ERE2.3 | Spatial and temporal modelling of renewable energy systems
Spatial and temporal modelling of renewable energy systems
Convener: Luis Ramirez CamargoECSECS | Co-conveners: Johannes Schmidt, Marianne ZeyringerECSECS
Orals
| Wed, 17 Apr, 14:00–17:57 (CEST)
 
Room 0.96/97
Posters on site
| Attendance Wed, 17 Apr, 10:45–12:30 (CEST) | Display Wed, 17 Apr, 08:30–12:30
 
Hall X4
Posters virtual
| Attendance Wed, 17 Apr, 14:00–15:45 (CEST) | Display Wed, 17 Apr, 08:30–18:00
 
vHall X4
Orals |
Wed, 14:00
Wed, 10:45
Wed, 14:00
This session addresses spatial and temporal modelling of renewable energy systems, both in a prospective as well as in a retrospective manner. Therefore, contributions which model the characteristics of future renewable energy systems are equally welcome as contributions which assess the characteristics of the past performance of renewable energies. Session contributions may reach from purely climate based assessments of simulated renewable generation time series, over assessments of land use to full energy system models used to better understand energy systems with high shares of renewables.

Studies may for instance:
Show the spatial and temporal variability of renewable energy sources, including resource droughts and complementarity between technologies and locations
Assess the resilience of energy systems to weather and climate extreme events, with a focus on infrastructure and resource adequacy
Derive scenarios for the spatial allocation of renewable energies based on climatic, technical, economic, or social criteria
Assess past spatial deployment patterns of renewables
Assess past impacts on land cover and land-use, including impacts on biodiversity and other environmental indicators
Explore and quantify impacts of wind and solar PV power deployment on the social and natural environment in a spatially explicit way
Derive integrated scenarios of energy systems with high shares of renewables (Including systems from the local scale e.g. in form of local Energy Communities to the national or continental scale).

The objective of the session is to provide an insight into recent advances in the field of renewable energy system modeling. The session welcomes research dedicated to climatic and technical issues, environmental impact assessments, and policy-making, forecasting and real time applications concerning renewable energy systems.

Session assets

Orals: Wed, 17 Apr | Room 0.96/97

Chairpersons: Luis Ramirez Camargo, Johannes Schmidt
14:00–14:05
Climate and energy
14:05–14:15
|
EGU24-4156
|
ECS
|
On-site presentation
Anna Nickoloff, Sophia Olim, Michael Eby, and Andrew Weaver

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.

14:15–14:25
|
EGU24-18667
|
ECS
|
On-site presentation
Joan Delort Ylla, Alexis Tantet, and Philippe Drobinski

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.

14:25–14:35
|
EGU24-15062
|
ECS
|
On-site presentation
Lieke van der Most, Karin van der Wiel, Winnie Gerbens-Leenes, René Benders, and Richard Bintanja

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.

14:35–14:45
|
EGU24-1511
|
ECS
|
On-site presentation
Denizhan Guven, Omer Lutfi Sen, and Mehmet Ozgur Kayalica

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.

14:45–14:55
|
EGU24-3907
|
On-site presentation
Doris Folini, Guillaume Senger, Boriana Chtirkova, Jan Wohland, and Martin Wild

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.

14:55–15:05
|
EGU24-1227
|
ECS
|
On-site presentation
Dongsheng Zheng, Dan Tong, Steven J. Davis, Yue Qin, Yang Liu, Rongchong Xu, Jin Yang, Xizhe Yan, and Qiang Zhang

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.

System modeling
15:05–15:15
|
EGU24-18985
|
ECS
|
Highlight
|
Virtual presentation
|
Isabelle Viole, Koen van Greevenbroek, and Claudia Cheng

How competitive can Norway – one of the main natural gas suppliers to the European Union (EU) – be at exporting hydrogen to the EU? We explore three scenarios in which Norway’s hydrogen export market may develop: A Business-as-usual, B Moderate Onshore, C Accelerated Offshore. Applying a sector-coupled energy system model, we examine the economic, social and environmental implications of each scenario. Given a variety of cost assumptions in shipping, CCS and electrolysis, the pathways result in wide ranges of potential costs of hydrogen from 2-7€/kg hydrogen. In the cheaper scenarios A and B we identify roadblocks in social acceptance in either the expansion of onshore wind turbines, or in resistance against CCS technologies. Environmental trade-offs in land use change follow suit. Any of the pathways discussed requires fast investments in the necessary infrastructure paired with measures to increase social acceptance and to alleviate environmental impacts. Nonetheless, we show that Norway could supply a significant share of the EU’s hydrogen demand in the near-term future.

How to cite: Viole, I., van Greevenbroek, K., and Cheng, C.: Can Norway save the European Union's hydrogen ambition for 2030?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18985, https://doi.org/10.5194/egusphere-egu24-18985, 2024.

