能源互联网中的数字孪生技术体系、应用与挑战

doi:10.11930/j.issn.1004-9649.202303037

中国电力 ›› 2024, Vol. 57 ›› Issue (1): 230-243.DOI: 10.11930/j.issn.1004-9649.202303037

能源互联网中的数字孪生技术体系、应用与挑战

刘曌¹(), 孙庆凯¹(), 许泽凯¹(), 吴潇雨²(), 王小君¹(), 吕金玲¹()

1. 北京交通大学电气工程学院，北京　100044
2. 国网能源研究院有限公司，北京　102209

收稿日期:2023-03-08 接受日期:2023-08-14 出版日期:2024-01-28 发布日期:2024-01-23
作者简介:刘曌（1991—），男，博士，讲师，从事电力系统稳定性分析与控制、人工智能技术在电力系统中的应用、电力系统优化调度等研究，E-mail：liuzhao1@bjtu.edu.cn
孙庆凯（1992—），男，通信作者，博士研究生，从事综合能源系统优化运行、人工智能技术在电力系统中的应用等研究，E-mail：19117021@bjtu.edu.cn
王小君（1978—），男，博士，教授，从事电力系统及其自动化、综合能源系统优化运行、人工智能技术在电力系统中的应用等研究，E-mail：xjwang1@bjtu.edu.cn
基金资助:
国家自然科学基金面上资助项目（基于群智进化的分布式能源系统群演化模型与优化引导方法，51977005）。

System, Applications and Challenges of Digital Twin Technology in Energy Internet

Zhao LIU¹(), Qingkai SUN¹(), Zekai XU¹(), Xiaoyu WU²(), Xiaojun WANG¹(), Jinling LV¹()

1. School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
2. State Grid Energy Research Institute Co., Ltd., Beijing 102209, China

Received:2023-03-08 Accepted:2023-08-14 Online:2024-01-28 Published:2024-01-23
Supported by:
This work is supported by General Program of National Natural Science Foundation of China (Research on Evolutionary Model and Optimization Method of Distributed Energy System Group Based on Agents Group Evolution, No.51977005)

摘要/Abstract

摘要：

随着传统能源互联系统逐步向能源互联网升级转化，仅基于机理建模的方法难以描述其高维性、非线性以及多能耦合性等特征。数字孪生技术可将真实系统精准映射到虚拟空间，对实现能源互联网的特征描述、运行分析、监控优化以及智能决策具有重要意义。首先，从数字孪生技术的发展出发，对能源互联网中的数字孪生技术体系进行分析，提出了涵盖“多源数据采集-模型构建-平台支撑-智能交互”的分层技术体系框架，细化了数字孪生技术在能源互联网中的应用价值；其次，详细阐述了数字孪生技术在能源互联网中的典型应用以及需要突破的难点，并给出了其当前的发展瓶颈；最后，对数字孪生技术在能源互联网中的发展路线进行了总结与展望。

关键词: 能源互联网, 数字孪生, 技术框架, 典型应用, 难点与展望

Abstract:

With the gradual upgrading and transformation of traditional energy interconnection system to energy Internet, it is difficult to describe its high dimensionality, nonlinear and multi energy coupling characteristics if only based on the mechanism modeling method. Digital twin technology can accurately map the real system to the virtual space, which is of great significance to the realization of the feature description, operation analysis, monitoring optimization and intelligent decision-making of the energy Internet. Firstly, from the development of digital twin technology, an analysis is made on the digital twin technology system in the energy Internet, and a hierarchical technology system framework is proposed, which covers “multi-source data acquisition-model building-platform support-intelligent interaction” and the application value of digital twin technology in the energy Internet is elaborated. Secondly, the typical applications of the digital twin technology in energy Internet and the current development bottleneck are introduced in detail. Finally, the development route of the digital twin technology in energy internet is summarized and envisaged.

