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Capacity witholding and strategic firm behavior : a agent based simulation aproach to the study of market power in a uniform price auction

Sand, Ole Christian
Master thesis
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http://hdl.handle.net/11250/196750
Utgivelsesdato
2013
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  • Master Thesis [2971]
Sammendrag
The relationship between market structure and market power is a focal point of debate in the study of liberalized electricity markets. The overall aim of this research is to assess the presence of market power and if possible, determine the implications for regulators and policy makers tasked with creating competitive liberalized markets. Previous studies utilizing agent-based simulation modeling have used strategic price bids to examine whether or not imperfect competition may exist wherein a firm may exhibit market power by offering a price in excess of their marginal cost (Bunn, et al., 2010). This thesis adds the strategic withdrawal of capacity to the model. That is, generators may exert market power in one of two ways. First, an agent can increase its offer price above the cost of production. The novel second option allows agents to selectively withhold capacity from the market.

The model uses 2011 market data from the United Kingdom and Wales, a framework which provides an interesting and pragmatic picture of market liberalization. Pragmatic insofar as competition is dominated by six major players along with a competitive fringe. With six major players, the market is on the verge of what may be considered a Cournot environment. Empirically speaking, however, since 1990's the market has appeared to become more competitive and there is little evidence of sustained market manipulation (Wolak & Patrick, 2001). The simulation model is a tool used to evaluate the effect of strategic firm behavior on a uniform price auction. The application does not attempt to predict or recreate price developments; nor does it represent market equilibrium. All of this is done within the confines of actual market participants and realistic measures of generation capacities coupled with historical demand data.

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