15:15–15:25
|
EGU24-19090
|
ECS
|
Virtual presentation
|
Koen van Greevenbroek, Johannes Schmidt, and Marianne Zeyringer

Hydrogen could play a crucial role in Europe's transition to carbon neutrality by 2050. However, the size and scope of the upcoming hydrogen sector is subject to great uncertainty due to unknown future costs, technological developments and competition with other energy carriers. The prospects of hydrogen imports from outside the EU is possibly subject to even greater uncertainty. Are hydrogen imports needed at all? Are they an essential element of the green transition? In the present work we use a multi-horizon energy system optimisation framework to investigate the rationale for EU hydrogen imports. In particular, we analyse when hydrogen imports may alleviate the most critical bottlenecks in achieving net carbon neutrality by 2050. The main bottlenecks of interest are rapid growth in renewable energy and hydrogen production. To ensure robustness of the results, we use near-optimal methods to map out a large variety of transition pathways under a number of different political and technological scenarios. The pathways are evaluated on cost and land-use impact inside Europe as well as potential upstream impacts of imported hydrogen. Using this holistic approach allows us to uncover when hydrogen imports are compelling and when they are dubious. 

How to cite: van Greevenbroek, K., Schmidt, J., and Zeyringer, M.: How important are hydrogen imports for European carbon neutrality?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19090, https://doi.org/10.5194/egusphere-egu24-19090, 2024.

15:25–15:35
|
EGU24-9038
|
ECS
|
On-site presentation
Samouro Dansokho, Alexis Tantet, Philippe Drobinski, and Anna Creti

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 KR (resp.~KSU) 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 KSU 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 KSU and KR 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.

15:35–15:45
|
EGU24-12640
|
ECS
|
On-site presentation
Dana Reulein and Dimitri Pinel

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.

Coffee break
Chairpersons: Johannes Schmidt, Luis Ramirez Camargo
16:15–16:17
16:17–16:27
|
EGU24-2792
|
ECS
|
Highlight
|
On-site presentation
Tsamara Tsani, Jann Michael Weinand, Tristan Pelser, Maximilian Hoffmann, Romanos Ioannidis, Rachel Maier, Stanley Risch, Felix Kullmann, Russell McKenna, and Detlef Stolten

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.

16:27–16:37
|
EGU24-21629
|
ECS
|
On-site presentation
Assessing Wind Energy Potential in Africa: A Comprehensive Analysis of Feasibility and Implementation Probability using GIS Data
(withdrawn)
Benedikt Haeckner, Christoph Zink, Maximilian Pfennig, and David Geiger
16:37–16:47
|
EGU24-22066
|
ECS
|
Virtual presentation
Luis Ramirez Camargo and Maria Luisa Lode

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.

16:47–16:57
|
EGU24-5238
|
ECS
|
On-site presentation
Mai Shi and Xi Lu

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.

16:57–17:07
|
EGU24-19996
|
On-site presentation
Paul Lehmann and Felix Reutter

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.

17:07–17:17
|
EGU24-20619
|
ECS
|
On-site presentation
GIS-based landscape scenicness estimation using machine learning for visual impact assessment of wind projects deployment in Europe
(withdrawn)
Ruihong Chen
17:17–17:27
|
EGU24-18680
|
ECS
|
On-site presentation
Kevin Walsh, Paul Holloway, and Aaron Lim

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.

17:27–17:37
|
EGU24-19844
|
On-site presentation
Christian Mikovits and Thomas Öberseder

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.

17:37–17:47
|
EGU24-10141
|
On-site presentation
Philipp Gärtner, Claudius Wehner, Jan Siegismund, Johannes Albert, and Johannes Zschache

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.

17:47–17:57
|
EGU24-11161
|
ECS
|
On-site presentation
Michael Klingler, Nadia Ameli, Jamie Rickman, and Johannes Schmidt

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.

Posters on site: Wed, 17 Apr, 10:45–12:30 | Hall X4

Display time: Wed, 17 Apr 08:30–Wed, 17 Apr 12:30
Chairpersons: Luis Ramirez Camargo, Johannes Schmidt
X4.150
|
EGU24-1723
Francisco Santos-Alamillos, David Pozo-Vázquez, Guadalupe Sánchez-Hernández, Antonio Jiménez-Garrote, Miguel López-Cuesta, Santiago DeFelipe-García, José Antonio Ruiz-Arias, and Joaquín Tovar-Pescador

The Spanish energy roadmap aims at producing around 80% of electricity from renewable energy by 2030, while reducing nuclear energy in a scenario of increasing demand. To this end, the target for installed capacity is 50 GW for wind energy (30 GW in 2022), 39 GW for solar PV (20 GW in 2022) and 2.5 GW for battery storage. Plans underway suggest even more ambitious goals.
We present the results of an analysis of the optimal spatial distribution of new wind and solar capacities in Spain. The study is carried out using the electrical system model PyPSA-Eur, which allows analyzing the optimal allocation and sizing of new renewable plants, taking into account the variability of generation and demand, energy costs, integration and the transmission issues. Two main scenarios are explored: 1) capital costs and 2) operational nuclear power amount (0/3/7 GW). The study assumes a 20% increase of demand by 2030 and a maximum total installed power of 160 GW. The generation and distribution networks used in PyPSA-Eur includes 9 nodes, homogeneously distributed in the study region. The model is fed with the Spanish High Resolution Renewable Energy and Demand (SHIRENDA) open access database for energy system analyses. Both combined the generation and distribution networks  and SHIRENDA allow adequately accounting for the very high spatial variability of the renewable resources in Spain. 
The results show that the new capacities should be installed in up to four of the nine regions (nodes) considered, although this strongly depends on the amount of nuclear energy. In particular, for scenarios with low nuclear power (0/3 GW) wind capacities should be installed mainly in the Galicia (northwest of the study area) and Aragón (northeast) regions, and solar PV in the regions of Murcia (southeast) and Aragón. For scenarios with fully operational nuclear energy (7 GW), the region of Andalusia (south) was also selected both for wind and solar PV. The intermediate nuclear power amount scenario (3 GW) is best from the costs standpoint. The curtailment is high (about 10%),  higher for wind, but reduces by 50% when nuclear energy is removed.
Overall, the results show that a homogeneous spatial distribution of new solar and wind capacities in the study region is far from optimal and that a better representation of the spatio-temporal variability of the renewable energy resources, as done in this study, is needed. Future work will explore the optimal ratio between solar PV and wind capacity, as well as the role of energy storage and demand management.

