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«By HOSSEIN HAJIMIRSADEGHI Bachelor of Science in Electrical Engineering University of Tehran Tehran, Iran, 2008 Advisor: Ashkan Rahmi-Kian Associate ...»

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Bachelor of Science in Electrical Engineering

University of Tehran

Tehran, Iran, 2008

Advisor: Ashkan Rahmi-Kian

Associate Professor, Faculty of Electrical and Computer

Engineering, University of Tehran

July, 2008






II.1. Nash Equilibrium

II.2. Coevolutionary Programming

II.2.A. Cooperative Coevolutionary Algorithm

II.2.A.a. Numerical Example

II.2.B. Competitive Coevolutionary Algorithm

II.2.B.a. Numerical Example

II.3. Iterative NE Search

II.3.A. Local Iterative NE Search

II.3.A.a. Numerical Example

II.3.B. Global Iterative NE Search

II.3.B.a. Numerical Example

II.4. Nash Equilibrium as a Minimum of a Function

II.4.A. Discrete Minimization

II.4.A.a. Numerical Example

II.4.B. Continuous Minimization

II.4.B.a. Local Profit Maximization

II.4.B.b. Global Profit Maximization

II.4.B.c. Numerical Example


III.1. Transmission Constrained Electricity Markets with Linear Demand Functions

III.1.A. Two-Bus Transmission Constrained Cournot Model

III.1.B. Three-Bus Transmission Constrained Cournot Model (Two Generator and Three Load)

III.1.C. Three-Bus Transmission Constrained Cournot Model

III.1.D. Four-Bus Transmission Constrained Cournot Model

iii Chapter Page III.2. Unconstrained Electricity Markets with a Total Nonlinear Demand Function

III.2.A. Cournot Model with Two Firms

III.2.B. Cournot Model with Three Firms

III.2.C. Cournot Model with Four Firms

III.2.D. Cournot Model with Six Firms

III.3. Transmission Constrained Electricity Markets with Nonlinear Demand Functions

III.3.A. Two-Bus Cournot Model

III.3.B. Three-Bus Cournot Model

III.4. Some Other Nonlinear Games

III.3.A. Uniform-Price Spot Market

III.3.A. A Simple Electricity Pool

III.3.A. A Dynamic Nonlinear Game




Appendix A. An Introduction to Invasive Weed Optimization

Appendix B. Discrete Invasive Weed Optimization

–  –  –

Finding Nash Equilibrium (NE) for nonlinear games is a challenging work due to existence of local Nash Equilibrium traps. So, devising algorithms that are capable of escaping from trapping in local optima and finding global solutions is needed for analysis of nonlinear games. Evolutionary Algorithms as the popular stochastic global search algorithms can be exploited for this purpose. In this thesis, Nash Equilibrium search approaches for nonlinear games are studied through a number of numerical examples and practical problems.

Coevolutionary programming, evolutionary iterative Nash Equilibrium search, and minimizing objective functions with embedded Nash Equilibria, using evolutionary algorithms, are the main methods discussed in this work. Also, local optimization algorithms are employed in some of the problems for comparison.

For practical simulations we apply the proposed algorithms to several nonlinear games in electricity market models with two to six players. Transmission-constrained electricity markets with linear and nonlinear demand functions and unconstrained electricity markets with a nonlinear total demand are the main case studies in this work. We adopt Invasive Weed Optimization for all the evolutionary computing purposes, and the efficiency of our proposed Coevolutionary Invasive Weed Optimization (CIWO) for finding global NE is shown in the simulations. Likewise, successful results of the proposed Discrete Invasive Weed optimization (DIWO) in NE search for games with discrete strategy spaces are provided.

–  –  –

John Nash’s formulation of noncooperative game theory was one of the great breakthroughs in the history of social science. Nash Equilibrium (NE) is a solution of a game involving two or more players in which no player has incentive to unilaterally change her action, so any change in strategies by any of the players lead that player to earn less.

