Posters

Abstract

Wind energy development leads to an increased mortality of birds through collisions with wind turbines, unless effective mitigation measures are put in place. A promising approach to reduce collision risk is predictive curtailment, i.e. wind turbine shutdown when the predicted collision risk is highest. For this approach, it is essential to optimise curtailment schemes so that the turbines’ energy production is reduced as little as possible. For bats, predictive curtailment has proved to effectively reduce collision mortality at low costs. However, for birds, its potential is largely unknown to date. In this case study, we assessed the benefits and costs of predictive curtailment for the Red Kite, a collision-prone raptor species, during the breeding season (March-August) in northeastern France. As predictor variables which could trigger curtailment, we considered day of the year, time of the day and wind speed. We combined the production data of an example wind farm with the probability of Red Kites to fly within the rotor height range, a proxy of collision risk, which we derived from GPS tracking. As a simulation, we applied a range of possible curtailment schemes defined by various sets of curtailment thresholds. We found that collision risk could be reduced by 50% with 5.4%, and by 80% with 19.7% of production loss (2.2% and 8.1% of loss when using the whole year as reference). For 80%, the most efficient curtailment scheme implied curtailment between 1 March and 18 August, between 0.15-0.85 of relative daytime (0 = sunrise, 1 = sunset) and when wind speed was below 8 m/s. This study provides a proof of concept of predictive curtailment for breeding raptors, expanding the practitioners’ toolbox of mitigation measures. This approach could be an alternative to other measures such as automated Shutdown on Demand systems, with minimum investment costs.