Ocean thermal energy conversion (OTEC) is a form of renewable energy that could potentially displace a significant amount of fossil-fuel generated electricity. Many multi-century simulations of the UVic Earth Systems Climate Model (UVic ESCM) are presented to better understand the climate change mitigation potential and the projected magnitude and significance of the impacts of widespread OTEC implementation at varying total power outputs (3, 5, 7, 10, and 15 TW). This study builds on previous research with the inclusion of a fully coupled atmospheric model, sea ice model, and comprehensive carbon cycle model. In high emission scenarios (Representative Concentration Pathway 8.5), OTEC was found to be able to briefly produce over 36 TW of power and power production rates of 6 TW and below were found to be sustainable on multi-millennial timescales. The study also included an emission reduction associated with OTEC that resulted in cumulative emission reductions of 1190-3600 Pg C by 2500 relative to a control scenario without OTEC deployment. Environmental impacts include globally averaged sea surface temperature decreases of 0.8-3ºC relative to control values, increased heat uptake at intermediate depths, and enhanced biological production. The implementation of OTEC was found to induce overturning cells in the North Pacific and cause significant relative increases in strength of the maximum Meridional Overturning Circulation globally with values ranging from 1.6 to 8.2 Sv by 2500, depending on the level of OTEC power generation. While caution is required and the engineering challenges would be large, early indications suggest that the large-scale implementation of OTEC could make a substantial contribution to climate change mitigation.

How to cite: Nickoloff, A., Olim, S., Eby, M., and Weaver, A.: Potential Climate Change Mitigation and Environmental Impacts from the Widespread Implementation of Ocean Thermal Energy Conversion , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-4156, https://doi.org/10.5194/egusphere-egu24-4156, 2024.

High-Variable Renewable Energies (VREs) power systems are becoming a cornerstone of climate change mitigation policies across Europe. At the same time, committed and potential climate change will impact energy systems as a whole, both from the supply and demand side. Current power systems are expected to change drastically in the future, in particular in terms of VRE penetration. Finding the best mix for a given country is a complex problem that depends on multiple social, economical and political criteria. Instead of prescribing future capacities, economically optimal VRE mixes that ensure system adequacy can be used. We focus in this study on the impact of climate change on these optimal VRE mixes, as well as on the associated system costs. The study is narrowed to the case of France, which is a highly temperature sensitive country with high VRE potential resources. An ensemble of six model pairs from the EURO-CORDEX (CMIP5) project is used to obtain the meteorological variables of interest under different levels of climate change. The open source software e4clim is used to determine the economically optimal mixes. Socioeconomic scenarios of electrification are derived to study the effect of an increased base load and temperature sensitivity. We find that for the case of France increasing climate change tends to decrease demand. The PV resource is not affected significantly whereas the wind resource decreases over the whole country and up to 10 % in some regions. We show that these impacts lead to changing optimal VRE mixes. Although the installed photovoltaic (PV) capacity is not affected by climate change, except for its geographic distribution under some socioeconomic scenarios, so does the installed wind capacity. No matter the socioeconomic scenario, installed wind capacity is found to be the adjustment variable when demand decreases due to climate change: even though the wind capacity factor decreases, less capacity needs to be installed. In parallel, increasing levels of climate change lead to decreased system total costs: less VRE generation capacity is installed and generation costs for the dispatchable producers are decreased. These cost differences are up to 10 % and amount up to 6 G€ depending on the socioeconomic scenario considered. We finally find that the system marginal cost is not significantly affected by climate change. Underestimating future climate change in planification could thus lead to stranded wind farm assets up to 10 % of the installed fleet, corresponding to up to a 2 G€ loss. If stranded assets are avoided by anticipating the right climate change scenario, then adverse impacts of climate change are found to be minimal, since the cost for dispatchable producers tends to decrease and the system marginal cost is not affected. If only economically optimal VRE mixes were considered here, those can then be put under suboptimal climatic and socioeconomic conditions, paving the way to take into account the uncertainty related to climate change and socioeconomic development when dealing with VRE mix planification issues.

How to cite: Delort Ylla, J., Tantet, A., and Drobinski, P.: Impact of climate change on high-VRE optimal mixes and system costs: the case of France., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18667, https://doi.org/10.5194/egusphere-egu24-18667, 2024.

