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, and analyze economic incentives to ensure reliable energy systems under current regulatory, market and tariff conditions.
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,including economic valuations of such impacts
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, assessments of environmental impacts, economic analysis of markets, policies and regulations, and forecasting applications , concerning renewable energy systems.
Orals: Fri, 2 May | Room -2.41/42
Coping with prolonged periods of low availability of wind and solar power, also referred to as "Dunkelflaute", emerges as a key challenge for realizing a decarbonized European energy system fully based on renewable energy sources. Here we investigate the role of long-duration electricity storage and geographical balancing in dealing with such variable renewable energy droughts. To this end, we combine renewable availability time series analysis and power sector modeling, using 36 historical weather years. We find that extreme drought events define long-duration storage operation and investment. The most extreme event in Europe occurred in the winter of 1996/97. Assuming policy-relevant interconnection, long-duration storage of 351 TWh or 7% of yearly electricity demand is required to deal with this event. As it affects many countries simultaneously, a storage capacity of 159 TWh or 3% of yearly electricity demand remains required even in the extreme case of unconstrained geographical balancing. Before and during Dunkelflaute events, we find complex interactions of long-duration storage with other flexibility options. Sensitivity analyses illustrate that firm zero-emission generation technologies would only moderately reduce long-duration storage needs. Thus, policymakers and system planners should prepare for a rapid expansion of long-duration storage capacity to safeguard the renewable energy transition in Europe. We further argue that using multiple weather years that include pronounced renewable energy droughts is required for weather-resilient energy system modeling.
How to cite: Schill, W.-P., Kittel, M., and Roth, A.: Coping with the Dunkelflaute: Power system implications of variable renewable energy droughts in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20413, https://doi.org/10.5194/egusphere-egu25-20413, 2025.
Addressing climate change and reducing greenhouse gas emissions requires integrating variable renewable energy sources, such as solar and wind, into national energy systems. However, periods of compounding low wind and solar potential, known as energy droughts or ‘Dunkelflaute’ conditions, present significant challenges for electricity grids. While some regional studies on energy droughts exist, there is no comprehensive global assessment of how future climate changes might affect these events.
Using temperature, radiation and wind projections from 13 global climate models, energy droughts are analyzed for the 1979-2100 period using coincidence threshold analysis for their detection (10th percentile of the marginal distributions of wind and solar potential). Our analysis reveals that for most regions of the world, the frequency and duration of energy droughts are projected to increase with rising levels of warming. For instance in India and in the Sahel region, the risk of compounding low wind and solar potential is twice as high in 2100 under the RCP8.5 scenario than during the 2000-2020 period.
We also show how different strategies of balancing solar and wind capacity such as minimizing the variance of the production instead of maximizing the mean can affect vulnerability to compound shocks; in some countries optimizing this balance could substantially decrease the frequency of compound energy droughts. We note that in some regions the risk of compound wind/solar energy droughts remains high regardless of the energy mix. This underscores the importance of adapting energy strategies to mitigate the risks of increasing energy droughts under future climate conditions.
This study emphasizes the need for a comprehensive approach to optimally align short-term mitigation goals with the long-term impacts of climate change.
How to cite: Lenoble, C., Guivarch, C., Kornhuber, K., Rivoire, P., Schmitt, J., Schwarzwald, K., Valla, M., and Vallet, A.: Global risks of renewable energy droughts under climate change, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15821, https://doi.org/10.5194/egusphere-egu25-15821, 2025.
Wind droughts, characterized by prolonged periods of low wind speeds, pose significant environmental and economic risks in many regions worldwide. These extreme events can severely disrupt electricity generation from wind farms, yet their drivers and potential impacts remain poorly understood. Here, using CMIP6 data under three future emissions scenarios (SSP126, SSP245, and SSP585), we identify robust increasing trends in the frequency and duration of wind droughts on both global and regional scales from 2015 to 2100. Notably, the duration of 25-year return events is projected to increase in northern mid-latitude regions, where declining cyclone frequency and weakening meridional thermal gradients are two key meteorological drivers for these trends. Furthermore, we highlight regions where record-breaking wind droughts (RWDs)—events deemed statistically impossible based on historical records—are more likely to occur in a warming climate. Regions such as eastern North America, western Russia, northeastern China, and north-central Africa have a higher probability for RWDs under future scenarios. This enhanced probability of wind droughts has important implications for wind farm site selection, a factor that has received limited attention in current assessments.
How to cite: Qu, M., Shen, L., Zeng, Z., Yang, B., Zhong, H., Yang, X., and Lu, X.: Prolonged wind droughts in a warming climate, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14142, https://doi.org/10.5194/egusphere-egu25-14142, 2025.
Power systems dominated by renewables are strongly affected by weather variability, affecting electricity demand and electricity generation across countries. In Europe, the interplay between electricity generation from sources such as wind, solar, and run-of-river hydropower, alongside electricity demand, can result in high residual load and associated renewable energy droughts (REDs). Concurrent REDs across multiple regions further challenge the increasingly interconnected European energy system. Understanding the compounding effects between energy sources and European regions is crucial to improving the reliability of power systems and reducing shortfalls. Here, we study such compounding effects during the season that is most affected by REDs by using weekly data of solar, wind, and river hydropower electricity generation and electricity demand derived from the PyPSA-Eur model forced with ERA5 weather data during 1941-2023 under present-day installed capacities. In the first step, by focussing on 129 small-scale areas in Europe, we find that wind electricity and electricity demand, including their interplay, are the primary contributors to REDs. Secondly, we explore spatially compounding effects by considering nine individual macro-regions, each composed of highly interconnected small-scale areas. Within each of the nine macro-regions, we find that anomalies in residual loads of small-scale areas compound to cause regionally aggregated REDs, particularly the most extreme REDs. Thirdly, in view of an increasingly interconnected European energy system, we inspect the interplay between shortfalls across the nine macro-regions, revealing that spatially compounding effects may enhance the risks for the energy system. The dependencies among residual loads of the nine macro-regions increase the probability of the regions simultaneously experiencing REDs. In addition, we find that the tendency of some macro-regions to experience REDs simultaneously increases extreme EU-aggregated shortfalls by 12% on average. This research underscores the need to consider compounding effects between electricity generation technologies and electricity demand across multiple regions in the design and optimization of electricity systems.
How to cite: Meng, Y., Zscheischler, J., Schmidt, J., and Bevacqua, E.: Compounding effects behind renewable energy droughts in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6607, https://doi.org/10.5194/egusphere-egu25-6607, 2025.
Extreme weather during winters have a significant impact on the security of supplies for power systems over the UK. Extremely low temperatures combined with low to moderate wind periods increase the net demand relative to the available renewable energy supply in the system potentially leading to disruptions and power cuts. This work introduces a novel way of shifting weather over time in a standard risk assessment framework to make maximum use of limited data on weather extremes in order to determine their worst-case possible impact if the demand is not moderated for example, by the Christmas or weekends during winters. The proposed method involves first mapping the historical weather from ERA-5 to electricity demand and generation time series using a regression model. The historical data is then scaled to different scenarios of demand and generation for current day and future. Once the rescaled series of demand and generation are obtained, a shifting operation in weather is performed forward/backward in time to obtain the final synthetic demand and generation series that can be used to assess risk of supply shortages. There is interesting application of both meteorology and power system engineering in this work and by shifting weather over time, more extreme weather days which fall on weekends or the Christmas period can be redistributed to peak demand periods (during weekdays) giving a more comprehensive perspective on how weather links to shortfall risks. Our analysis shows that for instance, during the extremely negative NAO winter of 2010-11, the effect of extremely low temperature and low-to-moderate wind conditions on demand and supply could have been worse even if the weather patterns had shifted slightly just by a few days. If a three day forward shift in weather patterns was observed, the shortfall risks increase from 1.9 to 2.9 days/winter as few colder days from the Christmas weeks are brought to the beginning of January for present day conditions. When the same shift in weather conditions are assumed in a higher weather sensitive scenario with increase in temperature sensitivity of demand from -0.6 GW/°C to -1.0 GW/°C and, increase in installed wind generation capacity from 15 GW each to 30 GW onshore and 20 GW offshore respectively in the future, the risk levels fall to 1.1 days/winter. This highlights how scaling up the wind generation capacity over the coming years will reduce reliance on conventional sources of energy and ensure a stable electricity supply, even under extreme conditions, while addressing the challenge of growing demand driven by increase in electrification in the future.
How to cite: Bhattacharya, A., Dent, C., Wilson, A., and Hegerl, G.: Assessing Shortfall Risk of UK power systems using shifts in winter weather Conditions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4357, https://doi.org/10.5194/egusphere-egu25-4357, 2025.
Within the EnergyProtect project the escalating risks posed by extreme weather events to renewable energy infrastructure and affects on production are tackled. As climate change intensifies storms, heatwaves, and heavy precipitation, the vulnerability of renewable energy systems demands advanced detection and prediction methods to ensure resilience. The project focuses on developing machine learning algorithms for detecting adverse weather patterns, as well as dynamical and machine learning physics-informed downscaling, to enable precise risk assessment and infrastructure protection strategies tailored to current and future conditions.
