Meta-Power: Next-Generation Smart Grid

doi:10.12096/j.2096-4528.pgt.22058

Abstract

Abstract:

Metaverse is a new stage of digital revolution,which transforms the universe to highly interactive and hyper-spatiotemporal eco-system supported by multiple technologies. The application of metaverse in the power system can further improve the informatization and intellectualization of the system,becoming the next generation smart grid. This paper proposed a new concept of meta-power to represent the power system introducing the metaverse. Multiple technologies of the meta-power can enhance the flexibility,security,and intelligence of the power system operation. High interactivity of the meta-power can enable the convenient and immersed power system monitoring and maintenance. Hyper-spatiotemporal feature of the meta-power can tackle the spatial and temporal constraints of the power system operation,and facilitate the evaluation and deduction of future energy development strategies.

Key words: metaverse, smart grid, digital twin, ubiquitous internet-of-things, artificial intelligence, advanced human-machine interaction, power system

CLC Number:

TK 01

Chao HUANG, Siqi BU, Qiyu CHEN, Hiu Hung LEE. Meta-Power: Next-Generation Smart Grid[J]. Power Generation Technology, 2022, 43(2): 287-304.

Figures/Tables 9

Fig. 1 Framework of meta-power

Fig. 2 Realistic power system framework

Fig. 3 Update flowchart of digital twin

Fig. 4 Virtual power system model

Fig. 5 Ubiquitous power internet of things

Fig. 6 Hadoop-based data management framework

Fig. 7 Development process of human-machine interaction system in the power industry

