SBIR-STTR Award

Revenue from wind energy is an important source of stable income for small and mid-size farms as the agricultural sector becomes more volatile. One of the biggest challenges facing wind energy development on small and mid-sized farms are the adverse effects of wind farms on bats. To reduce collision-related mortality of bats curtailment -- stopping wind turbines from spinning during times of high bat activity -- is employed. While beneficial for bats curtailment results in lost renewable energy and lost revenue which reduces the annual lease payment to small and mid-size farms. This project aims to develop next-generation smart curtailment algorithms that save just as many bats as current technologies for less lost renewable energy. Smart curtailment involves fitting an algorithm to environmental data to predict times that are risky for bats (times to curtail) or not (times to not curtail). No current technologies acknowledge the dynamic cost of curtailment across wind speeds. The project team will design and implement a dynamic cost function for decision tree algorithms. The dynamic cost function will allow the algorithm to optimize a smart curtailment regime by explicitly accounting for the cost of curtailment at different wind speeds (the cost of curtailment is proportional to a cubic function of wind speed). The new software will be applied to prototypical data to assess its efficacy. The proposed software solution will create a win-win-win for bat conservation small and mid-size farms that receive a share of wind energy revenues and renewable energy generation in the US.