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Game Theory Project Phase 1

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GAME THEORY Project Phase I

1. Introduction

Decision concerning electrical transmission network expansion has become harder in the last two decades. As a matter of fact, the power sector has been decentralized and the electricity market is now a competitive market. Thus a transmission expansion plan cannot follow the traditional planning criteria anymore. As a result, new techniques are developed in order to solve the transmission expansion problem TEP.

Usually different optimization methods are used to solve the TEP: genetic algorithm (GA), Tabu search (TS) and several heuristic and metaheuristic techniques. Those are called static scenarios. Dynamic scenarios are the one based on game theory which represents the behavior …show more content…

The model is applied to the Garver six-bus system for testing.

A form of non-cooperative games is defined in [5]. The concept of reference transmission network (RTN) is introduced. The RTN is considered to be the sub game perfect Nash equilibrium of the dynamic game. The game agents in this paper are the planner and all the power producers. Transmission expansion plans are proposed by the investors and the planner chooses one possible feasible plan. For each plan a multi-stage game is performed and every stage is formed by a number of spot market sessions. At every spot market an auction is organized and both power producers and consumers participate and bid. The Nash equilibrium at each stage is computed using diagonalization algorithm. As an illustrative example, A 3-bus system is used.

In [6] a transmission expansion search methodology is proposed using multiple optimization techniques. Once the optimal TEP is obtained, the costs of the included projects are evaluated considering the bargaining solution of the right-of-ways between investor and land owner, the asset cost and the level of effort of the corresponding agent (investor). A principle-agent game where the principle is the central planner and the agent is the investor is used to model the expected utility of the game participants. Finally the bidding competition occur in order to win the project contract. The optimal bid is concluded depending on the

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