Key words: energy Internet, digital twin, technology framework, typical application, difficulties and prospects

刘曌, 孙庆凯, 许泽凯, 吴潇雨, 王小君, 吕金玲. 能源互联网中的数字孪生技术体系、应用与挑战[J]. 中国电力, 2024, 57(1): 230-243.

Zhao LIU, Qingkai SUN, Zekai XU, Xiaoyu WU, Xiaojun WANG, Jinling LV. System, Applications and Challenges of Digital Twin Technology in Energy Internet[J]. Electric Power, 2024, 57(1): 230-243.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: https://www.electricpower.com.cn/CN/10.11930/j.issn.1004-9649.202303037

https://www.electricpower.com.cn/CN/Y2024/V57/I1/230

图/表 7

图 1 能源互联网含义

Fig.1 Meaning of the energy Internet

图 2 能源互联网数字孪生技术分层框架

Fig.2 Layered framework of the energy Internet digital twin technology

图 3 能源互联网数字孪生典型应用

Fig.3 Typical applications of the energy Internet digital twin

图 4 数字孪生在能源互联网监测分析中的难点

Fig.4 Application difficulties of digital twin in energy Internet monitoring and analysis

图 5 数字孪生在能源互联网模型构建中的难点

Fig.5 Application difficulties of digital twins in construction of energy Internet model

图 6 数字孪生在能源互联网规划与运行中的难点

Fig.6 Application difficulties of digital twin in energy Internet planning and operation

图 7 数字孪生在能源互联网综合评价中的难点

Fig.7 Application difficulties of digital twin in comprehensive evaluation of energy Internet

