Meta-Power: Digitalized Power Systems Driven by Metaverse
Keywords:
Artificial intelligence, Digital twins, Extended reality, Internet of Things, Metaverse, Power systemsAbstract
Metaverse is a transformative stage in the digital revolution, focusing on the development of an interactive and hyper-spatiotemporal ecosystem. This ecosystem is built upon various technologies, such as digital twins and extended reality. The application of the metaverse in power systems can significantly advance their digitalization level. This paper introduces a novel concept of meta-power to represent digitalized power systems driven by the metaverse. Supported by multiple technologies, the meta-power is a power ecosystem with high interactivity and hyper-spatiotemporal capabilities. The multi-technicity of meta-power enhances the stability, flexibility, reliability, safety, and economy of power systems. Furthermore, its high interactivity improves the convenience and immersion of power system monitoring and maintenance. Additionally, its hyper-spatiotemporal capability overcomes spatial and temporal limitations in power system operations and planning, providing benefits in evaluating and deducing future energy development strategies. This paper presents a comprehensive exploration of meta-power, encompassing its architecture, characteristics, enabling technologies, and application scenarios, aiming to provide theoretical and practical implications, respectively. At the theoretical level, this paper can stimulate research and development efforts in new metaverse technologies for power systems. At the practical level, it serves as a guide for power system digitalization, facilitating the advancement of a sustainable economy while ensuring the reliability and safety of power systems.
References
Y. Tao, J. Qiu, S. Lai, J. Zhao, and Y. Xue, “Carbon-oriented electricity network planning and transformation,” IEEE Transactions on Power Systems, vol. 36, no. 2, pp. 1034–1048, 2020.
C. Lei, Q. Wang, G. Zhou, S. Bu, N. Zhou, T. Lin, and F. Wei, “Probabilistic wind power expansion planning of bundled wind-thermal generation system with retrofitted coal-fired plants using load transfer optimization,” International Journal of Electrical Power & Energy Systems, vol. 151, pp. 109145, 2020.
Y. Ding, M. Mao, and L. Chang, “Conservative power theory and its applications in modern smart grid: Review and prospect,” Applied Energy, vol. 303, pp. 117617, 2021.
X. Fang, S. Misra, G. Xue, and D. Yang, “Smart grid - The new and improved power grid: A survey,” IEEE Communications Surveys and Tutorials, vol. 14, no. 4, pp. 944–980, 2012.
P. Siano, “Demand response and smart grids—A survey,” Renewable & Sustainable Energy Reviews, vol. 30, pp. 461–478, 2014.
B. K. Bose, “Artificial intelligence techniques in smart grid and renewable energy systems-some example applications,” Proceedings of the IEEE, vol. 105, no. 11, pp. 2262–2273, 2017.
Y. Mo et al., “Cyber-physical security of a smart grid infrastructure,” Proceedings of the IEEE, vol. 100, no. 1, pp. 195–209, 2012.
Y. Xue and X. Yu, “Beyond smart grid-cyber-physical-social system in energy future,” Proceedings of the IEEE, vol. 105, no. 12, pp. 2290–2292, 2017.
M. Martinsen, Y. Zhou, E. Dahlquist, J. Yan, and K. Kyprianidis, “Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future,” Applied Energy, vol. 339, pp. 120988, 2023.
J. Yu, N. Petersen, P. Liu, Z. Li, and M. Wirsum, “Hybrid modelling and simulation of thermal systems of in-service power plants for digital twin development,” Energy, vol. 260, pp. 125088, 2022.
Y. Wang et al., “A Survey on metaverse: Fundamentals, security, and privacy,” IEEE Communications Surveys and Tutorials, vol. 25, no. 1, pp. 319–352, 2023.
Y. Shen, Y. Liu, and Y. Tian, “Parallel sensing in metaverses: Virtual-real interactive smart systems for ‘6S’ sensing,” IEEE/CAA Journal of Automatica Sinica, vol. 9, no. 12, pp. 2047–2054, 2022.
A. Kusiak, “Manufacturing metaverse,” Journal of Intelligent Manufacturing, pp. 1–2, 2023.
S. M. Schöbel and J. M. Leimeister, “Metaverse platform ecosystems,” Electronic Markets, vol. 33, no. 1, p. 12–, 2023.
J. N. Njoku, C. I. Nwakanma, G. C. Amaizu, and D.-S. Kim, “Prospects and challenges of metaverse application in data-driven intelligent transportation systems,” IET Intelligent Transport Systems, vol. 17, no. 1, pp. 1–21, 2023.
A. Siyaev and G.-S. Jo, “Neuro-symbolic speech understanding in aircraft maintenance metaverse,” IEEE Access, vol. 9, pp. 154484–154499, 2021.
A. M. Al-Ghaili et al., “A review of metaverse’s definitions, architecture, applications, challenges, issues, solutions, and future trends,” IEEE Access, vol. 10, pp. 125835–125866, 2022.
