Artificial intelligence in smart grids: A bibliometric analysis and scientific mapping study

Georgios Lampropoulos

Abstract

The realization of sustainable development and sustainable development goals achievement are essential. Hence, the power sector digitalization is imminent. This bibliometric and mapping study aims to explore the use of artificial intelligence in smart grids and how the topic has evolved over the years. In total, ten research questions are set to be explored. The analysis includes 1,926 articles that were identified and retrieved from Scopus and Web of Science (WoS) over the period 2005 to 2022. The analysis includes the descriptive statistics of the related studies and the annual scientific production, the identification of the most relevant and impactful authors, articles, outlets, affiliations, and countries, and the examination of the most commonly used keywords. The most popular topics and the advancement of the research focus are also explored. The study examines the results, discusses the main findings, presents open issues, and suggests new research directions. The significant role of artificial intelligence in the realization of smart grids and the digitalization of the power sector to enable sustainable development and the achievement of sustainable development goals was evident.




Keywords


artificial intelligence; smart grid; power sector; renewable energy resources; sustainable development.

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References


M. L. Tuballa and M. L. Abundo, “A review of the development of smart grid technologies,” Renewable and Sustainable Energy Reviews, vol. 59, pp. 710–725, 2016, doi: 10.1016/j.rser.2016.01.011.

A. S. Musleh, G. Yao, and S. m. Muyeen, “Blockchain applications in smart Grid–Review and frameworks,” IEEE Access, vol. 7, pp. 86746–86757, 2019, doi: 10.1109/access.2019.2920682.

O. Majeed Butt, M. Zulqarnain, and T. Majeed Butt, “Recent advancement in smart grid technology: Future prospects in the electrical power network,” Ain Shams Engineering Journal, vol. 12, no. 1, pp. 687–695, 2021, doi: 10.1016/j.asej.2020.05.004.

I. Diahovchenko, M. Kolcun, Z. Čonka, V. Savkiv, and R. Mykhailyshyn, “Progress and challenges in smart grids: Distributed generation, smart metering, energy storage and smart loads,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 44, no. 4, pp. 1319–1333, 2020, doi: 10.1007/s40998-020-00322-8.

K. Moslehi and R. Kumar, “A reliability perspective of the smart grid,” IEEE Transactions on Smart Grid, vol. 1, no. 1, pp. 57–64, 2010, doi: 10.1109/tsg.2010.2046346.

H. Farhangi, “The path of the smart grid,” IEEE Power and Energy Magazine, vol. 8, no. 1, pp. 18–28, 2010, doi: 10.1109/mpe.2009.934876.

X. Fang, S. Misra, G. Xue, and D. Yang, “Smart grid — the new and improved power grid: A survey,” IEEE Communications Surveys & Tutorials, vol. 14, no. 4, pp. 944–980, 2012, doi: 10.1109/surv.2011.101911.00087.

M. a. Ponce-Jara, E. Ruiz, R. Gil, E. Sancristóbal, C. Pérez-Molina, and M. Castro, “Smart grid: Assessment of the past and present in developed and developing countries,” Energy Strategy Reviews, vol. 18, pp. 38–52, 2017, doi: 10.1016/j.esr.2017.09.011.

S. K. Rathor and D. Saxena, “Energy management system for smart grid: An overview and key issues,” International Journal of Energy Research, vol. 44, no. 6, pp. 4067–4109, 2020, doi: 10.1002/er.4883.

M. Farmanbar, K. Parham, Ø. Arild, and C. Rong, “A widespread review of smart grids towards smart cities,” Energies, vol. 12, no. 23, p. 4484, 2019, doi: 10.3390/en12234484.

M. B. Mollah et al., “Blockchain for future smart grid: A comprehensive survey,” IEEE Internet of Things Journal, vol. 8, no. 1, pp. 18–43, 2021, doi: 10.1109/jiot.2020.2993601.

L. Cheng, N. Qi, F. Zhang, H. Kong, and X. Huang, “Energy internet: Concept and practice exploration,” 2017, doi: 10.1109/ei2.2017.8245533.

A. Chehri, I. Fofana, and X. Yang, “Security risk modeling in smart grid critical infrastructures in the era of big data and artificial intelligence,” Sustainability, vol. 13, no. 6, p. 3196, 2021, doi: 10.3390/su13063196.

I. Alotaibi, M. A. Abido, M. Khalid, and A. V. Savkin, “A comprehensive review of recent advances in smart grids: A sustainable future with renewable energy resources,” Energies, vol. 13, no. 23, p. 6269, 2020, doi: 10.3390/en13236269.

