Analyzing interrelated markets in the electricity sector — The case of wholesale power trading in Germany

This paper reports on results from an agent-based simulation model that comprises three interrelated markets in the electricity sector: a day-ahead electricity market, a market for balancing power, and a carbon exchange for CO
₂
emission allowances. Agents seek to optimize trading strategies over the two electricity markets through reinforcement learning; they also integrate market results from emissions trading into their reasoning. Simulation outcomes show that the model is able to closely reproduce observed prices at the German power markets for the analysis period of 2006. The model is thus applicable for analyzing different market designs in order to derive evidence for policy advice; one example for such an analysis is given in this contribution.