参考文献 53

1	舒印彪, 赵勇, 赵良, 等. “双碳”目标下我国能源电力低碳转型路径[J]. 中国电机工程学报, 2023, 43 (5): 1663- 1672.
	SHU Yinbiao, ZHAO Yong, ZHAO Liang, et al. Study on low carbon energy transition path toward carbon peak and carbon neutrality[J]. Proceedings of the CSEE, 2023, 43 (5): 1663- 1672.
2	方仍存, 杨洁, 周奎, 等. 计及全生命周期碳成本的园区综合能源系统优化规划方法[J]. 中国电力, 2022, 55 (12): 135- 146. DOI
	FANG Rengcun, YANG Jie, ZHOU Kui, et al. An optimal planning method for park IES considering life cycle carbon cost[J]. Electric Power, 2022, 55 (12): 135- 146. DOI
3	王延峰, 申永鹏, 唐耀华, 等. 双碳目标下NB-IoT能源物联网安全构架及关键技术[J]. 电力系统保护与控制, 2022, 50 (8): 179- 187. DOI
	WANG Yanfeng, SHEN Yongpeng, TANG Yaohua, et al. Framework and key technologies for NB-IoT energy Internet of Things to achieve carbon peak and neutrality goals[J]. Power System Protection and Control, 2022, 50 (8): 179- 187. DOI
4	国家能源局. 关于加快推进能源数字化智能化发展的若干意见[EB/OL], (2023-04-02)[2023-04-10]. http://www.gov.cn/zhengce/zhengceku/2023-04/02/content_5749758.htm.
5	赵鹏, 蒲天骄, 王新迎, 等. 面向能源互联网数字孪生的电力物联网关键技术及展望[J]. 中国电机工程学报, 2022, 42 (2): 447- 458.
	ZHAO Peng, PU Tianjiao, WANG Xinying, et al. Key technologies and perspectives of power Internet of Things facing with digital twins of the energy Internet[J]. Proceedings of the CSEE, 2022, 42 (2): 447- 458.
6	唐文虎, 陈星宇, 钱瞳, 等. 面向智慧能源系统的数字孪生技术及其应用[J]. 中国工程科学, 2020, 22 (4): 74- 85. DOI
	TANG Wenhu, CHEN Xingyu, QIAN Tong, et al. Technologies and applications of digital twin for developing smart energy systems[J]. Strategic Study of CAE, 2020, 22 (4): 74- 85. DOI
7	陶飞, 刘蔚然, 张萌, 等. 数字孪生五维模型及十大领域应用[J]. 计算机集成制造系统, 2019, 25 (1): 1- 18.
	TAO Fei, LIU Weiran, ZHANG Meng, et al. Five-dimension digital twin model and its ten applications[J]. Computer Integrated Manufacturing Systems, 2019, 25 (1): 1- 18.
8	林峰, 肖立华, 商浩亮, 等. “双碳”背景下能源互联网数字孪生系统的设计及应用[J]. 电力科学与技术学报, 2022, 37 (1): 29- 34.
	LIN Feng, XIAO Lihua, SHANG Haoliang, et al. Design and application of energy Internet digital twin system under the background of “dual carbon”[J]. Journal of Electric Power Science and Technology, 2022, 37 (1): 29- 34.
9	ZHANG X H, LI K C, LI D Z, et al. Digital twin in energy Internet and its potential applications[C]//2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). Wuhan, China. IEEE, 2021: 2948–2953.
10	肖文磊, 曹宪, 赵罡, 等. 数字孪生的智能制造内涵及其在数控加工的应用[J]. 智能制造, 2021, (5): 30- 46.
11	王梦雪, 赵浩然, 田航, 等. 典型综合能源系统仿真与规划平台综述[J]. 电网技术, 2020, 44 (12): 4702- 4712.
	WANG Mengxue, ZHAO Haoran, TIAN Hang, et al. Review of typical simulation and planning platforms for integrated energy system[J]. Power System Technology, 2020, 44 (12): 4702- 4712.
12	BOSCHERT S, ROSEN R. Digital twin-the simulation aspect[M]//HEHENBERGER P, BRADLEY D. Mechatronic Futures. Cham: Springer, 2016: 59–74.
13	ZHOU M K, YAN J F, FENG D H. Digital twin framework and its application to power grid online analysis[J]. CSEE Journal of Power and Energy Systems, 2019, 5 (3): 391- 398.
14	TAO F, ZHANG H, LIU A, et al. Digital twin in industry: state-of-the-art[J]. IEEE Transactions on Industrial Informatics, 2019, 15 (4): 2405- 2415. DOI
15	刘彬, 张云勇. 基于数字孪生模型的工业互联网应用[J]. 电信科学, 2019, 35 (5): 120- 128.
