USING SIMULATED ANNEALING ALGORITHM FOR OPTIMAL BIDDING STRATEGY IN ELECTRIC MARKETS

This study represents an optimal strategy for bidding in power markets which includes Environmental Penalties using agent-based modeling. Environmental penalties are considered for fossil-fueled power plants due to CO2, NO2, … emissions. The total emission of these pollutants can be expressed as sum of a quadratic and an exponential function. It has been assumed demand is inelastic, and then the reward of each player (Gen-Co) is optimized. Each player has three decision variables and bids to market through gaming them. The method used for optimize is Simulated Annealing (SA) that applies to optimization the multi variable functions. The results obtained through SA, are compared with results of Particle Swarm Optimization (PSO) Algorithm and represented 14.6% and 34.4% improvement in total reward of the market and emission, respectively.