As renewable energy capacities continue to grow (rapidly), the European electricity system will become vulnerable to extreme events in the form of energy droughts—periods of low production coinciding with high demand. In this work, we use a large model ensemble of 1600 years of daily climate data in conjunction with an energy production and demand modelling framework and consider present-day installed capacities to compute the full distribution of renewable electricity production and demand in the present-day climate. This approach enables us to examine in detail the specific events at the tail of the distribution that pose the highest risks to energy security.

In particular, this study focuses on energy droughts occurring once every ten years in six European countries: Sweden, Norway, Italy, Spain, France, and Switzerland, chosen because of their specific renewable energy mix including hydropower. We analyze energy drought events and their corresponding meteorological conditions and find that energy droughts result from processes that cause (temporally) compounding impacts in the energy and meteorological system. These processes can turn what might have been short-term droughts into prolonged, cumulative energy crises. For instance, low reservoir inflows in spring quadruple the chance of prolonged energy droughts: reduced snowpack and rainfall lower hydro-availability but also dry-out subsoils, increasing the chance of heatwaves and thereby extending the energy problems into summer.

We identify and evaluate three compounding energy/climate conditions and quantify the associated risks. These results can inform the energy modelling community where high-risk meteorological conditions can be applied in power system models to optimize and analyze the robustness of future energy system designs, and provide insights on the specific characteristics of the risks of multiyear energy droughts to policymakers and energy companies.

How to cite: van der Most, L., van der Wiel, K., Gerbens-Leenes, W., Benders, R., and Bintanja, R.: Temporally compounding energy droughts in European electricity systems with hydropower, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15062, https://doi.org/10.5194/egusphere-egu24-15062, 2024.

The focus on global warming and climate change has prompted a substantial shift towards green energy technologies, which are crucial in shaping electricity generation capacity. Türkiye has actively been investing in renewable energy sources, such as wind, solar and geothermal, to reduce its dependency on imported fossil fuels and improve its energy security. In this study, we aimed to investigate the future of the electricity production in Türkiye under a changing climate using climate model projections and a machine learning algorithm. Thus, we first identified the most suitable Global Climate Models (GCMs) in simulating Türkiye's climate conditions, and then we evaluated how climate change, considering changing wind speeds, solar radiation, and temperature, will impact future electricity production in renewable energy output. We acquired historical data from 13 CMIP6 Global Climate Models, focusing on temperature, wind speed, and solar radiation parameters. Model resolution was standardized, and daily data for 120 grids in Türkiye were collected for 2010-2014. The performance of GCMs was assessed against ERA5/CRU-biased corrected datasets using metrics such as Kling-Gupta efficiency (KGE), modified index of agreement (md), and normalized root mean square error (nRMSE). A Multiple-criteria Decision Analysis (MCDA) method ranked the models based on performance, and Comprehensive rating metrics (MR) provided a unified score. Based on the result of MR, the top-performing models (ACCESS-CM2, INM-CM5–0, INM-CM4–8, and ACCESS-ESM-1-5) were ensembled, and then utilized to predict Türkiye's future climate using the Extreme Gradient Boosting Tree (XGBoost) algorithm. Projections were made for 2020-2064 under the SSP5-8.5 scenario. According to the results of the XGBoost forecast, solar power plant output is predicted to decrease across the country due to rising temperatures, with the largest drops in the Mediterranean (7.7-5.2%) and Eastern Black Sea (7.7-6.0%) regions. The Eastern Black Sea region, with low current solar potential, is deemed unsuitable for photovoltaic solar power plants in the future. Minimal decreases are anticipated in the Marmara (2.8-2.0%) and Southeastern Anatolia (2.8-4.4%) regions. Wind turbine electricity production is expected to increase, notably in Thrace (3.5-8.5%), northern Central Anatolia (3.5-8.5%), southern Southeastern Region (3.5-11.1%), and around Ağrı and Van provinces in Eastern Anatolia (3.5-6.0%). Conversely, the Eastern Black Sea, Uşak-Kütahya-Eskişehir-Bolu provinces in northwestern Anatolia (3.0-1.0%), and Mardin-Batman-Şırnak provinces in southeastern Anatolia (5.8-1.0%) may experience a decline in wind production potential. Overall, the study's findings align with existing literature, providing valuable insights into Turkey's future electricity production landscape under the influence of climate change and the transition to green energy technologies.