H. Ning, H. Wang, Y. Lin, W. Wang, S. Dhelim, F. Farha, J. Ding, and M. Daneshmand, “A Survey on the metaverse: The state-of-the-art, technologies, applications, and challenges,” IEEE Internet of Things Journal, 2023.
D. K. Baroroh, C.-H. Chu, and L. Wang, “Systematic literature review on augmented reality in smart manufacturing: Collaboration between human and computational intelligence,” Journal of Manufacturing Systems, vol. 61, pp. 696–711, 2021.
S. K. Jagatheesaperumal et al., “Semantic-aware digital twin for metaverse: A comprehensive review,” 2023, doi: 10.48550/arxiv.2305.18304.
A. K. Upadhyay and K. Khandelwal, “Metaverse: the future of immersive training,” Strategic HR Review, vol. 21, no. 3, pp. 83–86, 2022.
L.-H. Lee, T. Braud, P. Zhou, L. Wang, D. Xu, Z. Lin, A. Kumar, C. Bermejo, and P. Hui, “All one needs to know about metaverse: A complete survey on technological singularity, virtual ecosystem, and research agenda,” arXiv preprint arXiv:2110.05352, 2021.
E. H. Korkut and E. Surer, “Visualization in virtual reality: A systematic review,” Virtual Reality: the Journal of the Virtual Reality Society, vol. 27, no. 2, pp. 1447–1480, 2023.
X. Wang, J. Wang, C. Wu, S. Xu, and W. Ma, “Engineering Brain: Metaverse for future engineering,” AI in Civil Engineering, vol. 1, no. 1, 2022.
M. Casini, “Extended reality for smart building operation and maintenance: A review,” Energies, vol. 15, no. 10, p. 3785–, 2022.
J. C. P. Cheng, K. Chen, and W. Chen, “State-of-the-art review on mixed reality applications in the AECO industry,” Journal of Construction Engineering and Management, vol. 146, no. 2, 2020.
V. T. Truong, L. Le, and D. Niyato, “Blockchain meets metaverse and digital asset management: A comprehensive survey,” IEEE Access, vol. 11, pp. 26258–26288, 2023.
G. D. Ritterbusch and M. R. Teichmann, “Defining the metaverse: A systematic literature review,” IEEE Access, vol. 11, pp. 12368–12377, 2023.
J. Smart, J. Cascio, and J. Paffendorf. “Metaverse roadmap: Pathway to the 3D web,” Metaverse: A cross-industry public foresight project, 2007.
J. Dionisio, W. III, and R. Gilbert, “3D Virtual worlds and the metaverse: Current status and future possibilities,” ACM Computing Surveys, vol. 45, no. 3, pp. 1–38, 2013.
L. U. Khan, Z. Han, D. Niyato, M. Guizani, and C. S. Hong, “Metaverse for wireless systems: Vision, enablers, architecture, and future directions,” 2022, doi: 10.48550/arxiv.2207.00413.
B. Falchuk, S. Loeb, and R. Neff, The Social Metaverse: Battle for Privacy, vol. 37, no. 2. New York: IEEE, 2018, pp. 52–61.
T. Huynh-The et al., “Blockchain for the metaverse: A Review,” Future Generation Computer Systems, vol. 143, pp. 401–419, 2023.
Y. Fu, C. Li, F. R. Yu, T. H. Luan, P. Zhao, and S. Liu, “A survey of blockchain and intelligent networking for the metaverse,” IEEE Internet of Things Journal, vol. 10, no. 4, pp. 3587–3610, 2023.
Z. Abou El Houda and B. Brik, “Next-power: Next-generation framework for secure and sustainable energy trading in the metaverse,” Ad Hoc Networks, vol. 149, p. 103243–, 2023.
C. Zhang and S. Liu, “Meta-Energy: When integrated energy internet meets metaverse,” IEEE/CAA Journal of Automatica Sinica, vol. 10, no. 3, pp. 580–583, 2023.
R. V. Yohanandhan, R. M. Elavarasan, R. Pugazhendhi, M. Premkumar, L. Mihet-Popa, and V. Terzija, “A holistic review on Cyber-Physical Power System (CPPS) testbeds for secure and sustainable electric power grid – Part – I: Background on CPPS and necessity of CPPS testbeds,” International Journal of Electrical Power & Energy Systems, vol. 136, p. 107718–, 2022.
H. Mohammadi Moghadam, H. Foroozan, M. Gheisarnejad, and M.-H. Khooban, “A survey on new trends of digital twin technology for power systems,” Journal of Intelligent & Fuzzy Systems, vol. 41, no. 2, pp. 3873–3893, 2021.
S. M. A. A. Abir, A. Anwar, J. Choi, and A. S. M. Kayes, “IoT-enabled smart energy grid: Applications and challenges,” IEEE Access, vol. 9, pp. 50961–50981, 2021.