G. Dileep, “A survey on smart grid technologies and applications,” Renewable Energy, vol. 146, pp. 2589–2625, 2020, doi: 10.1016/j.renene.2019.08.092.

M. Ghorbanian, S. H. Dolatabadi, M. Masjedi, and P. Siano, “Communication in smart grids: A comprehensive review on the existing and future communication and information infrastructures,” IEEE Systems Journal, vol. 13, no. 4, pp. 4001–4014, 2019, doi: 10.1109/jsyst.2019.2928090.

E. Esenogho, K. Djouani, and A. M. Kurien, “Integrating artificial intelligence internet of things and 5G for Next-Generation smartgrid: A survey of trends challenges and prospect,” IEEE Access, vol. 10, pp. 4794–4831, 2022, doi: 10.1109/access.2022.3140595.

R. Bayindir, I. Colak, G. Fulli, and K. Demirtas, “Smart grid technologies and applications,” Renewable and Sustainable Energy Reviews, vol. 66, pp. 499–516, 2016, doi: 10.1016/j.rser.2016.08.002.

M. Ghorbanian, S. H. Dolatabadi, and P. Siano, “Big data issues in smart grids: A survey,” IEEE Systems Journal, vol. 13, no. 4, pp. 4158–4168, 2019, doi: 10.1109/jsyst.2019.2931879.

S. D. Ramchurn, P. Vytelingum, A. Rogers, and N. R. Jennings, “Putting the ’smarts’ into the smart grid,” Communications of the ACM, vol. 55, no. 4, pp. 86–97, 2012, doi: 10.1145/2133806.2133825.

S. Uludag, K.-S. Lui, W. Ren, and K. Nahrstedt, “Secure and scalable data collection with time minimization in the smart grid,” IEEE Transactions on Smart Grid, vol. 7, no. 1, pp. 43–54, 2016, doi: 10.1109/tsg.2015.2404534.

B. K. Bose, “Power electronics, smart grid, and renewable energy systems,” Proceedings of the IEEE, vol. 105, no. 11, pp. 2011–2018, doi: 10.1109/JPROC.2017.2745621.

Y. Yu, J. Yang, and B. Chen, “The smart grids in China—A review,” Energies, vol. 5, no. 5, pp. 1321–1338, 2012, doi: 10.3390/en5051321.

Y. Duan, J. S. Edwards, and Y. K. Dwivedi, “Artificial intelligence for decision making in the era of big data – evolution, challenges and research agenda,” International Journal of Information Management, vol. 48, pp. 63–71, 2019, doi: 10.1016/j.ijinfomgt.2019.01.021.

G. Lampropoulos, “Artificial intelligence, big data, and machine learning in industry 4.0,” in Encyclopedia of data science and machine learning, 2022, pp. 2101–2109, doi: 10.4018/978-1-7998-9220-5.ch125.

J. Lee, H. Davari, J. Singh, and V. Pandhare, “Industrial artificial intelligence for industry 4.0-based manufacturing systems,” Manufacturing Letters, vol. 18, pp. 20–23, 2018, doi: 10.1016/j.mfglet.2018.09.002.

G. Lampropoulos, K. Siakas, V. Julio, and R. Olaf, “Artificial intelligence, blockchain, big data analytics, machine learning and data mining in traditional CRM and social CRM: A critical review,” in 21st IEEE/WIC/ACM international joint conference on web intelligence and intelligent agent technology (WI-IAT), 2022, pp. 504–510, doi: 10.1109/WI-IAT55865.2022.00080.

P. Boza and T. Evgeniou, “Artificial intelligence to support the integration of variable renewable energy sources to the power system,” Applied Energy, vol. 290, p. 116754, 2021, doi: 10.1016/j.apenergy.2021.116754.

W. Lyu and J. Liu, “Artificial intelligence and emerging digital technologies in the energy sector,” Applied Energy, vol. 303, p. 117615, 2021, doi: 10.1016/j.apenergy.2021.117615.

A. C. Serban and M. D. Lytras, “Artificial intelligence for smart renewable energy sector in Europe—Smart energy infrastructures for next generation smart cities,” IEEE Access, vol. 8, pp. 77364–77377, 2020, doi: 10.1109/access.2020.2990123.

U. K. Das et al., “Forecasting of photovoltaic power generation and model optimization: A review,” Renewable and Sustainable Energy Reviews, vol. 81, pp. 912–928, 2018, doi: 10.1016/j.rser.2017.08.017.

O. Ellegaard and J. A. Wallin, “The bibliometric analysis of scholarly production: How great is the impact?” Scientometrics, vol. 105, no. 3, pp. 1809–1831, 2015, doi: 10.1007/s11192-015-1645-z.