How to cite: Guven, D., Sen, O. L., and Kayalica, M. O.: Türkiye's Renewable Energy Outlook: GCM-Based Analysis and Future Projections Using the Extreme Gradient Boosting Algorithm, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1511, https://doi.org/10.5194/egusphere-egu24-1511, 2024.

In the context of climate science and climate change, extreme events take a prominent place because of their potentially devastating impacts on various aspects of society, from economic losses to premature deaths. Much effort has gone, in particular, into the study of heat waves and droughts. Extreme events in surface solar downwelling radiation (SSR) have, by contrast, gained little interest so far. This neglect is at odds with the prominent role that photo-voltaic (PV) energy production, which feeds on SSR, is to play in the future.

Based on daily-mean data from nine global climate models participating in the pre-industrial control experiment (piControl) of the Coupled Model Intercomparison Project-Phase 6 (CMIP6), we provide a descriptive analysis of extreme events in surface solar radiation (SSR) arising from internal variability of the climate system, with a geographical focus on central Europe, where we also anchor our analysis in 38 years of observed daily mean SSR data. Two kinds of extreme events are investigated: sustained radiation events (SREs, periods of L consecutive days with extremely high or low SSR on each single day) and cumulative radiation events (CREs, yearly minimum mean SSR over a period of L days). To explore the role of extreme SSR events in PV energy generation, we use the Global Solar Energy Estimator (GSEE, https://github.com/renewables-ninja/gsee).

Selected findings from our analysis include the following. In central Europe, the frequency of SREs shows an exponential dependence on L, their duration in days. High SREs are more frequent than low SREs over global land. CREs in central Europe are well described by Generalized Extreme Value statistics with a negative shape parameter, similar to wind and temperature extremes. PV production associated with low SREs in central Europe is roughly linear in SSR with little sensitivity to panel orientation, while for high SREs PV production depends non-linearly on SSR and sensitivity to panel orientation is pronounced. PV production of high SRE events in winter greatly exceeds PV production of low SRE events in summer. Our results are a first step in examining the characteristics and relevance of SSR extreme events, highlighting the need for further studies.

How to cite: Folini, D., Senger, G., Chtirkova, B., Wohland, J., and Wild, M.: Extreme surface solar radiation events and implications for PV energy generation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3907, https://doi.org/10.5194/egusphere-egu24-3907, 2024.

Extreme power shortage events, especially occurred in wind-solar hybrid supply systems, are longstanding serious threats to safeguard energy security and socioeconomic stabilization. Here, 43 years of hourly reanalysis climatological data are leveraged to examine historical trends in defined extreme long-duration and low-reliability events in wind-solar systems worldwide. We find interannual and decadal uptrends in the two types of defined extreme power shortage events regardless of their frequency, duration, and intensity since 1980. For instance, duration of extreme low-reliability events worldwide has increased by 5.39 hours (0.113 hours y⁻¹ on average) between 1980–2000 and 2001–2022. However, such ascending trends are unevenly distributed worldwide, with a higher variability in low- and middle-latitude developing countries but a smaller change in high-latitude developed countries. This observed uptrends in extreme power shortage events are primarily driven by increases in extremely low wind speeds instead of solar radiation. However, the changes in power shortage events and extremely low wind speeds are strongly disproportionated. Only 8.80% change in extremely low wind speed gives rise to over 30% variability in extreme power shortage events, despite a mere 1.26% change in average wind speed. Our findings underline that wind-solar hybrid supply systems will probably suffer from weakened power security if such upwards trends persist in a warmer coming future.

How to cite: Zheng, D., Tong, D., Davis, S. J., Qin, Y., Liu, Y., Xu, R., Yang, J., Yan, X., and Zhang, Q.: Increases in extreme power shortage events of wind-solar supply systems worldwide, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1227, https://doi.org/10.5194/egusphere-egu24-1227, 2024.

The adequacy of electricity systems is strongly linked to the level of Variable Renewable Energies (VREs) penetration. To ensure supply-demand balance and in the absence of other sources of flexibility, Dispatchable Units (DUs) must be operated in a more flexible manner due to the variability of REs. We expect the DUs schedule to be strongly affected by the flexibility needed from base DUs in response to increasing VREs and taking flexibility into account may lead to using some peak DUs that would be unused in a standard merit order dispatch.

We develop and apply to France a methodology to assess the system cost response to the flexibility costs change due to VREs integration at the regional scale and the impact of the latter on DUs depending on their merit order position. 