K. Sharma and L. M. Saini, “Power-line communications for smart grid: Progress, challenges, opportunities and status,” Renewable & Sustainable Energy Reviews, vol. 67, pp. 704–751, 2017.
K. Zhou, C. Fu, and S. Yang, “Big data driven smart energy management: From big data to big insights,” Renewable & Sustainable Energy Reviews, vol. 56, pp. 215–225, 2016.
C. Feng, Y. Wang, Q. Chen, Y. Ding, G. Strbac, and C. Kang, “Smart grid encounters edge computing: Opportunities and applications,” Advances in Applied Energy, vol. 1, p. 100006–, 2021.
R. Machlev et al., “Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities,” Energy and AI, vol. 9, p. 100169–, 2022.
B. Rodiv, “Industry 4.0 and the new simulation modelling paradigm,” Organizacija, vol. 50, no. 3, pp. 193–207, 2017.
M. Grieves, “Intelligent digital twins and the development and management of complex systems,” Digital Twin, vol. 2, no. 8, pp. 8, 2022.
F. Tao, M. Zhang, Y. Liu, and A. Y. Nee, “Digital twin driven prognostics and health management for complex equipment,” CIRP Annals, vol. 67, no. 1, pp. 169–172, 2018.
F. Tao and Q. Qi, “Make more digital twins,” Nature, vol. 573, no. 7775, pp. 490–491, 2019.
F. Tao, B. Xiao, Q. Qi, J. Cheng, and P. Ji, “Digital twin modeling,” Journal of Manufacturing Systems, vol. 64, pp. 372–389, 2022.
Q. Qi, F. Tao, T. Hu, N. Anwer, A. Liu, Y. Wei, L. Wang, and A. Nee, “Enabling technologies and tools for digital twin,” Journal of Manufacturing Systems, vol. 58, pp. 3–21, 2021.
A. Thelen, X. Zhang, O. Fink, Y. Lu, S. Ghosh, B. D. Youn, M. D. Todd, S. Mahadevan, C. Hu, and Z. Hu, “A comprehensive review of digital twin–part 2: Roles of uncertainty quantification and optimization, a battery digital twin, and perspectives,” Structural and Multidisciplinary Optimization, vol. 66, no. 1, pp. 1, 2023.
J. P. Spinti, P. J. Smith, and S. T. Smith, “Atikokan Digital Twin: Machine learning in a biomass energy system,” Applied Energy, vol. 310, pp. 118436, 2022.
J. P. Spinti, P. J. Smith, S. T. Smith, and O. H. Diaz-Ibarra, “Atikokan Digital Twin, Part B: Bayesian decision theory for process optimization in a biomass energy system,” Applied Energy, vol. 334, pp. 120625, 2023.
H. Song, M. Song, and X. Liu, “Online autonomous calibration of digital twins using machine learning with application to nuclear power plants,” Applied Energy, vol. 326, pp. 119995, 2022.
G. Zhao, Z. Cui, J. Xu, W. Liu, and S. Ma, “Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit,” Energy, vol. 254, pp. 124492, 2022.
S. de Lopez Diz, R. M. Lopez, F. J. R. Sanchez, E. D. Llerena, and E. J. B. Pena, “A real-time digital twin approach on three-phase power converters applied to condition monitoring,” Applied Energy, vol. 334, pp. 120606, 2023.
Z. Huang, K. Soh, M. Islam, and K. Chua, “Digital twin driven life-cycle operation optimization for combined cooling heating and power-cold energy recovery (CCHP-CER) system,” Applied Energy, vol. 324, pp. 119774, 2022.
J. Qu, Q. Wang, J. Zhang, H. Zhao, G. Wu, and X. Li, “3-D transient finite-element analysis and experimental investigation of short-circuit dynamic stability for air circuit breaker,” IEEE Transactions on Components, Packaging and Manufacturing Technology, vol. 5, no. 11, pp. 1610–1617, 2015.
K. Yamazaki and M. Matsumoto, “3-D finite element meshing for skewed rotor induction motors,” IEEE Transactions on Magnetics, vol. 51, no. 3, pp. 1–4, 2015.
P. Moutis and O. Alizadeh-Mousavi, “Digital twin of distribution power transformer for real-time monitoring of medium voltage from low voltage measurements,” IEEE Transactions on Power Delivery, vol. 36, no. 4, pp. 1952–1963, 2020.
J. M. Reniers and D. A. Howey, “Digital twin of a MWh-scale grid battery system for efficiency and degradation analysis,” Applied Energy, vol. 336, pp. 120774, 2023.
C. Canizares, T. Fernandes, E. Geraldi, L. Gerin-Lajoie, M. Gibbard, I. Hiskens, J. Kersulis, R. Kuiava, L. Lima, F. DeMarco et al., “Benchmark models for the analysis and control of small-signal oscillatory dynamics in power systems,” IEEE Transactions on Power Systems, vol. 32, no. 1, pp. 715–722, 2016.