Central to EnergyProtect is a three-tiered methodology for weather risk detection and resilience assessment:
- Machine Learning detection methods: Advanced pattern detection algorithms integrate atmospheric domain knowledge to identify and classify high-risk weather patterns. These models improve detection accuracy by combining meteorological parameters with infrastructure-specific indicators.
- High-Resolution Physics-Aware and dynamical Downscaling: Convection-permitting models at 1–2 km resolution enable detailed simulations of localized extreme weather events, addressing the challenges of complex terrain where traditional models often fail.
- Probabilistic Risk Assessment: Infrastructure vulnerability data is combined with detected weather patterns to quantify resilience under various scenarios, incorporating economic incentives and regulatory frameworks to support adaptation.
Here, we show initial results of the adverse atmspheric event detection algrithms threatening energy infrastructure. Different methods, classical weather pattern and machine learning algorithms, are investigated. Historic events such as the storm Boris and events defined with the industry stakeholders are evaluated. Additinally, a first set of dynamical downscaled climate scenarios is used for selected adverse weather types to evalute the methods skills across the different resolution scales.
How to cite: Schicker, I., Lexer, A., Lehner, S., Bügelmayer-Blaschek, M., Lampert, J., Papazek, P., Hasel, K., Thiele, P., Baier, K., and Spiekermann, R.: Enhancing Energy System Resilience Through Advanced Detection of Extreme Weather Events, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14830, https://doi.org/10.5194/egusphere-egu25-14830, 2025.
As renewable energy, particularly wind power, becomes a cornerstone of global energy strategies, the accuracy of wind prediction models has critical implications for grid stability and economic efficiency. This study introduces a novel deep learning framework designed to significantly enhance the resolution and accuracy of wind data, thereby improving predictive models for wind power generation. Utilizing a combination of high-resolution Numerical Weather Prediction (NWP) data and lower-resolution reanalysis data, our model reconstructs wind data at a scale necessary for effective wind farm planning and operation. Employing advanced techniques such as Fast Fourier Transform (FFT) and Radially Averaged Power Spectral Density (RAPSD), the model analyzes multi-scale variability in wind patterns. This approach allows for a detailed examination of both large-scale atmospheric flows and finer meteorological phenomena—crucial for accurate wind prediction. In the spatial domain, a Uniform Filter segregates fine-scale from broad-scale features, enhancing the model’s ability to capture essential details without losing the context of overarching weather patterns. Generative Adversarial Networks (GANs) are a pivotal component of our methodology. These networks train to model the statistical distribution of wind features with high fidelity, bridging the gap between theoretical accuracy and practical applicability. By integrating stochastic and deterministic training elements, our model balances the randomness inherent in fine-scale wind variability with the necessary coherence of large-scale patterns. Preliminary tests demonstrate that our model achieves a Root Mean Square Error (RMSE) of 1.83 m/s, representing a significant improvement of 0.16 m/s compared to existing meteorological models. When deployed in real-world scenarios, such as a wind farm, the model shows a 23% improvement with a Normalized Mean Absolute Error (NMAE) of 0.17 in wind power prediction, enhancing both the reliability and economic viability of wind energy projects. This study not only advances the technical capabilities of wind data modeling but also provides a robust framework for the practical application of these improvements in wind power prediction. The deep learning approach outlined here holds considerable promise for transforming wind energy management and deployment, setting a new standard for precision in renewable energy technologies.
Keywords: Wind Data Downscaling, Multi-Scale Integration, Meteorological Gridded Data, Deep Learning, Wind Energy Management, Wind Farm Development
How to cite: Ding, J.-W. and Hsieh, I.-Y. L.: Deep Learning for Wind Power: Enhancing Prediction Accuracy through High-Resolution Data Reconstruction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5650, https://doi.org/10.5194/egusphere-egu25-5650, 2025.
Limiting global warming below 1.5°C calls for achieving energy systems with net-zero carbon dioxide (CO2) emissions likely by 2040, and the pledged actions under current policies cannot meet this target. Few studies have optimized global deployment of photovoltaic and wind power, leading to high uncertainties in the capacity and costs of mitigation. Here we present a strategy involving construction of 22,821 photovoltaic, onshore-wind, and offshore-wind plants in 192 countries to minimize the levelized cost of electricity. We identify a large potential of cost reduction by combining coordination of energy storage and power transmission, dynamics of learning, trade of minerals, and development of supply chains. Our optimization increases the capacity of photovoltaic and wind power, accompanied by a reduction in costs of abatement from $140 (baseline) to $33 per tonne CO2. Our study provides a roadmap for achieving energy systems with net-zero CO2 emissions, emphasizing the physical, financial, and socioeconomic challenges.
How to cite: Wang, Y., Wang, R., Tanaka, K., Ciais, P., Penuelas, J., Balkanski, Y., Sardans, J., Hauglustaine, D., Cao, J., Chen, J., Wang, L., Tang, X., and Zhang, R.: Global spatiotemporal optimization of photovoltaic and wind power to achieve the 1.5 °C target, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-139, https://doi.org/10.5194/egusphere-egu25-139, 2025.
How can large-scale energy system optimisation models attain high spatial resolution while remaining computationally tractable, with the aim of better representing spatial variability and assessing the environmental and societal impacts of scenarios?
Energy system optimisation models are a widely-used approach to generate and study scenarios for techno-economically feasible system designs that meet emission reduction targets. These models have a particular strength in representing the spatial-temporal variability of renewable generation and demand and the energy system’s capability to balance them.
Spatial detail is crucial for these models: first, to represent variability and flexibility needs accurately, and second, when the goal is to assess environmental and societal impacts of scenarios. However, models with extensive scope (continental or national) are limited by computational resources, which requires spatial aggregation. Regional models that resolve greater spatial detail, on the other hand, are usually limited in spatial scope and are not necessarily consistent with the broader context.
Here, we present, test and compare different downscaling methods that increase the spatial resolution of energy models for system design and operation using a 2-step approach. The first step involves running an aggregated model at low resolution, which provides boundary conditions for the second downscaling step, which yields a feasible solution at the desired high spatial resolution.
We compare the methods, some of them described in the literature, some of them entirely novel, with respect to design goals like consistency, approximation, variety and computational complexity reduction in a simple test setting, thereby providing original insights on their trade-offs. Our findings support an informed use of downscaling methods for energy system optimisation models, with a wide range of applications in refining large-scale models and incorporating local societal and environmental information into energy system scenarios.
How to cite: Launer, J., Lombardi, F., Tindemans, S., and Pfenninger-Lee, S.: From continental to local: Downscaling energy system models to detect local barriers and benefits, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15951, https://doi.org/10.5194/egusphere-egu25-15951, 2025.
Achieving global decarbonisation targets requires strategic planning to ensure efficient capital allocation. Transparent and reproducible energy modelling workflows can help address the complexities of renewable integration and synthetic fuel production, including the associated CO2 infrastructure requirements. This presentation compares insights from two widely used energy system models—PyPSA and REMix—applied to New Zealand, showcasing the scalability and reproducibility of open-source frameworks for investment decision support. Both tools deliver a highly resolved (in time, space, and technologies) energy transition for New Zealand.
PyPSA-NZ provides a sector-coupled analysis of hydrogen export strategies under varying renewable electricity shares and regulatory frameworks. Results underscore the need for rapidly scaling renewable shares to attract early investments in e-fuels while allowing for long-term emissions reductions. A critical interplay exists between domestic electricity demand, renewable expansion rates, and international energy trade.
In parallel, REMix-NZ identifies key infrastructure needs, including a 13-fold increase in power generation capacity—primarily from solar photovoltaics—supplemented by substantial new storage capacity, in addition to the existing hydropower fleet, to ensure reliable energy supply. In scenarios, we also explore e-fuel exports to the Pacific Islands.
We compare these two open-source modelling frameworks to highlight their differences and complementarities. We aim to provide actionable insights into decarbonisation pathways and hydrogen export strategies. The presentation concludes with lessons learned, addressing model development and implementation challenges while advocating for open, accessible tools to advance energy simulation and policy-making.
How to cite: Haas, J., Canessa, R., Schumm, L., Klausen, C., Dempsey, D., and Peer, R.: Advancing Transparent Energy System Modeling for Decarbonization Strategies: Lessons from New Zealand on eFuels and CO2 Infrastructure, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13789, https://doi.org/10.5194/egusphere-egu25-13789, 2025.
The Korean energy system is undergoing significant transformation, actively deploying renewable energy sources in response to climate change. The government has set ambitious targets to reduce greenhouse gas emissions by 40% by 2030 and achieve carbon neutrality by 2050, compared to 2018 levels. According to the national roadmap released in 2023, the share of renewable energy in electricity generation is expected to exceed 21% by 2030 and 30% by 2036. For instance, over the past decade (i.e., 2013–2023), we made remarkable strides as the total renewable energy installation has increased by 595%, and when considering only solar and wind energy, this figure rises to 1,596%. In this context, accelerating the energy system transition in Korea requires sophisticated modelling tools to investigate efficient system design and evidence-based policy decisions.
