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Agent-Based Computational Economics

Part of the Contributions to Management Science book series (MANAGEMENT SC.)

As outlined in Chap. 2 the electricity sector is characterized by technical constraints, multiple interlinked markets, and an oligopolistic structure with vertical integration. These aspects make electricity markets rank among the most complex of all markets, and push most classical modeling methods to their limits (for a discussion of prevalent electricity market modeling methods, the reader is referred to Ventosa et al., 2005). Equilibrium models do not consider strategic bidding behavior or assume that players have all relevant information about the other players' characteristics and behavior; they also disregard the consequences of learning effects from daily repeated interaction (Rothkopf, 1999). Game theoretical analysis yields insights into specific aspects of electricity trading (Wilson, 2002), but is usually limited to stylized trading situations among few actors, and places rigid —oftentimes unrealistic —assumptions on the player' behavior. Human subject experiments are difficult to apply to electricity market research, as some expertise is necessary to realistically imitate the bidding behavior of a power generator. Hence, experiments are deemed appropriate for simple electricity trading scenarios only.

The complexity of the electricity sector and its high importance for a competitive economy calls for new modeling methods that facilitate gaining insights into various aspects of power markets. Agent-based (AB) modeling is one appealing new methodology that has the potential to overcome some of the aforementioned shortcomings of optimization or equilibrium modeling methods.Within the last ten years, more and more researchers have developed electricity market models with adaptive software agents. This field of research is still growing and maturing.

Keywords

Electricity Market Spot Market Bidding Strategy Uniform Price Independent System Operator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2008

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