Y. Latreche, H. Bouchekara, K. Naidu, H. Mokhlis, and W. Dahalan, “Comprehensive review of radial distribution test systems,” TechRxiv, no. 1, pp. 1–65, 2020.
X. He, Q. Ai, R. C. Qiu, and D. Zhang, “Preliminary exploration on digital twin for power systems: Challenges, framework, and applications,” arXiv preprint arXiv:1909.06977, 2019.
M. H. Cintuglu, O. A. Mohammed, K. Akkaya, and A. S. Uluagac, “A survey on smart grid cyber-physical system testbeds,” IEEE Communications Surveys & Tutorials, vol. 19, no. 1, pp. 446–464, 2016.
U. Adhikari, T. Morris, and S. Pan, “WAMS cyber-physical test bed for power system, cybersecurity study, and data mining,” IEEE Transactions on Smart Grid, vol. 8, no. 6, pp. 2744–2753, 2016.
Y. Qin, X. Wu, and J. Luo, “Data-model combined driven digital twin of life-cycle rolling bearing,” IEEE Transactions on Industrial Informatics, vol. 18, no. 3, pp. 1530–1540, 2021.
A. Saad, S. Faddel, and O. Mohammed, “IoT-based digital twin for energy cyber-physical systems: Design and implementation,” Energies, vol. 13, no. 18, pp. 4762, 2020.
A. W. Momber, T. Möller, D. Langenkämper, T. W. Nattkemper, and D. Brün, “A Digital Twin concept for the prescriptive maintenance of protective coating systems on wind turbine structures,” Wind Engineering, vol. 46, no. 3, pp. 949–971, 2022.
P. Jain, J. Poon, J. P. Singh, C. Spanos, S. R. Sanders, and S. K. Panda, “A digital twin approach for fault diagnosis in distributed photovoltaic systems,” IEEE Transactions on Power Electronics, vol. 35, no. 1, pp. 940–956, 2020.
E. Söderäng, S. Hautala, M. Mikulski, X. Storm, and S. Niemi, “Development of a digital twin for real-time simulation of a combustion engine-based power plant with battery storage and grid coupling,” Energy Conversion and Management, vol. 266, p. 115793–, 2022.
X. Tang, Y. Ding, J. Lei, H. Yang, and Y. Song, “Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications,” Energy Reports, vol. 8, pp. 1423–1431, 2022.
Y. Sun, Y. Shi, Q. Hu, C. Xie, and T. Su, “DTformer: An efficient digital twin model for loss measurement in UHVDC transmission systems,” IEEE Transactions on Power Systems, pp. 1–13, 2023.
L. Sui, X. Guan, C. Cui, H. Jiang, H. Pan, and T. Ohtsuki, “Graph learning empowered situation awareness in internet of energy with graph digital twin,” IEEE Transactions on Industrial Informatics, vol. 19, no. 5, pp. 7268–7277, 2023.
M. M. S. Khan, J. Giraldo, and M. Parvania, “Real-time cyber attack localization in distribution systems using digital twin reference model,” IEEE Transactions on Power Delivery, pp. 1–12, 2023.
Y.-Y. Hong and G. F. D. Apolinario, “Ancillary services and risk assessment of networked microgrids using digital twin,” IEEE Transactions on Power Systems, pp. 1–15, 2022.
M. A. M. Yassin, A. Shrestha, and S. Rabie, “Digital twin in power system research and development: Principle, scope, and challenges,” Energy Reviews, vol. 2, no. 3, 2023.
P. Spudys, N. Afxentiou, P.-Z. Georgali, E. Klumbyte, A. Jurelionis, and P. Fokaides, “Classifying the operational energy performance of buildings with the use of digital twins,” Energy and Buildings, vol. 290, p. 113106–, 2023.
V. Bocullo et al., “A Digital Twin Approach to City Block Renovation Using RES Technologies,” Sustainability, vol. 15, no. 12, p. 9307–, 2023.
G. Cheng, Y. Lin, A. Abur, A. Gomez-Exposito, and W. Wu, “A survey of power system state estimation using multiple data sources: PMUs, SCADA, AMI, and beyond,” IEEE Transactions on Smart Grid, 2023.
S. Galli, A. Scaglione, and Z. Wang, “For the grid and through the grid: The role of power line communications in the smart grid,” Proceedings of the IEEE, vol. 99, no. 6, pp. 998–1027, 2011.
E. Ancillotti, R. Bruno, and M. Conti, “The role of communication systems in smart grids: Architectures, technical solutions and research challenges,” Computer Communications, vol. 36, no. 17–18, pp. 1665–1697, 2013.