In this study, we developed and validated a Korea-specific energy system model, PyPSA-KR, built upon the PyPSA framework. PyPSA’s key strengths—scalability, visualization, flexibility, and openness—enable the incorporation of high-resolution spatial and temporal data, integration across multiple energy sectors, and the examination of emerging technologies and market structures. By validating the PyPSA-KR model for the Korean power sector, we confirmed its capability to effectively reflect the country’s unique conditions and to analyze optimal capacity expansions and the role of renewable energy in meeting the 2030 midway target.
This model provides a valuable analytical framework for macro-energy research, including scenario exploration that accounts for traditional energy phase-outs, renewable energy expansion strategies, grid reinforcement, and energy storage deployment. By delivering reproducible and transparent results, our study establishes a robust foundation for future energy system modeling in Korea, ultimately facilitating strategic decision-making toward carbon neutrality. The PyPSA-KR not only supports policy development, infrastructure planning, and investment decisions but also contributes to ensuring long-term sustainability and energy security within the Korean energy system.
How to cite: Kwak, K. and Woo, J.: PyPSA-KR: Implementation and analysis of optimal renewable energy strategies for the 2030 midway milestone in the Korean energy system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2122, https://doi.org/10.5194/egusphere-egu25-2122, 2025.
The transition to a climate-neutral energy system poses spatial planning challenges due to the growing dependence on decentralized renewable energy and the uneven distribution of supply, demand, and infrastructure. While Energy system models (ESM) can be instrumental in identifying energy transition pathways and they are able to assess the effects of energy and climate policies, they usually lack the adequate incorporation of spatial elements, e.g., conflicts among different land claims. The integration of ESM with spatial models can help in identifying the impact of spatial elements on the energy transition pathways by considering, e.g., socioeconomic dynamics, land use conflicts, and infrastructure constraints. This study aims to develop a modeling framework to explore interactions between spatial planning and the design of a climate-neutral energy system. For this purpose, we improve the spatial resolution of an ESM and design a spatial model that incorporates spatial planning scenarios and ESM requirements. Moreover, we elaborated the parameter exchange between these two models.
For enhancing the ESM resolution, we develop a nested approach to increase spatial granularity in five steps. First, we provide the spatial input data such as solar and wind potential in high resolution, e.g., in 20 km2. Second, an initial clustering is performed to generate the desired number of nodes for the country (e.g., 30 nodes), which we refer to as the full-resolution ESM. Then, the country is divided into macro regions (e.g., 5 macro regions) through a second clustering to cost-optimize the ESM at lower resolution. The energy system optimization is then performed individually for each macro region at full resolution. Finally, all optimized macro-regions are then combined to achieve a national-scale ESM at full resolution.
For integrating spatial planning, we use national spatial planning scenarios to guide land use allocation within high-resolution spatial grids. We designed a spatial optimization model to allocate required energy system components and other land use demands, ensuring the energy system is spatially feasible at minimum cost. This methodology employs a recursive platform to exchange feedback between the spatial model and ESM that enable the iterative improvement to obtain more reliable results. For example, the ESM may initially determine the placement of wind farms based on land availability and suitability criteria for wind energy at each node. However, if the spatial model cannot accommodate the required wind farm area in a specific region due to competing land use claims, it provides feedback to modify the ESM in subsequent iterations. Each individual component of this framework has been tested in separate studies, and this comprehensive framework is part of our future work for the case of the Netherlands.
How to cite: Javanmardi, K., Fattahi, A., Ramirez Camargo, L., van der Hilst, F., and Faaij, A.: Integration of spatial planning and energy system modeling at the national leve, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21547, https://doi.org/10.5194/egusphere-egu25-21547, 2025.
While participatory research seeks to engage stakeholders, young people—who will bear the consequences of recent policy decisions—remain largely overlooked, as evidenced by climate strikes and environmental movements highlighting their growing distrust in the energy transition process. In this interdisciplinary study, we conducted workshops with students to develop diverse scenarios assessing the impact of pupils’ socio-techno-economic choices on the snapshots of a 2050 net-zero electricity system for Norway. We use those scenarios in an electricity system model for Norway. Our results indicate that pupil landscape preferences reduce Norway’s land-based capacity potential by more than half, resulting in an approximate 9% increase in the Levelized Cost of Electricity, from 790 to 880 NOK/MWh. Given that 68% of pupils favored offshore wind, integrating their technological preferences reduces the onshore wind portfolio from 33 GW to nearly zero in the most conservative scenario, accompanied by a reduction in solar capacity from 30 GW to 6 GW. Regional preferences lead to a concentrated allocation of new offshore wind installations predominantly in the west and south of Norway, areas with existing hydropower, potentially inducing local socio-political issues. Moreover, pupils supported new transmission lines and electricity trading under the condition of self-sufficiency, reducing system costs by approximately 8%, providing a win-win scenario for Norway and Europe. The cumulative impact of pupil choices significantly depends on how they are prioritized. The median system cost for achieving a net-zero emission system could likely be reduced by 7%-8% if Norway prioritizes investments in transmission infrastructure locally and through interconnections with neighboring countries while honoring youth preferences. Building on evolving participatory research, this study not only provides a framework for fostering mutual understanding with youth but also demonstrates their capacity for meaningful participation in energy transition discussions, thus promoting an inclusive and swift energy transition.
How to cite: Javed, M. S., Fossheim, K., Guzik, M., Seibt, B., and Zyringer, M.: How can youth perspectives shape Norway's 2050 electricity system?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18405, https://doi.org/10.5194/egusphere-egu25-18405, 2025.
Positive Energy Districts (PEDs) are a transformative approach to urban energy systems, targeted towards energy self-sufficiency, reduced carbon emissions, and improved energy equity. They aim to generate as much energy as they consume, often by integrating renewable energy sources, energy storage, and demand-side management. Among various renewable energy technologies, solar photovoltaics (PV) are increasingly deployed on rooftops to meet the neighbourhood’s electricity demand, while heat pumps are utilised for efficient space heating. The electricity generated from solar PV can power heat pumps, improving overall energy efficiency for a building. However, widespread solar PV adoption, especially during the summer months, generates excess energy that can lead to grid congestion. Therefore, not all neighbourhoods in a city may transition into a PED without substantial grid upgrades or expansions. In this study, we aim to identify neighbourhoods where solar PVs and heat pumps can achieve a net-positive energy balance in an optimum way.
We analyse the energy demand of buildings in the neighbourhoods of Den Burg Texel, an island in The Netherlands, focusing on identifying optimal neighbourhoods for PED implementation. To assess the feasibility of neighbourhoods for PED implementation, we simulate the electricity demand profiles of the buildings, combining typical electricity usage and potential demand from heat pumps. Using a 5R1C building thermal model, we simulate heat demand profiles for residential neighbourhoods, incorporating local weather data, building geometries, and occupancy patterns. We model three levels of insulation: existing, usual refurbishment, and advanced refurbishment, based on TABULA database (Loga et al., 2016). To evaluate renewable energy potential, we simulate solar PV generation with varying penetration levels, accounting for roof orientation, shading, and local climate conditions. For this analysis, we use the Time Series Initialization for Buildings (tsib) Python package (Kotzur, 2018), with local weather inputs from COSMO-REA6 reanalysis data.
We compute technical indicators such as unfulfilled demand, loss of power supply probability, excess energy, grid stability, and storage capacity requirements for all neighbourhoods. Our analysis suggests that integrating rooftop solar PV systems and heat pumps, along with insulation refurbishments, can significantly increase energy self-sufficiency in all the neighbourhoods. However, adopting solar PVs and using heat pumps in poorly insulated buildings can increase grid congestion, especially during peak generation or high heating demand in winter. Building refurbishments that lower the heat demand helps mitigate the challenges by reducing energy consumption. Based on the technical indicators, we identify neighbourhoods where solar PV systems and heat pumps can achieve a net-positive energy balance while minimising the challenges. Finally, we discuss how different neighbourhood characteristics influence the technical feasibility of the transition of a neighbourhood into a PED.
Kotzur, L. (2018). Future grid load of the residential building sector (PhD Thesis). RWTH Aachen University. https://scholar.archive.org/work/6ffkvyknnjgc3hb5uewdaklxua/access/wayback/http://publications.rwth-aachen.de/record/752116/files/752116.pdf
Loga, T., Stein, B., & Diefenbach, N. (2016). TABULA building typologies in 20 European countries—Making energy-related features of residential building stocks comparable. Energy and Buildings, 132, 4–12. https://doi.org/10.1016/j.enbuild.2016.06.094
How to cite: Joshi, M. Y., Ricker, B., and Ramirez Camargo, L.: Variability of Technical Challenges across neighbourhoods in transitioning to Solar-Powered Positive Energy Districts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15149, https://doi.org/10.5194/egusphere-egu25-15149, 2025.
