Operator’s Bidding Strategies in the Liberalized Italian Power Market

Eric Guerci, Mohammad Ali Rastegar () and Silvano Cincotti ()
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Mohammad Ali Rastegar: University of Genova
Silvano Cincotti: GREQAM

Chapter Chapter 5 in Artificial Economics, 2009, pp 53-66 from Springer

Abstract: 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.

Keywords: Electricity Market; Combine Cycle; Reinforcement Learning Algorithm; Historical Performance; Federal Energy Regulatory Commission (search for similar items in EconPapers)
Date: 2009
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnechp:978-3-642-02956-1_5

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DOI: 10.1007/978-3-642-02956-1_5

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