How to cite: Santos-Alamillos, F., Pozo-Vázquez, D., Sánchez-Hernández, G., Jiménez-Garrote, A., López-Cuesta, M., DeFelipe-García, S., Ruiz-Arias, J. A., and Tovar-Pescador, J.: Analysis of the optimal allocation of wind and solar PV capacities in a decarbonized power system in Spain using PyPSA, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1723, https://doi.org/10.5194/egusphere-egu24-1723, 2024.

X4.151
|
EGU24-3822
|
ECS
|
Dimitrios Chatzopoulos, Athanasios Zisos, Nikos Mamassis, and Andreas Efstratiadis

The hydrometeorological processes associated with renewables are characterized by substantial spatiotemporal variability, and thus uncertainty, which is addressed through decentralized planning, thus taking advantage of scaling effects. The objective of this work is to provide a comprehensive investigation of the role of scale regarding solar photovoltaic production in Greece, which is one of the predominant renewables. By implementing macroscopic criteria in terms of solar potential (e.g., topography-adjusted radiation indices), we select a sufficient sample of well-distributed locations in Greece. For these points, hourly radiation and temperature data, derived from satellite products, are retrieved and validated against ground observations. Following this, we formulate a detailed simulation procedure that accounts for the two physical drivers and the panel characteristics (i.e., efficiency and temperature impacts due to heating), and we configure the baseline scenario by computing the individual production of each site. Next, to highlight the added value of distributed production and quantify the scaling effects in PV power production, we follow a Monte Carlo approach by randomly distributing PVs across the selected locations, to eventually provide a statistical analysis on the spatial and temporal domain and over different PV technologies.

How to cite: Chatzopoulos, D., Zisos, A., Mamassis, N., and Efstratiadis, A.: The benefits of distributed grid production: An insight on the role of spatial scale on solar PV energy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3822, https://doi.org/10.5194/egusphere-egu24-3822, 2024.

X4.152
|
EGU24-9590
|
ECS
Benjamin Biewald and William Zappa

Weather-driven periods of low electricity production from renewable energy sources (RES) can result in so-called ‘energy droughts’, sometimes known by the German term ‘Dunkelflaute’. When these weather phenomena occur over a large geographical area for extended periods of time, and coincide with periods of high electricity demand due to cold temperatures (‘kalte Dunkelflaute’), these events pose a risk for maintaining resource adequacy in a future power system relying significantly on RES.

The most robust way of identifying energy droughts is to use hourly electricity market simulations to capture both the demand- and supply-side effects of different climate years, but these simulations are computationally intensive to perform and Transmission System Operators (TSOs) can typically only consider 30-40 historical climate years in resource adequacy studies. However, as vastly more climate data is becoming available from future climate projections, a robust way to identify ‘kalte Dunkelflauten’ from climate data alone is needed in order to identify challenging years to consider in resource adequacy studies. A variety of approaches for defining and analysing energy droughts can be found in the literature such as those which detect singular events by defining a threshold for renewable energy production (production drought) or how much (net) load is covered by RES (supply drought) (e.g. Raynaud et al., 2018), and those which use statistical methods to assess the risk of an energy drought within a predefined timespan (e.g. Ruhnau & Qvist, 2022). However, there is no clear consensus on which is the best method.

In this study we will present an evaluation of different methods to assess the risk of occurrence of such ‘kalte Dunkelflaute’ events, and validate these methods by comparing with results from detailed hourly simulations, with a focus on the Netherlands and Germany. By applying different detection methods to both existing and projected RES capacity, and using both historical and future climate data from a Pan-European Climate Database, we compare past and future risks posed by energy droughts.  As extreme ‘Dunkelflaute’ events are rare but their impact may be severe, comparing different approaches of how to statistically evaluate these events is an important contribution to evaluating resource adequacy, and assessing the resilience of the future energy.

 

References

  • Raynaud, B. Hingray, B. François & J.D. Creutin (2018). Energy droughts from variable renewable energy sources in European climates. Renewable Energy, 125, 578-589, https://doi.org/10.1016/j.renene.2018.02.130.
  • Ruhnau & S. Qvist (2022). Storage requirements in a 100% renewable electricity system: extreme events and inter-annual variability. Environmental Research Letters, 17(4), 044018, https://doi.org/10.1088/1748-9326/ac4dc8

How to cite: Biewald, B. and Zappa, W.: Evaluation of different methods for detecting 'kalte Dunkelflaute' events with respect to climate change projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9590, https://doi.org/10.5194/egusphere-egu24-9590, 2024.