D. Kopse, U. Rudez, and R. Mihalic, “Applying a wide-area measurement system to validate the dynamic model of a part of European power system,” Electric Power Systems Research, vol. 119, pp. 1–10, 2015.
M. A. Cruz, H. R. Rocha, M. H. Paiva, J. A. L. Silva, E. Camby, and M. E. Segatto, “PMU placement with multi-objective optimization considering resilient communication infrastructure,” International Journal of Electrical Power & Energy Systems, vol. 141, pp. 108167, 2022.
S. Bu, L. G. Meegahapola, D. P. Wadduwage, and A. M. Foley, “Stability and dynamics of active distribution networks (ADNs) with D-PMU technology: A review,” IEEE Transactions on Power Systems, vol. 38, no. 3, pp. 2791–2804, 2022.
B. Sivaneasan, E. Gunawan, and P. So, “Modeling and performance analysis of automatic meter-reading systems using PLC under impulsive noise interference,” IEEE Transactions on Power Delivery, vol. 25, no. 3, pp. 1465–1475, 2010.
R. R. Mohassel, A. Fung, F. Mohammadi, and K. Raahemifar, “A survey on advanced metering infrastructure,” International Journal of Electrical Power & Energy Systems, vol. 63, pp. 473–484, 2014.
S. Li, F. Luo, J. Yang, G. Ranzi, and J. Wen, “A personalized electricity tariff recommender system based on advanced metering infrastructure and collaborative filtering,” International Journal of Electrical Power & Energy Systems, vol. 113, pp. 403–410, 2019.
M. R. Ahmed, J. M. Cano, P. Arboleya, L. S. Ramon, and A. Y. Abdelaziz, “DSSE in European-type networks using PLC-based advanced metering infrastructure,” IEEE Transactions on Power Systems, vol. 37, no. 5, pp. 3875–3888, 2022.
R. Smolenski, P. Szczesniak, W. Drozdz, and L. Kasperski, “Advanced metering infrastructure and energy storage for location and mitigation of power quality disturbances in the utility grid with high penetration of renewables,” Renewable and Sustainable Energy Reviews, vol. 157, pp. 111988, 2022.
R. Gore and S. P. Valsan, “Big data challenges in smart grid IoT(WAMS) deployment,” in Proc. 8th Int. Conf. Commun. Syst. Netw. (COMSNETS), Bengaluru, India, 2016, pp. 1–6.
M. Annunziata, G. Bell, R. Buch, S. Patel, and N. Sanyal, Powering the future: Leading the digital transformation of the power industry, GE Power Digit. Solutions, Boston, MA, USA, 2016. [Online]. Available: https://www.gepower.com/content/dam/gepowerpw/global/en_US/documents/industrial%20internet%20and%20big%20data/powering-the-future-whitepaper.pdf.
J. S. Vardakas, N. Zorba, and C. V. Verikoukis, “A survey on demand response programs in smart grids: Pricing methods and optimization algorithms,” IEEE Communication and Survey Tutorials., vol. 17, no. 1, pp. 152–178, 1st Quart., 2015.
J. H. Fernandez, A. Omri, and R. Di Pietro, “Power grid surveillance: Topology change detection system using power line communications,” International Journal of Electrical Power & Energy Systems, vol. 145, pp. 108634, 2023.
K. Xia, J. Ni, Y. Ye, P. Xu, and Y. Wang, “A real-time monitoring system based on ZigBee and 4G communications for photovoltaic generation,” CSEE Journal of Power and Energy Systems, vol. 6, no. 1, pp. 52–63, 2020.
H. Hui, Y. Ding, Q. Shi, F. Li, Y. Song, and J. Yan, “5G network-based Internet of Things for demand response in smart grid: A survey on application potential,” Applied Energy, vol. 257, pp. 113972, 2020.
S. Lee, B. Kang, K. Cho, D. Kang, K. Jang, L. Park, and S. Park, “Design and implementation for data protection of energy IoT utilizing OTP in the wireless mesh network,” Energy Procedia, vol. 141, pp. 540–544, 2017.
Y. Yan, Y. Qian, H. Sharif, and D. Tipper, “A survey on smart grid communication infrastructures: Motivations, requirements and challenges,” IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 5–20, 2012.
R. Singh, S. V. Akram, A. Gehlot, D. Buddhi, N. Priyadarshi, and B. Twala, “Energy System 4.0: Digitalization of the Energy Sector with Inclination towards Sustainability,” Sensors (Basel, Switzerland), vol. 22, no. 17, p. 6619–, 2022.
D. Syed, A. Zainab, A. Ghrayeb, S. S. Refaat, H. Abu-Rub, and O. Bouhali, “Smart grid big data analytics: Survey of technologies, techniques, and applications,” IEEE Access, vol. 9, pp. 59564–59585, 2020.
