Complementarity Modeling In Energy Markets

roblems 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). 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. A natural question is why concentrate on energy markets for this complementarity approach? In a nutshell, complementarity models generalize: a. on-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. s it turns out, energy or other markets that have game theoretic aspects are best modeled by complementarity problems. 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. As such, complementarity models are a very general and flexible modeling format. 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. conomic and engineering problems that aren’t specifically derived from optimization problems (e.g., spatial price equilibria) d. 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. optimization problems via their Karush-Kuhn-Tucker conditions b.

Books > Business and Management

Complementarity Modeling In Energy Markets