X4.153
|
EGU24-10308
|
ECS
Generating an ensemble of hourly renewable energy from a suite of daily weather parameters sets for energy system modelling: a two-step modelling approach
(withdrawn)
Negar Vakilifard and Charles Rougé
X4.154
|
EGU24-12011
|
ECS
Luna Bloin-Wibe, Leonard Göke, Jonas Savelsberg, and Jan Wohland

Transitioning to renewable energy will be instrumental in mitigating the devastating effects of climate change. Because of the many unknowns in the design and dispatch of future energy systems, quantifying climate risk in the energy sector is challenging: in particular, renewable energy production and heating demand is highly reliant on meteorological conditions, which are variable in nature and shifting due to climate change.

It is therefore important to use large samples of renewable generation and demand, for current and future climates, in energy system modeling. However, lacking standardized ways to translate between the climate and energy model world, most existing studies rely on different assumptions and draw from a limited sample of available climate variables.

To this end, we created a modular climate-to-energy pipeline: starting with hourly output from the climate model CESM2, it bias corrects, translates, and scales to the various inputs of energy system models. We base the conversion on open-source tools: GSEE for solar power generation, windpowerlib for wind from climate model levels and demand.ninja for heating and cooling demand. The resulting pipeline ensures consistency of variables, with inputs and outputs tailorable to specific needs.

We use the pipeline to analyze seasonal cycles of energy generation and demand under different weather conditions, for current and future climates deploying the AnyMOD.jl framework for energy system modeling. Because of the modular approach, the pipeline could easily be adapted for other climate models and time-series, providing better evidence for climate-informed energy system planning.

How to cite: Bloin-Wibe, L., Göke, L., Savelsberg, J., and Wohland, J.: CESM2energy: a modular climate-to-energy pipeline , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12011, https://doi.org/10.5194/egusphere-egu24-12011, 2024.

X4.155
|
EGU24-15765
Amna Bibi, Ben Marzeion, Muhammad Shafeeque, and Gerald Lohmann

Europe's energy transition towards renewable sources is imperative for achieving sustainability and mitigating climate change. However, the intermittency of solar and wind power necessitates a detailed evaluation of their combined potential. This study analyzes the spatiotemporal distribution and variability of solar and wind power resources across Europe from 1979-2022, using ERA5 reanalysis data.

Empirical Orthogonal Function (EOF) analysis is used to characterize the spatiotemporal patterns of variability in irradiance and wind speed up to multidecadal timescales. Also, the variance of the estimates of the capacity factor (CF, i.e., electricity generation normalized to the installed capacity) is compared with EOF patterns.

Results show that the leading three modes of EOF represent the most variance in spatial distribution of irradiance and wind speed over Europe, with significant interannual and interdecadal fluctuations influencing spatiotemporal distribution. The temporal variance for offshore and onshore wind exhibits larger spatial heterogeneity. The spatial heterogeneity of the variance of solar CF is lower than that of wind power CF, but its amplitude is much higher in most regions. There is a negative linear correlation between the variance and mean of CF for both solar and wind power.

Southern Europe shows the lowest intermittency in solar power, while eastern and northern Europe exhibit a lower intermittency of onshore wind. Offshore wind potential is high over the Norwegian and Mediterranean Seas. We also identify areas of maximum complementary between solar and wind power resources, attempting to use large-scale datasets and established knowledge of patterns of climate variability to fulfill local-scale renewable energy requirements best. Future research will focus on developing advanced hybrid models to integrate diverse renewable energy sources, exploring their synergistic potentials.

How to cite: Bibi, A., Marzeion, B., Shafeeque, M., and Lohmann, G.: Assessing the Spatiotemporal Variability and Complementarity of Renewable Energy Resources across Europe, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15765, https://doi.org/10.5194/egusphere-egu24-15765, 2024.

X4.156
|
EGU24-20555
Helber Gomes, Dirceu Herdies, Luiz Fernando Santos, João Augusto Hackerott, Mário Quadro, Fabricio Daniel dos Santos Silva, Robinson Semolini, Bruno Dantas Cerqueira, and Djanilton Henrique Moura Junior

The effects of climate change are present in all segments of society in general, and especially in the energy segment. Brazil plays a leading role in the use of renewable energy, with the majority of its matrix coming from renewable sources. In this sense, evaluating the impact of meteorological variables on the injected energy load is fundamental for the efficient use of energy. The present study aims to analyze the influences of meteorological variables on the energy load demand in Brasília - Federal District, Brazil. We analyzed observed data from the National Institute of Meteorology (INMET) weather station, reanalysis data from the National Center for Environmental Prediction (NCEP), reanalysis from the European Center for Medium-Range Weather Forecast (ECMWF), Modern-Era Retrospective Analysis for Research and Application Aerosol Reanalysis (MERRA-2) and South American Mapping of Temperature (SAMeT). Pearson's correlation coefficient was used to quantify the linear relationship between the observed data from the meteorological station (INMET) and the monthly load injected in Brasília in the period 2016-2022. Subsequently, statistical metrics, commonly used for model checking, were applied to regularly spaced global numerical model datasets with assimilation: CFSR (NCEP), ERA5 (ECMWF), MERRA2 (NASA), and SAMET (INPE). A high direct correlation of the injected monthly load with the monthly averages of maximum and average temperatures (0.65 and 0.51), respectively, and an inverse correlation with the observed average relative humidity (-0.50) was noted. Furthermore, the representativeness of temperatures from the data sets was investigated, aiming to expand the analysis to other regions that do not have meteorological station data. In validating the maximum and average temperature, it was possible to identify a high representative potential of the sets covered. Highlights include SAMET and ERA5, which presented the highest correlation coefficients (higher than 0.90) and standard deviation proportional to observational data.