Changes in the system cost due to flexibility are diagnosed from a residual demand for regional VRE mixes at different penetration levels optimized by the e4clim model. e4clim is a minimal optimal VRE investment model based on the minimization of a system cost assuming that dispatchable costs are a function of the aggregated dispatchable production only. Considering that the standard merit order holds and for prescribed marginal costs of production, the DUs are ranked by loadpoint and defined by their marginal and rental costs. Moreover, at time scales greater than 1 hour, there are few hard flexibility constraints. It is therefore assumed that flexibility can be modeled as costs, for instance because of the extra fatigue and human resources induced by more flexible operation of DUs. Among the different forms of flexibility, we focus on ramps and start-ups. Each producer is assigned a marginal ramp (resp.~ start-up) cost proportional to its fixed cost by a coefficient K_R (resp.~K_SU) determined using real data.

The variable costs of flexibility are obtained by multiplying these marginal costs by the ramps and start-ups diagnosed from e4clim.

For the reference value of K_SU and 50% penetration of VREs, we find that the variable cost of start-ups contributes to 7% of the system cost and that is 3.6 times larger than the ramps contribution. Secondly, the base DUs have flexibility costs higher than the maximum flexibility cost without VRE. The middle producers see theirs decrease and they completely cancel out for the last producers since they are no longer used. Finally, for large VRE penetration (≥20%), we find that PV induces twice the flexibility need induced by wind and mostly affects base DUs while wind impacts all DUs more homogeneously. 

Although flexibility costs are lower than production costs, considering them in the optimization of DUs could reduce the system cost and result in a dispatch different from the standard merit order. Furthermore, flexibility costs could be significantly reduced by considering them in the optimization of the technological and geographical distribution of VREs. Finally, the sensitivity of our results to the estimates of the coefficients K_SU and K_R calls for more empirical studies of the marginal costs of flexibility. 

How to cite: Dansokho, S., Tantet, A., Drobinski, P., and Creti, A.: Impact of flexibility costs on electricity systems depending on regional wind and PV capacities with an application to France., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9038, https://doi.org/10.5194/egusphere-egu24-9038, 2024.

It is widely acknowledged that relying on a single energy source is not viable and a mix of energy sources and carriers is required to achieve carbon neutrality [1]. Hydrogen has come to the forefront of discussion, particularly due to its potential for long-term storage.

Computational models based on mathematical optimization have been widely used in the literature, to better understand the role of hydrogen in energy systems with high shares of variable renewable energy sources (VRES). These models optimize dispatch and investment decisions for multiple energy sources and carriers over several decades. However, to maintain a holistic view of the system with reasonable complexity, it is common to decrease the temporal resolution.

Clustering hourly VRES time-series to a reduced set of representative periods is a particularly popular method in the literature. However, there is an inherent trade-off between short- and long-term dynamics: For example, clustering days enables a more accurate representation of diurnal features compared to clustering hours, although at the expense of considering seasonal trends. In the optimization problem, these features can have a direct impact on investments in short- or long-term storage, and, ultimately in VRES. Moreover, the clustered data suffers from a loss of chronology which is important for modeling long-term (hydrogen) storage and providing accurate operational and investment signals.

To address the research gap raised by [2], the approach of this modeling exercise is to analyze the performance of clustering hours (1) versus clustering days or weeks (2) in the context of storage and VRES investments. The analysis is based on different scenarios for VRES optimized in the energy system model GENeSYS-MOD co-developed by TU Berlin. To improve the shortcomings of (1) and (2), chronological clustering [3] and additional storage constraints [4] are tested and evaluated. Ultimately, the goal is not to derive normative conclusions for time-series aggregation methods and (hydrogen) storage modeling, but instead highlight different configurations and their performance under various settings.

[1]        L. Fan, Z. Tu, and S. H. Chan, ‘Recent development of hydrogen and fuel cell technologies: A review’, Energy Reports, vol. 7, pp. 8421–8446, Nov. 2021, doi: 10.1016/j.egyr.2021.08.003.

[2]        L. E. Kuepper, H. Teichgraeber, N. Baumgärtner, A. Bardow, and A. R. Brandt, ‘Wind data introduce error in time-series reduction for capacity expansion modelling’, Energy, vol. 256, p. 124467, Oct. 2022, doi: 10.1016/j.energy.2022.124467.

