| 29 Sep 2025
Investigation on the Impacts of Smart Curtailment for Bat Fatality Mitigation in Alberta
Abstract. As wind energy continues to expand in Canada, it is increasingly important to balance power generation with wildlife conservation. For migratory bat species, the risk of interactions with wind turbines varies throughout the year. In response to environmental conditions, curtailing turbine operation during periods of higher risk has been shown to reduce bat fatalities. This study models seasonal turbine curtailment scenarios across wind farms in the Canadian province of Alberta to estimate the resulting energy and economic impacts. High-resolution weather data were used to reconstruct complete wind speed records and simulate turbine output. The modeled power output was closely aligned with real production data reported by the province's energy operator. Results indicate that curtailment outcomes vary significantly depending on wind speed thresholds, seasonal wind conditions, and curtailment duration. Across all scenarios, smart curtailment reduced energy and financial losses by 20–40 % compared to blanket curtailment, highlighting the benefits of using meteorological and behavioral triggers. These findings provide practical insights for minimizing energy loss while supporting conservation goals.
General comments to the manuscript:
abstract and introduction were well and clearly written. Overall, the aim of the study is also well-aligned with the scope of WES. The manuscript present results of applied relevance with the potential to accelerate the field of wind development through adaptive control of wind turbine operation to address sustainability, environmental and socioeconomic considerations.
I did, however, find the methodology and results sections to be somewhat scrambled and less well-structured, as such affecting the presentation of the study negatively with several details lacking (see specific comments) that I would suggest including for improved transparency of the study design and reproducibility.
The authors quantify power production losses based on modelled blanket and smart curtailment schemes (based on temperature and precipitation criteria) across different cut-in speeds relevant for bat activity and find that the proposed smart curtailment scenario reduces power losses and, consequently, costs. They also include the realistic consideration that the costs of developing, implementing and maintaining a smart curtailment system are not factored into the analysis but it would be relevant to relate a prospected cost of such a system to the predicted revenue gained from smart curtailment.
A substantial effort is put into the generation of gap-free meteorological data series based on data from three weather stations in the surrounding area of each wind farm, but some are at considerable distance (up to >50 km away although it is not specified if distance from a given weather station is measured to the center of each wind farm (WF) or the closest edge of the WF and the WF area was not included. I therefore question whether the models would be more accurate and support the aim of the study more clearly if weather parameters had been extracted for the actual wind turbines/WF site coordinates from the global Copernicus ERA5 dataset. There could be perfectly good arguments against it but perhaps it would be an idea to discuss why the chosen approach was used instead.
The manuscript also lacks a comprehensive discussion section (none is currently included) at present it merely includes half a page of concluding remarks with a single reference. Consequently, the outcomes are not linked to previous research and results and does not consider the importance of smart versus blanket curtailment from an ecology perspective, even though it had likely implications for bat management and conservation as well.
Specific comments (and a disclaimer: I am not a statistics expert, which may also be reflected in some of my comments, and would therefore recommend that the manuscript also be reviewed by someone with more insight into the modelling approach used):
Background l. 27, regarding white-nose syndrome: white-nose is not a stressor at global level, consider specifying either which migratory bat populations (e.g., North-American) or (my preference) broaden the sentence overall to 'increasing global risks to migratory bats' but specify 'on a continental scale' for white-nose.
Table 1. temporal window: what is the reasoning behind the two different nightly schedules? Suggest including this in the otherwise thorough description of the scenarios in the paragraphs above the table. Does the shorter temporal window used for peak season not impact the outcome?
Methodology l. 60-61: which criteria are behind 'consistent' and 'high-quality'?
Table 2: suggest including overall area of each wind farm
l. 70, following ‘weather stations’: Consider including reference to table A1 and info about the height of weather station measurements already here. I realize that both are included further below but was missing the information when reading this section.
l. 72, ‘strong alignment’: would be good to show this alignment graphically or back it with results of data analysis. How where they compared?
l. 82: ‘surface roughness length’ was an unfamiliar term to me, recommend to include a short definition
l. 100-101: I understand this argument but for the applied scenario, losses would have to be considered as well. Would suggest, as minimum, to provide a range (min/max) of expected/typical losses and indicate how much variation is expected between wind farms and whether this is expected to influence results, or if not, then why not. This ties to line 104-105 also, if any of the parameters mentioned here are covariates of the losses component, then I am unsure how it can just be treated as a constant. Maybe in other words, as a non-technical outsider, I have no way of evaluating how much this simplification might influence the outcome of the study.
l. 103: timesteps not explained/defined previously, what are the timesteps?
l. 103: ‘a linear interpolation’, should it be the linear interpolation or if this is a reference to a specific type of such then specify.
l. 111, NLCD: if already in reference list, then useful to add abbreviation there to make it easier to find, if not, then consider adding link here.
l. 113: This paragraph and figure 1 should be part of the results section
l. 116: there are quite a few abbreviations in play, so perhaps remind the reader what AESO is (e.g. actual power output data (AESO))
l. 116: initial versus gap-filled: is the only difference that 'initial' has gaps in weather data? Suggest to make this a bit more clear or introduce earlier in methods how these differ. In Figure 1, the legend says modelled and initial but are they not both modelled? Using initial and gap-filled would perhaps be more instructive, provided that 'initial' is explained in the text above.
L. 115, ‘consistently aligned more closely’: I only detect a minute difference for July and August 2022 and for all three months in 2023. Suggest changing Y-axis to show the difference more clearly plus include deviation measures and/or include and refer to actual values and uncertainty estimates in the appendix/supplementary materials.
Figure 1: It seems to me that the results per wind farm would be useful as well, consider adding, if feasible. If not, then an idea for a follow up study, perhaps?
l. 122-124: How relevant are first and second decimals for percentages a) relative to realistic uncertainty and b) to the broad scope in terms of costs? Is it warranted to include decimals and is the difference significant?
l. 138-142: suggest moving this: first sentence to results, the rest to discussion.
1. 138, ‘calibration methods’: where are these described? Use the same term there to make more apparent what was considered calibration methods.
l. 155, ‘power curves…’: the equation does not show the plots, perhaps rephrase to the power curves arising from eq. 7? It might be instructive to show a graphical example of the/a power curve(s).
l. 162, ‘distributional metrics’: please explain
1. 162-164: sentence should be part of results.
l. 167, ‘binned into 4 – 11 m/s intervals’: Unclear to me what these means, what are the intervals?
l. 169-173: recommend to move this to results and please review my suggestion about using Copernicus weather data.
l. 183: perhaps useful to indicate how often/how many hours/how large a fraction this was relevant for to support the statement 'slight underestimate'
l. 187, ‘meteorological sensors’: where there no meteorological sensors on the turbines on-site? Suggest to include in methods why such were not considered for the analysis.
l. 194-215: This all belongs to methods from my perspective and takes away from the aim of the results; to summarize main outcomes. Please review carefully and re-structure methods and results accordingly.
l. 220: suggest to also include before 5.5 m/s: ‘at the lowest cut-in speed of 5.5 m/s…
Figure 4-6: explain in figure text what grey point are (e.g. excluded outliers)
l. 229: ‘broadly consistent’: this statement could be a bit more specific, e.g. modelled production losses are consistently slightly higher than those estimated from back-calculated hub-height wind speeds but smart curtailment consistently leads to lower losses for both datasets.
l. 271: ‘significant’: how was significance tested?
Technical corrections:
l. 83: delete ‘s’ from theses
l. 238, ‘Peak Season’: check for consistency in capitalization of categories throughout section