Fig. 8 Four scenarios of meta-power

Fig. 9 Framework of parallel learning

References 78

1	申洪，周勤勇，刘耀，等．碳中和背景下全球能源互联网构建的关键技术及展望[J]．发电技术，2021，42(1)：8-19． doi:10.12096/j.2096-4528.pgt.20113
	SHEN H， ZHOU Q Y， LIU Y，et al ．Key technologies and prospects for the construction of global energy internet under the background of carbon neutral[J]．Power Generation Technology，2021，42(1)：8-19． doi:10.12096/j.2096-4528.pgt.20113
2	童光毅．基于双碳目标的智慧能源体系构建[J]．智慧电力，2021，49(5)：1-6． doi:10.3969/j.issn.1673-7598.2021.05.002
	TONG G Y ．Construction of smart energy system based on dual carbon goal[J]．Smart Power，2021，49(5)：1-6． doi:10.3969/j.issn.1673-7598.2021.05.002
3	SILBERMAN S ．The energy web[EB/OL]．(2001-07-01)[2022-02-21]．．
4	张瑶，王傲寒，张宏．中国智能电网发展综述[J]．电力系统保护与控制，2021，49(5)：180-187．
	ZHANG Y， WANG A H， ZHANG H ．Overview of smart grid development in China[J]．Power System Protection and Control，2021，49(5)：180-187．
5	谢清玉，张耀坤，李经纬．面向智能电网的电力大数据关键技术应用[J]．电网与清洁能源，2021，37(12)：39-46． doi:10.3969/j.issn.1674-3814.2021.12.006
	XIE Q Y， ZHANG Y K， LI J W ．Application of key technologies of power big data in smart grids[J]．Power System and Clean Energy，2021，37(12)：39-46． doi:10.3969/j.issn.1674-3814.2021.12.006
6	SMART J， CASCIO J， PAFFENDORF J ．Metaverse roadmap：pathway to the 3D web[R]．Metaverse：A Cross-Industry Public Foresight Project，2007．
7	NING H， WANG H， LIN Y，et al ．A survey on metaverse：the state-of-the-art，technologies，applications，and challenges[EB/OL]．(2021-11-18)[2022-02-21]．．
8	LEE L H， BRAUD T， ZHOU P，et al ．All one needs to know about metaverse：a complete survey on technological singularity，virtual ecosystem，and research agenda[EB/OL]．(2021-11-18)[2022-02-21]．．
9	SIYAEV A， JO G S ．Towards aircraft maintenance metaverse using speech interactions with virtual objects in mixed reality[J]．Sensors，2021，21(6)：2066． doi:10.3390/s21062066
10	NINAGAWA C ．Virtual power plant integration technology[M]．Singapore：Springer Press，2022． doi:10.1007/978-981-16-6148-8
11	米阳，王鹏，邓锦，等．孤岛交直流混合微电网群分层协调控制[J]．电力系统保护与控制，2021，49(20)：1-8．
	MI Y， WANG P， DENG J，et al ．Hierarchical coordinated control of island AC/DC hybrid microgrids[J]．Power System Protection and Control，2021，49(20)：1-8．
12	KHODAEI A， BAHRAMIRAD A， SHAHIDEHPOUR M ．Microgrid planning under uncertainty[J]．IEEE Transactions on Power Systems，2015，30(5)：2417-2425． doi:10.1109/tpwrs.2014.2361094
13	FLUHR J， AHLERT K H， WEINHARDT C ．A stochastic model for simulating the availability of electric vehicles for services to the power grid[C]//2010 43rd Hawaii International Conderence on System Sciences．2010：1-10． doi:10.1109/hicss.2010.33
14	Gartner ．Gartner identifies the top 10 strategic technology trends for 2018[R]．U S：Gartner，2018． doi:10.1049/et.2018.1021
15	RODIC B ．Industry 4.0 and the new simulation modelling paradigm[J]．Organizacija，2017，50(3)：193-207． doi:10.1515/orga-2017-0017
16	HARGREAVES P A， MECROW B C， HALL R ． Calculation of iron loss in electrical generators using finite-element analysis[J]．IEEE Transactions on Industry Applications，2012，48(5)：1460-1466． doi:10.1109/tia.2012.2209851
17	SANTOS H F DOS， SADOWSKI N， BATISTELA N J，et al ．Synchronous generator fault investigation by experimental and finite-element procedures[J]．IEEE Transactions on Magnetics，2016，52(3)：1-4． doi:10.1109/tmag.2015.2480546
18	RAMAYANTI S， BUDIANTORO P ．Design analysis of solar panel structure LAPAN-constellation satellite using finite element analysis[J]．IOP Conference Series：Materials Science and Engineering，2021，1041(1)：12020． doi:10.1088/1757-899x/1041/1/012020
19	TAO F， ZHANG M， LIU Y，et al ．Digital twin driven prognostics and health management for complex equipment[J]．CIRP Annals，2018，67(1)：169-172． doi:10.1016/j.cirp.2018.04.055
20	HUSSAIN M R， REFAAT S S ．Improvement in three-dimension al finite element modeling for high voltage power transformer in time domain[C]//2021 IEEE 19th International Power Electronics and Motion Control Conference (PEMC)．2021：460-466． doi:10.1109/pemc48073.2021.9432566
