Turkish Journal of Electrical Engineering and Computer Sciences




In a competitive electricity market, generation companies need suitable bidding models to maximize their profit. Therefore, each supplier will bid strategically for choosing the bidding coefficients to counter the competitors' bidding strategies. In this paper, the optimal bidding strategy problem is solved using a novel algorithm based on the shuffled frog leaping algorithm (SFLA). It is a memetic metaheuristic that is designed to seek a global optimal solution by performing a heuristic search. It combines the benefits of the genetic-based memetic algorithm (MA) and the social behavior-based particle swarm optimization (PSO). This allows it to have a more precise search that avoids the premature convergence and selection of the operators. Therefore, the proposed method overcomes the short comings of the selection of operators and premature convergence of the genetic algorithm (GA) and PSO method. The most important merit of the proposed method is its high convergence speed. The proposed method is numerically verified through computer simulations on an IEEE 30-bus system consisting of 6 suppliers and a practical 75-bus Indian system consisting of 15 suppliers. The results show that the SFLA takes less computational time and produces higher profits compared to PSO and the GA.


Electricity market, bidding strategies, market clearing price, genetic algorithm, particle swarm optimization, shuffled frog leaping algorithm

First Page


Last Page