Modelling of bidding strategies on the day-ahead electricity market

Electricity generation coming from variable renewable energy sources is and has been increasing significantly. Because of the unpredictability attached to their behavior, fossil fuel power plants and other renewables have to offer a higher degree of flexibility compared to the past. In Germany, one of the countries with a greater involvement in renewable energy generation, the share of renewable energy sources has grown from 14.6% in 2012 to a 29.2% in 2018. Consequently, fossil fuel power plants are forced into more start up and down operations, making cycling costs higher. This thesis, under the project of improving the agent-based simulation of the DLR's Institute of Engineering Thermodynamics of Stuttgart, AMIRIS, contributes to the assessment of cycling costs in the bidding of power point operators by creating new bidding strategies for AMIRIS that cover the aforementioned cycling cost. These strategies are designed with the overall objective of maximizing the power plant operator's profit. The new designed bidding strategies are first implemented in a Python simulation before being introduced in the agent-based environment. Consequently, results of both simulations are presented in this report, always paying close attention to the comparison between bidding with these new strategies and bidding under short term marginal cost. In AMIRIS, when applying these new strategies to a very small part of the power plant park, the day-ahead electricity market clearing is almost identical, and these new bidding strategies yield higher profit to the power plant operator than bidding under short term marginal cost. On the contrary, when applying these new designed bidding strategies to the whole power plant park, the marginal clearing price suffers greatly from non-accounted interaction between the operators bidding and its impact on the clearing of the market