Reprint
Smart Energy Management for Smart Grid
Edited by
September 2023
262 pages
- ISBN978-3-0365-8272-6 (Hardback)
- ISBN978-3-0365-8273-3 (PDF)
This is a Reprint of the Special Issue Smart Energy Management for Smart Grid that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
This reprint focuses on the fundamentals of smart grids control, with a special emphasis on solutions for electrical distribution grids with decentralized and local production, high renewable energy penetration and load and weather forecasts with uncertainties. Special attention is given to the power quality of smart grids. Techniques like blockchain, IoT and machine learning are analysed and discussed in order to carry out this task for smart grids.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
cold ironing; electrification; operation management; renewable energy source; seaport microgrids; shipboard microgrid; maritime; energy; cap and trade; blockchain; demand-side management; energy policy; energy trading; cost–benefit analysis; smart grid; hosting capacity; efficiency; technology; economic; the EU; willingness to pay; policy; cold ironing; energy management system; optimal sizing; renewable energy sources; seaport microgrids; maritime; HOMER; aggregator; ancillary services; distributed resources; enabled virtual unit; energy storage systems; heuristic greedy-indexing; Monte Carlo; virtual power plant; theft attacks; long short term memory; gated recurrent unit; deep learning techniques; machine learning techniques; electricity theft detection; smart grids; renewable energy sources; energy demand control; smart appliances; elastic energy management algorithm; GRASP algorithm; IoT; energy management; forecast uncertainties; microgrids; optimization; renewable energy integrations; DSM; BGSA; BPSO; smart grid; load categorizing; DUC; load shifting; Internet of Things; power quality monitoring; power grid measurements; transient detection; transient characterization; deep learning; convolutional neural networks; energy community; microgrid; local control