M. Aria and C. Cuccurullo, “Bibliometrix: An r-tool for comprehensive science mapping analysis,” Journal of Informetrics, vol. 11, no. 4, pp. 959–975, 2017, doi: 10.1016/j.joi.2017.08.007.

N. Donthu, S. Kumar, D. Mukherjee, N. Pandey, and W. M. Lim, “How to conduct a bibliometric analysis: An overview and guidelines,” Journal of Business Research, vol. 133, pp. 285–296, 2021, doi: 10.1016/j.jbusres.2021.04.070.

M. Gusenbauer and N. R. Haddaway, “Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of google scholar, PubMed, and 26 other resources,” Research Synthesis Methods, vol. 11, no. 2, pp. 181–217, 2020, doi: 10.1002/jrsm.1378.

J. Zhu and W. Liu, “A tale of two databases: The use of web of science and scopus in academic papers,” Scientometrics, vol. 123, no. 1, pp. 321–335, Feb. 2020, doi: 10.1007/s11192-020-03387-8.

C. Jiang, H. Zhang, Y. Ren, Z. Han, K.-C. Chen, and L. Hanzo, “Machine learning paradigms for Next-Generation wireless networks,” IEEE Wireless Communications, vol. 24, no. 2, pp. 98–105, 2017, doi: 10.1109/mwc.2016.1500356wc.

M. Q. Raza and A. Khosravi, “A review on artificial intelligence-based load demand forecasting techniques for smart grid and buildings,” Renewable and Sustainable Energy Reviews, vol. 50, pp. 1352–1372, 2015, doi: 10.1016/j.rser.2015.04.065.

Y. Wang, Q. Chen, T. Hong, and C. Kang, “Review of smart meter data analytics: Applications, methodologies, and challenges,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 3125–3148, 2019, doi: 10.1109/tsg.2018.2818167.

S. Sobri, S. Koohi-Kamali, and N. Abd. Rahim, “Solar photovoltaic generation forecasting methods: A review,” Energy Conversion and Management, vol. 156, pp. 459–497, 2018, doi: 10.1016/j.enconman.2017.11.019.

M. Ozay, I. Esnaola, F. T. Yarman Vural, S. R. Kulkarni, and H. V. Poor, “Machine learning methods for attack detection in the smart grid,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 8, pp. 1773–1786, 2016, doi: 10.1109/tnnls.2015.2404803.

V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, and J. Pineau, “An introduction to deep reinforcement learning,” Foundations and Trends in Machine Learning, vol. 11, no. 3–4, pp. 219–354, 2018, doi: 10.1561/2200000071.

D. Alahakoon and X. Yu, “Smart electricity meter data intelligence for future energy systems: A survey,” IEEE Transactions on Industrial Informatics, vol. 12, no. 1, pp. 425–436, 2016, doi: 10.1109/tii.2015.2414355.

E. Chemali, P. J. Kollmeyer, M. Preindl, R. Ahmed, and A. Emadi, “Long Short-Term memory networks for accurate State-of-Charge estimation of li-ion batteries,” IEEE Transactions on Industrial Electronics, vol. 65, no. 8, pp. 6730–6739, 2018, doi: 10.1109/tie.2017.2787586.

X. Zhou, S. Chen, Z. Lu, Y. Huang, S. Ma, and Q. Zhao, “Technology features of the new generation power system in china,” Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, vol. 38, no. 7, pp. 1893–1904, 2018, doi: 10.13334/j.0258-8013.pcsee.180067.

J. Zhang, Q. Yu, F. Zheng, C. Long, Z. Lu, and Z. Duan, “Comparing keywords plus of WOS and author keywords: A case study of patient adherence research,” Journal of the Association for Information Science and Technology, vol. 67, no. 4, pp. 967–972, 2016, doi: 10.1002/asi.23437.

H. Yousuf, A. Y. Zainal, M. Alshurideh, and S. A. Salloum, “Artificial intelligence models in power system analysis,” in Artificial intelligence for sustainable development: Theory, practice and future applications, 2021, pp. 231–242. doi: 10.1007/978-3-030-51920-9_12

V. Franki, D. Majnarić, and A. Višković, “A comprehensive review of artificial intelligence (AI) companies in the power sector,” Energies, vol. 16, no. 3, p. 1077, 2023, doi: 10.3390/en16031077.

O. A. Omitaomu and H. Niu, “Artificial intelligence techniques in smart grid: A survey,” Smart Cities, vol. 4, no. 2, pp. 548–568, 2021, doi: 10.3390/smartcities4020029.


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