Abstract
Smart grids enable a two-way data-driven flow of electricity, allowing systematic communication along the distribution line. Smart grids utilize various power sources, automate the process of energy distribution and fault identification, facilitate better power usage, etc. Artificial Intelligence plays an important role in the management of power grids, making it even smarter. With the help of Artificial Intelligence and Internet of Things, smart grids can optimize the energy consumption, provide continuous feedback on usage, and monitor live usage statistics, thereby making the energy intelligent. Smart grids require specific hardware to continuously monitor and adapt to the requirements of the system. By enabling energy intelligence, we empower building-level and city-level optimizations that make use of green energy, thereby contributing more toward sustainable development. Thus, the multifaceted energy management system uses sustainable and renewable energy sources, combined with smart devices to provide a two-way communication system to optimize the end-to-end distribution of energy, beneficial to both suppliers and consumers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ali ABMS, Azad S. Demand forecasting in smart grid. In: Ali ABMS, editor. Smart grids: opportunities, developments, and trends. London, London: Springer; 2013. p. 135–50.
Ali SS, Choi BJ. State-of-the-art artificial intelligence techniques for distributed smart grids: a review. Electronics. 2020;9:1030.
Ben Slama S. Prosumer in smart grids based on intelligent edge computing: a review on artificial intelligence scheduling techniques. Ain Shams Eng J. 2021; https://doi.org/10.1016/j.asej.2021.05.018.
Carpinone A, et al. Markov chain modeling for very-short-term wind power forecasting. Electr Power Syst Res. 2015;122:152–8.
Compieta P, et al. Exploratory spatio-temporal data mining and visualization. J Visual Lang Comput. 2007;18:255–79.
Directorate-General for Internal Policies of the Union. Artificial intelligence in smart cities and urban mobility : how can artificial intelligence applications be used in urban mobility and smart cities and how can their deployment be facilitated. Publications Office of the European Union; 2021.
Daki H, et al. Big data management in smart grid: concepts, requirements and implementation. J Big Data. 2017;4:1–19.
Dhend MH, Chile RH. Fault diagnosis of smart grid distribution system by using smart sensors and Symlet wavelet function. J Electron Test. 2017;33:329–38.
Duffy P, et al. Energy sources and supply grids – the growing need for storage. In: Energy storage options and their environmental impact; 2018. p. 1–41.
Essayeh C, et al. Energy management strategies for smart green MicroGrid systems: a systematic literature review. J Electr Comput Eng. 2021;2021: https://doi.org/10.1155/2021/6675975
Fabbri G, et al. Impact of V2G/G2V technologies on distributed generation systems. In: 2014 IEEE 23rd international symposium on industrial electronics (ISIE); 2014. p. 1677–82.
He M, et al. Multiple timescale dispatch and scheduling for stochastic reliability in smart grids with wind generation integration. In: 2011 proceedings IEEE INFOCOM; 2011. p. 461–5.
Hersent O, et al. The Internet of Things: key applications and protocols. Wiley; 2012.
Hlalele TS, et al. Faults classification and identification on smart grid: part-A status review. Proc Manuf. 2019;35:601–6.
Hossein Motlagh N, et al. Internet of Things (IoT) and the energy sector. Energies. 2020;13:494.
Hosseinzadeh J, et al. Fault detection and classification in smart grids using augmented K-NN algorithm. SN Appl Sci. 2019;1:1627.
Hussain T, et al. Smart and intelligent energy monitoring systems: a comprehensive literature survey and future research guidelines. Int J Energy Res. 2021;45:3590–614.
Jiao J. Application and prospect of artificial intelligence in smart grid. IOP Conf Ser Earth Environ Sci. 2020;510:022012.
Khan B, Singh P. Selecting a meta-heuristic technique for smart micro-grid optimization problem: a comprehensive analysis. IEEE Access. 2017;5:13951–77.
Kim M. Toward smart microgrid with renewable energy: an overview of network design, security, and standards. In: Lecture notes in computer science. Berlin Heidelberg: Springer; 2013. p. 142–56.