How to cite: Gomes, H., Herdies, D., Santos, L. F., Hackerott, J. A., Quadro, M., Silva, F. D. D. S., Semolini, R., Cerqueira, B. D., and Junior, D. H. M.: The Influence of Meteorological Variables on Energy Demand in the Federal District of Brazil, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20555, https://doi.org/10.5194/egusphere-egu24-20555, 2024.

X4.157
|
EGU24-1409
|
ECS
|
Josefin Winberg, Yann Clough, Cecilia Larsson, Johan Ekroos, and Henrik Smith

Bioenergy is expected to play a key role in the transition from fossil fuels to renewable energy sources, with biomass from grass and forestry pointed out as some of the main bioenergy sources in Northern Europe. The increased demand for biomass creates incentives for regional biofuel markets, assumed to replace imported biofuels in the substitution of fossil fuels in industries and the transport sector. In our study, we use coupled modelling of economic and ecological systems to investigate the potential landscape-scale impacts on biodiversity from increased production of lignocellulosic biomass for energy purposes in a farm-forest mosaic region in Southern Sweden. As a first step, we use the empirical and spatially explicit agent-based model AgriPoliS (Happe et al., 2006) to predict how profit-maximizing farmers respond to increased demand and price of ley biomass for energy purposes by changed farm structures and land use within a region, in response to. We expect that increased use of ley and grass biomass for energy could have a negative effect on fodder production, which in turn negatively affects dairy and livestock farming, ultimately with negative impacts on biodiversity if semi-natural grasslands (SNG) are abandoned or afforested. The impact on biodiversity from the resulting land-use changes is modelled in a second step, using a countryside species-area relationship model (cSAR) based on existing field data. By coupling the two models, we can predict the ecological impacts of changes in energy policies or markets, to ultimately understand if there are any tipping points for how much grass biomass can be used for energy until we have a decline in SNG and their associated biodiversity.

How to cite: Winberg, J., Clough, Y., Larsson, C., Ekroos, J., and Smith, H.: Coupling economic & ecological models - the effect on biodiversity from energy grass production , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1409, https://doi.org/10.5194/egusphere-egu24-1409, 2024.

X4.158
|
EGU24-3162
|
ECS
Marco Tangi and Alessandro Amaranto

Multi-energy systems (MESs), which integrate various technologies and energy vectors in a single unified framework, have proven to be effective and flexible tools for addressing the challenges faced by energy systems in a changing world. These include the need for decarbonization, increasing penetration of renewable energy sources, a push for decentralization and independence in energy markets, and rapidly shifting socio-economic and climatic conditions.

However, traditional modeling tools for MESs planning and management have several shortcomings. Existing multi-energy planning and modeling frameworks often prioritize minimizing a single monetary objective. Even when multiple objectives are considered, they are often monetized, reducing the problem to a single-objective approach and limiting the exploration of possible solutions. Additionally, MES planning struggles to account for uncertainties arising from climate and socio-economic variables, especially with the rise of non-programmable energy sources and frequent disruptions in global supply chains and energy stability.

The work hereby presented aims to overcome these limitations by developing modeling frameworks that allow for exploring various configurations of multi-energy systems based on non-comparable objectives. The goal is to extract trade-off solutions through optimization algorithms under different future scenarios. The framework integrates the single-objective configuration model CALLIOPE with multi-objective evolutionary algorithms to explore the decision space thoroughly. Multiple algorithms are tested, and the best-performing algorithm is used to extract optimal configurations under alternating scenarios of renewable energy generation potential and energy prices.

The new framework is tested on a synthetic case study based on the Sulcis Iglesiente (SI) Province in Sardinia, Italy, a region facing socio-economic challenges exacerbated by the planned phase-out of a local coal power plant. The analysis considers opportunities for investing in renewable resources, expanding the local renewable power pool, installing energy storage batteries, and transitioning from gas and oil boilers to heat pumps and biomass generators. Objectives such as air quality, energy independence, economic considerations, and emission targets are taken into account.

Results demonstrate that the new methodology allows for the extraction of multiple optimal configurations of the multi-energy system, incorporating different technology combinations based on the relative importance of objectives. Among the tested algorithms, EpsMOEA and OMOPSO perform the best, thoroughly exploring the decision space and returning unique optimal configurations. Scenario analysis reveals that the attractiveness of certain technologies, especially for heat generation, is highly sensitive to different objectives and scenarios. In contrast, others, such as onshore wind plants, remain favorable regardless of circumstances.

