Posters

Abstract

Background Protecting bat populations is a critical challenge for the wind energy sector, as environmental curtailment measures implemented to reduce bat mortality often result in significant energy production losses. Curtailment compliance and effectiveness are commonly assessed through external reporting, limiting operators’ ability to independently verify regulatory adherence and operational performance. This study presents an automated, data-driven methodology for monitoring bat curtailment effectiveness through the development of an internally generated Key Performance Indicator (KPI). Objectives The primary objective of this project was to develop a tool capable of: -    verifying compliance with regulatory curtailment criteria, including temperature thresholds, wind speed thresholds, and time windows; -    identifying anomalies such as missing, delayed, or unjustified curtailments in order to reduce avoidable production losses; -    quantifying energy losses by cross-referencing SCADA production data with curtailment triggering conditions. Methodology The KPI was developed using a Python-based framework interfacing with the API of second-layer web scada platform to extract turbine operational data, meteorological parameters, and sunrise/sunset timings. Regulatory thresholds, such as temperatures above 15 °C, wind speeds below 6 m/s, and nighttime operating windows, were explicitly defined and integrated into the analysis. An hourly comparison was performed to assess, for each time step, whether curtailment conditions were met and whether turbines responded appropriately by reducing power output. The results were structured into a compliance matrix distinguishing compliant curtailments from deviations, thereby enabling transparent, reproducible, and objective performance assessment. Case Study The methodology was applied to a wind fam in the north-east of France, comprising seven Senvion MM92 turbines, over the bat protection period from April to October 2024. The analysis revealed an average curtailment compliance rate of 95%, with noticeable variability across individual turbines. Identified discrepancies were primarily attributed to SCADA communication delays, localized meteorological variability, and sensor-related issues. Results and Discussion The results indicate that static curtailment strategies generally achieve high levels of regulatory compliance while effectively contributing to bat protection. However, several turbines exhibited anomaly rates exceeding 10%, often associated with sensor miscalibration or data inconsistencies. These findings highlight the operational value of automated KPIs in identifying underperforming assets and enabling targeted corrective actions, such as sensor recalibration or SCADA system optimization, to reduce unnecessary energy losses and improve curtailment effectiveness. A key limitation of the methodology lies in its reliance on high-quality, high-resolution SCADA data, which is essential to ensure the accuracy and reliability of the KPI. Conclusion The proposed automated KPI provides a robust and reproducible solution for evaluating bat curtailment effectiveness in wind farms, combining regulatory rigor with data-driven operational analysis. It enables operators to independently demonstrate environmental compliance, minimize unjustified production losses, and engage stakeholders using objective evidence. The methodology is scalable and transferable, with strong potential for extension to other curtailment types and for future integration of predictive analytics to further optimize curtailment strategies.