[3]        S. Pineda and J. M. Morales, ‘Chronological Time-Period Clustering for Optimal Capacity Expansion Planning With Storage’, IEEE Trans. Power Syst., vol. 33, no. 6, pp. 7162–7170, Nov. 2018, doi: 10.1109/TPWRS.2018.2842093.

[4]        L. Kotzur, P. Markewitz, M. Robinius, and D. Stolten, ‘Time series aggregation for energy system design: Modeling seasonal storage’, Applied Energy, vol. 213, pp. 123–135, Mar. 2018, doi: 10.1016/j.apenergy.2018.01.023.

How to cite: Reulein, D. and Pinel, D.: Time-Series Aggregation in Energy System Models: Navigating the trade-offs between short-term and long-term dynamics, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12640, https://doi.org/10.5194/egusphere-egu24-12640, 2024.

The energy transition necessitates the massive deployment of large-scale wind turbines and solar photovoltaics (PV). However, numerous countries, including Germany, have experienced setbacks in the form of project cancellations and delays that impede the installation of these technologies, which are driven by various non-technical factors. Local opposition, prompted by concerns over the visual impact of renewable energy technologies on the surrounding landscape, is one of these. Past studies have sought to tackle this problem by incorporating the visibility of wind turbines into planning considerations and potential analyses. However, these analyses have been limited to small regions and do not account for the visibility of other renewable technologies, such as solar PV.

This study employs a nationwide, integrated, reverse-viewshed analysis, potential analysis, and techno-economic analysis. Furthermore, we evaluate the effects of designing renewable energy systems that are not visible in scenic or densely-populated areas on the remaining energy potential, energy system costs, and technological choices necessary to achieving net zero emissions by 2045. Installations visible from areas with different scenicness ratings (1–9) and population density thresholds are used to define scenarios to account for the sensitivity of visual impacts on different degrees of landscape scenicness and viewed by different population segments.

Our research reveals that wind turbines with the highest levelized cost of electricity (LCOE) in Germany coincide with those that are visible from the most scenic landscapes (scenicness level 9). Therefore, minimizing the visual impact of wind turbines by placing them out of sight from the most scenic landscape areas could align with cost-effectiveness objectives. However, if the visibility restrictions become too strict (e.g., that they not be visible from scenicness levels ≥ 5 or population densities ≥ 300 people/km²), there will not be enough wind power potential (e.g., the remaining 6.8 TWh/year or 2.4 TWh/year) to cost-effectively achieve German climate targets. Instead, PV systems, with a lower visual impact, would be more favorable and selected in the optimization to meet energy demands.

How to cite: Tsani, T., Weinand, J. M., Pelser, T., Hoffmann, M., Ioannidis, R., Maier, R., Risch, S., Kullmann, F., McKenna, R., and Stolten, D.: Minimizing visual impacts of renewable energy technologies and its implications for potential, costs, and energy transformation pathways: A nationwide study on Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2792, https://doi.org/10.5194/egusphere-egu24-2792, 2024.

Energy communities (ECs) are expected to play a major role in the European Energy transition. However, quantitative evidence shows that these only represent a minor share of the installed capacity compared to commercial large scale installations. We argue that understanding better the conditions that facilitate of ECs emergence, will contribute to develop adequate strategies to foster their creation. In previous research we conducted an exploratory data analysis to understand the relation between the availability and quality of variable renewable energy sources (VRES) and ECs (Ramirez Camargo et al., 2023). This was done by calculating 38 indicators of VRES availability and quality for NUTS3 regions derived from four decades of ERA5 data, together with a data set of energy cooperatives (most common organizational form of ECs) as a proxy for ECs with less than 1,000 entries. With the publication of an extensive data set of citizen-led energy initiatives, agglomerating all sorts of ECs and with more than 10,000 entries (Wierling et al., 2023), we replicated the previously proposed methodology. The main results from previous research hold at the continental level:  There is a slight predominance of citizen-led energy initiatives where wind resources are high and opposite results for solar resources. Nevertheless, the considerably higher data availability allows for a detailed analysis at the country level. We observe that while countries with large numbers of citizen-led energy initiatives, such as Germany, drive what we observed at the continental level, there are countries such as Denmark and Ireland with high positive correlation between citizen-led energy initiatives and wind power capacity factors. There are also clear exceptions to the rule, such as the Czech Republic, with a high positive correlation to solar resources that reaches 0.731. At the country level, just as at the continental level, we see that clusters of citizen-led energy initiatives develop where VRES availability is high but it also becomes more evident that there are large differences in the concentration of citizen-led energy initiatives between NUTS3 regions of individual countries. Finally, we see a large unexploited potential for development of ECs in the regions of the continent that are rich in solar resources.

