MODELING OF BIDDING PRICES IN POWER MARKETS USING CLUSTERING AND FUZZY ASSOCIATION RULES

In this paper, a strategy for discovering patterns over the continuous domain of bidding prices is proposed. In particular, the proposed method represents bidding functions as points in a multidimensional space where a clustering algorithm is applied. Also, as a result of this method, a dramatic reduction over the search space of bidding strategies is achieved. In addition, some relations of dominance over bidding strategies are found, improving the pattern recognition process of agents' bidding behavior. This method is applied on the bidding prices database for some GENCOs of the Colombian power (electricity) market. Furthermore, an application of some data mining algorithms is presented with the purpose of quantifying some hypothesis formulated on the effect of hydrology over both spot and bidding prices.