21	QU J， WANG Q， ZHANG J，et al ．3-D transient finite-element analysis and experimental investigation of short-circuit dynamic stability for air circuit breaker[J]．IEEE Transactions on Components，Packaging and Manufacturing Technology，2015，5(11)：1610-1617． doi:10.1109/tcpmt.2015.2475300
22	WANG Z， LIU Y， KONG X，et al ．Galloping characteristics of 10 kV overhead transmission line using finite element analysis method[C]//2021 International Conference on Electrical Materials and Power Equipment (ICEMPE)．Chingqing，China：IEEE，2021：1-4． doi:10.1109/icempe51623.2021.9509102
23	CONTI S， DILETTOSO E， RIZZO S A ． Electromagnetic and thermal analysis of high voltage three-phase underground cables using finite element method[C]//2018 International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe)．2018：1-6． doi:10.1109/eeeic.2018.8525354
24	YAMAZAKI K， MATSUMOTO M ．3-D finite element meshing for skewed rotor induction motors[J]．IEEE Transactions on Magnetics，2015，51(3)：1-4． doi:10.1109/tmag.2014.2348568
25	TSAO B， CHMIEL B， DEVILLIER A，et al ．Transient finite element simulation of a Lithium-ion battery pack thermal management system based on latent heat system materials[J]．ECS Transactions，2021，104(1)：61-71． doi:10.1149/10401.0061ecst
26	CANIZARES C， FERNANDES T， GERALDI E，et al ．Benchmark models for the analysis and control of small-signal oscillatory dynamics in power systems[J]．IEEE Transactions on Power Systems，2017，32(1)：715-722． doi:10.1109/tpwrs.2016.2561263
27	LATRECHE Y， BOUCHEKARA H R E H， NAIDU K，et al ．Comprehensive review of radial distribution test systems[EB/OL]．(2020-06-20)[2022-02-21]．． doi:10.36227/techrxiv.12578648
28	HE X， AI Q， QIU R C，et al ．Preliminary exploration on digital twin for power systems：challenges，framework，and applications[EB/OL]．(2019-09-16)[2022-02-21]．．
29	AGHAMOLKI H G， MIAO Z， FAN L ．A hardware-in-the-loop SCADA testbed[C]//2015 North American Power Symposium (NAPS)，Charlotte，USA：IEEE，2015：1-6． doi:10.1109/naps.2015.7335093
30	YANG Y， JIANG H T， MCLAUGHLIN K，et al ．Cybersecurity test-bed for IEC 61850 based smart substations[C]//2015 IEEE Power & Energy Society General Meeting．Denver，USA：IEEE，2015：1-5． doi:10.1109/pesgm.2015.7286357
31	CINTUGLU M H， MOHAMMED O A， AKKAYA K，et al ．A survey on smart grid cyber-physical system testbeds[J]．IEEE Communications Surveys & Tutorials，2017，19(1)：446-464． doi:10.1109/comst.2016.2627399
32	SONG X， JIANG T， SCHLEGEL S，et al ．Investigation of inventive tuning algorithm for the realization of digital twins of inverter model in inverter-dominated power distribution grid[C]//2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)．Bucharest，Romania：IEEE，2019：1-6． doi:10.1109/isgteurope.2019.8905551
33	SAAD A， FADDEL S， MOHAMMED O ．IoT-based digital twin for energy cyber-physical systems：design and implementation[J]．Energies，2020，13(18)：4762． doi:10.3390/en13184762
34	LE F C， TAO Y ．Intelligent energy dispatching robot and its knowledge automation：information，physics and social integration：frame-work，technology and challenges[J]．Procedia CSEE，2018，38(1)：25-40．
35	杨挺，翟峰，赵英杰，等．泛在电力物联网释义与研究展望[J]．电力系统自动化，2019，43(13)：9-20． doi:10.7500/AEPS20190418015
	YANG T， ZHAI F， ZHAO Y J，et al ．Interpretation and research prospects of ubiquitous power internet of things[J]．Automation of Electric Power Systems，2019，43(13)：9-20． doi:10.7500/AEPS20190418015
36	GALLI S， SCAGLIONE A， WANG Z ．For the grid and through the grid：the role of power line communications in the smart grid[J]．Proceedings of the IEEE，2011，99(6)：998-1027． doi:10.1109/jproc.2011.2109670
37	ANCILLOTTI E， BRUNO R， CONTI M ．The role of communication systems in smart grids：architectures，technical solutions and research challenges[J]．Computer Communications，2013，36(17/18)：1665-1697． doi:10.1016/j.comcom.2013.09.004
38	YANG B， YAMAZAKI J， SAITO N，et al ．Big data analytic empowered grid applications：is PMU a big data issue[C]//2015 12th International Conference on the European Energy Market (EEM)．Lisbon，Potugal：IEEE，2015：1-4． doi:10.1109/eem.2015.7216718