	LIU Bin, ZHANG Yunyong. Application of digital twin model based industrial Internet[J]. Telecommunications Science, 2019, 35 (5): 120- 128.
16	HE X, CHU L, QIU R C, et al. A novel data-driven situation awareness approach for future grids-using large random matrices for big data modeling[J]. IEEE Access, 2018, 6, 13855- 13865. DOI
17	沈沉, 曹仟妮, 贾孟硕, 等. 电力系统数字孪生的概念、特点及应用展望[J]. 中国电机工程学报, 2022, 42 (2): 487- 499.
	SHEN Chen, CAO Qianni, JIA Mengshuo, et al. Concepts, characteristics and prospects of application of digital twin in power system[J]. Proceedings of the CSEE, 2022, 42 (2): 487- 499.
18	贺兴, 艾芊, 朱天怡, 等. 数字孪生在电力系统应用中的机遇和挑战[J]. 电网技术, 2020, 44 (6): 2009- 2019.
	HE Xing, AI Qian, ZHU Tianyi, et al. Opportunities and challenges of the digital twin in power system applications[J]. Power System Technology, 2020, 44 (6): 2009- 2019.
19	王成山, 董博, 于浩, 等. 智慧城市综合能源系统数字孪生技术及应用[J]. 中国电机工程学报, 2021, 41 (5): 1597- 1608.
	WANG Chengshan, DONG Bo, YU Hao, et al. Digital twin technology and its application in the integrated energy system of smart city[J]. Proceedings of the CSEE, 2021, 41 (5): 1597- 1608.
20	刘林, 张运洲, 王雪, 等. 能源互联网目标下电力信息物理系统深度融合发展研究[J]. 中国电力, 2019, 52 (1): 2- 9.
	LIU Lin, ZHANG Yunzhou, WANG Xue, et al. Research on deep integration of power cyber physical system under energy Internet goals[J]. Electric Power, 2019, 52 (1): 2- 9.
21	雷煜卿, 仝杰, 张树华, 等. 能源互联网感知层技术标准体系研究[J]. 供用电, 2021, 38 (7): 14- 20, 33.
	LEI Yuqing, TONG Jie, ZHANG Shuhua, et al. Research on technical standard system of energy internet sensing layer[J]. Distribution & Utilization, 2021, 38 (7): 14- 20, 33.
22	肖祥武, 王丰, 王晓辉, 等. 面向工业互联网的智慧电厂仿生体系架构及信息物理系统[J]. 电工技术学报, 2020, 35 (23): 4898- 4911.
	XIAO Xiangwu, WANG Feng, WANG Xiaohui, et al. Bionic structure and cyber-physical system for intelligent power plant oriented to the industrial Internet[J]. Transactions of China Electrotechnical Society, 2020, 35 (23): 4898- 4911.
23	詹谭博驰. 基于数字孪生的电网数字化转型研究[J]. 能源与环保, 2021, 43 (11): 262- 266.
	ZHAN Tanbochi. Research on digital transformation of power grid based on digital twins[J]. China Energy and Environmental Protection, 2021, 43 (11): 262- 266.
24	魏一雄, 郭磊, 陈亮希, 等. 基于实时数据驱动的数字孪生车间研究及实现[J]. 计算机集成制造系统, 2021, 27 (2): 352- 363.
	WEI Yixiong, GUO Lei, CHEN Liangxi, et al. Research and implementation of digital twin workshop based on real-time data driven[J]. Computer Integrated Manufacturing Systems, 2021, 27 (2): 352- 363.
25	包哲. 基于智能算法的综合能源系统动态建模仿真与协同运行优化集成研究[D]. 北京: 华北电力大学, 2020.
	BAO Zhe. Study on coupled dynamic simulation and synergistic operation optimization of integrated energy system based on intelligent algorithm[D]. Beijing: North China Electric Power University, 2020.
26	TALER J, DZIERWA P, TALER D, et al. Optimization of the boiler start-up taking into account thermal stresses[J]. Energy, 2015, 92, 160- 170. DOI
27	高学伟. 数字孪生建模方法及其在热力系统优化运行中的应用研究[D]. 北京: 华北电力大学, 2021.
	GAO Xuewei. Research on digital twin modeling method and its application to optimal operation of thermalpower system [D]. Beijing: North China Electric Power University, 2021.
28	陶飞, 张贺, 戚庆林, 等. 数字孪生模型构建理论及应用[J]. 计算机集成制造系统, 2021, 27 (1): 1- 15.