The speed and scale needed to transition to a clean energy grid requires an integrated approach to address challenges in variability of energy resources and grid load. This research provides novel insights into the optimal use of otherwise-curtailed energy by analyzing the impacts of deploying different levels of renewable capacity on the cost, curtailment, and emissions reductions of future grid scenarios. The focus on New York State is motivated by a combination of its substantial energy demand stemming from large population centers, sizable offshore wind energy pipeline, enormous offshore wind energy potential, and ambitious clean energy targets. The extent to which supply is projected to outpace demand is quantified by employing grid load data in conjunction with high spatial and temporal resolution wind energy datasets with wind speeds at relevant hub heights for modern wind turbines. The resulting model captures a range of possible renewable capacity buildout scenarios mirroring existing state energy policy, and reports the quantity and temporal variation of curtailed energy for each. The study further identifies the conditions under which it would be most efficient to use this excess energy to fulfill requirements for technology such as green hydrogen electrolysis, carbon capture systems, or battery storage. This information can facilitate decision-making for strategic grid integration planning, including investment decisions around infrastructure that will help decarbonize hard-to-abate sectors. This study aims to enhance grid planning that will better serve end users by providing reliable and low-cost clean energy and support the burgeoning net zero carbon economy.
How to cite: von Krauland, A.-K., Modi, V., Goldberg, D., Xie, J., and Anastasiou, D.: Characterizing New York State Offshore Wind Energy Curtailment to Support Net Zero Technology Growth, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14107, https://doi.org/10.5194/egusphere-egu25-14107, 2025.
The operation of large offshore wind farms decreases wind speeds in and around the wind farm areas. Wind farm wakes can significantly impact annual energy production, especially in areas with high installed capacity density. We simulate the atmospheric flow during one typical year to estimate the wind resources for the North and South Baltic Seas using three scenarios: no wind farms, wind farms as installed in November 2021, and future wind farm deployment in 2030. We use two wind farm parameterisations in the WRF mesoscale model to model the wind farm wakes. The simulation’s wind speed climatology with and without wind farms is evaluated against a few available tall mast observations. Maps and spatial transects are created to illustrate the potential reductions in wind speed, capacity factors, load hours, and the distances needed for the wind to recover to its background values.
Based on simulations from this study (the first of its kind using nearly 40,000 individual wind turbines of over 400 different types), the yearly average (or over 20% in some regions). The wake’s impact on the capacity factor reductions can be detected from a distance of 20 up to 80 km downstream of the wind farms. This distance mainly depends on the installed capacity density, the extent of the wind farm and the background wind speed. Using an additional post-processing tool, we can calculate the production for each wind turbine in the domain and compare their production under various scenarios and parameterisations. The simulations also show that large wind farms can affect fields other than wind speed at hub height. They show a decrease in 2-m temperature and an increase in boundary layer height, particularly in summer. An increase in cloud fraction at the wind farm locations, particularly in winter, can also be detected in the modelling results. Although the mean annual changes in these quantities are not statistically significant at 95%, under particular stability conditions or seasons, they are.
How to cite: Hahmann, A. N., G. Alonso-de-Linaje, N., Imberger, M., Fischereit, J., Peña, A., and Badger, J.: Modelling large-scale wind farms and their climatic effects in the North and South Baltic Seas, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10750, https://doi.org/10.5194/egusphere-egu25-10750, 2025.
Europe’s strategy to decarbonize the energy sector in accordance with climate mitigation goals requires a high penetration of variable renewable energy sources, particularly offshore wind energy. As capacity is projected to expand significantly over the coming decades, the kinetic energy depletion from the atmosphere by large-scale wind farms cannot be ignored. Despite their implications, these regional-scale effects are typically underrepresented in the literature compared to intra-farm wake effects, which are successfully mitigated through adequate turbine spacing. In this study, we employ the ETHOS.REFLOW renewable energy potential workflow manager to evaluate the technical potential of the North Sea’s offshore wind resources in a fully reproducible manner. A comprehensive ocean eligibility assessment is conducted to assess the available areas for deployment, followed by explicit placement of individual turbines over the remaining areas. To assess the relationship between deployment density and efficiency losses, we model three distinct deployment scenarios for each sea region. Our approach involves an analysis of two major reanalysis-based meteorological time series datasets corrected for bias using observational wind data. We expand on a previously defined simple physics-based regional energy budget model accounting for the horizontal and vertical influx of kinetic energy and energy losses from conversion to electrical energy, surface friction, wakes and downward outflux, modelling the total power yield and reduction factors at a national level. Finally, we employ a cost model to calculate the levelized cost of energy for wind farms at a national level and compare the results for multiple scenarios, both with and without accounting for atmospheric kinetic energy removal. Our findings indicate a decline in cost-efficiency at large deployment scales related to efficiency losses as a result of atmospheric kinetic energy extraction. These findings are highly informative for energy system planners and policymakers given Europe’s planned intensification of its offshore wind sector over the next decade.
How to cite: Pelser, T.: North Sea offshore techno-economic wind potential incorporating regional atmospheric energy budgets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6211, https://doi.org/10.5194/egusphere-egu25-6211, 2025.
Offshore wind energy can play a significant role in meeting Colombia's electricity demand and decarbonising its energy system. This study explores the techno-economic potential of wind energy in the Colombian Caribbean Sea. We develop a methodological framework that uses scenario analysis to evaluate area availability for offshore wind energy, assess the technical potential at a regional scale and estimate the cost of technology deployment.
To analyse the available area, technical, environmental, social and traditional offshore activity constraints were considered. We formulated three scenarios with different levels of restrictions and potential offshore activity co-existence. For the available area in each scenario, the annual energy production was estimated using bias-corrected wind-speed data from the ERA5 reanalysis and the power curve of the 15 MW IEA turbine model. To estimate a spatially explicit LCOE, we build a cost structure from recent literature in which water depth, distance to onshore connection and turbine rating are the key variables of the cost functions.
The LCOE map reveals promising areas for wind energy development, many of which are located close to the coastline and shallow waters. However, it was found that under a scenario of high restrictions, the area is significantly reduced, and a large portion of the potential would be located in deeper waters and farther from the coastline, where the LCOE is higher, making the technology less competitive. Even though the technical potential would be sufficient to meet all of Colombia's installed capacity projected by 2050, the economically viable potential would be a fraction of it.
Our study presents an analysis that helps to understand the impact of various space management options on resource assessment and costs of offshore wind deployment. These results offer valuable insights to policymakers currently generating the country's policy and regulation for offshore wind development.
How to cite: Suarez Bermudez, L., Hahmann Robinovich, A., Faaij, A., and Ramirez Camargo, L.: Exploring the potential of wind energy in the Caribbean Sea, Colombia., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15108, https://doi.org/10.5194/egusphere-egu25-15108, 2025.
Climatic change mitigation strategies include the reduction of fossil fuels dependency and the increase of energy mix contribution from renewable sources. Oceanic renewable energy sources emerge as a promising alternative to diversify the energy mix. In the southwestern tropical Atlantic off Brazil, the Ocean Thermal Energy Conversion (OTEC) and the potential energy from surface currents were investigated. Time series of 40 years (1983 - 2022) of water temperature data (surface and 1000 m depth) were used to estimate thermal gradients. The temporal gradients showed no significant differences between the months over the annual cycle, with maximum thermal gradients >20ºC throughout the study region. The spatial gradient showed high thermal efficiency coefficients throughout the study region (h > 0.8), mainly in the North and Northeastern. The combined analysis of thermal efficiency and distance from the coast showed three points with the highest thermal efficiency ratings (h > 0.85) and the shortest distance (<30 km) for the effective implementation of an Ocean Thermal Energy Conversion-OTEC projects. Furthermore, the presence of the strong western boundary subsurface North Brazil Undercurrent (NBUC) in this region lead to the investigation of the current power density (CPD) at different vertical levels. The results showed four hotspots for marine current energy exploitation with CPD higher than 1000 W m-2, two of them related to the NBUC at depths between 150 and 250 m. All the hotspots identified were a consequence of flow-topography interactions, in particular because of changes in current dynamics due to coastline and shelf-break isobaths direction changes. We compared the hotspots in terms of closeness to the coast, closeness to oil and gas exploration blocks, stability of current core and absence of deep reef system at the subjacent shelf. Our results indicate that, besides the challenges of current core being in deeper layers, the undercurrent provides a stronger and seasonally stabler CPD than the surface currents. Finally, current and OTEC technologies can promotes access to clean, non-intermittent and sustainable energy sources, reducing greenhouse gas emissions and contributing to the mitigation of climate change.
How to cite: Araujo, M., Lima, T., Queiroz, S., Noriega, C., Silva, M., and Moura, M.: Assessing ocean renewable energy off Brazil, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2371, https://doi.org/10.5194/egusphere-egu25-2371, 2025.
How to cite: Pavez Orrego, C., Helth Gaukås, N., Småbråten, D. R., Flores, A., Monsalve, E., Barbosa, N., and Comte, D.: EVIBES Energy Harvesting from Natural and Anthropogenic Vibrations: Preliminary results , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17673, https://doi.org/10.5194/egusphere-egu25-17673, 2025.
