dc.contributor |
Faria, Pedro |
|
dc.contributor |
Vale, Zita |
|
dc.date |
2023-02-02T16:46:03Z |
|
dc.date |
2023-02-02T16:46:03Z |
|
dc.date |
2023 |
|
dc.date.accessioned |
2023-02-18T19:28:55Z |
|
dc.date.available |
2023-02-18T19:28:55Z |
|
dc.identifier |
ONIX_20230202_9783036550558_143 |
|
dc.identifier |
https://directory.doabooks.org/handle/20.500.12854/96742 |
|
dc.identifier |
https://mdpi.com/books/pdfview/book/6688 |
|
dc.identifier |
https://mdpi.com/books/pdfview/book/6688 |
|
dc.identifier.uri |
http://localhost:8080/xmlui/handle/CUHPOERS/249585 |
|
dc.description |
The Special Issue “Demand Response in Smart Grids” includes 11 papers on a variety of topics. The success of this Special Issue demonstrates the relevance of demand response programs and events in the operation of power and energy systems at both the distribution level and at the wide power system level. This reprint addresses the design, implementation, and operation of demand response programs, with focus on methods and techniques to achieve an optimized operation as well as on the electricity consumer. |
|
dc.format |
image/jpeg |
|
dc.language |
eng |
|
dc.publisher |
MDPI - Multidisciplinary Digital Publishing Institute |
|
dc.rights |
open access |
|
dc.subject |
demand response |
|
dc.subject |
population evolution |
|
dc.subject |
irrational behavior |
|
dc.subject |
Markov state |
|
dc.subject |
non-cooperative game |
|
dc.subject |
solid electric thermal storage |
|
dc.subject |
cyber–physical model |
|
dc.subject |
K-means |
|
dc.subject |
support vector machine |
|
dc.subject |
neural network |
|
dc.subject |
data analysis |
|
dc.subject |
demand response (DR), load profile clustering |
|
dc.subject |
k-means |
|
dc.subject |
targeting of customer |
|
dc.subject |
aggregator |
|
dc.subject |
ramp period |
|
dc.subject |
real-time simulation |
|
dc.subject |
multi-objective optimization |
|
dc.subject |
social welfare metrics |
|
dc.subject |
energy allocation |
|
dc.subject |
load disaggregation |
|
dc.subject |
percentage total harmonic distortion and non-intrusive identification of load pattern |
|
dc.subject |
charging strategy |
|
dc.subject |
optimization |
|
dc.subject |
electricity pricing |
|
dc.subject |
electric vehicle |
|
dc.subject |
flexibility |
|
dc.subject |
flexibility market |
|
dc.subject |
home energy management system |
|
dc.subject |
energy flexibility |
|
dc.subject |
industrial energy management |
|
dc.subject |
demand side management |
|
dc.subject |
control approaches |
|
dc.subject |
energy management |
|
dc.subject |
optimization method |
|
dc.subject |
objective function |
|
dc.subject |
control constraints |
|
dc.subject |
Demand Response |
|
dc.subject |
dynamic thermal rating |
|
dc.subject |
hosting capacity |
|
dc.subject |
transformer |
|
dc.subject |
n/a |
|
dc.subject |
bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues |
|
dc.subject |
bic Book Industry Communication::T Technology, engineering, agriculture::TB Technology: general issues::TBX History of engineering & technology |
|
dc.title |
Demand Response in Smart Grids |
|
dc.resourceType |
book |
|
dc.alternateIdentifier |
9783036550558 |
|
dc.alternateIdentifier |
9783036550565 |
|
dc.alternateIdentifier |
10.3390/books978-3-0365-5056-5 |
|
dc.licenseCondition |
Attribution 4.0 International |
|
dc.identifierdoi |
10.3390/books978-3-0365-5056-5 |
|
dc.relationisPublishedBy |
46cabcaa-dd94-4bfe-87b4-55023c1b36d0 |
|
dc.relationisbn |
9783036550558 |
|
dc.relationisbn |
9783036550565 |
|
dc.pages |
240 |
|
dc.placepublication |
Basel |
|