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Data-Intensive Computing in Smart Microgrids
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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.
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Keywords
- AMI
- automatic generation control
- battery energy storage systems
- big data analytics
- Cloud computing
- data-intensive smart application
- deep learning
- demand response
- demand response programs
- electricity consumption
- electricity load forecasting
- electricity theft detection
- electricity thefts
- energy management
- energy trade contract
- extreme learning machine
- feature selection
- fog computing
- genetic algorithm
- green community
- green data center
- Grid Search
- imbalanced data
- intelligent control methods
- load forecasting
- microgrid
- multi-objective energy optimization
- n/a
- NB-PLC
- Optimization Techniques
- photovoltaic
- processing time
- real time power management
- Real-Time Systems
- renewable energy
- Renewable energy sources
- Resource allocation
- Response Time
- Scheduling
- SG
- single/multi-area power system
- Smart grid
- Smart Grids
- Smart meter
- soft computing control methods
- support vector machine
- Technology, engineering, agriculture
- Technology: general issues
- TL
- virtual inertial control
- Wind
Links
DOI: 10.3390/books978-3-0365-1628-8Editions