In November, 1949, the proceedings of National Academy of Science received a short note from Nash which was published the next year [35]. In this assay, Nash gave general definition of equilibrium for normal-form games, and he neatly sketched an argument using the Kakutani fixed-point theorem to prove that equilibria in randomized strategies must exist for any finite normal-form game. Also, in 1951, he presented his outstanding article “Noncooperative Games” [36] in which he argued that his noncooperative equilibrium concept, together with von Neumann’s normal form gives a complete general methodology to analyze all games. Furthermore, he showed the efficiency and importance of his proposed equilibrium in a number of interesting examples, illustrating problems which have concerned game theorists ever since, including a game with one Pareto-inefficient equilibria like Prisoners’ Dilemma.

Historically, the concept of NE was developed before Nash in literature by a number of famous scientists. Antoine Augostin Cournot in his brilliant book [37], constructed a theory of oligopolistic firms that includes monopolists and perfect competitors as limiting extremes (1838). In fact, we may speak of Cournot as the founder of oligopoly theory [38].

Another prominent work was done by John von Neumann and Oskar Morgenstern who introduce the concept of mixed strategy NE for special case of zero-sum games [39].

Also, Bertrand (1883) to Felner (1949) found specific models of oligopoly which had some applied predictions [40], [41].

Since its development, NE plays an important role in game theory and has been used for modeling problems in a variety of areas like economics, biology, engineering, political science, computer science, philosophy, etc.

In this thesis we aim to find Nash Equilibrium for nonlinear games. Although rigorous mathematical frameworks have been devised to approach games with linear manipulation, however, considerable amount of attention have been dedicated in recent years for NE search in the case of nonlinear games. In this respect, the terms of evolutionary game theory [34] and Natural selection in biology and life science have been employed by economics and engineers to have more insights to the concept of Nash Equilibrium. In fact, evolutionary game theory in a computational scheme is the application of natured-inspired models of change in generations of populations to game theory.

In this study, Evolutionary Algorithms as the popular means of global search are exploited to find NE for nonlinear noncooperative games. We try to put all the common methods of NE search in evolutionary frameworks and analyze their performance through a large number of numerical simulations for finding global NE. The oligopolistic games in electricity markets are the practical problems which are studied in this work.

The remainder of thesis is arranged as follows. Chapter II explains methods and approaches for NE search in games with two or more players. A numerical example is solved with each proposed method to identify the efficiency, advantages and drawbacks of the algorithms and also to have a comparison between evolutionary and nonevolutionary frameworks.

Chapter III provides a large number of practical simulations for different models of games in electricity markets. Moreover, performance of the proposed methods in chapter II for solving complex games with fairly large number of players is investigated.

Finally, chapter IV delivers the decisive message of my work and clears the perspective for future works.

–  –  –

Many techniques have been developed for searching Nash Equilibrium (NE) in game theory problems. All the approaches are inspired by NE definition which is maximizing the payoff, given other players’ strategies. The simplest method which can be applied to two or three player games, is finding the intersection of best response curves (reaction curves) by drawing or Algebra. For graphical approach, some geometric techniques have been also proposed to come up with more than two player problems [17]. Algebra can improve the method to solve games with several players, but it can be applied to problems with simple mathematical manipulations. This algorithm is commonly used in Cournot or Bertrand models of electricity markets with linear demand functions, using the first-order condition for maximizing each player’s payoff [1], [18], [19].

Iterative NE search in which players repeatedly maximize their payoff by turn is another method that is applied to more complex problems. The profit maximization problem which is embedded in this method can be solved by local or global optimization algorithms. In literature, local search is more popular and have been employed in [3], [28] and [26], however in [9], a GA-based algorithm is also presented for profit maximization.

