Operator's bidding strategies in the liberalized Italian power market
Abstract
This paper studies the Italian wholesale electricity market by means of a realistic agent-based computational model of the day-ahead market session, of the thermal-power production pool and of the Italian high-voltage transmission network. The aim of the paper is twofold. Firstly, it studies how the strategic behavior of the thermal power plants can influence the level of price at a national level. Secondly, it performs an empirical validation of the computational model over a period of one month which enables to assess the validity of the proposed model. In particular, three scenarios are studied and compared, i.e., the historical performance, a marginal cost based case and a strategic case where generation companies learn according to a reinforcement learning algorithm their best strategy. Results show that the strategic model reproduces real price dynamic during low- and medium- demand periods, whereas during peak-hours the strategic model tends to underestimate historical performances.