Particle swarm optimization based optimal bidding strategy in an open electricity market
In an electricity market generating companies and large consumers need suitable bidding models to maximize their profits. Therefore, each supplier and large consumer will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. In this paper, bidding strategy problem modeled as an optimization problem and solved using Particle Swarm Optimization (PSO). PSO shares many similarities with evolutionary computation techniques such as Genetic Algorithm (GA). The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. A numerical example with six suppliers and two large consumers is used to illustrate the essential features of the proposed method and the results are compared with the Genetic Algorithm (GA) approach. Test results indicate that the proposed algorithm outperforms the Genetic Algorithm approach with respect to total profit and convergence time.