000 03490nam a22005895i 4500
001 978-3-319-73177-3
003 DE-He213
005 20220801220450.0
007 cr nn 008mamaa
008 180205s2018 sz | s |||| 0|eng d
020 _a9783319731773
_9978-3-319-73177-3
024 7 _a10.1007/978-3-319-73177-3
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aTEC009000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aOvalle, Andrés.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_950462
245 1 0 _aGrid Optimal Integration of Electric Vehicles: Examples with Matlab Implementation
_h[electronic resource] /
_cby Andrés Ovalle, Ahmad Hably, Seddik Bacha.
250 _a1st ed. 2018.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2018.
300 _aXV, 219 p. 90 illus., 88 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v137
505 0 _aIntroduction -- Centralized Approach -- Dynamic Programming and Potential Game Approach -- Evolutionary Game Theory Approach Part I: Mixed Strategist Dynamics -- Evolutionary Game Theory Approach Part II: Escort Dynamics.
520 _aThis book is a compilation of recent research on distributed optimization algorithms for the integral load management of plug-in electric vehicle (PEV) fleets and their potential services to the electricity system. It also includes detailed developed Matlab scripts. These algorithms can be implemented and extended to diverse applications where energy management is required (smart buildings, railways systems, task sharing in micro-grids, etc.). The proposed methodologies optimally manage PEV fleets’ charge and discharge schedules by applying classical optimization, game theory, and evolutionary game theory techniques. Taking owner’s requirements into consideration, these approaches provide services like load shifting, load balancing among phases of the system, reactive power supply, and task sharing among PEVs. The book is intended for use in graduate optimization and energy management courses, and readers are encouraged to test and adapt the scripts to their specific applications.
650 0 _aComputational intelligence.
_97716
650 0 _aRenewable energy sources.
_94906
650 0 _aMathematical optimization.
_94112
650 0 _aGame theory.
_96996
650 1 4 _aComputational Intelligence.
_97716
650 2 4 _aRenewable Energy.
_913722
650 2 4 _aOptimization.
_950463
650 2 4 _aGame Theory.
_96996
700 1 _aHably, Ahmad.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_950464
700 1 _aBacha, Seddik.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_950465
710 2 _aSpringerLink (Online service)
_950466
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319731766
776 0 8 _iPrinted edition:
_z9783319731780
776 0 8 _iPrinted edition:
_z9783319892382
830 0 _aStudies in Systems, Decision and Control,
_x2198-4190 ;
_v137
_950467
856 4 0 _uhttps://doi.org/10.1007/978-3-319-73177-3
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c78596
_d78596