Solar photovoltaic (PV) systems have emerged as one of the most effective technologies for converting sunlight directly into electricity through the photovoltaic effect. This rapid growth demonstrates the crucial role PV systems play in the global shift toward renewable energy. These systems exhibit remarkable versatility, capable of being deployed at diverse scales—from small residential setups that empower individual households to large utility-scale solar farms that contribute significantly to national grids. However, Complex multidimensional structure in urban environments have substantial impacts on solar PV energy harnessing. The intricate interplay of buildings, infrastructure, and urban geometry creates shading patterns and reflections that significantly affect the actual solar energy yields. However, satellite-derived estimates of PV potential often ignore these urban complexities, leading to substantial overestimations.
To tackle this issue, this study aims to propose a robust and cost-effective framework for quantifying the extent of overestimation by integrating high-resolution geostationary remote sensing imagery with LiDAR-based urban morphology data. First, we propose a hierarchical strategy for accurate large-scale solar position computation by sampling from the Solar Position Algorithm. Subsequently, the original global horizontal irradiance is decomposed into its primary solar constituents—beam, circumsolar, and isotropic—using solar position parameters. The digital surface model derived from LiDAR data simulates the effects of urban shading and sky occlusion on solar irradiance. The digital surface model derived from LiDAR data simulates the effects of urban shading and sky occlusion on solar irradiance. Ultimately, this method will enable the generation of accurate high-resolution solar energy potential maps and facilitate an analysis of the spatiotemporal characteristics of solar energy distribution patterns.
We use Hong Kong as the testbed, given its characteristic high-rise, high-density urbanization with multiple detailed data sources. Our framework is validated using eight in-situ ground measurements, showing a reduction in RMSE from 1.510 to 1.230 and an improvement in MAPE from 50.52% to 35.73%. Focusing on rooftop areas, our findings reveal that Hong Kong's overall solar energy potential in 2020 is 79.08 billion kWh, compared to 94.20 billion kWh estimated from direct satellite observations—a discrepancy of 19.11%, which highlights a significant overestimation. Our high-resolution maps have immense utility for urban planning and sustainable development, providing a precise tool for optimizing solar energy deployment in dense urban environments. These insights will aid in fostering more efficient and equitable energy solutions, contributing to the sustainable growth of urban areas.
How to cite: Wang, H. and Chen, B.: Substantial overestimation of satellite-derived rooftop solar energy potential in multidimensional urban environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10356, https://doi.org/10.5194/egusphere-egu25-10356, 2025.
Shifting to renewable energy sources is crucial for reducing greenhouse gas emissions and mitigating the effects of climate change. Photovoltaic (PV) systems are an essential technology for generating renewable energy. These systems show considerable variability in location and time due to their various scales, installation types, and geographic distribution. Accurately identifying and segmenting PV installations—whether rooftop or ground-mounted—is essential for assessing energy potential, monitoring system performance, and informing land use and regulatory strategies.
This study introduces a new method for segmentation of high-resolution photovoltaic (PV) systems by combining geoinformation, remote sensing data, and deep learning techniques. Unlike previous research that concentrated on specific types of PV installations, our approach allows for the simultaneous prediction of multiple categories of PV systems at a spatial resolution of 0.2 meters. By utilizing automatic labeling techniques and integrating datasets such as OpenStreetMap, we employ a state-of-the-art deep learning framework to improve the segmentation process and provide accurate spatial insights into PV deployment patterns. The proposed method achieves an overall accuracy of nearly 80 %, demonstrating its effectiveness in capturing the diverse characteristics of PV installations across various environments and instilling confidence in its reliability.
Our approach has practical applications in various areas, including assessing the spatial and temporal variability of renewable energy systems, evaluating infrastructure resilience to climate and weather extremes, and quantifying the land-use impacts associated with the expansion of renewable energy. This research aids in creating integrated scenarios for energy systems that incorporate a significant proportion of renewable resources by connecting technical, environmental, and economic aspects. The findings provide valuable tools for stakeholders involved in energy system modeling, urban planning, and policy development, ultimately advancing the goal of a sustainable and resilient energy transition.
How to cite: Keller, S. and Krikau, S.: High-Resolution Segmentation of Photovoltaic Systems: Leveraging Geoinformation and Deep Learning for Enhanced Renewable Energy Assessment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16214, https://doi.org/10.5194/egusphere-egu25-16214, 2025.
In order to achieve the Paris climate target of limiting global warming to below 2 °C compared to pre-industrial levels, the German government has launched the Climate Action Plan 2050. According to this plan, Germany is to achieve climate neutrality by 2045, which will require a comprehensive transformation of the energy sector, involving the gradual phasing-out of high-emission coal-fired power generation and the continuous expansion of renewable energies, especially wind and photovoltaic (PV). However, the low energy density of renewables leads to an immense demand for land. This results in major landscape changes, thereby triggering substantial land-use conflicts. Depending on the legal framework, the spatial patterns of these energy landscapes can vary considerably. The objective of our study is to analyze and visualize the potential spatio-temporal patterns of renewable energies for two regions that differ considerably from each other in terms of population size, economic structure, local industry, and natural potential for renewable energies. The first region under consideration is the rural Allgäu planning region, located in the far southwest of Bavaria, Southern Germany, the second is the governmental district of Cologne in Western Germany. The core of the study is the development of a dynamic distribution algorithm for the wind and PV locations for the modelling period 2023 – 2045. It facilitates the adjustment of assumed legal and consumption scenarios to estimate their respective impacts. The outputs of the algorithm are transferred to a geographic information system for visualization. Regional estimates of future electricity demand as well as wind and PV potentials serve as input data. The findings indicate that the objective of achieving climate neutrality in the Allgäu region by the year 2045 is, in principle, feasible across all scenarios examined. This assertion is accompanied by the recognition that achieving this objective will necessitate substantial alterations to the existing landscape, with some of these alterations being concentrated in specific areas. The magnitude of these anticipated changes varies significantly between the various scenarios considered in this study. The analysis indicates that the availability of sufficient PV potential is a prerequisite for the realization of climate neutrality in all scenarios, whilst the availability of wind potential is consistently found to be inadequate, with its capacity being exhausted well in advance of the conclusion of the modelling period. Conversely, the analysis indicates that attaining climate neutrality remains unfeasible in the Cologne region, even under scenarios of maximal renewable energy expansion, necessitating substantial alterations to the landscape. The study underscores the necessity for comprehensive assessments of regional factors to ascertain the viability of achieving climate neutrality.
How to cite: Schlenker, F., Petrovic, D., Bosch, S., and Kunstmann, H.: Energy landscapes with less than two degrees of global warming – a comparative case study of two German regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17401, https://doi.org/10.5194/egusphere-egu25-17401, 2025.
Wind turbines are essential to the renewable energy transition but can negatively impact local property values. To address these externalities and improve local acceptance, financial participation schemes are frequently discussed and adopted. However, these schemes often fail to account for regional disparities in property value losses, limiting their cost-effectiveness. This study evaluates the cost-effectiveness of three financial participation schemes designed to offset property value losses near wind turbines.
We used Causal Forests to quantify turbine-induced property value losses, utilizing data from ImmobilienScout24 (2000–2022) and the Core Energy Market Data Register (CEMDR). Property values were modeled based on proximity to turbines (0–1 km, 1–2 km, and 2–3 km) and socio-demographic factors. The cleaned dataset of 682,576 observations was analyzed to estimate Conditional Average Treatment Effects (CATEs). Generalized Additive Models (GAMs) extrapolated these effects to unobserved areas, providing spatially comprehensive property value loss estimates.
Compensation schemes analyzed included payments per kWh, per kW, and per turbine, distributed either by area or the number of houses within a 3 km radius. Payment ranges spanned €0.0–0.2 per kWh or kW and €0–1,000,000 per turbine.
We find that wind turbines reduce property values by 3.6% within 1 km, 2.4% at 1–2 km, and 0.9% at 2–3 km. GAM-based extrapolation revealed regional disparities: while most areas report minimal losses, extreme cases range from -€27.66 million to €4.33 million per 1 km². Total estimated property value losses related to current wind power deployment in Germany amount to €21.9 billion. To evaluate the schemes, we compare the total transfer required to offset 50% of the damages (€10.9 billion) and identify the corresponding tariff levels.
The most cost-effective scheme is the household-based per-turbine payment (€29.09 billion at €55,000 per turbine), followed by household-based per-kW tariffs (€33.54 billion at €3.1/kW) and area-based per-kW schemes (€37.30 billion at €4.2/kW), which better align with localized property losses than energy production-based models. Per-kWh schemes involve the highest transfers and overcompensation, particularly under area-based distributions (€39.81 billion at €1.8/kWh). Household-based per-kWh models (€35.29 billion at €1.3/kWh) slightly reduce overcompensation but remain less efficient. All schemes exhibit substantial targeting errors, overcompensating some communities while undercompensating others.
Our results also evaluate current financial participation schemes in Germany. At the national level, the Renewable Energy Act (EEG) provides €0.002 per kWh, covering only 14.6% of total damages. At the state level, Brandenburg's €10,000 per turbine payment covers 15.1%, and Saxony-Anhalt's €0.06 per kW tariff covers 57.3%. These policies inadequately address regional disparities in turbine-induced property losses.
