the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The eco-conscious wind turbine: bringing societal value to design
Helena Canet
Adrien Guilloré
Abstract. Wind turbines are designed to minimize the cost of energy, a metric aimed at making wind competitive with other energy-producing technologies. However, now that wind energy is competitive, how can we increase its value for society? And how much would a societal gain cost other stakeholders, such as investors or consumers? This paper tries to answer these questions from the perspective of wind turbine design.
Although wind turbines produce green renewable energy, they also generate various impacts on the environment, as all human endeavours. Among all impacts, the present work adopts the environmental effects produced by a turbine over its entire life cycle, expressed in terms of CO2-equivalent emissions. A new approach to design is proposed, whereby Pareto fronts of solutions are computed to define optimal trade-offs between economic and
environmental goals.
The new proposed methodology is demonstrated on the redesign of a baseline 3 MW wind turbine at two locations in Germany, differing for typical wind speeds. Among other results, it is found that, in these conditions, a 1 % increase in the cost of energy can buy about a 5 % decrease in the environmental impact of the turbine. Additionally, it is also observed that in the specific case of Germany, very low specific-power designs are typically favored, because they produce more energy at low wind speeds, where both the economic and environmental values of wind are higher.
Although limited to the sole optimization of wind-generating assets at two different locations, these results suggest the existence of new opportunities for the future development of wind energy where, by shifting the focus slightly away from a purely cost-driven short-term perspective, longer-term benefits for the environment (and, in turn, for society) may be obtained.
Helena Canet et al.
Status: closed
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RC1: 'Comment on wes-2022-37', Pietro Bortolotti, 22 Jul 2022
This is a great paper that certainly deserves publication in WES. I’d like to congratulate the authors for their hard work and I only have a couple minor comments to further improve their article:
Section 2.1: there are several more LCOE+ metrics in literature than the ones that you report here. I miss why you chose LVOE and NVOE opposed to others, for example PLCOE, which is recommended by Mai et al, 2021.
Sections 3.2 and 3.4 should be expanded. I understand that you are scaling masses and costs solely from rotor diameter and hub height. Your inputs must also include fixed quantities such as rated power and max tip speed (?), which help estimate gearbox and generator torque. A couple extra sentences would help. Also, to show the validity of the assumptions, you should report masses and costs for the baseline WT and show that the absolute values match reasonably well with literature, for example with turbine capital cost numbers provided in https://www.nrel.gov/docs/fy22osti/81209.pdf
Page 12, line 322: “A representative scenario of 50% incineration and 50% landfilling is assumed here, as described in Vestas (2011, 2013a, b).” This is surprising to me, I thought that the vast majority of blades ended up in landfills. I looked at some references, for example https://doi.org/10.1016/j.rser.2021.111847 and https://doi.org/10.1177/1048291116676098, and I struggle to find hard numbers. Probably, percentages change from country to country. This said, the references that you provide also don’t seem very solid. Some extra literature and possibly a couple more sentences are recommended to support your assumption.
Figure 9: why is the y axis so tiny? I cannot interpret this plot: I do not see the drop in price with wind speed and I don’t understand what the red markers represent (is it a box-whisker plot?). The caption doesn’t help me much either.
Citation: https://doi.org/10.5194/wes-2022-37-RC1 -
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2022-37/wes-2022-37-AC1-supplement.pdf
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AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
-
RC2: 'Comment on wes-2022-37', Dominic von Terzi, 24 Jul 2022
The manuscript of Canet et al. on "The eco-conscious wind turbine: bringing societal value to design" is a timely and important contribution that shows a way forward how to quantify and trade the value of wind energy beyond the economical cost of energy. This way it can facilitate discussions beyond speculation and preconceived notions.
There a key insights in the work that may be stressed even more clearly by the authors in the abstract and conclusions than they already do:
- similar as in economic metrics, like LCoE, one needs to look at the difference between value and costs (and not costs alone), here it is that wind energy also displaces CO2 production by an order of magnitude more that it produces.
- "value-based metrics are location- and time-dependent quantities", so here the merit order in the electricity market needs to be accounted for to quantify the CO2 displacement effect.
- There are likely trades possible at little economic costs, or even none, that benefit society at large if quantified and traded in design, e.g. via multi-dsciplinary design analysis and optimization (MDAO).The authors are very much aware of the limitations of their study, but here a few points to consider, although these likely make their conclusions rather stronger:
- In their MDAO, rating of the turbines was kept constant. This is reasonable at first, but when a larger rotor was found to be beneficial for societal impact, some economic penalty (compared to a pure LCoE optimiztion) had to be paid. However, for this larger rotor, a larger rating may then pay off for LCoE.