39	THOLOMIER D， Kang H， CVOROVIC B ．Phasor measurement units：functionality and applications[C]//2009 Power Systems Conference．Clemson，USA：IEEE，2009：1-12． doi:10.1109/psamp.2009.5262468
40	MAHMOOD A， AAMIR M， ANIS M I ．Design and implementation of AMR smart grid system[C]//2008 IEEE Canada Electric Power Conference．Karachi，Pakistan：IEEE，2008：1-6． doi:10.1109/epc.2008.4763340
41	LEWIS R P， IGICT P， ZHOU Z ．Assessment of communication methods for smart electricity metering in the UK[C]//2009 IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE)．Valencia，Spain：IEEE，2009：1-4． doi:10.1109/sae.2009.5534884
42	ZHANG D， SUN B， FAN H ．Remote online power system monitoring system based on multiinformation acquisition technology[C]//2004 International Conference on Power System Technology．Singapore：IEEE，2004：1147-1151．
43	SYED D， ZAINAB A， GHRAYEB A，et al ．Smart grid big data analytics：survey of technologies，techniques，and applications[J]．IEEE Access，2020，9：59564-59585． doi:10.1109/access.2020.3041178
44	魏梦飒，陆增洁，徐建清，等．基于大数据模型的电网风电场群总功率预测[J]．电子设计工程，2019，27(23)：64-67．
	WEI M S， LU Z J， XU J Q，et al ．Prediction of total power of grid-connected wind farm based on big data model[J]．Electronic Design Engineering，2019，27(23)：64-67．
45	赵川，孙华利，王国平，等．基于大数据的电力信息系统网络安全分析[J]．电子设计工程，2019，27(23)：148-152．
	ZHAO C， SUN H L， WANG G P，et al ．Network security analysis of power information system based on big data[J]．Electronic Design Engineering，2019，27(23)：148-152．
46	赵俊华，文福拴，薛禹胜，等．云计算：构建未来电力系统的核心计算平台[J]．电力系统自动化，2010，34(48)：1-8．
	ZHAO J H， WEN F S， XUE Y S，et al ．Cloud computing：the core computing platform for building future power systems[J]．Automation of Electric Power Systems，2010，34(48)：1-8．
47	TONG J， WU H， LIN Y，et al ．Fog-computing-based short-circuit diagnosis scheme[J]．IEEE Transactions on Smart Grid，2020，11(4)：3359-3371． doi:10.1109/tsg.2020.2964805
48	PENG N， LIANG R， WANG G，et al ．Edge computing based fault location in distribution networks by using asynchronous transient amplitudes at limited nodes[J]．IEEE Transactions on Smart Grid，2021，12(1)：574-588． doi:10.1109/tsg.2020.3009005
49	LIU Y， YANG C， JIANG L，et al ．Intelligent edge computing for IoT-based energy management in smart cities[J]．IEEE Network，2019，33(2)：111-117． doi:10.1109/mnet.2019.1800254
50	IBRAHIM M S， DONG W， YANG Q ．Machine learning driven smart electric power systems：current trends and new perspectives[J]．Applied Energy，2020，272：115237． doi:10.1016/j.apenergy.2020.115237
51	OGUZ K ．Big data applications in the energy sector[M]．Portugal：Instituto Superior Tecnico，2017． doi:10.23919/ropaces.2017.7916057
52	JIAO R， HUANG X， MA X，et al ．A model combining stacked auto encoder and back propagation algorithm for short-term wind power forecasting[J]．IEEE Access，2018，6：17851-17858． doi:10.1109/access.2018.2818108
53	ONETO L， LAURERI F， ROBBA M，et al ．Data-driven photovoltaic power production nowcasting and forecasting for polygeneration microgrids[J]．IEEE Systems Journal，2017，12(3)：2842-2853． doi:10.1109/jsyst.2017.2688359
54	KONG W， DONG Z Y， JIA Y，et al ． Short-term residential load forecasting based on LSTM recurrent neural network[J]．IEEE Transactions on Smart Grid，2017，10(1)：841-851．
55	WANG S， WANG X， WANG S，et al ．Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting[J]．International Journal of Electrical Power & Energy Systems，2019，109：470-479． doi:10.1016/j.ijepes.2019.02.022
56	RAFIEI M， NIKNAM T， AGHAEI J，et al ．Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine[J]．IEEE Transactions on Smart Grid，2018，9(6)：6961-6971． doi:10.1109/tsg.2018.2807845
57	YIN L， GAO Q， ZHAO L，et al ．Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids[J]．Energy，2020，191：116561． doi:10.1016/j.energy.2019.116561
58	ZHANG X S， LI Q， YU T，et al ．Consensus transfer Q-learning for decentralized generation command dispatch based on virtual generation tribe[J]．IEEE Transactions on Smart Grid，2016，9(3)：2152-2165． doi:10.1109/tsg.2016.2607801
59	ISLAM M， LEE G， HETTIWATTE S N，et al ．Calculating a health index for power transformers using a subsystem-based GRNN approach[J]．IEEE Transactions on Power Delivery，2017，33(4)：1903-1912． doi:10.1109/tpwrd.2017.2770166
