Bidding Strategies for Generation Companies in a Day-ahead Market using Fuzzy Adaptive Particle Swarm Optimization

This paper presents a methodology based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) for the preparation of optimal bidding strategies corresponding unit commitment by Generation companies (Gencos) in order to gain maximum profits in a day-ahead electricity market. In a competitive electricity market with limited number of suppliers, Gencos are facing an oligopoly market rather than a perfect competition. Under oligopoly market environment, each Genco may increase its own profit through a favorable bidding strategy. In FAPSO the inertia weight is tuned using fuzzy IF/THEN rules. The fuzzy rule-based systems are natural candidates to design inertia weight, because they provide a way to develop decision mechanism based on specific nature of search regions, transitions between their boundaries and completely dependent on the problem. The proposed method is tested with a numerical example and results are compared with Genetic Algorithm (GA) and different versions of PSO. The results show that fuzzying the inertia weight improve the search behavior, solution quality and reduced computational time compared to GA and different versions of PSO. Key-Words: Bidding Strategy, Electricity Market, Fuzzy Inference, Market Clearing Price (MCP), Particle Swarm Optimization (PSO).