Kim WH, et al. Real-time energy monitoring and controlling system based on ZigBee sensor networks. Proc Comput Sci. 2011;5:794–7.
Koutitas G. The smart grid: anchor of the smart city. In: McClellan S, et al., editors. Smart cities: applications, technologies, standards, and driving factors. Cham: Springer International Publishing; 2018. p. 53–74.
Latora V, Marchiori M. Economic small-world behavior in weighted networks. Eur Phys J B Condens Matter Complex Syst. 2003;32:249–63.
Liu T, et al. The impact mechanism and scenario simulation of energy internet on transition. Discrete Dyn Nat Soc. 2021;2021 https://doi.org/10.1155/2021/5549991.
Lusseau D. The emergent properties of a dolphin social network. Proc Biol Sci. 2003;270(Suppl 2):S186–8.
Ma Z, et al. The role of data analysis in the development of intelligent energy networks. IEEE Netw. 2017;31:88–95.
Meliani M, et al. Energy management in the smart grid: state-of-the-art and future trends. Int J Eng Business Manag. 2021;13:18479790211032920.
Mohanty SP, et al. Everything you wanted to know about smart cities: the Internet of things is the backbone. IEEE Consumer Electron Magaz. 2016;5:60–70.
Nga DV, et al. Visualization techniques in smart grid. Smart Grid Renew Energy. 2012;03:175–85.
Petinrin OJ, Shaaban M. Overcoming challenges of renewable energy on future smart grid. TELKOMNIKA Indones. J Electr Eng. 2012;10: https://doi.org/10.11591/telkomnika.v10i2.675
Phuangpornpitak N, Tia S. Opportunities and challenges of integrating renewable energy in smart grid system. Energy Procedia. 2013;34:282–90.
Purvins A, Labbate A. Automated energy management in distributed electricity systems: an EEPOS approach. Int J Green Energy. 2017;14:1034–47.
Reddy SS, et al. Review of stochastic optimization methods for smart grid. Front Energy. 2017;11:197–209.
Shahinzadeh H, et al. Internet of Energy (IoE) in smart power systems. In: 2019 5th conference on knowledge based engineering and innovation (KBEI); 2019. p. 627–36.
Shariff SM, et al. A state of the art review of electric vehicle to grid (V2G) technology. IOP Conf Ser Mater Sci Eng. 2019;561:012103.
Spahiu P, Evans IR. Protection systems that verify and supervise themselves. In: 2011 2nd IEEE PES international conference and exhibition on innovative smart grid technologies; 2011. p. 1–4.
Stenull O, Janssen HK. Nonlinear random resistor diode networks and fractal dimensions of directed percolation clusters. Phys Rev E Stat Nonlinear Soft Matter Phys. 2001;64:016135.
You M, et al. A versatile software defined smart grid testbed: artificial intelligence enhanced real-time co-evaluation of ICT systems and power systems. IEEE Access. 2020;8:88651–63.
Zhang Y, et al. Big data analytics in smart grids: a review. Energy Inform. 2018;1:1–24.
Zhou S, et al. Application research of big data real-time processing technology in smart grid. IOP Conf Ser: Earth Environ Sci. 2021;696:012044.
Zhu T, et al. Emergent technologies in big data sensing: a survey. Int J Distrib Sens Netw. 2015;2015:1–13.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Eapen, N.G., Harsha, K.G., Kesan, A. (2023). Energy Intelligence: The Smart Grid Perspective. In: Vijayalakshmi, S., ., S., Balusamy, B., Dhanaraj, R.K. (eds) AI-Powered IoT in the Energy Industry. Power Systems. Springer, Cham. https://doi.org/10.1007/978-3-031-15044-9_3
Download citation
DOI: https://doi.org/10.1007/978-3-031-15044-9_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-15043-2
Online ISBN: 978-3-031-15044-9
eBook Packages: EnergyEnergy (R0)