In conclusion, our analysis demonstrates that financial participation can help to offset a substantial part of the property value losses produced by wind power deployment. However, fully compensating losses is financially impractical with existing models due to significant overpayment. Consequently, a combination of refined financial schemes, other localized benefits, e.g., through community ownership, and procedural participation is essential for fostering public acceptance.
How to cite: Vallapurackal, J., Heuer, F., Lehmann, P., Meier, J.-N., and Sommer, S.: Spatial Analysis of Financial Participation Schemes to Offset Property Value Losses near Wind Turbines, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19395, https://doi.org/10.5194/egusphere-egu25-19395, 2025.
Posters on site: Fri, 2 May, 08:30–10:15 | Hall X4
The long-duration energy storage (LDES) plays a critical role in enhancing the resilience of hybrid solar-wind energy systems, particularly during periods of prolonged low resource availability, known as energy droughts. In this work the energy droughts refer to extended periods when solar and wind generation are simultaneously insufficient to meet demand, posing significant challenges to ensuring power system reliability.
This study investigates the demand for LDES in mitigating energy droughts using 40 years of capacity factor historical data for solar and wind resources in Poland. To relate production from variable renewable energy sources (VRES) to electricity demand, a time series of hourly load data was developed with artificial neural networks, utilizing weather parameters and type of the day (working/holiday) as explanatory variables. By analyzing historical variability and the complementarity of VRES, considering different shares of solar and wind power in the energy mix, this research identifies the temporal patterns of energy droughts and quantifies their impact on energy shortage duration and deficits. To overcome these events, we evaluate the power capacity and discharge duration requirements for LDES solutions in a 100% renewable energy system scenario.
Here we show that the most extreme energy drought events in terms of both duration and energy deficit, occurred in the winter of 2005/2006. Reducing energy droughts to zero would require oversizing the VRES system to cover 160% of the multiannual mean demand, with a solar-to-wind ratio of 30:70. In addition, it would be necessary to implement energy storage capable of sustaining the mean load for 9 days, which is equal to 4.15 TWh of energy storage capacity.
The findings underscore the importance of optimizing the balance between solar and wind energy contributions and deploying substantial long-duration energy storage to ensure the resilience and reliability of a fully renewable energy system based on VRES. These insights provide a foundation for designing energy strategies that address the challenges posed by energy droughts.
The results presented in this study build upon the work conducted as part of project no. 2022/47/B/ST8/01113 funded by the National Science Centre (Narodowe Centrum Nauki) titled: Method to quantify the energy droughts of renewable sources based on historical and climate change projections data.
How to cite: Ostraszewski, M. and Jurasz, J.: Role of long-duration energy storage in solar-wind hybrid systems for drought mitigation: case study of Polish energy system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1259, https://doi.org/10.5194/egusphere-egu25-1259, 2025.
The integration of wind power into electricity markets has introduced significant challenges in Europe, most notably the cannibalization effect, where increased wind power generation leads to reductions in electricity prices. This study explores the spatiotemporal patterns of wind power cannibalization in Finland from 2015 to 2023. We developed a wind power production model for Finland using ERA5 reanalysis data and reference VESTAS V112-3000 kW turbine specifications. The model converts wind speeds at 100 meters to power output using validated power curves, enabling spatiotemporal analysis of the cannibalization effect and its regional variations. The analysis revealed that wind power production strongly influenced electricity prices, with a notable example in 2022, where production shows the strongest negative correlation with prices (-66.35 EUR/MWh per GW). In October 2022, wind power explained 60% of electricity price variability, emphasizing its substantial role in market dynamics. Spatially, the most pronounced effects occurred along Finland's western coast (Ostrobothnia) and the Gulf of Bothnia, regions with high wind power generation potential. Seasonal analysis further highlighted winter as the peak period for both wind power production and the cannibalization effect. These findings provide valuable insights for optimizing wind farm siting while maintaining economic viability in the Nordic electricity market.
How to cite: Bibi, A. and Marzeion, B.: Wind Power Cannibalization in Finland: A Spatiotemporal Analysis Using ERA5 Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2588, https://doi.org/10.5194/egusphere-egu25-2588, 2025.
The era of mobility has transitioned to electric vehicles (EVs), with energy—particularly renewable energy sources—forming an inextricable link to environmentally friendly power. However, the growing adoption of EVs increases the load on the power grid, and the integration of renewables introduces variability due to their intermittent generation. This mismatch between power demand and power generation can cause issues such as voltage and frequency instability. To mitigate these effects, the flexibility of EV charging can be utilized to optimize grid operations. Further, Vehicle-to-Grid (V2G) technology, which enables bidirectional power flow between the power grid and the vehicle’s battery, offers a dynamic approach to energy management. For medium and heavy-duty (MHD) EVs, the attributes of flexible charging and V2G operations make them particularly attractive candidates for enhancing grid stability. This study explored the potential to manage charging loads and harness surplus renewable energy using MHD EVs. Photovoltaic (PV) systems and EVs were strategically matched both temporally and spatially to create synergy that flattens the net load profile. Additionally, potential sites for bidirectional charging stations were identified through Geographical Information Systems (GIS) analysis, utilizing V2G technology to effectively utilize excess generated energy. This approach promotes sustainable mobility and contributes to grid stability in an environmentally friendly manner.
How to cite: Ku, J. and Park, H.-D.: Application of GIS analysis for PV and V2G optimization with medium and heavy-duty electric vehicles to enhance grid stability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3881, https://doi.org/10.5194/egusphere-egu25-3881, 2025.
Snow cover significantly impacts the energy generation of solar photovoltaic (PV) systems, often leading to prolonged periods of low energy production, or "energy droughts." These events are critical to understanding the reliability of PV systems, particularly in regions prone to snow accumulation. In this study, we analyze the effect of snow cover on solar PV generation across Poland for the year 2023 using data from meteorological stations, real PV systems, and multiple modeling approaches. Our analysis utilizes daily snow cover data from 681 meteorological stations, of which 118 were selected based on data completeness. Additionally, sub-hourly (15-minute) data from 174 real PV systems were collected, with 129 systems included in the final analysis due to achieving 95%+ data completeness. The real PV generation was compared against estimates derived from three modeling sources: the JRC PV-GIS tool (using SARAH3 and ERA-5 datasets) and Renewables Ninja (using MERRA-2 reanalysis).The results reveal a significant overestimation of PV generation by the reanalysis-based data sources during the days with snow cover. On average, for a day with an 11 cm snow cover, measured PV generation was only 0.33% of the generation observed on snow-free days. In contrast, MERRA-2 suggested 5.36%, ERA-5 estimated 3.52%, and SARAH3 provided the most accurate estimation at 1.18%, though it still overestimated real generation by nearly a factor of 4. On another snow-covered day with a 15 cm snow depth, real PV output was only 0.98% of typical snow-free generation. However, ERA-5 estimated this output at 58.75%, MERRA-2 at 14.7%, and SARAH3 at 7.98%.These discrepancies highlight a systemic tendency of reanalysis (and also satellite measurements) based tools to overlook the magnitude of low-generation events caused by snow cover. Such overestimations could lead to inaccuracies in energy yield predictions and hinder effective planning for both small- and large-scale power systems. The findings underscore the need for enhanced PV system modeling that accounts for snow-related energy droughts, ensuring greater accuracy in assessing the reliability and resilience of solar energy systems in snowy regions.This study provides valuable insights for improving the integration of PV systems in power systems modeling, emphasizing the critical need for accurate snow-cover correction mechanisms in widely used solar generation estimation tools.
The results reported here build on the works conducted as a part of the project no. 2022/47/B/ST8/01113 funded by the National Science Centre (Narodowe Centrum Nauki) titled: Method to quantify the energy droughts of renewable sources based on historical and climate change projections data. A complementary part of this work also refers to no. BPN/BEK/2023/1/00278 funded by the National Agency for Academic Exchange (Narodowa Agencja Wymiany Akademickiej) titled: Harvesting the Elements: Investigating the Economic Value of Complementarity Between Solar and Wind Energy Resources in Large-Scale Power Systems under Extreme Events
How to cite: Jurasz, J., Ostraszewski, M., Stachura, G., Bochenek, B., Ciapała, B., Cui, Y., and Kies, A.: Energy Droughts in Solar PV Generation Caused by Snow Cover, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6203, https://doi.org/10.5194/egusphere-egu25-6203, 2025.
The transition towards renewable energy sources presents resilience challenges to power systems, especially during climate extremes such as Dunkelflauten (prolonged periods of low wind and solar power), hydrological droughts and heatwaves. For Africa, energy transition planning is underexplored, including the potential impact of climate change on future power systems. We will analyse the impact of various climate extremes on electricity generation, demand, transmission and distribution across different spatiotemporal scales. By examining historical climate extremes and potential future ones under climate change, including compound events, we will assess the possible consequences and adaptive measures for power systems. The research will focus on identifying key characteristics of climate extremes such as frequency, intensity, extent, and duration across diverse regions and periods, and how these translate into consequences for power systems, with a specific focus on case studies for African countries. The research intends to form the foundation for proposing adaptations to power system models to make them more "climate extremes-aware".
