The Influence of Different Degradation Characteristics on the Greenhouse Gas Emissions of Silicon Photovoltaics: A Threefold Analysis
Abstract
:1. Introduction
2. Goal and Scope
- Non-linear performance degradation: In [7], we used PV module and system field data to show that performance degradation does not always follow a linear path. In the same study, it was demonstrated that even at the same failure threshold, the degradation profile taken to reach this threshold influences the lifetime yield of a given PV module or system. Therefore, in this study, we assess the variations in GHG emissions that can be caused by different non-linear degradation profiles at the same defined failure threshold in comparison with a linear degradation profile.
- Climate-specific degradation rate: The climatic conditions of a specific location will influence the stress that is acting on the PV module. Degradation rates of PV modules have been shown to be climate dependent [12,18]. Therefore, the PV service lifetime and the lifetime energy yield vary from location to location. In this study, climate specific degradation rates are evaluated and applied in the calculation of GHG emissions instead of using a constant degradation rate for the different climate locations. Irradiation values for the lifetime yield calculation are adapted according to the location.
- Climate change: Global climate change will cause a change in climate variables (like temperature and solar irradiation), which is described in the form of different climate change scenarios in several studies [19]. Based on our knowledge about the effect of climatic conditions on the degradation mechanism and in turn, GHG emissions, it is to be expected that climate change will have an effect on PV degradation rates, service lifetime, lifetime yield and hence GHG emissions. Therefore, we assess how different climate change scenarios will cause changes in GHG emissions from PV electricity.
3. Methods and Materials
3.1. Lifetime Yield and PV System
3.2. Modelling the Degradation Aspects
3.2.1. Modelling the Impact of Non-Linear PV Performance Degradation
3.2.2. Modelling the Impact of Location Specific Degradation Rate
3.2.3. Estimating the Impact of Climate Change
- SSP1—sustainability: also called the “Green road”, where the world shifts gradually, but pervasively, toward a more sustainable path and emphasizes more inclusive development respective the environmental boundaries.
- SSP5—fossil fueled development: described as “taking the highway” to increase faith in competitive markets, innovation, and participatory societies to produce rapid technological progress and development of human capital as the path to sustainable development. This scenario pushes society to increase the exploitation of abundant fossil fuel resources and the adoption of resource and energy-intensive lifestyles worldwide.
3.3. Climatic and PV Data Sources and Uncertainties
- To demonstrate the non-linearity in PV performance degradation, we used the data and methods of extracting degradation trends of the PV systems in our previous study [7]. The underlying values contain an average relative uncertainty of 7.0%, which is outstanding in comparison with similar studies [25].
- To assess the impact of climate change, the data used is extracted from the ground measurements of the German meteorological service (Deutscher Wetterdienst: DWD) [27]. The data consist of long-time series from the 1940s to the present of global horizontal irradiation (GHI), ambient temperature, wind speed, and relative humidity. The modelled climate change data for scenarios SSP119 and SSP585 have been retrieved through the Earth System Grid Federation (ESGF) Peer-to-Peer (P2P) enterprise system [28]. Further details on the generation of climate change scenarios are given in [29,30]. Data projecting climate change scenarios into the future naturally contain high uncertainty. In this analysis, uncertainty is reduced by comparing two different scenarios (best-case and worst-case). Still, our analysis only contains climate change projections from one source [19]. Future analysis should ideally consider the combination of different datasets generated by different research institutions in the frame of the IPCC.
3.4. Statistical Analysis
- for assessing the effect of non-linear performance degradation, P is the CO emission evaluated using non-linear degradation scenarios and m is the CO emission value evaluated using the linear degradation.
- for assessing the effect of climate change, p is the value of climate variable or CO emission evaluated in 2021 and m is the value of climate variable or CO emission evaluated in 2100.
4. Results
4.1. Assessing the Effect of Non-Linear Performance Degradation on the GHG Emissions of PV Electricity
4.2. Assessing the Effect of Climate Specific Degradation Rates on the GHG Emissions of PV Electricity
4.3. Assessing the Impact of Climate Change on the GHG Emissions of PV Electricity
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A. Maps of Total Degradation Rate and Solar Irradiation
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Parameter | Values |
---|---|
Solar irradiation | 1331 kWh/(m year) (baseline) |
Performance ratio | 0.85 |
Degradation rate | 0.7%/year (baseline) |
Plant size | 3000 Wp |
Module efficiency | 20.11% |
Module area | 1.85 m |
Maximum power | 372.3 Wp |
Power/Area | 201.24 Wp/m |
Scenario | Parameter () | Parameter () | k (%/Year) | Lifetime (Years) |
---|---|---|---|---|
Linear | - | - | 0.70 | 30.0 |
S01 | 1.0 | 0.33 | 0.70 | 30.0 |
S02 | 0.7 | 0.40 | 0.70 | 30.0 |
S03 | 0.3 | 0.93 | 0.70 | 30.0 |
S04 | 0.2 | 1.94 | 0.70 | 30.0 |
Temperature-Precipitation (TP) Zones | Irradiation (H) Zones |
---|---|
A: Tropical climate | K: Very high irradiation |
B: Desert climate | H: High irradiation |
C: Steppe climate | M: Medium irradiation |
E: Temperate climate | L: Low irradiation |
D: Cold climate | |
F: Polar climate |
Increase in | Increase in | Increase in | Increase in | ||
---|---|---|---|---|---|
Station | Scenario | Solar | Module | Degradation | CO |
Irradiation | Temperature | Rate | Emission | ||
Station A | ss119 | 0.63% | 0.11% | 7.96% | 5.32% |
ss585 | 0.21% | 33.65% | 93.71% | 93.84% | |
Station B | ss119 | 2.35% | 3.24% | 8.95% | 7.38% |
ss585 | 1.02% | 58.86% | 109.28% | 105.12% |
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Herceg, S.; Kaaya, I.; Ascencio-Vásquez, J.; Fischer, M.; Weiß, K.-A.; Schebek, L. The Influence of Different Degradation Characteristics on the Greenhouse Gas Emissions of Silicon Photovoltaics: A Threefold Analysis. Sustainability 2022, 14, 5843. https://doi.org/10.3390/su14105843
Herceg S, Kaaya I, Ascencio-Vásquez J, Fischer M, Weiß K-A, Schebek L. The Influence of Different Degradation Characteristics on the Greenhouse Gas Emissions of Silicon Photovoltaics: A Threefold Analysis. Sustainability. 2022; 14(10):5843. https://doi.org/10.3390/su14105843
Chicago/Turabian StyleHerceg, Sina, Ismail Kaaya, Julián Ascencio-Vásquez, Marie Fischer, Karl-Anders Weiß, and Liselotte Schebek. 2022. "The Influence of Different Degradation Characteristics on the Greenhouse Gas Emissions of Silicon Photovoltaics: A Threefold Analysis" Sustainability 14, no. 10: 5843. https://doi.org/10.3390/su14105843