A NEW APPROACH FOR BIDDING STRATEGY OF GENCOS USING PARTICLE SWARM OPTIMIZATION COMBINED WITH SIMULATED ANNEALING METHOD

This paper describes a procedure that uses particle swarm optimization (PSO) combined with the simulated annealing (SA) to analyze the bidding strategy of Generating Companies (Gencos) in an electricity market where they have incomplete information about their opponents. In the proposed methodology, Gencos prepare their strategic bids according to the Supply Function Equilibrium (SFE) model and they change their bidding strategies until Nash equilibrium points are obtained. Nash equilibrium points constitute a central solution concept in the game theory and are computed with solving a global optimization problem. In this paper a new computational intelligence technique is introduced that can be used to solve the Nash optimization problem. This new procedure, namely PSO-SA is based on the PSO algorithm and SA method. SA method is used to avoid becoming trapped in local minima or maxima and improve the velocity’s function of particles. The performance of the PSO-SA procedure is compared with the results of other computational intelligence techniques such as PSO, Genetic Algorithm (GA), and a mathematical method (GAMS/DICOPT). Keywords– Energy market, deregulation, Nash equilibrium point, optimal bidding strategy, particle swarm, simulated annealing

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