Ramirez Camargo, L., Lode, M., & Coosemans, T. (2023, Januar 13). Assessing the relevance of renewable energy resources availability for the existence of Energy Cooperatives in Europe. Volume 29: Closing Carbon Cycles – A Transformation Process Involving Technology, Economy, and Society: Part IV. Applied Energy Conference 2022. https://doi.org/10.46855/energy-proceedings-10327

Wierling, A., Schwanitz, V. J., Zeiss, J. P., von Beck, C., Paudler, H. A., Koren, I. K., Kraudzun, T., Marcroft, T., Müller, L., Andreadakis, Z., Candelise, C., Dufner, S., Getabecha, M., Glaase, G., Hubert, W., Lupi, V., Majidi, S., Mohammadi, S., Nosar, N. S., … Zoubin, N. (2023). A Europe-wide inventory of citizen-led energy action with data from 29 countries and over 10000 initiatives. Scientific Data, 10(1), Article 1. https://doi.org/10.1038/s41597-022-01902-5

How to cite: Ramirez Camargo, L. and Lode, M. L.: Evaluating the relevance of the availability of variable renewable energy resources for the existence of citizen-led energy initiatives in Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-22066, https://doi.org/10.5194/egusphere-egu24-22066, 2024.

Rooftop solar photovoltaics (RSPV) play a pivotal role in enabling countries and cities to transit to renewable energy and achieve net-zero emissions. Effective RSPV deployment hinges on understanding its spatiotemporal patterns and a city’s capacity to integrate it, considering the challenges of supply-demand inconsistency and grid security. Despite its importance, there is a lack of high-resolution data on RSPV in terms of both power generation and accommodation potential.

From the perspective of RSPV technical potential, its assessment has much larger complexity than utility-scale PV systems, as individual rooftop rather than a large site serves as the smallest unit for the assessment. Given the difficulty in mapping rooftop and its available space for RSPV installation, high resolution mapping of RSPV technical potential of an entire large country remains challenging. Current literatures on this topic reach a spatial resolution on 10-100 km² scale, which is still hard to demonstrate details within cities, and fail to account rooftop availability for each individual pixel. From the perspective of RSPV deployment potential, current literatures tend to aggregate total RSPV supply with grid demand. As different types of buildings have different load intensity and patterns, such simplification would underestimate the variability of load-accommodation ability for RSPV.

To tackle these challenges, we develop an integrated framework that combines high-resolution RSPV potential assessment with consumption optimization based on building-related loads. For the technical potential evaluation, we employ a machine learning model, which integrates ~30 variables from different remote sensing images and spatial explanatory data, to quantify building rooftop area and height distribution on 1 km² scale. A rooftop availability analysis is then applied for each 1 km² pixel based on its building density, height and property. The RSPV capacity and hourly electricity potential are then calculated through combining available rooftop and radiation modelling. For the consumption analysis model, we first use building simulation to model the hourly power demand for different buildings (urban residential, public, industry and rural) in different cities. Combining hourly RSPV potential and building-related loads, we then optimize the RSPV deployment by profit maximization, with the constraint of a series grid-accommodation scenarios. Specifically, the grid-accommodation scenarios include minimum self-consumption and maximum peak-valley difference.

We apply our framework to China as a case, with a potential mapping for 3,596,668 1*1km² pixels and deployment analysis for 369 prefecture-city for each kind of buildings. The results show that the total RSPV potential in mainland China amounts to 2785 GW, with 4631 TWh annual electricity potential. Urban residential, public, industry and rural buildings respectively takes up 7.6%，7.0%，24.9% and 60.5% for total potential. We quantify the deployable RSPV capacity under various local consumption and peak-valley difference constraints, ranking different building types in different cities based on levelized cost of energy (LCOE), value of solar (VOS), and emission reduction potential. The study concludes by discussing pathways to achieve renewable energy targets based on these findings.

How to cite: Shi, M. and Lu, X.: High spatiotemporal resolution mapping of rooftop solar technical and deployment potential in China, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5238, https://doi.org/10.5194/egusphere-egu24-5238, 2024.

