Bidding Strategies for Generation Companies Using Adaptive Fuzzy Particle Swarm Optimization in a Day Ahead Market

This paper focuses on market systems based on sealed-bid auctions. In this market, participants submit their offers to sell and to buy to the market operator, who determines the Market Clearing Price (MCP). The study presents a methodology based on Fuzzy Adaptive Particle Swarm Optimization (FAPSO) for the preparation of optimal bidding strategies corresponding to unit commitment by Generation companies (Gencos) in order to gain maximum profits in a day-ahead electricity market. Instead of perfect competition, Gencos faces an oligopoly market in a competitive electricity market with few suppliers. Each Genco may boost its own profit in an oligopolistic market by using an advantageous bidding method.

In FAPSO the inertia weight is tuned using fuzzy IF/THEN rules. The fuzzy rule-based systems are natural candidates for design inertia weight because they provide a way to develop decision mechanisms based on the 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 fuzzing the inertia weight improves the search behavior, solution quality and reduced computational time compared to GA and different versions of PSO. As a result, the final solution lands at the global optimum, which avoids premature convergence and permits a faster convergence.