TY - BOOK ID - 14308474 TI - Complementarity modeling in energy markets AU - Gabriel, Steven A. AU - Conejo, Antonio J. AU - Fuller, J. David PY - 2013 SN - 1441961224 1489986758 1441961232 9781441961228 PB - New York: Springer, DB - UniCat KW - Commodity exchanges -- Computer simulation. KW - Electric utilities -- Finance -- Computer simulation. KW - Energy resources. KW - Power resources. KW - Commodity exchanges KW - Power resources KW - Management KW - Finance KW - Business & Economics KW - Investment & Speculation KW - Management Theory KW - Computer simulation KW - Computer simulation. KW - Energy KW - Energy resources KW - Power supply KW - Commodities exchange KW - Commodity markets KW - Exchanges, Commodity KW - Exchanges, Produce KW - Produce exchanges KW - Business. KW - Operations research. KW - Decision making. KW - Management science. KW - Macroeconomics. KW - Business and Management. KW - Operation Research/Decision Theory. KW - Macroeconomics/Monetary Economics//Financial Economics. KW - Operations Research, Management Science. KW - Natural resources KW - Energy harvesting KW - Energy industries KW - Futures market KW - Commercial products KW - Produce trade KW - Speculation KW - Operations Research/Decision Theory. KW - Economics KW - Operational analysis KW - Operational research KW - Industrial engineering KW - Management science KW - Research KW - System theory KW - Economics. KW - Finance. KW - Quantitative business analysis KW - Problem solving KW - Operations research KW - Statistical decision KW - Deciding KW - Decision (Psychology) KW - Decision analysis KW - Decision processes KW - Making decisions KW - Management decisions KW - Choice (Psychology) KW - Decision making KW - Commodity exchanges - Computer simulation UR - https://www.unicat.be/uniCat?func=search&query=sysid:14308474 AB - This 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. ER -