Renewable energies (RES) infrastructure may imply local benefits and burdens. Local benefits might be, e.g., energy autonomy or trade tax income. Local burdens may be reflected, e.g., in house price losses or local opposition by citizens. From a justice perspective, this leads to our overarching research question: What would a just spatial distribution of local burdens and benefits of RES infrastructure look like and how could distributive spatial justice be achieved? With respect to the first part of the question (what would be a just distribution of benefits and burdens?), different answers may exist depending on one's understanding of distributive justice. With regard to the second part of the question (how to achieve a just distribution of benefits and burdens?), there are basically two possible approaches. Firstly, the distribution of local benefits can be addressed. By modifying the institutional framework and/or the spatial infrastructure deployment the spatial distribution of benefits can be adjusted to achieve a distribution of benefits and burdens being considered as just. Secondly, the distribution of local burdens can be targeted and affected in order to achieve a distribution of benefits and burdens considered as just. That is the focus of this paper. We assume local burdens being solely influenced by infrastructure deployment and local benefits being spatially equivalent to the burdens, thus not requiring separate consideration.

To examine our overarching research question, we use a numerical optimization model. We apply the model to the future spatial deployment of onshore wind power and utility scale solar photovoltaics (PV) in Germany in a fully renewable system. We optimize the deployment for given energy production targets with respect to a cost-effectiveness criterion and with respect to various alternative spatial distributive justice understandings. These relate to the equality principle, ability principle, and benefit-principle. By doing so, we shed light on three sub-questions: (1) Can spatial distributive justice of the RES deployment be improved and if so, to what extent? Our results show that, due to regional RES potential limitations, perfect justice cannot be achieved for any of the assumed concepts of justice. But our results also show that the current infrastructure allocation can be assessed as relatively unjust with regard to all assumed concepts of justice, and considerable improvements in justice would be possible by redistributing deployment in space. (2) What relevance do different normative assumptions have for the spatial distributional justice of the RES deployment? Our results reveal that the justice assessment of an allocation depends largely on the understanding of justice that is assumed. In addition, our optimizations demonstrate that it is easier to establish distributive justice between larger and fewer regions than between smaller and more regions. (3) To what extent are there trade-offs between pursuing spatial distributive justice and cost-effectiveness? We find that optimizing the RES deployment by levelized costs of electricity (LCOE) is comparatively unfavorable with respect to the assumed justice concepts. In turn, optimizing the spatial allocation of RES deployment by the assumed justice concepts increases LCOE by 1%-14%, compared to the cost-optimal allocation.

How to cite: Lehmann, P. and Reutter, F.: Spatial distributive justice for onshore wind power and utility-scale solar PV deployment – Optimizations for the case of Germany, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19996, https://doi.org/10.5194/egusphere-egu24-19996, 2024.

The Irish Government’s Climate Action Plan aims to increase renewable energy generation capacity to 22GW by 2030, with at least 5GW of this to be produced by offshore wind. The potential to harness and develop this resource is significant; however not all areas offshore are suitable. Moreover, not all routes from windfarms to land are suitable for the submarine cables needed to transfer the energy produced offshore back to the onshore grid.

This research utilizes geospatial analysis to identify the optimal route selection for offshore export cables. The area under consideration for this research encompasses the South and West coasts of Ireland in the North Celtic Sea and Eastern Atlantic Ocean, areas under intense development for offshore wind, particularly floating wind platforms.

To achieve this, a geospatial repository of publicly available data was compiled to ensure the key features related to cable route feasibility were included in the spatial analysis. These layers included bathymetric, geological, and ecological data, as well as information on human activities in the area, to assess the potential hazards to a submarine cable within a particular region. Each variable was assessed as to its importance in the route selection model using criteria weights derived from the Analytical Hierarchical Process and expert opinion of a panel of industry representatives. The resulting data layers were combined into a suitability map of the seabed using a Weighted Overlay Analysis.

Individual offshore wind sites and coastal landfall sites were selected based on proposed developments. GIS route selection methods were then implemented, principally the least-cost path algorithm, to identify the optimal route.

The combined criteria map produced in this project classifies regions off the South and West coasts of Ireland into zones of suitability for cable routes and highlights the main areas along the coast most appropriate for cable landfall sites.  By using automated route selection tools in GIS along the suitability map surface, realistic paths along the seabed can be quickly designed to allow for adequate burial of the cable, and avoidance of obstacles, hazards and zones of exclusion.

