Bioenergy is seen as a solution to multiple topical challenges (i.e. an increasing energy demand, scarcity of fossil fuels, climate change). Therefore it is extensively politically fostered in many countries. One effect of this fostering is land use change which can have numerous unintended ecological and social side-effects. Examples include increasing carbon dioxide emissions through the destruction of terrestrial carbon stocks (e.g. when deforestation takes place) or the reduction of food supplies when land users switch to bioenergy production. This doctoral thesis investigates the land use change that is triggered through an increasing bioenergy demand. This is done with the method of agent-based modeling (ABM). In Part I of this study we present a stylized ABM that comprises markets and policy instruments as drivers of land use change. In Part II we apply the model to investigate the impact of an increasing bioenergy demand in the land use system. We evaluate the changing land use patterns from the perspective of climate change mitigation. We are able to characterize technological, ecological and economic framework conditions that will likely lead to unwanted side-effects. In addition, we ask whether regionally optimized policy instruments can enable sustainable bioenergy production. Our model allows the comparison of differently designed instruments. We show that specific designs of policy instruments are effective to mitigate the unwanted side effects on the climate. However, their application leads to the occurrence of a new socio-economic side-effect. Part III of this thesis is dedicated to further methodological development of the ABM method. The potential of ABMs is often hampered by a lack of transparent model descriptions. We address this issue by the provision of a standardized protocol that is adapted for the description of human decisions. It is an extension of the widely-used ODD protocol which adds questions for informations that were found to be missing for the understanding of ABMs in a literature survey. We further present an overview of existent techniques of model descriptions in ABM and evaluate their suitability to address different purposes. Overall, this thesis contributes to an improved understanding of the impact of a bioenergy demand as a new driver of land use change. The results allow to disentangle the effects of economic, ecological and technological framework conditions on the impact of bioenergy and to evaluate policy options to counteract unwanted side effects.