Abstract
The day-ahead spot market and the markets for control reserve power are among the most important markets for short-term trading of power plant capacities in Germany. Especially the prices for control reserve power have been varying severely in the last years. Considering the applied pay-as-bid auction design and market power of certain market participants, strategic bidding behavior must be assumed. Such strategic behavior cannot be modelled by fundamental simulation models alone. Thus, it is necessary to simulate individual bidding curves and trading decisions of market participants in order to simulate realistic market results and prices. Therefore, the aim of this paper is to model the bidding considerations on the day-ahead and control reserve power markets, to implement them in an agent based simulation model using a reinforcement learning algorithm and to investigate the resulting market prices.
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