The methodologies presented in this work signify a significant step forward in finding optimal planning and management solutions for multi-energy systems. They successfully capture the intrinsic complexity of the problems considered, supporting the search for integrated, efficient, participatory, and sustainable solutions.

How to cite: Tangi, M. and Amaranto, A.: Designing integrated and resilient multi-energy systems via multi-objective optimization and scenario analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3162, https://doi.org/10.5194/egusphere-egu24-3162, 2024.

X4.159
|
EGU24-6895
|
ECS
Olalekan Omoyele, Silvana Matrone, Maximilian Hoffmann, Emanuele Ogliari, Jann Michael Weinand, Sonia Leva, and Detlef Stolten

Energy system optimization has become an indispensable tool for planning the energy transition. However, model accuracy has traditionally been limited to hourly resolution due to data availability and computational complexity. This study quantifies resolution-induced inaccuracies in hourly and sub-hourly energy system optimization models. It focuses on a self-sufficient residential building by converting minutely-resolved renewable supply and demand data from Milan, Italy into data at five-, ten-, 15-, 30-, and 60-minute intervals using both averaging and sampling methods.

The average hourly resolution shows an underestimation of 1.71% in the total annualized cost of the system compared to the minutely resolution. In the electrical sub-system, the photovoltaic inverter is predominantly affected, being twice as large at minutely resolution in order to handle supply and demand peaks on the sub-hourly scale. To test for reliability, the operational performance of the optimal system layouts obtained from different resolutions is tested with minutely-resolved data. Our results show that system designs obtained for lower resolutions are infeasible for minutely data with lost loads of up to 89.37 kWh per year or 1.37% of annual electricity demand. Depending on the value of the lost load cited in the literature, this accounts for up to €893.67 of yearly inconvenience costs. A second method based on regular sampling (i.e., taking every 60th value of the original time series) shows either an under- or overestimation of the total costs depending on the selected sample (there are 60 in total), with a tendency towards conservative design layouts. The two methods (sampling and averaging) reveal that hourly resolution could be sufficient with respect to total system cost approximations, but is unacceptable for sizing dynamically-operated components and strict reliability requirements.

Future research should seek to provide higher-resolved data on intermittent renewable energy sources and appropriately handle the resulting increased computational complexity of energy system models.

How to cite: Omoyele, O., Matrone, S., Hoffmann, M., Ogliari, E., Weinand, J. M., Leva, S., and Stolten, D.: Impact of Sub-Hourly Resolution on the Design and Reliability of Residential Energy System Models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6895, https://doi.org/10.5194/egusphere-egu24-6895, 2024.

X4.160
|
EGU24-11505
|
ECS
Esraa Elmaddah, Marco Gaetani, Fabrizio Fattori, and Mario Motta

Recently, the Italian government has revised its national energy and climate plan (NECP) and has significantly increased its objective for wind energy capacity. However, climate change itself can affect the availability of wind resources, due to the increasing frequency of extreme weather conditions, and possible shifts in the mean climate conditions. Meanwhile, RSE has been developing the Meteorological Reanalysis Italian Dataset (MERIDA) to monitor climate variability in Italy over the last 20 years. MERIDA consists of a dynamic downscaling of the ERA5 global reanalysis using the WRF-ARW limited area model and provides hourly data. While MERIDA has a spatial resolution of 7 km, RSE has also developed MERIDA High-resolution for Renewable Energy Sources (HRES) with 4 km spatial resolution. MERIDA HRES represents an upgrade of MERIDA to describe the most relevant meteorological variables for applications related to renewable energy e.g. wind, air temperature, solar radiation.

This work presents an assessment of both MERIDA and MERIDA HRES hourly datasets for the estimation of wind power production in Italy. A comparative analysis has been conducted based on three different types of wind variables, namely: wind speed at 100 m height from MERIDA HRES, 100 m height wind speed extrapolated from MERIDA HRES wind speed at 10 m height, and 100 m height wind speed extrapolated from MERIDA wind speed at 10 m height. A wind power density model has been also developed as part of this work to estimate the wind energy production using wind speed variables. A validation for the results has been conducted vs the hourly actual wind energy output at bidding zone level based on historical data (from ENTSO-E). The results present the impact of both changes in spatial resolution and extrapolation of MERIDA datasets on the expected wind energy output vs the actual energy output.

How to cite: Elmaddah, E., Gaetani, M., Fattori, F., and Motta, M.: Assessment of a Climate Reanalysis Product for Estimating Hourly Wind Energy Production in Italy, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11505, https://doi.org/10.5194/egusphere-egu24-11505, 2024.