The findings of this research indicate that distance from existing coastal substations is a key factor in terms of the economic viability of a cable route. Many windfarms will require more than one export cable, and with several windfarm proposals within the same coastal region, bottle necks at suitable landfall sites may be expected.

The results of the study provide a useful tool for policy makers and developers in the planning stage. In a broader context, these findings can be upscaled, customised and applied at a national level for other countries to allow a systematic approach to offshore renewable energy development.

How to cite: Walsh, K., Holloway, P., and Lim, A.: Optimising Submarine Cable Routes from Offshore Windfarms – Site Suitability Mapping, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18680, https://doi.org/10.5194/egusphere-egu24-18680, 2024.

A wind power plant's development causes a range of disturbances, both short-term and long-term. Wind turbine pads, access roads, substations, service buildings, and other equipment that physically occupy land or produce impermeable surfaces are examples of these disruptions. Development in forested areas, where more land must be removed around each turbine, is linked to extra direct impacts. Although the land cleared around a turbine pad does not produce impermeable surfaces, the quality of the ecosystem may be significantly degraded as a result of this alteration.

This work includes the outcomes derived from a sequence of data analytics. The analyses entail aggregating unprocessed data obtained from Copernicus Sentinel-2 satellite images covering the timeframe from 2015 to 2023. The development of algorithms tailored to distinct regions (such as EU countries and sub-regions) to identify alterations, together with the subsequent statistical examination of the alteration data, are essential elements of this procedure. The change dataset has a spatial resolution of 10m x 10m, which is the same as the input data from Sentinel-2. It is a binary raster dataset that visually shows the changes happening below and near the wind turbine sites that were built between 2015 and 2023. The statistical analysis includes the evaluation of this data and the examination of the changing raster, land-cover data, the biogeographical area, and terrain data. The statistical calculations are conducted for both individual wind turbines and wind parks comprising many wind turbines. 

How to cite: Mikovits, C. and Öberseder, T.: Analysis of Land Use Change at Wind Turbine Sites, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19844, https://doi.org/10.5194/egusphere-egu24-19844, 2024.

The ongoing energy transition presents us with the challenge of finding sustainable and efficient ways to generate renewable energy. In this context, wind energy plays a decisive role and contributes significantly to increasing the share of renewable energies in the energy mix. Strategic energy planning, effective repowering, decommissioning and the seamless integration of wind energy into the power grid require accurate location information of existing wind turbines. In this work, we present a) the precise, automated, Germany-wide identification and localization of wind turbines using high-resolution planetary data and b) an innovative approach to detect wind turbine activity using Sentinel-2 data. The detection of wind turbine activity is based on the different blade positions, including their shadow position, which is caused by slightly offset recording times of the spectral bands. The change is highlighted and classified using a Convolutional Neural Network. The presentation also discusses possible limitations and peculiarities of the methods used and emphasizes the relevance of remote sensing-based monitoring for the wind energy industry and environmental monitoring.

How to cite: Gärtner, P., Wehner, C., Siegismund, J., Albert, J., and Zschache, J.: Site monitoring and activity detection of wind turbines with Planet and Sentinel-2 satellite data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10141, https://doi.org/10.5194/egusphere-egu24-10141, 2024.

Large-scale wind and solar photovoltaic (PV) infrastructures are rapidly expanding in Brazil. These low-carbon technologies can exacerbate land struggles rooted in historical inequities in land ownership, lack of regulation and weak governance. Here, we trace how green grabbing, i.e. the large-scale appropriation and control of (undesignated) public lands, both formally legal and illicit, for the development of wind and solar PV, has developed in Brazil throughout 2000 to 2021. We find that global investors and owners, mainly from Europe, are involved in 78% of wind and 96% of solar PV parks, occupying 2,148 km2 and 102 km2 of land, respectively. We also show that land privatization is the prevalent land tenure regime for securing access to and control over land, indicating significant transformations of prior (undesignated) public and common land. We conclude that green grabbing is a persistent, critical phenomenon in Brazil, requiring transparency and vigilant monitoring of land claims and tenure modifications.

How to cite: Klingler, M., Ameli, N., Rickman, J., and Schmidt, J.: Large-scale green grabbing for wind and solar PV development in Brazil, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11161, https://doi.org/10.5194/egusphere-egu24-11161, 2024.