Data-Intensive Computing in Smart Microgrids

Dear Colleagues,

Microgrids have recently emerged as the building blocks of smart grids, combining distributed renewable energy sources, energy storage devices, and load management 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, and consumption data).

The application of big data analysis techniques (e.g., forecasting, classification, and clustering) to such data can optimize power generation and operation in real-time by accurately predicting electricity demands, discovering electricity consumption patterns, and developing dynamic pricing mechanisms. The efficient and intelligent analysis of the data will enable smart microgrids to detect and restore 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 to achieve holistic smart energy management, including energy generation, transmission, distribution, and demand-side management.

We are inviting submissions of relevant original research articles or comprehensive reviews to Special Issue of Energies on “Data-Intensive Computing in Smart Microgrids”. The topics of the Special Issue include, but are not limited to, the following:

• Energy data-intensive analytics in smart microgrids
• Data-driven management and control of smart microgrids
• Big data management in smart grids and microgrids
• Cloud and fog computing in smart microgrids operations and management
• Applications of network science in modeling and the analysis of smart microgrids
• Data-driven dynamic pricing mechanisms and strategies in smart grids
• Intelligent decision-making in smart microgrids
• Demand-side management in smart microgrids
• Energy load/demand forecasting for residential, commercial, and/or industrial consumers
• Electricity price forecasting for residential, commercial, and/or industrial consumers
• Power forecasting from renewable energy resources (e.g., solar, wind)
• Management of data for Advanced Metering Infrastructure (AMI) in smart grids
• Data-driven renewable energy integration in smart grids
• Blockchain integration in smart grid and microgrids

Assist. Prof. Dr. Herodotos Herodotou
Guest Editor

Manuscript Submission Information

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