Simulating the performance of market-based policies for renewable energy using learning trading agents

Different instruments are available to support electricity generation from renewable energy sources. However, it is still controversial and highly debated which policy leads to preferable results for society. The European Commission has recently stated that a policy relying on a competitive allocation mechanism forcing market players to reveal their real generation costs is preferable. The commission recommends to either allocating feed-in premiums through a tender mechanisms, or to implement a renewable quota obligation scheme. As any other market-based mechanism, both systems are vulnerable to manipulation by market participants. This paper analyzes and compares the performance of the above mentioned mechanisms by simulating the behavior of market participants as adaptive learning agents.