S. Zhang, A. Pandey, X. Luo, M. Powell, R. Banerji, L. Fan, A. Parchure, and E. Luzcando, “Practical adoption of cloud computing in power systems–Drivers, challenges, guidance, and real-world use cases,” IEEE Transactions on Smart Grid, vol. 13, no. 3, pp. 2390–2411, 2022.
Y. Zhang, L. Wang, and Y. Xiang, “Power system reliability analysis with intrusion tolerance in SCADA systems,” IEEE Transactions on Smart Grid, vol. 7, no. 2, pp. 669–683, 2015.
Y. Zhang, L. Wang, Y. Xiang, and C.-W. Ten, “Inclusion of SCADA cyber vulnerability in power system reliability assessment considering optimal resources allocation,” IEEE Transactions on Power Systems, vol. 31, no. 6, pp. 4379–4394, 2016.
F. Aminifar, M. Fotuhi-Firuzabad, M. Shahidehpour, and A. Safdarian, “Impact of WAMS malfunction on power system reliability assessment,” IEEE Transactions on Smart Grid, vol. 3, no. 3, pp. 1302–1309, 2012.
D. Liu, H. Liang, X. Zeng, Q. Zhang, Z. Zhang, and M. Li, “Edge computing application, architecture, and challenges in ubiquitous power internet of things,” Frontiers in Energy Research, vol. 10, pp. 850252, 2022.
J. Tong, H. Wu, Y. Lin, Y. He, and J. Liu, “Fog-computing-based short-circuit diagnosis scheme,” IEEE Transactions on Smart Grid, vol. 11, no. 4, pp. 3359–3371, 2020.
N. Peng, R. Liang, G. Wang, P. Sun, C. Chen, and T. Hou, “Edge computing-based fault location in distribution networks by using asynchronous transient amplitudes at limited nodes,” IEEE Transactions on Smart Grid, vol. 12, no. 1, pp. 574–588, 2020.
R. S. Kumar and E. Chandrasekharan, “A parallel distributed computing framework for Newton–Raphson load flow analysis of large interconnected power systems,” International Journal of Electrical Power & Energy Systems, vol. 73, pp. 1–6, 2015.
V. Veerasamy, L. M. I. Sampath, S. Singh, H. D. Nguyen, and H. B. Gooi, “Blockchain-based decentralized frequency control of microgrids using federated learning fractional-order recurrent neural network,” IEEE Transactions on Smart Grid, 2023.
Y. Liu, C. Yang, L. Jiang, S. Xie, and Y. Zhang, “Intelligent edge computing for IoT-based energy management in smart cities,” IEEE Network, vol. 33, no. 2, pp. 111–117, 2019.
A. M. Azmy, “Optimal power flow to manage voltage profiles in interconnected networks using expert systems,” IEEE Transactions on Power Systems, vol. 22, no. 4, pp. 1622–1628, 2007.
F. Aminifar, M. Abedini, T. Amraee, P. Jafarian, M. H. Samimi, and M. Shahidehpour, “A review of power system protection and asset management with machine learning techniques,” Energy Systems, pp. 1–38, 2021.
M. S. Ibrahim, W. Dong, and Q. Yang, “Machine learning driven smart electric power systems: Current trends and new perspectives,” Applied Energy, vol. 272, pp. 115237, 2020.
M. De Lange, R. Aljundi, M. Masana, S. Parisot, X. Jia, A. Leonardis, G. Slabaugh, and T. Tuytelaars, “A continual learning survey: Defying forgetting in classification tasks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 7, pp. 3366–3385, 2021.
R. Jiao, X. Huang, X. Ma, L. Han, and W. Tian, “A model combining stacked auto encoder and back propagation algorithm for short-term wind power forecasting,” IEEE Access, vol. 6, pp. 17851–17858, 2018.
L. Oneto, F. Laureri, M. Robba, F. Delfino, and D. Anguita, “Data-driven photovoltaic power production nowcasting and forecasting for polygeneration microgrids,” IEEE Systems Journal, vol. 12, no. 8, pp. 2842–2853, 2017.
W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network,” IEEE Transactions on Smart Grid, vol. 10, no. 1, pp. 841–851, 2017.
S. Wang, X. Wang, S. Wang, and D. Wang, “Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting,” International Journal of Electrical Power & Energy Systems, vol. 109, pp. 470–479, 2019.
M. Rafiei, T. Niknam, J. Aghaei, M. Shafie-Khah, and J. P. Catalao, “Probabilistic load forecasting using an improved wavelet neural network trained by generalized extreme learning machine,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6961–6971, 2018.
L. Yin, Q. Gao, L. Zhao, and T. Wang, “Expandable deep learning for real-time economic generation dispatch and control of three-state energies based future smart grids,” Energy, vol. 191, pp. 116561, 2020.
X. S. Zhang, Q. Li, T. Yu, and B. Yang, “Consensus transfer Q-learning for decentralized generation command dispatch based on virtual generation tribe,” IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 2152–2165, 2016.
