Ebook: Complementarity Modeling in Energy Markets

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.

---

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.

---

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.

Content:
Front Matter....Pages i-xxvi
Introduction and Motivation....Pages 1-29
Optimality and Complementarity....Pages 31-69
Some Microeconomic Principles....Pages 71-125
Equilibria and Complementarity Problems....Pages 127-179
Variational Inequality Problems....Pages 181-220
Optimization Problems Constrained by Complementarity and Other Optimization Problems....Pages 221-262
Equilibrium Problems with Equilibrium Constraints....Pages 263-321
Algorithms for LCPs, NCPs and VIs....Pages 323-384
Some Advanced Algorithms for VI Decomposition, MPCCs and EPECs....Pages 385-432
Natural Gas Market Modeling....Pages 433-476
Electricity and Environmental Markets....Pages 477-513
Multicommodity Equilibrium Models: Accounting for Demand-Side Linkages....Pages 515-560
Back Matter....Pages 561-629

---

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.

Content:
Front Matter....Pages i-xxvi
Introduction and Motivation....Pages 1-29
Optimality and Complementarity....Pages 31-69
Some Microeconomic Principles....Pages 71-125
Equilibria and Complementarity Problems....Pages 127-179
Variational Inequality Problems....Pages 181-220
Optimization Problems Constrained by Complementarity and Other Optimization Problems....Pages 221-262
Equilibrium Problems with Equilibrium Constraints....Pages 263-321
Algorithms for LCPs, NCPs and VIs....Pages 323-384
Some Advanced Algorithms for VI Decomposition, MPCCs and EPECs....Pages 385-432
Natural Gas Market Modeling....Pages 433-476
Electricity and Environmental Markets....Pages 477-513
Multicommodity Equilibrium Models: Accounting for Demand-Side Linkages....Pages 515-560
Back Matter....Pages 561-629
....