Benjamin F. Hobbs

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. 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. conomic and engineering problems that aren’t specifically derived from optimization problems (e.g., spatial price equilibria) d. 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). 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? s 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.

Planning, operating, and policy making in the electric utility and natural gas sectors involves important trade-offs among economic, social, and environmental criteria. These trade-offs figure prominently in ongoing debates about how to meet growing energy demands and how to restructure the world's power industry. Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods reviews practical tools for multicriteria (also called multiobjective) decision analysis that can be used to quantify trade-offs and contribute to more consistent, informed, and transparent decision making. These methods are designed to generate and effectively communicate information about trade-offs; to help people form, articulate, and apply value judgments in decision making; and to promote effective negotiation among stakeholders with competing interests. Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods includes explanations of a wide range of methods, tutorial applications that readers can duplicate, a detailed review of energy-environment applications, and three in-depth case studies.

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. 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. conomic and engineering problems that aren’t specifically derived from optimization problems (e.g., spatial price equilibria) d. 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). 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? s 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.

Planning, operating, and policy making in the electric utility and natural gas sectors involves important trade-offs among economic, social, and environmental criteria. These trade-offs figure prominently in ongoing debates about how to meet growing energy demands and how to restructure the world's power industry. Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods reviews practical tools for multicriteria (also called multiobjective) decision analysis that can be used to quantify trade-offs and contribute to more consistent, informed, and transparent decision making. These methods are designed to generate and effectively communicate information about trade-offs; to help people form, articulate, and apply value judgments in decision making; and to promote effective negotiation among stakeholders with competing interests. Energy Decisions and the Environment: A Guide to the Use of Multicriteria Methods includes explanations of a wide range of methods, tutorial applications that readers can duplicate, a detailed review of energy-environment applications, and three in-depth case studies.