M. Islam, G. Lee, S. N. Hettiwatte, and K. Williams, “Calculating a health index for power transformers using a subsystem-based GRNN approach,” IEEE Transactions on Power Delivery, vol. 33, no. 4, pp. 1903–1912, 2017.
T. S. Abdelgayed, W. G. Morsi, and T. S. Sidhu, “A new approach for fault classification in microgrids using optimal wavelet functions matching pursuit,” IEEE Transactions on Smart Grid, vol. 9, no. 5, pp. 4838–4846, 2017.
H. Livani and C. Y. Evrenosoglu, “A machine learning and wavelet-based fault location method for hybrid transmission lines,” IEEE Transactions on Smart Grid, vol. 5, no. 1, pp. 51–59, 2013.
F. Golestaneh, P. Pinson, and H. B. Gooi, “Very short-term nonparametric probabilistic forecasting of renewable energy generation- with application to solar energy,” IEEE Transactions on Power Systems, vol. 31, no. 5, pp. 3850–3863, 2016.
M. Khodayar, J. Wang, and M. Manthouri, “Interval deep generative neural network for wind speed forecasting,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3974–3989, 2019.
A. Alcántara, I. M. Galván, and R. Aler, “Direct estimation of prediction intervals for solar and wind regional energy forecasting with deep neural networks,” Engineering Applications of Artificial Intelligence, vol. 114, p. 105128–, 2022.
X. Zhan, L. Kou, M. Xue, J. Zhang, and L. Zhou, “Reliable long-term energy load trend prediction model for smart grid using hierarchical decomposition self-attention network,” IEEE Transactions on Reliability, vol. 72, no. 2, pp. 1–13, 2023.
S.-V. Oprea and A. Bara, “Machine learning algorithms for short-term load forecast in residential buildings using smart meters, sensors and big data solutions,” IEEE Access, vol. 7, pp. 177874–177889, 2019.
K. Chen, K. Chen, Q. Wang, Z. He, J. Hu, and J. He, “Short-term load forecasting with deep residual networks,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3943–3952, 2019.
R. Wang, S. Bu, and C. Y. Chung, “Real-time joint regulations of frequency and voltage for TSO-DSO coordination: A deep Reinforcement learning-based approach,” IEEE Transactions on Smart Grid, pp. 1–1, 2023.
K. Zhang, J. Zhang, P.-D. Xu, T. Gao, and D. W. Gao, “Explainable AI in deep reinforcement learning models for power system emergency control,” IEEE Transactions on Computational Social Systems, vol. 9, no. 2, pp. 419–427, 2022.
J. Duan et al., “Deep-reinforcement-learning-based autonomous voltage control for power grid operations,” IEEE Transactions on Power Systems, vol. 35, no. 1, pp. 814–817, 2020.
D. Zhang et al., “A bi-level machine learning method for fault diagnosis of oil-immersed transformers with feature explainability,” International Journal of Electrical Power & Energy Systems, vol. 134, p. 107356–, 2022.
J. Xu et al., “Probabilistic prognosis of wind turbine faults with feature selection and confidence calibration,” IEEE Transactions on Sustainable Energy, pp. 1–15, 2023.
S. S. Shuvo and Y. Yilmaz, "Predictive maintenance for increasing EV charging load in distribution power system," in 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), Tempe, AZ, USA, 2020, pp. 1-6.
Shuang Wu, Le Zheng, Wei Hu, Rui Yu, and Baisi Liu, “Improved deep belief network and model interpretation method for power system transient stability assessment,” Journal of Modern Power Systems and Clean Energy, vol. 8, no. 1, pp. 27–37, 2020.
J. Wen, S. Bu, and F. F. Li, “Two-level ensemble methods for efficient assessment and region visualization of maximal frequency deviation risk,” IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 1–1, 2023.
N. Veerakumar, J. L. Cremer, and M. Popov, “Dynamic incremental learning for real-time disturbance event classification,” International Journal of Electrical Power & Energy Systems, vol. 148, p. 108988–, 2023.
T. Masood and J. Egger, “Augmented reality in support of Industry 4.0–Implementation challenges and success factors,” Robotics and Computer-Integrated Manufacturing, vol. 58, pp. 181–195, 2019.
A. Shamsuzzoha, R. Toshev, V. Vu Tuan, T. Kankaanpaa, and P. Helo, “Digital factory–Virtual reality environments for industrial training and maintenance,” Interactive Learning Environments, vol. 29, no. 8, pp. 1339–1362, 2021.
S. L. Ullo, P. Piedimonte, F. Leccese, and E. De Francesco, “A step toward the standardization of maintenance and training services in C4I military systems with mixed reality application,” Measurement, vol. 138, pp. 149–156, 2019.
H. Laaki, Y. Miche, and K. Tammi, “Prototyping a digital twin for real time remote control over mobile networks: Application of remote surgery,” IEEE Access, vol. 7, pp. 20325–20336, 2019.