	TAO Fei, ZHANG He, QI Qinglin, et al. Theory of digital twin modeling and its application[J]. Computer Integrated Manufacturing Systems, 2021, 27 (1): 1- 15.
29	赵琦, 王新迎, 乔骥. 数据驱动的能源互联网建模与仿真关键技术[J]. 电力信息与通信技术, 2020, 18 (1): 39- 45.
	ZHAO Qi, WANG Xinying, QIAO Ji. Key technologies of data-driven energy interconnection modeling and simulation[J]. Electric Power Information and Communication Technology, 2020, 18 (1): 39- 45.
30	STRUŠNIK D, GOLOB M, AVSEC J. Artificial neural networking model for the prediction of high efficiency boiler steam generation and distribution[J]. Simulation Modelling Practice and Theory, 2015, 57, 58- 70. DOI
31	SMREKAR J, ASSADI M, FAST M, et al. Development of artificial neural network model for a coal-fired boiler using real plant data[J]. Energy, 2009, 34 (2): 144- 152. DOI
32	黄天恩, 郭庆来, 孙宏斌, 等. 模型-数据混合驱动的电网安全特征选择和知识发现关键技术与工程应用[J]. 电力系统自动化, 2019, 43 (1): 95- 101, 208.
	HUANG Tianen, GUO Qinglai, SUN Hongbin, et al. Hybrid model and data driven concepts for power system security feature selection and knowledge discovery: key technologies and engineering application[J]. Automation of Electric Power Systems, 2019, 43 (1): 95- 101, 208.
33	靳尧飞, 应雨龙, 李靖超, 等. 基于模型与数据混合驱动的燃气轮机气路故障诊断方法[J]. 热力发电, 2021, 50 (9): 66- 71, 93.
	JIN Yaofei, YING Yulong, LI Jingchao, et al. A gas path circuit diagnosis method for gas turbine based on model and data hybrid drive[J]. Thermal Power Generation, 2021, 50 (9): 66- 71, 93.
34	沈沉, 贾孟硕, 陈颖, 等. 能源互联网数字孪生及其应用[J]. 全球能源互联网, 2020, 3 (1): 1- 13. DOI
	SHEN Chen, JIA Mengshuo, CHEN Ying, et al. Digital twin of the energy Internet and its application[J]. Journal of Global Energy Interconnection, 2020, 3 (1): 1- 13. DOI
35	SONG Y K, CHEN Y, HUANG S W, et al. Efficient GPU-based electromagnetic transient simulation for power systems with thread-oriented transformation and automatic code generation[J]. IEEE Access, 2018, 6, 25724- 25736. DOI
36	王妍. 基于需求侧管理的区域能源系统控制策略研究[D]. 天津: 天津大学, 2018.
	WANG Yan. Research on control strategy of regional energy system based on demand side management[D]. Tianjin: Tianjin University, 2018.
37	梁佳欣. 微能源网经济与环境效益研究及系统优化设计[D]. 济南: 山东大学, 2018.
	LIANG Jiaxin. Economic and environmental benefits Research and system optimization design of micro energy grid [D]. Jinan: Shandong University, 2018.
38	韩笑, 郭剑波, 蒲天骄, 等. 电力人工智能技术理论基础与发展展望(一): 假设分析与应用范式[J]. 中国电机工程学报, 2023, 43 (8): 2877- 2891.
	HAN Xiao, GUO Jianbo, PU Tianjiao, et al. Theoretical foundation and directions of electric power artificial intelligence(Ⅰ): hypothesis analysis and application paradigm[J]. Proceedings of the CSEE, 2023, 43 (8): 2877- 2891.
39	高仕斌, 高凤华, 刘一谷, 等. 自感知能源互联网研究展望[J]. 电力系统自动化, 2021, 45 (5): 1- 17.
	GAO Shibin, GAO Fenghua, LIU Yigu, et al. Prospect of research on self-aware energy Internet[J]. Automation of Electric Power Systems, 2021, 45 (5): 1- 17.
40	白浩, 周长城, 袁智勇, 等. 基于数字孪生的数字电网展望和思考[J]. 南方电网技术, 2020, 14 (8): 18- 24, 40.
	BAI Hao, ZHOU Changcheng, YUAN Zhiyong, et al. Prospect and thinking of digital power grid based on digital twin[J]. Southern Power System Technology, 2020, 14 (8): 18- 24, 40.
41	乔骥, 郭剑波, 范士雄, 等. 人在回路的电网调控混合增强智能初探: 基本概念与研究框架[J]. 中国电机工程学报, 2023, 43 (1): 1- 15.
