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008 120719s2013 xxu| s |||| 0|eng d
020 _a9781441961235
_9978-1-4419-6123-5
024 7 _a10.1007/978-1-4419-6123-5
_2doi
050 4 _aHD30.23
072 7 _aKJT
_2bicssc
072 7 _aKJMD
_2bicssc
072 7 _aBUS049000
_2bisacsh
082 0 4 _a658.40301
_223
100 1 _aGabriel, Steven A.
_eauthor.
245 1 0 _aComplementarity Modeling in Energy Markets
_h[electronic resource] /
_cby Steven A. Gabriel, Antonio J. Conejo, J. David Fuller, Benjamin F. Hobbs, Carlos Ruiz.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2013.
300 _aXXVI, 630 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aInternational Series in Operations Research & Management Science,
_x0884-8289 ;
_v180
505 0 _aIntroduction and Motivation -- Optimality and Complementarity -- Some Microeconomic Principles -- Equilibria and Complementarity Problems -- Variational Inequality Problems -- Optimization Problems Constrained by Optimization Problems -- Equilibrium Problems with Equilibrium Constraints -- Algorithm for LCPs, NCPs, and VIs -- Some Advanced Algorithms for VI Decomposition, MPCCs and EPECs -- Natural Gas Market Modeling -- Electricity and Environmental Markets -- Multicommodity Equilibrium Models: Accounting for Demand-Side Linkages.
520 _aThis addition to the ISOR series  introduces complementarity models in a straightforward and approachable manner and uses them to carry out an in-depth analysis of energy markets, including formulation issues and solution techniques.   In a nutshell, complementarity models generalize: a. optimization problems via their Karush-Kuhn-Tucker conditions b. non-cooperative games in which each player may be solving a separate but related optimization problem with potentially overall system constraints (e.g., market-clearing conditions) c. economic and engineering problems that aren’t specifically derived from optimization problems (e.g., spatial price equilibria) d. problems in which both primal and dual variables (prices) appear in the original formulation (e.g., The National Energy Modeling System (NEMS) or its precursor, PIES). As such, complementarity models are a very general and flexible modeling format. A natural question is why concentrate on energy markets for this complementarity approach?  As it turns out, energy or other markets that have game theoretic aspects are best modeled by complementarity problems.  The reason is that the traditional perfect competition approach no longer applies due to deregulation and restructuring of these markets and thus the corresponding optimization problems may no longer hold.  Also, in some instances it is important in the original model formulation to involve both primal variables (e.g., production) as well as dual variables (e.g., market prices) for public and private sector energy planning.  Traditional optimization problems can not directly handle this mixing of primal and dual variables but complementarity models can and this makes them all that more effective for decision-makers.
650 0 _aBusiness.
650 0 _aOperations research.
650 0 _aDecision making.
650 0 _aManagement science.
650 0 _aMacroeconomics.
650 1 4 _aBusiness and Management.
650 2 4 _aOperation Research/Decision Theory.
650 2 4 _aMacroeconomics/Monetary Economics//Financial Economics.
650 2 4 _aOperations Research, Management Science.
700 1 _aConejo, Antonio J.
_eauthor.
700 1 _aFuller, J. David.
_eauthor.
700 1 _aHobbs, Benjamin F.
_eauthor.
700 1 _aRuiz, Carlos.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781441961228
830 0 _aInternational Series in Operations Research & Management Science,
_x0884-8289 ;
_v180
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4419-6123-5
912 _aZDB-2-SBE
942 _2Dewey Decimal Classification
_ceBooks
999 _c43411
_d43411