X4.161
|
EGU24-15405
|
Mathias Rosendal

Large-scale energy system modelling plays a crucial role in the debate on energy system decarbonisation. It is common to use a bidding zone representation to model the European energy system due to the structure of the electricity market. However, this may underestimate infrastructure constraints at higher spatial resolutions. This question has been investigated in the literature, while methods for aggregating highly resolved data remain a research gap. In this study we explore various spatial resolutions using the sector-coupled energy system model Balmorel with a focus on the Danish energy system. The modelling framework will encompass detailed geospatial data of existing Danish power plants in combination with the atlite module for generating variable renewable energy (VRE) production profiles at different geographical locations. Utilising the further developed modelling framework in Balmorel, the impact of applying various spatial resolutions is thus investigated from a bidding zone, NUTS2, NUTS3, to municipal spatial resolution. Preliminary results indicate that transmission costs are underestimated at low spatial resolution. However, they remain a small part of total system costs at very high spatial resolution. Large operational differences are observed, which will be investigated further. These results will be discussed considering spatial aggregation methods and used to inform further research on a similar investigation at the European scale to advance the modelling of sector-coupled energy system models with high penetrations of VRE.

 

How to cite: Rosendal, M.: Analysis of Various Spatial Resolutions for Modelling Sector-Coupled Energy Systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15405, https://doi.org/10.5194/egusphere-egu24-15405, 2024.

X4.162
|
EGU24-17971
|
ECS
|
Joseph Vernier, Sylvain Edouard, Baptiste Amiot, Mike Van Iseghem, Martin Ferrand, Didier Combes, Guillaume Schuchardt, and Patrick Massin
Currently, our understanding of the impact of agrivoltaic systems on the crops is limited. The presence of panels modifies the micro-climate and therefore the radiative, thermal, and aeraulic exchanges between the crop and its surrounding (S. Edouard, 2022). These modifications can lead to a loss of agricultural production, but also to a crop protection against meteorological events. Crop models, such as DSSAT, are not suitable to study the impact of solar panels on crop growth as spatial and temporal averages in the models hide spatial heterogeneities caused by the panels, and the sub-daily phenomena are not simulated. Computational fluid dynamic (CFD) allows high-fidelity simulations of multi-physics problems on different time and length scales (such as thermal hydraulics in power plants, or the drag of a wind farm). First CFD simulations applied to agrivoltaics have been carried out by (S. Zainaly, 2023), and by (H. J. Wiliams, 2023). Through Joseph Vernier’s PhD thesis, EDF R&D has initiated CFD modeling applied to agrivoltaics.
 
The CFD solver code_saturne simulates the flow over the panels, as well as the radiation, the temperature, and the humidity fields. Moreover, a 2 layers force-restore soil model computes the energy and the water exchanges between the soil and the atmosphere. The effect of the micro-climate on the photosynthesis and the plant stomatal resistance must be considered to accurately predict the plant growth. That is why, the soil-plant-atmosphere continuum model (A. Tuzet, 2003) has been implemented in code_saturne and a simplified study case composed of four solar panels has been built. First simulations of the modifications of the micro-climate by the solar panels and how it impacts the crops are very promising. Indeed, spatial heterogeneities are well simulated for the radiation, and the soil temperature (Figure 1-4), as well as for the wind speed (Figure 5, 6), the plant temperature, the photosynthesis, and the evapotranspiration. Simulations of the impact of shading on the soil water balance reveals that the plant’s energy balance is locally modified in a complex fashion that depends on the agrivoltaic power plant geometry. Water stress is considered, and it interferes with the plant's ability to photosynthesize and to transpire. Thanks to the coupling of code_saturne and the soil-plant-atmosphere continuum model, the plant state is simulated along the day for different weather conditions and agrivoltaic configurations. This is a first step towards a deeper understanding of the physical interactions within a photovoltaic system.

How to cite: Vernier, J., Edouard, S., Amiot, B., Van Iseghem, M., Ferrand, M., Combes, D., Schuchardt, G., and Massin, P.: CFD, a radiative model, and a plant model to capture the interactions between solar panels, the atmosphere, the soil, and the plants in agrivoltaic configurations., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17971, https://doi.org/10.5194/egusphere-egu24-17971, 2024.

X4.163
|
EGU24-21977
|
ECS
Max Kleinebrahm, Jann Michael Weinand, Elias Naber, Russell McKenna, Armin Ardone, and Wolf Fichter

Rising energy procurement costs and declining capital costs for renewable technologies are provoking interest in self-sufficiency for individual buildings. In this study, we evaluate the potential of self-sufficient energy supply for 41 million freestanding single-family buildings in the European building stock under current and future (2050) conditions. We employ spatial microsimulation to derive a synthetic building stock, identify 4000 representative buildings and calculate weather-robust cost-minimal energy systems using a high-performance computing cluster. Subsequently, we train surrogate models to transfer the optimization results to the entire European building stock. Our analyses show that buildings in regions with low seasonality, high solar radiation and high electricity procurement costs have high potential for self-sufficiency. Under current techno-economic conditions, 53% of the 41 million buildings are technically able to supply themselves independently from external infrastructures by only using local rooftop solar radiation, and this proportion could increase to 75% by 2050. By paying a premium of up to 50% compared to grid-dependent systems with electrified heat supplies, building owners could make over two million buildings fully energy self-sufficient by 2050.

How to cite: Kleinebrahm, M., Weinand, J. M., Naber, E., McKenna, R., Ardone, A., and Fichter, W.: Two million European single-family homes could abandon the grid by 2050, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21977, https://doi.org/10.5194/egusphere-egu24-21977, 2024.