	QIAO Ji, GUO Jianbo, FAN Shixiong, et al. Human-in-the-loop hybrid-augmented intelligence method for power system dispatching: basic concept and research framework[J]. Proceedings of the CSEE, 2023, 43 (1): 1- 15.
42	蒲天骄, 陈盛, 赵琦, 等. 能源互联网数字孪生系统框架设计及应用展望[J]. 中国电机工程学报, 2021, 41 (6): 2012- 2029.
	PU Tianjiao, CHEN Sheng, ZHAO Qi, et al. Framework design and application prospect for digital twins system of energy Internet[J]. Proceedings of the CSEE, 2021, 41 (6): 2012- 2029.
43	俞小勇, 秦丽文, 桂海涛, 等. 新一代人工智能在配电网智能感知与故障诊断中的应用[J]. 南方电网技术, 2022, 16 (5): 34- 43. DOI
	YU Xiaoyong, QIN Liwen, GUI Haitao, et al. Application of new generation artificial intelligence technology in intelligent sensing and fault detection of power distribution network[J]. Southern Power System Technology, 2022, 16 (5): 34- 43. DOI
44	叶飞宇, 张曦, 林俊光, 等. 数字孪生技术在楼宇型综合能源供应的应用[J]. 煤气与热力, 2021, 41 (6): 15- 18, 42.
	YE Feiyu, ZHANF Xi, LIN Junguang, et al. Application of digital twin technology in building-type integrated energy supply[J]. Gas & Heat, 2021, 41 (6): 15- 18, 42.
45	于军琪, 聂己开, 赵安军, 等. 基于特征挖掘的ARIMA-GRU短期电力负荷预测[J]. 电力系统及其自动化学报, 2022, 34 (3): 91- 99.
	YU Junqi, NIE Jikai, ZHAO Anjun, et al. ARIMA-GRU short-term power load forecasting based on feature mining[J]. Proceedings of the CSU-EPSA, 2022, 34 (3): 91- 99.
46	刘凯. 基于改进BP神经网络的短期负荷预测研究[D]. 南京: 河海大学, 2005.
	LIU Kai. Short-term load forecasting based on improved BP neural network[D]. Nanjing: Hohai University, 2005.
47	严兴煜, 高赐威, 陈涛, 等. 数字孪生虚拟电厂系统框架设计及其实践展望[J]. 中国电机工程学报, 2023, 43 (2): 604- 619.
	YAN Xingyu, GAO Ciwei, CHEN Tao, et al. Framework design and application prospect for digital twin virtual power plant system[J]. Proceedings of the CSEE, 2023, 43 (2): 604- 619.
48	李明节, 陶洪铸, 许洪强, 等. 电网调控领域人工智能技术框架与应用展望[J]. 电网技术, 2020, 44 (2): 393- 400. DOI
	LI Mingjie, TAO Hongzhu, XU Hongqiang, et al. The technical framework and application prospect of artificial intelligence application in the field of power grid dispatching and control[J]. Power System Technology, 2020, 44 (2): 393- 400. DOI
49	刘世成, 张东霞, 朱朝阳, 等. 能源互联网中大数据技术思考[J]. 电力系统自动化, 2016, 40 (8): 14- 21, 56. DOI
	LIU Shicheng, ZHANG Dongxia, ZHU Chaoyang, et al. A view on big data in energy Internet[J]. Automation of Electric Power Systems, 2016, 40 (8): 14- 21, 56. DOI
50	黄蓉. 计算机网络安全与数据完整性技术探究[J]. 网络安全技术与应用, 2021, (4): 57- 58.
51	冯浩. 数字孪生数据驱动设计过程复杂性合成及演化机理研究[D]. 天津: 河北工业大学, 2020.
	FENG Hao. Research on design-centric complexity synthesis and evolution mechanism driven by digital twin data[D]. Tianjin: Hebei University of Technology, 2020.
52	蒲天骄, 张中浩, 谈元鹏, 等. 电力人工智能技术理论基础与发展展望(二): 自主学习与应用初探[J]. 中国电机工程学报, 2023, 43 (10): 3705- 3718.
	PU Tianjiao, ZHANG Zhonghao, TAN Yuanpeng, et al. Theoretical primer and directions of electric power artificial intelligence(Ⅱ): self-directed learning and preliminary application[J]. Proceedings of the CSEE, 2023, 43 (10): 3705- 3718.
53	张辰源, 陶飞. 数字孪生模型评价指标体系[J]. 计算机集成制造系统, 2021, 27 (8): 2171- 2186. DOI
	ZHANG Chenyuan, TAO Fei. Evaluation index system for digital twin model[J]. Computer Integrated Manufacturing Systems, 2021, 27 (8): 2171- 2186. DOI

