  Auctions have become the primary instrument for promoting renewable energy
around the world. However, the data published on such auctions are typically
limited to aggregated information (e.g., total awarded capacity, average
payments). These data constraints hinder the evaluation of realisation rates
and other relevant auction dynamics. In this study, we present an algorithm to
overcome these data limitations in German renewable energy auction programme by
combining publicly available information from four different databases. We
apply it to the German solar auction programme and evaluate auctions using
quantitative methods. We calculate realisation rates and - using correlation
and regression analysis - explore the impact of PV module prices, competition,
and project and developer characteristics on project realisation and bid
values. Our results confirm that the German auctions were effective. We also
found that project realisation took, on average, 1.5 years (with 28% of
projects finished late and incurring a financial penalty), nearly half of
projects changed location before completion (again, incurring a financial
penalty) and small and inexperienced developers could successfully participate
in auctions.