- Often their optimization led to an optimum design at boundaries, e.g. the lowest specific power allowed. Besides the question what bounds to choose, here, allowing for a variable rating could also help.
- Sensitivity analysis and uncertainty quantification will be important for (future) robust designs using their methodology.
- The authors found the "environmental net value at the two locations (...) very similar". Is this due to being in the same electricity market with the same merit order?Overall, I consider this well-written and well-structured manuscript a significant contribution to the literature and are looking forward to follow up work by the authors, our research community and beyond!
Citation: https://doi.org/10.5194/wes-2022-37-RC2 -
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2022-37/wes-2022-37-AC1-supplement.pdf
-
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
-
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2022-37/wes-2022-37-AC1-supplement.pdf
Status: closed
-
RC1: 'Comment on wes-2022-37', Pietro Bortolotti, 22 Jul 2022
This is a great paper that certainly deserves publication in WES. I’d like to congratulate the authors for their hard work and I only have a couple minor comments to further improve their article:
Section 2.1: there are several more LCOE+ metrics in literature than the ones that you report here. I miss why you chose LVOE and NVOE opposed to others, for example PLCOE, which is recommended by Mai et al, 2021.
Sections 3.2 and 3.4 should be expanded. I understand that you are scaling masses and costs solely from rotor diameter and hub height. Your inputs must also include fixed quantities such as rated power and max tip speed (?), which help estimate gearbox and generator torque. A couple extra sentences would help. Also, to show the validity of the assumptions, you should report masses and costs for the baseline WT and show that the absolute values match reasonably well with literature, for example with turbine capital cost numbers provided in https://www.nrel.gov/docs/fy22osti/81209.pdf
Page 12, line 322: “A representative scenario of 50% incineration and 50% landfilling is assumed here, as described in Vestas (2011, 2013a, b).” This is surprising to me, I thought that the vast majority of blades ended up in landfills. I looked at some references, for example https://doi.org/10.1016/j.rser.2021.111847 and https://doi.org/10.1177/1048291116676098, and I struggle to find hard numbers. Probably, percentages change from country to country. This said, the references that you provide also don’t seem very solid. Some extra literature and possibly a couple more sentences are recommended to support your assumption.
Figure 9: why is the y axis so tiny? I cannot interpret this plot: I do not see the drop in price with wind speed and I don’t understand what the red markers represent (is it a box-whisker plot?). The caption doesn’t help me much either.
Citation: https://doi.org/10.5194/wes-2022-37-RC1 -
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2022-37/wes-2022-37-AC1-supplement.pdf
-
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
-
RC2: 'Comment on wes-2022-37', Dominic von Terzi, 24 Jul 2022
The manuscript of Canet et al. on "The eco-conscious wind turbine: bringing societal value to design" is a timely and important contribution that shows a way forward how to quantify and trade the value of wind energy beyond the economical cost of energy. This way it can facilitate discussions beyond speculation and preconceived notions.
There a key insights in the work that may be stressed even more clearly by the authors in the abstract and conclusions than they already do:
- similar as in economic metrics, like LCoE, one needs to look at the difference between value and costs (and not costs alone), here it is that wind energy also displaces CO2 production by an order of magnitude more that it produces.
- "value-based metrics are location- and time-dependent quantities", so here the merit order in the electricity market needs to be accounted for to quantify the CO2 displacement effect.
- There are likely trades possible at little economic costs, or even none, that benefit society at large if quantified and traded in design, e.g. via multi-dsciplinary design analysis and optimization (MDAO).The authors are very much aware of the limitations of their study, but here a few points to consider, although these likely make their conclusions rather stronger:
- In their MDAO, rating of the turbines was kept constant. This is reasonable at first, but when a larger rotor was found to be beneficial for societal impact, some economic penalty (compared to a pure LCoE optimiztion) had to be paid. However, for this larger rotor, a larger rating may then pay off for LCoE.
- Often their optimization led to an optimum design at boundaries, e.g. the lowest specific power allowed. Besides the question what bounds to choose, here, allowing for a variable rating could also help.
- Sensitivity analysis and uncertainty quantification will be important for (future) robust designs using their methodology.
- The authors found the "environmental net value at the two locations (...) very similar". Is this due to being in the same electricity market with the same merit order?Overall, I consider this well-written and well-structured manuscript a significant contribution to the literature and are looking forward to follow up work by the authors, our research community and beyond!
Citation: https://doi.org/10.5194/wes-2022-37-RC2 -
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2022-37/wes-2022-37-AC1-supplement.pdf
-
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
-
AC1: 'Comment on wes-2022-37', Carlo L. Bottasso, 17 Nov 2022
The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2022-37/wes-2022-37-AC1-supplement.pdf
Helena Canet et al.
Helena Canet et al.
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