[1]	陈志永, 韩璟琳, 刘洋, 李光毅, 蒋雨, 蔡瑞天. 基于关键帧提取与图像识别的电网设施数字化重建[J]. 中国电力, 2024, 57(9): 217-223.
[2]	何永胜, 罗丹, 鲁宗相. 特高压阀侧套管数字孪生系统集成设计方法[J]. 中国电力, 2024, 57(6): 165-173.
[3]	安佳坤, 杨书强, 王涛, 贺春光, 张菁, 袁超, 窦春霞. 电动汽车聚合下的微能源互联网优化调度策略[J]. 中国电力, 2023, 56(5): 80-88.
[4]	韩伟, 段文岩, 杜兴伟, 姚峰, 马伟东, 刘磊. 基于数字孪生的在运安控系统故障诊断方法[J]. 中国电力, 2023, 56(11): 121-127.
[5]	周原冰, 杨方, 余潇潇, 江涵. 中国能源电力碳中和实现路径及实施关键问题[J]. 中国电力, 2022, 55(5): 1-11.
[6]	陆旭, 陈影, 许中平, 张海全, 慕春芳, 陈恺, 孙毅. 面向5G边缘计算网络的联合需求响应与任务卸载策略[J]. 中国电力, 2022, 55(10): 209-218.
[7]	刘林, 李苏秀, 张桦, 张希凤, 刘颖琦, 陈星彤. 能源互联网商业模式迭代体系与方法初探[J]. 中国电力, 2022, 55(1): 203-213.
[8]	李春龙, 黄辉, 梁云, 柴谦益, 赵若涵, 胡长悦. 面向电力传感器的环境能量收集技术发展趋势及面临的挑战[J]. 中国电力, 2021, 54(2): 27-35.
[9]	马令希, 付学谦. 考虑农业-气象-能源耦合的农业能源互联网理论及应用[J]. 中国电力, 2021, 54(11): 115-124.
[10]	王利利, 张琳娟, 许长清, 武宏波, 李翼铭, 黄玉晶. 能源互联网背景下园区用户画像及成熟度评价模型研究[J]. 中国电力, 2020, 53(8): 19-28.
[11]	徐杰彦, 许雯旸, 褚渊, 晋远, 康旭源, 陈征. 区域尺度住宅建筑日用电负荷模型构建方法研究[J]. 中国电力, 2020, 53(8): 29-39.
[12]	霍沫霖, 郭磊, 张哲. 区域能源互联网的发展现状与政策建议[J]. 中国电力, 2020, 53(12): 241-247.
[13]	朱西平, 付迁, 李恣霖, 罗健, 王登松. 能源互联网多能量枢纽合作议价模型[J]. 中国电力, 2020, 53(11): 89-96.
[14]	龙涛, 别朝红. 面向终端能源互联网的能效优化调度[J]. 中国电力, 2019, 52(6): 2-10.
[15]	王哲, 赵宏大, 朱铭霞, 周霞, 解相朋, 谢宏福, 臧必鹏. 电力无线专网在泛在电力物联网中的应用[J]. 中国电力, 2019, 52(12): 27-38.

能源互联网中的数字孪生技术体系、应用与挑战

System, Applications and Challenges of Digital Twin Technology in Energy Internet

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 53

相关文章 15

编辑推荐

Metrics

模态框（Modal）标题

能源互联网中的数字孪生技术体系、应用与挑战

System, Applications and Challenges of Digital Twin Technology in Energy Internet

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 53

相关文章 15

编辑推荐

Metrics