ．A multi-agent reinforcement learning method for distribution system restoration considering dynamic network reconfiguration[J]．Applied Energy，2024，372：123625． doi:10.1016/j.apenergy.2024.123625
71	刘祺，王承民，谢宁，等．新型配电系统中考虑电动汽车差异化行为特性的充换电站规划方法[J]．智慧电力，2024，52(9)：18-24．
	LIU Q， WANG C M， XIE N，et al ．Charging and swapping stations planning method considering differentiated behavior characteristics of electric vehicles in new distribution system[J]．Smart Power，2024，52(9)：18-24．
72	王安宁，范荣奇，张旸，等．基于多特征量判据的新型配电系统早期故障检测[J]．中国电力，2024，57(9)：181-193．
	WANG A N， FAN R Q， ZHANG Y，et al ．Multiple characteristics criterion based incipient fault detection of distribution systems[J]．Electric Power，2024，57(9)：181-193．
73	郭镥，刘永礼，张媛，等．实用化数据同步技术在新型配电系统规划多人协同机制中的应用[J]．发电技术，2024，45(2)：363-372．
	GUO L， LIU Y L， ZHANG Y，et al ．Application of practical data synchronization technology in multi person cooperation mechanism of new distribution system planning[J]．Power Generation Technology，2024，45(2)：363-372．
74	张汪洋，樊艳芳，侯俊杰，等．基于集成深度神经网络的配电网分布式状态估计方法[J]．电力系统保护与控制，2024，52(3)：128-140．
	ZHANG W Y， FAN Y F， HOU J J，et al ．Distribution network distributed state estimation method based on an integrated deep neural network[J]．Power System Protection and Control，2024，52(3)：128-140．
75	黄奕俊，肖健，彭依明，等．基于边缘计算的新型电力系统分布式状态估计[J]．电力科学与技术学报，2025，40(1)：77-84．
	HUANG Y J， XIAO J， PENGY M，et al ．Distributed state estimation of new power system based on edge computing[J]．Journal of Electric Power Science and Technology，2025，40(1)：77-84．
76	张明泽，栾文鹏，艾欣，等．基于边缘计算的台区短期负荷预测方法[J]．电测与仪表，2024，61(4)：93-99．
	ZHANG M Z， LUAN W P， AI X，et al ．Short-term substation load forecasting method based on edge computing[J]．Electrical Measurement & Instrumentation，2024，61(4)：93-99．
77	张丽，李世情，艾恒涛，等．基于改进Q学习算法和组合模型的超短期电力负荷预测[J]．电力系统保护与控制，2024，52(9)：143-153．
	ZHANG L， LI S Q， AI H T，et al ．Ultra-short-term power load forecasting based on an improved Q-learning algorithm and combination model[J]．Power System Protection and Control，2024，52(9)：143-153．
78	黄一川，宋瑜辉，荆朝霞．基于联邦学习的能源聚合服务商负荷预测[J]．电力建设，2025，46(1)：37-47．
	HUANG Y C， SONG Y H， JING Z X ．Federated learning-based load forecasting for energy aggregation service providers[J]．Electric Power Construction，2025，46(1)：37-47．
79	CAO D， ZHAO J， HU W，et al ．Deep reinforcement learning enabled physical-model-free two-timescale voltage control method for active distribution systems[J]．IEEE Transactions on Smart Grid，2022(13-1).DOI：10.1109/TSG.2021.3113085．
80	李付强，张文朝，潘艳，等．基于改进深度确定性策略梯度算法的电压无功优化策略[J]．智慧电力，2024，52(5)：1-7．
	LI F Q， ZHANG W C， PAN Y，et al ．Reactive voltage optimization strategy based on improved depth deterministic strategy gradient algorithm[J]．Smart Power，2024，52(5)：1-7．
81	ZHAO J，MEMBER， LI F，et al ．Deep reinforcement learning based model-free on-line dynamic multi-microgrid formation to enhance resilience[EB/OL]．(2022-05-06)[2024-10-18]．． doi:10.1109/tsg.2022.3160387
82	SI R， QIAO J， WANG X，et al ．A transferable multi-agent reinforcement learning method for distribution service restoration[C]//2023 IEEE International Conference on Systems，Man，and Cybernetics (SMC)．Hawaii，USA：IEEE，2023：1866-1871． doi:10.1109/smc53992.2023.10394147
83	CHOLLET F ．On the measure of intelligence[EB/OL]．(2019-11-06)[2024-10-18]．. doi:10.1023/a:1010650624155
	01547v2． doi:10.1023/a:1010650624155
84	SCHAUL T， TOGELIUS J， SCHMIDHUBER J ．Measuring intelligence through games[EB/OL]．(2011-09-06)[2024-10-18]．．
85	ZHANG T ．Parallel system based quantitative assessment and self-evolution for artificial intelligence of active power corrective control[J]．CSEE Journal of Power and Energy Systems，2023，10(1)：13-28．
86	HERNÁNDEZ-LOBATO J M， GELBART M A， ADAMS R P，et al ．A general framework for constrained Bayesian optimization using information-based search[J]．Journal of Machine Learning Research，2016，17(1)：5549-5601．
87	WANG Z， GEHRING C， KOHLI P，et al ．Batched large-scale Bayesian optimization in high-dimensional spaces[C]//International Conference on Artificial Intelligence and Statistics．Playa Blanca，Canary Islands：PMLR，2018：745-754．
88	LÉVESQUE J C， GAGNÉ C， SABOURIN R ．Bayesian hyperparameter optimization for ensemble learning[EB/OL]．(2016-05-20)[2024-10-18]．．org/abs/1605.06394v1．
89	LÉVESQUE J C， DURAND A， GAGNÉ C，et al ．Bayesian optimization for conditional hyperparameter spaces[C]//2017 International Joint Conference on Neural Networks (IJCNN)．Anchorage，Alaska：IEEE，2017：286-293． doi:10.1109/ijcnn.2017.7965867

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电力系统智能计算的关键技术及应用展望

Key Technologies and Application Prospects of Intelligent Computing in Power Systems

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