In recent years, with development of Soft Computing [23], and increasing growth of Biomimicry [24], and Bioinspired Computing in a variety of applications, there has been a considerable attention to evolutionary game theory and computational intelligence for game learning and simulation of electricity markets [3], [7]-[9], [13], [14], [22], [32], [33]. Coevolutionary programming is the most popular technique for this purpose. In [3], a novel Hybrid Coevolutionary is applied to solve constrained-transmission electricity markets, and in [8], a GA-based coevolutionary algorithm is exploited to simulate a simple electricity pool. Besides coevolutionary algorithms, learning methods in agentbased approach have also been used to study imperfect competition in electricity markets [5], [20], [21]. In fact, these days, agent-based economics is a rigorous opponent of game theory to simulate electricity markets.

Another approach for searching NE is characterization of NEs in terms of minima of a function and then minimizing this objective function. This method was firstly employed in finding mixed strategy NEs [13], [14], but recently a similar technique was introduced in [7] to identify pure NE in games with a large number of players. It seems that more in

investigations are needed to understand the efficiency of this model.

Section 1, provides a quick review for the concept of Nash Equilibrium. In section 2, Coevolutionary Programming to find NE is explained, while Iterative NE Search algorithms are described in section 3. Finally, section 4 summarizes methods of modeling Nash Equilibrium as a minimum of a function. Note that for each section a numerical example is simulated and the results are interpreted.

A general multi-player game consists of an index set = {1, 2, 3, …, N} called player’s

1. Nash Equilibrium set and an index set = {1, 2, 3, …, K} as the stages of the game, showing the allowable strategy spaces = { }, and receive a payoff of (, ), where ∈ number of moves for each player. In each stage, players take strategies from a set of

–  –  –

{ ∗, } is characterized in (2.1).

. Pure strategy Nash Equilibrium (NE) is a point where no player can obtain a higher ∗ profit by unilateral movement. The satisfying NE condition for the combined strategy ∀,∀ ∈,, ≥ (, ) ∗ ∗ ∗ (2.1) As we will use the term local NE in this dissertation, here a definition of that from [3] is

–  –  –

, ∗ ∗ ∗ ∗ (2.2)

–  –  –

2. Coevolutionary Programming In [10], coevlolutioanry algorithm (CEA) is defined as “an evolutionary algorithm that employs a subjective internal measure for fitness assessment.” The term subjective internal measure means that fitness for the individuals are measured based on their interaction with each other and this fitness value influences their evolution in some way.

This is a general definition for coevolutionary algorithm which most the coevolutionary computation researchers agree, however there are controversy on some topics like what precisely is the nature of interaction? Should the interacting individual be in different populations? Do they have to treat concurrently? [10] The answer to these questions is beyond the scope of this survey, but in this thesis, we focus on multi-population models in which the fitness for individuals is measured by their interaction with individuals in other populations. In the following two parts we define Cooperative and Competitive Coevolutionary Algorithms and present coevolutionary frameworks to find Nash Equilibrium for game theory problems.

A. Cooperative Coevolutionary Algorithm In Cooperative CEA, each population represents a piece of a larger problem and the populations evolve their own pieces in interaction with each other to solve the larger problem. A general cooperative coevolutionary framework for is explained in Algorithm 1.

–  –  –

1. For population, all populations

1.1. Initialize population

2. For population, all populations

2.1. Evaluate population with collaborators

3. t:=0

4. do

4.1. For population, all populations 4.1.1. Evolutionary Process to make the next generation 4.1.2. Evaluate next generation with collaborators

4.2. t:= t+1

5. Repeat 4 until terminating criteria is met For evaluating part, each individual is combined with its collaborators from other populations to form a complete solution and the objective function is evaluated.

Terminating criteria can be satisfied by falling short of the acceptable tolerance for changes in strategies or exceeding the maximum number of iterations. In evolutionary process, any evolutionary algorithm (EA) can be exploited, like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Mimetic Algorithm (MA), Simulated Annealing (SA). We employ Invasive Weed Optimization (IWO), a novel EA proposed by Mehrabian and Lucas [2], for all the evolutionary computation purposes throughout this dissertation (See Appendix A).

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