Generation bidding strategy in a pool based electricity market using Shuffled Frog Leaping Algorithm

• A new paradigm based on Shuffled Frog Leaping Algorithm (SFLA) is proposed.
• It combines the advantages of both Memetic Algorithm (MA) and PSO and overcome the drawbacks of PSO.
• A local search algorithm is applied to improve only the frog with the worst fitness (not all frogs) in each cycle. As a result, frogs tend to move towards the best position, which avoids premature convergence and permits a faster convergence.
• The proposed method is numerically verified through computer simulations on IEEE 30-bus system and practical 75-bus Indian System.
• The result shows that the proposed algorithm can generate better quality solution within shorter computational time and stable convergence characteristics compared to FAPSO, PSO and GA.

In an electricity market generation companies need suitable bidding models to maximize their profits. Therefore, each supplier will bid strategically for choosing the bidding coefficients to counter the competitors bidding strategy. In this paper optimal bidding strategy problem is solved using a novel algorithm based on Shuffled Frog Leaping Algorithm (SFLA). It is memetic meta-heuristic 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). Due to this it has better precise search which avoids premature convergence and selection of operators. Therefore, the proposed method overcomes the short comings of selection of operators and premature convergence of Genetic Algorithm (GA) and PSO method. Important merit of the proposed SFALA is that faster convergence. The proposed method is numerically verified through computer simulations on IEEE 30-bus system consist of 6 suppliers and practical 75-bus Indian system consist of 15 suppliers. The result shows that SFLA takes less computational time and producing higher profits compared to Fuzzy Adaptive PSO (FAPSO), PSO and GA.