Adopting appropriate bidding strategies under uncertain conditions and security constraints in deregulated power markets

In deregulated power markets, individuals endeavor to increase their revenues. The issue which has the most considerable importance in profit maximization is bidding strategy. However, market elements encounter difficulties when they engage in decision making process and the most challenging one is imperfect knowledge of their rival´s behavior. Therefore, the participants should find ways to model the ubiquitous uncertainty in the market. Individuals can exploit statistical properties of their rival´s behavior parameters from market historical data and use them for making economical decisions. In this research, we try to maximize profit from the point of view of a producer. To accomplish this, we model the rival´s bids as Gaussian joint stochastic variables and employ Monte Carlo approach to find the optimal bid for a specific participant. In addition, we use particle swarm optimization as a strong optimization tool in finding optimal bidding strategies in each iteration of Monte Carlo process, and consider security conditions in market clearing mechanism. At last, we assess the proposed method via a numerical example and discuss the obtained results.