How to cite: Mast, T., Sterl, S., Thiery, W., and Gupta, R.: Building Resilient Power Systems in Africa: Adapting to Climate Extremes and Energy Transition Challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6997, https://doi.org/10.5194/egusphere-egu25-6997, 2025.
High electricity generation costs remain a significant barrier to wind energy adoption. Projections of a 37–49% cost reduction by 2050 have driven the expansion of offshore wind farms (OWFs), which benefit from larger installations and abundant wind resources. However, climate change poses risks to OWFs, with extreme weather events (EWEs) potentially exposing turbines to conditions beyond their design limits. This study develops a multi-criteria decision analysis (MCDA)-based framework to optimize OWF siting in the UK Exclusive Economic Zone (EEZ), ensuring resilience to EWEs and future wind variability.
The framework evaluates future high wind events (HWE) exceeding turbine cut-out speeds and extreme loading thresholds, alongside low wind events (LWE) impacting power generation stability. Using high-resolution UK climate projections (UKCP18), the study integrates critical datasets—mean wind speed, gusts, temperature, and pressure—into site selection. Three MCDA methods (Vikor, Topsis, Cocoso) were identified as most effective based on strong correlation tests and applied to assess ten factors across three climate periods.
Results indicate that 17 MW turbines align with industry trends, while repowering existing OWFs in the East is less favorable due to future ranking declines. The Northwest emerges as the preferred region for new installations, offering greater resilience to climate impacts and ranking stability.
This work supports planners in strategic wind power capacity distribution, reducing variability, enhancing turbine resilience, and integrating climate projections into OWF planning. The framework provides a robust tool for adaptive and sustainable wind energy development in a changing climate.
How to cite: Abdelaziz, S. A. M., Sparrow, S., Hua, W., and Wallom, D.: Framework for Offshore wind farms’ future location planning in the UK exclusive economic zone, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7443, https://doi.org/10.5194/egusphere-egu25-7443, 2025.
The development of rooftop photovoltaic (RPV) systems presents significant advantages for energy and territorial planning, including the utilization of existing urban spaces and the decentralization of energy production. This study investigates the factors influencing RPV adoption across Italy, focusing on economic, demographic, environmental, and socio-cultural dimensions.
Using a multi-scale framework, we analyzed adoption patterns at national, electric market area, and regional levels through four complementary modeling techniques: linear regression, random forests, correlation analysis, and spatial econometric regression. Random forests proved effective in capturing complex, non-linear interactions, while spatial econometric models highlighted the influence of geographic proximity on adoption rates.
Key drivers of RPV adoption include population density, income levels, and characteristics of the built environment, which were consistently significant across scales. Environmental factors such as altitude gained prominence at more localized levels, emphasizing the importance of territorial context. Additionally, neighborhood effects and peer influence emerged as critical in shaping adoption patterns, revealing the interplay of social and spatial dynamics. The multi-scale approach reveals the nuanced influence of variables, showing how their significance shifts depending on the spatial scale of analysis.
How to cite: Amaranto, A., Aiello, M., Galbiati, I., Ronchetti, G., Tangi, M., and Garofalo, E.: Exploring the Drivers of Residential Photovoltaic Adoption in Italy: Insights from Multi-Model and Multi-Scale Analyses, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10516, https://doi.org/10.5194/egusphere-egu25-10516, 2025.
Effectively integrating variable renewable energy sources (VRES) into energy systems requires understanding their patterns and dynamics. Combining multiple VRES can take advantage of their complementary features, reducing energy storage needs, optimizing transmission infrastructure, and enhancing system reliability to meet energy demands.
This study assesses the evolution of complementarity at the country level between solar, run-of-river hydro (RoR), and wind (onshore plus offshore) power generation in seven European Union countries (Spain, France, Germany, Croatia, Italy, Poland, and Portugal) and the contribution of these VRES to load service during the period from 2016 to 2024. The metrics employed correspond to the stability coefficient (Cstab) proposed by Sterl et al. in 2018 and the total temporal complementarity index (kt) presented by Canales et al. in 2020 since both allow the simultaneous evaluation of complementarity between three VRES. The study used public net electricity generation data from the European Network of Transmission System Operators for Electricity (ENTSO-E). Countries were selected based on the availability of information for the three VRES during the aforementioned period, and a heterogeneous spatial distribution. No Nordic country had enough information available for at least one of the 3 VRES; consequently, they were omitted. For Cstab estimation by country and year, the base VRES is defined as the one with the highest average production. The maximum annual generation serves as the basis for calculating capacity factors, but this is a limitation to acknowledge since generating units may be added or removed throughout the year.
Under these considerations, the Cstab and kt metrics indicate that systems whose main VRES is wind power benefit from complementarity, improving their load-serving capacity. In 2024, Croatia and Portugal achieved a kt value of up to 0.65 and mean Cstab values >0.40, suggesting strong complementarity. Croatia’s highest mean Cstab was 0.48 in spring, while Portugal’s peak was 0.44 in winter. Both countries had their lowest values in summer, with Croatia at 0.41 and Portugal at 0.33. For comparison, Portugal had a mean Cstab of 0.26 in 2016, the only figure above 0.20 in the dataset. Due to the lower daily-scale variation in RoR generation, the mean Cstab averaged >0.10 for France and Italy (where RoR predominates) during the study period. However, in Italy, wind and solar energy exhibit complementarity (Cstab >0.40), which is noteworthy as these sources are more viable options than RoR for increasing installed capacity in these countries. This trend is further supported by the evolution of the contribution of these VRES to meeting the load, which increased from a simple average of around 27% in 2016 to 37% in 2024. This growth was primarily driven by an over 500% increase in installed solar capacity in some countries like Poland and Portugal, while RoR has remained constant or declined.
This study contributes to better comprehending the integration of multiple VRES and how complementarity metrics can be included in practical applications and assessments, supporting global efforts toward decarbonization and sustainable energy development.
Acknowledgements:
- Canales: ULAM NAWA programme Agreement: BPN/ULM/2022/1/00092/U/00001.
- Jurasz: Narodowe Centrum Nauki Project: 2022/47/B/ST8/01113.
How to cite: Canales, F. A. and Jurasz, J.: Complementarity between Solar, Run-of-River Hydro, and Wind Power in European Countries: An assessment from ENTSO-E data for 2016-2024, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12289, https://doi.org/10.5194/egusphere-egu25-12289, 2025.
Wind power deployment has experienced massive growth in the last two decades. To achieve climate mitigation goals and decarbonize our energy production much more installed capacity is necessary. However, this growth has to be in line with the minimization of the environmental and societal impacts, and requires the support and acceptance of citizens and local stakeholders. Access to information about possibilities of wind power deployment and its impacts is scattered and in many cases only available to experts in the wind power industry. To make this information available to a wider public, simplify high level assessments of wind power installations and objectivize discussions about individual projects, we have developed the WIMBY interactive map and forum. It was developed using a design thinking approach, featuring co-creation workshops with targeted end-user groups to define and prioritize the tool’s technical requirements. Participants included 45 professionals from universities, consultancies, and NGOs, with expertise ranging from meteorology to law, including engineers, biologists, and social science experts, and 10 representatives of interested audiences (e.g. local communities, activists and landowners), specialized users (e.g. transmission system operators, consultants, wind power engineers) and education (e.g. secondary school teachers). The co-creation process involved five steps: defining the target audience, conducting user interviews to collect their needs and concerns, defining and assessing user flows, developing wireframes, and creating interactive prototypes for testing. The input of this co-creation process provides valuable information for the next step: implementation. This is executed as an iterative process with a first complete version that will be tested thoroughly and further refined. The WIMBY interactive map spans the entire European continent and offers two modes: exploratory and planning. In exploratory mode, users can visualize wind energy layers, including wind speeds, capacity factors, existing wind farms, landscape metrics, and collision risks for birds and bats. In planning mode, the tool optimizes wind turbine locations by maximizing energy output. After the locations are defined the tool calculates several associated environmental and societal impacts. A life cycle assessment provides total CO2 emissions, while noise, shadow-flicker, land use, sea change, and job creation are estimated on the fly using open-source models. Regulation warnings/alerts for each location are also displayed. Finally, to enable cross-exchange and direct feedback on prompted simulations, an associated online community is being developed as an open discussion forum. The WIMBY interactive map and forum goes beyond the New European Wind Atlas by also providing options to optimize locations of individual turbines and delivering comprehensive information about a wide range of potential environmental and societal impacts for hypothetical wind farms. The first functional version of the WIMBY interactive map has already been successfully tested in three pilot regions with a diverse set of stakeholders, corroborating the expectations and consolidating the design. In this poster presentation you also get the opportunity to test Version 1 of the WIMBY interactive map.
How to cite: Ramirez Camargo, L., Hueting, R., Bakelants, L., Giorgi, G., Davis, N. N., and Hahmann, A. N.: A tool for high level planning and impacts assessment of wind farms in Europe: The WIMBY interactive map and forum, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13686, https://doi.org/10.5194/egusphere-egu25-13686, 2025.
