Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review

Abstract

Meteorological changes urge engineering communities to look for sustainable and clean energy technologies to keep the environment safe by reducing CO2 emissions. The structure of these technologies relies on the deep integration of advanced data-driven techniques which can ensure efficient energy generation, transmission, and distribution. After conducting thorough research for more than a decade, the concept of the smart grid (SG) has emerged, and its practice around the world paves the ways for efficient use of reliable energy technology. However, many developing features evoke keen interest and their improvements can be regarded as the next-generation smart grid (NGSG). Also, to deal with the non-linearity and uncertainty, the emergence of data-driven NGSG technology can become a great initiative to reduce the diverse impact of non-linearity. This paper exhibits the conceptual framework of NGSG by enabling some intelligent technical features to ensure its reliable operation, including intelligent control, agent-based energy conversion, edge computing for energy management, internet of things (IoT) enabled inverter, agent-oriented demand side management, etc. Also, a study on the development of data-driven NGSG is discussed to facilitate the use of emerging data-driven techniques (DDTs) for the sustainable operation of the SG. The prospects of DDTs in the NGSG and their adaptation challenges in real-time are also explored in this paper from various points of view including engineering, technology, et al. Finally, the trends of DDTs towards securing sustainable and clean energy evolution from the NGSG technology in order to keep the environment safe is also studied, while some major future issues are highlighted. This paper can offer extended support for engineers and researchers in the context of data-driven technology and the SG.

6G Networks and the AI Revolution—Exploring Technologies, Applications, and Emerging Challenges

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Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

“…In addition, the stability issues that occur during the switching process need to be further explored. Advanced technologies such as feature extraction, big data, and intelligent decision-making should be utilized for system better control [42].…”

Bio-Inspired Design of Future Solar Power Systems for Smart Grid Applications

“…Ahsan [21] says that ANNs can be used for load prediction in the smart grid, and energy resource planning. These capabilities are examined in the context of the next generation smart grid.…”

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“…It focuses on the manufacturing of solar panels and demonstrates the feasibility of producing flexible solar panels. This review shows that bio-inspired models find applications in renewable solar energy systems, with respect to insolation prediction and management related configuration [17][18][19][20][21]. The use of bio-inspired models to determine solar panel geometry is yet to significantly benefit from bio-inspired models as seen in [22][23][24][25].…”