T. Nishitsuji, T. Kakue, D. Blinder, T. Shimobaba, and T. Ito, “An interactive holographic projection system that uses a hand-drawn interface with a consumer CPU,” Scientific Reports, vol. 11, no. 1, pp. 147, 2021.
D. L. Gomes Jr, A. C. de Paiva, A. C. Silva, G. Braz Jr, J. D. S. de Almeida, A. S. de Araujo, and M. Gattas, “Augmented visualization using homomorphic filtering and Haar-based natural markers for power systems substations,” Computers in Industry, vol. 97, pp. 67–75, 2018.
F. Gorski, D. Grajewski, P. Bun, and P. Zawadzki, “Study of interaction methods in virtual electrician training,” IEEE Access, vol. 9, pp. 118242–118252, 2021.
Z. Lin, F. Wen, Y. Ding, Y. Xue, S. Liu, Y. Zhao, and S. Yi, “WAMS-based coherency detection for situational awareness in power systems with renewables,” IEEE Transactions on Power Systems, vol. 33, no. 5, pp. 5410–5426, 2018.
C.-H. Chae, N.-J. Jung, and K.-H. Ko, “Mobile power facilities maintenance system using augmented reality,” Journal of Electrical Engineering & Technology, vol. 17, no. 2, pp. 1357–1369, 2022.
C. Tu, X. He, Z. Shuai, and F. Jiang, “Big data issues in smart grid–A review,” Renewable and Sustainable Energy Reviews, vol. 79, pp. 1099–1107, 2017.
N. Bazmohammadi, A. Madary, J. C. Vasquez, H. B. Mohammadi, B. Khan, Y. Wu, and J. M. Guerrero, “Microgrid digital twins: Concepts, applications, and future trends,” IEEE Access, vol. 10, pp. 2284–2302, 2021.
L. G. Meegahapola, S. Bu, D. P. Wadduwage, C. Y. Chung, and X. Yu, “Review on oscillatory stability in power grids with renewable energy sources: Monitoring, analysis, and control using synchrophasor technology,” IEEE Transactions on Industrial Electronics, vol. 68, no. 1, pp. 519–531, 2020.
C. Lei, S. Bu, Q. Wang, N. Zhou, L. Yang, and X. Xiong, “Load transfer optimization considering hot-spot and top-oil temperature limits of transformers,” IEEE Transactions on Power Delivery, vol. 37, no. 3, pp. 2194–2208, 2021.
A. Saad, S. Faddel, T. Youssef, and O. A. Mohammed, “On the implementation of IoT-based digital twin for networked microgrids resiliency against cyber-attacks,” IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5138–5150, 2020.
M. M. S. Khan, J. A. Giraldo, and M. Parvania, “Attack detection in power distribution systems using a cyber-physical real-time reference model,” IEEE Transactions on Smart Grid, vol. 13, no. 2, pp. 1490–1499, 2021.
P. Andrianesis and M. Caramanis, “Distribution network marginal costs: Enhanced AC OPF including transformer degradation,” IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 3910–3920, 2020.
C. Huang, S. Bu, H. H. Lee, K. W. Chan, and W. K. Yung, “Prognostics and health management for induction machines: A comprehensive review,” Journal of Intelligent Manufacturing, pp. 1–26, 2023.
P. Jain, J. Poon, J. P. Singh, C. Spanos, S. R. Sanders, and S. K. Panda, “A digital twin approach for fault diagnosis in distributed photovoltaic systems,” IEEE Transactions on Power Electronics, vol. 35, no. 1, pp. 940–956, 2019.
C. Li, T. Shahsavarian, M. A. Baferani, N. Wang, J. Ronzello, and Y. Cao, “High temperature insulation materials for DC cable insulation–Part III: Degradation and surface breakdown,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 28, no. 1, pp. 240–247, 2021.
J. K. Nowocin, “Microgrid risk reduction for design and validation testing using controller hardware in the loop,” Ph.D. dissertation, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, 2017.
J. Wen, S. Bu, and F. Li, “Two-level ensemble methods for efficient assessment and region visualization of maximal frequency deviation risk,” IEEE Transactions on Power Systems, vol. 38, no. 1, pp. 643–655, 2022.
A. Ayala Garcia, I. Galvan Bobadilla, G. Arroyo Figueroa, M. Perez Ramirez, and J. Munoz Roman, “Virtual reality training system for maintenance and operation of high-voltage overhead power lines,” Virtual Reality, vol. 20, pp. 27–40, 2016.
D. Checa, J. J. Saucedo-Dorantes, R. A. Osornio-Rios, J. A. Antonino-Daviu, and A. Bustillo, “Virtual reality training application for the condition-based maintenance of induction motors,” Applied Sciences, vol. 12, no. 1, pp. 414, 2022.