With the phase-out of nuclear power in Switzerland by 2035, planning the nation's energy system towards a predominantly renewable future is both a critical and complex challenge. Optimization models offer valuable insights by enabling the exploration of temporal dynamics and spatial configurations of renewable energy deployments, in particular also for the spatially distributed sources wind and solar. Addressing the uncertainties of the evolving renewable landscape driven by many exogenous parameters requires models that can integrate diverse climate data sources, emerging technology features and adapt to modifications of existing ones.
We introduce OREES (Optimized Renewable Energy by Evolution Strategy), a generic optimization framework for spatial wind turbine and solar panel allocation based on a genetic algorithm and able to support economic, social and environmental objectives. OREES integrates diverse inputs, including radiation, wind and hydrological data, load demand, grid characteristics, existing infrastructure, and a detailed hydro-power system modeling, to optimize the spatial configuration of renewable energy projects while minimizing imports, costs, and/or biodiversity impacts. Its flexibility enables seamless incorporation of emerging and improving technologies, and contextual scalability, from national systems to urban or alpine scenarios. This model's scalability enables the validation of urban-scale projects within the broader context of national-level modeling.
OREES exemplifies a methodology for dynamically evaluating renewable energy deployment strategies, bridging diverse spatial and temporal dimensions, and supporting stakeholders in navigating the complexities of energy transitions. Using the example of Switzerland, with its strong climate heterogeneity, we show how an optimal placement of wind and solar installations allows for a stable supply with minimal import. We further show that the system is able to accommodate further constraints such as a preference for one or the other technology.
How to cite: Cintas, A.: From national to local scale: The optimal renewable integration strategy in Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15802, https://doi.org/10.5194/egusphere-egu25-15802, 2025.
The energy transition demands careful planning, considering economic, technical, social, and resource constraints. In Europe, while electrification targets are ambitious, marine renewables remain underexplored. This study aspires to enhance the PyPSA-Eur framework to create PyPSA-Eur-MREL by integrating all marine renewable (bottom fixed offshore wind, floating wind, floating solar, wave energy, tidal energy) sources using high-resolution datasets (≤ 5Km) to the energy system model of Europe for electricity. The study evaluates power output, deployment strategies, and packing densities of the energy carriers, projecting the impact of marine renewables across Europe for 2030, both in terms of power security, area-usage, with respect to greenfield generation, and the Offshore Energy Strategy (OES). As base scenario the ERA5 dataset is utilised, our higher spatio-temporal resolution data, install more marine renewables, reduce energy storage needs by 73%, minimise wind energy installed capacity by 50%, lower system curtailments by 60%, finally system costs for a 2030 fully renewable system drop by 40% per year. The presence of marine renewables offers cost savings, improves demand matching, and have a higher spatial energy density, highlighting their critical role in decarbonisation of the electricity sector.
How to cite: Mezilis, L. and Lavidas, G.: Benefits of marine renewable energies and high-resolution datasets for energy systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16576, https://doi.org/10.5194/egusphere-egu25-16576, 2025.
The Wind4Future project addresses the effects of climate change on the wind climate and the expansion of wind energy production in Austria. Wind is an important renewable energy source, and wind power has made considerable progress, particularly in eastern Austria, but changes in wind patterns due to climate change can impact both existing and planned wind generation sites.
The project's key goals are to: (1) evaluate changes in wind energy generation due to climate change in eastern Austria, (2) assess the impact of climate change on the achievement of national energy and climate goals, and (3) draft a whitepaper on challenges and opportunities for wind energy under climate change under climate change.
To achieve these results, the project applies a multi-step approach based on wind speed data, climate model simulations, machine learning, and scenario analysis. Advanced interpolation algorithms are used to generate high-resolution wind speed data and machine learning algorithms are used to extrapolate the wind speed fields to hub heights above 100 m and to model wind farm performance. The wind power potential is calculated for selected current and future wind turbine types, using a scenario approach. In collaboration with industry partners, future wind power generation scenarios are developed considering wind potential, geography, infrastructure, and turbine technology advancements.
By integrating these methods, the project analyzes the effects of climate change on Austria's wind energy potential and generates valuable data on future wind climate and power production, addressing gaps in existing knowledge. The results will be summarized in a whitepaper to inform political decision-makers and industry about the implications of climate change for wind energy in Austria.
How to cite: Lexer, A., Schicker, I., Tilg, A.-M., Andre, K., Heidenhofer, M., Janisch, S., and Bisko, N.: Assessing the Effects of Climate Change on the Future Wind Energy Landscape in Austria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17946, https://doi.org/10.5194/egusphere-egu25-17946, 2025.
Wind energy infrastructure projects have increased in the last decade due to their low carbon emission impact, making wind energy a crucial element in Europe's transition to a net zero society. However, wind energy infrastructure has location-specific natural and social impacts. Opposition to wind energy deployment has increased across Europe, and in many countries threatens achieving climate targets. Despite this, a comprehensive, multi-dimensional analysis of these trade-offs is missing.
Here, we address this gap by using an electricity system model at NUTS2 resolution, enhanced with novel spatial data on social (e.g. quantification of landscape senicness) and natural (e.g. bird and bat strikes) impacts. We explore how social and natural constraints impact the costs and design of future net-zero European electricity systems. Our findings allow policy makers and the public to make informed decisions on where to prioritise wind energy or other technologies.
How to cite: Zeyringer, M., Price, J., Vågerö, O., Valenzuela, G., Etard, A., Chen, R., Visconti, P., and Ramirez Camargo, L.: Multi-Dimensional Trade-Offs of Wind Energy in Europe's Net-Zero Electricity System , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19739, https://doi.org/10.5194/egusphere-egu25-19739, 2025.
Variable renewable energy droughts, also referred to as "Dunkelflaute'', emerge as a challenge for realizing climate-neutral energy systems based on variable wind and solar power. Using data on 38 historic weather years and an advanced identification method, we characterize European drought events for on- and offshore wind power, solar photovoltaics, and policy-relevant renewable technology portfolios. We show that drought characteristics heavily depend on the chosen threshold. Additionally, single-threshold analysis fails to detect heterogeneous drought patterns and therefore may lead to incomplete drought characterization. Using single thresholds, as common in the literature, is thus not advisable. Applying a multi-threshold framework, we quantify how the complementarity of wind and solar power temporally and spatially alleviates drought frequency, duration, and severity within (portfolio effect) and across countries (balancing effect). We further identify the most extreme droughts and show how these drive major discharging periods of long-duration storage in a fully renewable European energy system. Such events comprise sequences of shorter, contiguous droughts of varying severity. In a perfectly interconnected Europe, the most extreme drought event occurred in the winter of 1996/97 and lasted 55 days. Yet, the average renewable portfolio availability during this event was still 47% of its long-run mean. In individual countries, such events may last substantially longer and exhibit even lower average availability. For example, the most extreme storage-defining drought in Germany lasted 109 days and occurred in the winter of 1995/96. As extreme droughts may span across the turn of years, single calendar-year planning horizons are not suitable for modeling weather-resilient future energy scenarios.
How to cite: Schill, W.-P. and Kittel, M.: Quantifying the Dunkelflaute: An analysis of variable renewable energy droughts in Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20956, https://doi.org/10.5194/egusphere-egu25-20956, 2025.
Posters virtual: Mon, 28 Apr, 14:00–15:45 | vPoster spot 4
EGU25-9146 | Posters virtual | VPS16
Wave atlas of French Polynesia – Application on wave energy integration into the electrical mixMon, 28 Apr, 14:00–15:45 (CEST) | vP4.9
A new wave energy atlas database for French Polynesia
A wave reanalysis was carried out over the whole French Polynesian ZEE, using Météo-France's MFWAM model at 0.05° resolution, derived from the WAM model with a spatial resolution of 5 km, a three-hourly step, and a temporal depth of 30 years. These results have been published on the ODATIS open data platform.
Compared with existing reanalysis, the islands are better modelled, and it leads to better estimates of wave conditions and propagation.
It has been applied to wave energy evaluation over the whole French Polynesia.
Simulating the integration of 10 MW wave energy in Tahiti: challenges and opportunities
Sea state data from the atlas were used as input for simulating the production of 10 MW wave energy through several systems. These simulations were carried out for integration into the Tahitian power grid at several injection points and considering existing and planned renewable energies plants.
Over the same past time period, the island's electricity mix was modelled, showing the complementarity of wave energy with the other renewable energies found on the island, in particular photovoltaics and hydropower.
Tahiti presents (as in 2022) an electrical mix of 64% Oil, 29% Hydro, 7% PV (including roofs and plants connected to the Grid). New PV plants + batteries are studied by local stakeholders for the next years, involving other types of issues (land & recycling).
Our work highlights the advantages and challenges of integrating wave energy into the grid and raises the question of the methodology's replicability on other islands.
How to cite: Dubois, C., Chabbert, H., Reveil, M., and Vitrac, V.: Wave atlas of French Polynesia – Application on wave energy integration into the electrical mix , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9146, https://doi.org/10.5194/egusphere-egu25-9146, 2025.
