Reprint
Distributed Energy Resources Management 2018
Edited by
January 2020
286 pages
- ISBN978-3-03928-170-1 (Paperback)
- ISBN978-3-03928-171-8 (PDF)
This book is a reprint of the Special Issue Distributed Energy Resources Management 2018 that was published in
Chemistry & Materials Science
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary
The Special Issue Distributed Energy Resources Management 2018 includes 13 papers, and is a continuation of the Special Issue Distributed Energy Resources Management. The success of the previous edition shows the unquestionable relevance of distributed energy resources in the operation of power and energy systems at both the distribution level and at the wider power system level. Improving the management of distributed energy resources makes it possible to accommodate the higher penetration of intermittent distributed generation and electric vehicle charging. Demand response programs, namely the ones with a distributed nature, allow the consumers to contribute to the increased system efficiency while receiving benefits. This book addresses the management of distributed energy resources, with a focus on methods and techniques to achieve an optimized operation, in order to aggregate the resources namely in the scope of virtual power players and other types of aggregators, and to remunerate them. The integration of distributed resources in electricity markets is also addressed as an enabler for their increased and efficient use.
Format
- Paperback
License
© 2020 by the authors; CC BY-NC-ND license
Keywords
clustering; demand Response; distributed generation; smart grids; demand-side management; multi-agent system; distributed coordination; distributed energy resources; swarm intelligence; virtual power plant; distributed energy resources; multi-agent technology; bidding strategy; stackelberg dynamic game; aggregator; distribution system operator; distributed energy resources; local flexibility market; flexibility service; distributed energy; comprehensive benefits; multi-agent synergetic estimation; synergistic optimization strategy; control system; fault-tolerant control; algorithm design and analysis; IoT (Internet of Things); nonlinear control; optimization; DSM; microgrid; solar; wind; teaching-learning; microgrid; energy storage system; distributed generator; frequency control; active power control; autonomous control; droop control; frequency bus-signaling; batteries; energy storage; microgrids; optimal scheduling; particle swarm optimization; power system management; smart grid; supply and demand; trade agreements; low voltage networks; multi-period optimal power flow; multi-temporal optimal power flow; active distribution networks; unbalanced networks; indoor environment quality; occupant comfort; building climate control; healthy building; energy efficiency; adaptability; decentralized energy management system; local energy trading; multi-agent system; optimization; smart grid; demand response; distributed generation; particle swarm optimization; prosumer; n/a