Data-Intensive Computing in Smart Microgrids

Edited by
August 2021
238 pages
  • ISBN978-3-0365-1627-1 (Hardback)
  • ISBN978-3-0365-1628-8 (PDF)

This book is a reprint of the Special Issue Data-Intensive Computing in Smart Microgrids that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
Physical Sciences

Microgrids have recently emerged as the building block of a smart grid, combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions. At the same time, rapid advancements in sensor and metering technologies, wireless and network communication, as well as cloud and fog computing are leading to the collection and accumulation of large amounts of data (e.g., device status data, energy generation data, consumption data).

The application of big data analysis techniques (e.g., forecasting, classification, clustering) on such data can optimize the power generation and operation in real time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. An efficient and intelligent analysis of the data will enable smart microgrids to detect and recover from failures quickly, respond to electricity demand swiftly, supply more reliable and economical energy, and enable customers to have more control over their energy use. Overall, data-intensive analytics can provide effective and efficient decision support for all of the producers, operators, customers, and regulators in smart microgrids, in order to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management. This book contains an assortment of relevant novel research contributions that provide real-world applications of data-intensive analytics in smart grids and contribute to the dissemination of new ideas in this area.

  • Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
electricity load forecasting; smart grid; feature selection; Extreme Learning Machine; Genetic Algorithm; Support Vector Machine; Grid Search; AMI; TL; SG; NB-PLC; fog computing; green community; resource allocation; processing time; response time; green data center; microgrid; renewable energy; energy trade contract; real time power management; load forecasting; optimization techniques; deep learning; big data analytics; electricity theft detection; smart grids; electricity consumption; electricity thefts; smart meter; imbalanced data; data-intensive smart application; cloud computing; resource allocation; real-time systems; smart grid; multi-objective energy optimization; smart grid; renewable energy sources; wind; photovoltaic; demand response programs; energy management; battery energy storage systems; photovoltaic; demand response; scheduling; smart grid; automatic generation control; single/multi-area power system; intelligent control methods; microgrid; smart grid; renewable energy sources; virtual inertial control; demand response; soft computing control methods; n/a