Posters virtual: Wed, 17 Apr, 14:00–15:45 | vHall X4

Display time: Wed, 17 Apr 08:30–Wed, 17 Apr 18:00
vX4.25
|
EGU24-13182
|
ECS
Maggie Davis and Matthew Langholtz

In the pursuit of net-zero targets, the United States Department of Energy releases the fourth in a series of national biomass resource assessments. Building on the studies conducted in 2005, 2011, and 2016, the Billion-ton 2023 (BT23) report, provides an advancement in the understanding of biomass resources in terms of quantity, spatial distribution, and economic accessibility. The goals of this report are to update to latest available input data (e.g., costs, yields, and economic inputs) and ensure equitable access to the latest biomass resource data and that results are findable, accessible, interoperable, and reusable (FAIR) through a data new portal. The assessment unveils nuanced regional variations in biomass availability, ranging from the immediate potential of forest wastes to the maturation of the market for woody energy crops cultivated on agricultural land. This presentation provides an assessment of renewable carbon resources potentially available from the forested and agricultural land bases in the CONUS. The analysis of biomass resources extends to forested landscapes, assessed using the Forestry Sustainability and Economic Analysis Model (ForSEAM). Additional biomass resources on agricultural land are modeled using the Policy Analysis System Model (POLYSYS), a partial-equilibrium linear programming model with a focus on the agricultural producer response. In collaboration with the U.S. Forest Service (USDA-FS), waste-based woody resources are assessed using Forest Inventory and Analysis (FIA) data and the Bioregional Inventory Originated Simulation Under Management (BioSUM) model. BioSUM models two case studies to determine the potential for trees and other waste resources to be harvested from forests, fostering resilience against the growing threat of wildfires. Throughout these analyses, sustainability constraints are incorporated including the net regeneration of forested stands, limitations on harvesting on steep slopes, and other good practices that would need to be applied based on local conditions. By providing detailed insights into woody biomass suitability for energy production, this research lays the groundwork for near-term woody biomass resource potential and a mature-market potential contributing to a developing bioeconomy. This comprehensive analysis underscores the pivotal role of biomass resources in steering the U.S. toward net-zero targets.

How to cite: Davis, M. and Langholtz, M.: Woody Biomass for the Developing Bioeconomy, a Billion-ton Report Update, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13182, https://doi.org/10.5194/egusphere-egu24-13182, 2024.

vX4.26
|
EGU24-19829
|
ECS
Konstantina Pyrgaki, Pavlos Krassakis, Andreas Karavias, Theodoros Zarogiannis, Evangelia Zygouri, Anna Mpatsi, and Nikolaos Koukouzas

The WINTER project, funded by the EU, aims to develop a web GIS interactive platform that will be used as a tool for the management of coal regions in transition. The platform will guide and engage stakeholders by sharing best practices and addressing transition challenges in pilot regions at different transition stages.  The applied methodology involved a three-step process: 1) developing a geodatabase to import and standardize geospatial datasets; 2) training and implementing a Machine Learning (ML) approach [1]; and 3) identifying and quantifying land cover (LC) changes from 2018 to 2021. Particularly in Western Macedonia (Greece), the Amynteo mine, illustrated a green transition (Figure 1), converting mining areas to bare soil, vegetation and water bodies, indicating strong reclamation potential. In contrast, the Ptolemaida mine, still operational, illustrated minimal land cover changes. In Poland and specifically, in Konin region results highlighted mining expansion, affecting agricultural and wetland areas. On the other hand, the Kazimierz mine, which is already at a closure phase, exhibited a significant green transition, with a marked increase in vegetation land cover. 

The following step was the assessment for the potential for Renewable Energy Source (RES) implementation utilizing, open-source geospatial datasets, considering factors like elevation/slope, wind speed, solar radiation, and land cover/land use were used. Scenarios were designed to identify preliminary suitable areas for Photovoltaic (PV) and Wind Parks (WP) installations. In Western Macedonia, potential sites were identified adjacent to the Ptolemaida mine limits, with a significant area suitable for PV parks. In Konin, the analysis within mine boundaries revealed similar suitability for PV and WP, showing the highest potential for RES implementation. Specifically, in Western Macedonia, the potentially suitable areas for PV was higher, up to 34% of the total studied area, in contrast to the 12%-18% range observed in Konin's mines. Additionally, the potentially suitable sites for WP in Western Macedonia seem to be related due to geomorphological differences, whereas in Konin, the suitability analysis based on results within the boundaries of the open-pit mine.

The present study has received funding from the Research Fund for Coal and Steel—2020, under grant agreement No. 101057228 (WINTER).

[1] Krassakis, P.; Karavias, A.; Nomikou, P.; Karantzalos, K.; Koukouzas, N.; Kazana, S.; Parcharidis, I. Geospatial Intelligence and Machine Learning Technique for Urban Mapping in Coastal Regions of South Aegean Volcanic Arc Islands. Geomatics 2022, 2, 297-322. https://doi.org/10.3390/geomatics2030017

How to cite: Pyrgaki, K., Krassakis, P., Karavias, A., Zarogiannis, T., Zygouri, E., Mpatsi, A., and Koukouzas, N.: Spatiotemporal evolution and renewable energy potential in coal regions in transition, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19829, https://doi.org/10.5194/egusphere-egu24-19829, 2024.