60	PENG X， YANG F， WANG G，et al ．A convolutional neural network-based deep learning methodology for recognition of partial discharge patterns from high-voltage cables[J]．IEEE Transactions on Power Delivery，2019，34(4)：1460-1469． doi:10.1109/tpwrd.2019.2906086
61	YAO S， KANG Q， ZHOU M，et al ．Intelligent and data-driven fault detection of photovoltaic plants[J]．Processes，2021，9(10)：1711． doi:10.3390/pr9101711
62	ABDELGAYED T S， MORSI W G， SIDHU T S ．A new approach for fault classification in microgrids using optimal wavelet functions matching pursuit[J]．IEEE Transactions on Smart Grid，2017，9(5)：4838-4846． doi:10.1109/tsg.2017.2672881
63	LIVANI H， EVRENOSOGLU C Y ．A machine learning and wavelet-based fault location method for hybrid transmission lines[J]．IEEE Transactions on Smart Grid，2013，5(1)：51-59． doi:10.1109/tsg.2013.2260421
64	LAAKI H， MICHE Y， TAMMI K ．Prototyping a digital twin for real time remote control over mobile networks：application of remote surgery[J]．IEEE Access，2019，7：20325-20336． doi:10.1109/access.2019.2897018
65	GOMES Jr D L， DE PAIVA A C， SILVA A C，et al ．Augmented visualization using homomorphic filtering and Haar-based natural markers for power systems substations[J]．Computers in Industry，2018，97：67-75． doi:10.1016/j.compind.2018.01.010
66	GORSKI F， GRAJEWSKI D，BUN P，et al ．Study of interaction methods in virtual electrician training[J]．IEEE Access，2021，9：118242-118252． doi:10.1109/access.2021.3106826
67	宋慧娟．基于混合现实的人工智能在电力巡检中的应用[J]．电网与清洁能源，2020，36(2)：75-79．
	SONG H J ．Application of artificial intelligence based on mixed reality in power inspection[J]．Power System and Clean Energy，2020，36(2)：75-79．
68	肖东裕，谢旭琛，王慧豪，等．基于虚拟成像技术的变电站运维系统设计[J]．通信电源技术，2019，36(8)：46-47．
	XIAO D Y， XIE X C， WANG H H，et al ．Design of substation operation and maintenance system based on virtual imaging technology[J]．Telecom Power Technology，2019，36(8)：46-47．
69	ZHANG P， LI F， BHATT N ．Next-generation monitoring，analysis，and control for the future smart control center[J]．IEEE Transactions on Smart Grid，2010，1(2)：186-192． doi:10.1109/tsg.2010.2053855
70	CHAE C H， JUNG N J， KO K H ．Mobile power facilities maintenance system using augmented reality[J]．Journal of Electrical Engineering & Technology，2021：1-13． doi:10.1007/s42835-021-00942-y
71	DAVIDSON E M， MCARTHUR S D J， MCDONALD J R，et al ．Applying multi-agent system technology in practice：automated management and analysis of SCADA and digital fault recorder data[J]．IEEE Transactions on Power Systems，2006，21(2)：559-567． doi:10.1109/tpwrs.2006.873109
72	TU C， HE X， SHUAI Z，et al ．Big data issues in smart grid：a review[J]．Renewable and Sustainable Energy Reviews，2017，79：1099-1107． doi:10.1016/j.rser.2017.05.134
73	BAZMOHAMMADI N， MADARY A， VASQUEZ J C，et al ．Microgrid digital twins：concepts，applications，and future trends[J]．IEEE Access，2021，10：2284-2302． doi:10.1109/access.2021.3138990
74	周博文，奚超，李广地，等．元宇宙在电力系统中的应用[J]．发电技术，2022，43(1)：1-9． doi:10.12096/j.2096-4528.pgt.21144
	ZHOU B W， XI C， LI G D，et al ．Metaverse application in power systems[J]．Power Generation Technology，2022，43(1)：1-9． doi:10.12096/j.2096-4528.pgt.21144
75	SAAD A， FADDEL S， YOUSSEF T，et al ．On the implementation of IoT-based digital twin for networked microgrids resiliency against cyber attacks[J]．IEEE Transactions on Smart Grid，2020，11(6)：5138-5150． doi:10.1109/tsg.2020.3000958
76	JAIN P， POON J， SINGH J P，et al ．A digital twin approach for fault diagnosis in distributed photovoltaic systems[J]．IEEE Transactions on Power Electronics，2019，35(1)：940-956． doi:10.1109/tpel.2019.2911594
77	BRANDTSTAEDTER H， LUDWIG C， HUBNER L，et al ．Digital twins for large electric drive trains[C]//2018 Petroleum and Chemical Industry Conference Europe (PCIC Europe)．2018：1-5． doi:10.23919/pciceurope.2018.8491413
78	NOWOCIN J K ．Microgrid risk reduction for design and validation testing using controller hardware in the loop[D]．Massachusetts：Massachusetts Institute of Technology，2017．

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