Big Data Analytics Framework for Smart Grids

The text discusses the use of big data analytics in smart grid operations, covering smart power generation, transmission, and distribution, energy management systems, artificial intelligence, and machine learning-based computing. It presents a state-of-the-art analysis of big data analytics and its applications in power grids, exploring the role of the internet of things, artificial intelligence, and machine learning. It also examines edge analytics for integration of generation technologies and decision-making approaches. The text provides a comprehensive study and assessment of the research and development in electrical utility grids, including operational technology, storage, processing, and communication systems. It is an ideal reference text for students and researchers in electrical engineering, electronics and communications engineering, computer engineering, and information technology.

Format: Hardback
Length: 232 pages
Publication date: 22 December 2023
Publisher: Taylor & Francis Ltd

The power industry is undergoing a significant transformation due to the integration of advanced technologies such as smart grids, big data analytics, and the Internet of Things (IoT). These technologies have the potential to revolutionize the way we generate, transmit, and distribute electricity, improving efficiency, reliability, and sustainability.

One of the key challenges facing the power industry is the management of large amounts of data generated by various sensors and devices in smart grids. Big data analytics can help address this challenge by enabling the analysis of this data in real-time, allowing for more accurate and timely decision-making.

Smart power generation is another area where big data analytics is playing a significant role. By analyzing data from renewable energy sources, such as solar and wind, power grid operators can optimize the generation of electricity and reduce the reliance on fossil fuels. This can help reduce greenhouse gas emissions and improve air quality.

Smart transmission and distribution networks are also being developed using big data analytics. By analyzing data from power grid sensors and devices, operators can identify potential faults and outages before they occur, allowing for more efficient and reliable power distribution.

Artificial intelligence and machine learning are also being used in smart grids to improve energy management and optimize power distribution. These technologies can analyze large amounts of data and make predictions about energy demand, allowing for more efficient use of resources and reducing energy waste.

Edge analytics is another important area of research in smart grids. This refers to the processing of data at the edge of the network, closer to the devices and sensors generating the data. Edge analytics can reduce the amount of data that needs to be transmitted to the central data center, improving the speed and reliability of the network.

Decision-making approaches in smart grids are also being improved using big data analytics. By analyzing data from various sources, such as weather forecasts, energy demand patterns, and grid performance, operators can make more informed decisions about how to manage the grid.

However, there are also some research limitations associated with the use of big data analytics in smart grids. For example, the data generated by smart grids can be complex and heterogeneous, making it challenging to analyze and interpret. Additionally, there are concerns about the security and privacy of the data, particularly in the context of the IoT.

To address these research limitations, further research is needed to incorporate big data analytics into power system design and operational frameworks. This could involve developing new algorithms and techniques for analyzing complex data, as well as developing new security and privacy protocols for the IoT.

In conclusion, the integration of smart grids, big data analytics, and the IoT is transforming the power industry, providing new opportunities for efficiency, reliability, and sustainability. Big data analytics is playing a critical role in addressing the challenges facing the industry, such as managing large amounts of data, optimizing power generation, and improving energy management. However, further research is needed to fully exploit the potential of these technologies and incorporate them into power system design and operational frameworks.

Weight: 620g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032392905