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 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: Tue, 5 May, 08:30–12:30 | Room -2.43
Renewable energy sources play a major role in future net-zero energy systems. However, achieving energy system resilience remains challenging, since renewables strongly depend on weather fluctuations, and future energy systems are subject to major design uncertainty. Existing literature mostly treats these types of uncertainty separately. Therefore, the assessment and comparison of uncertainties surrounding climate change and energy system design, and particularly their interactions, is insufficiently understood.
To close this gap, we evaluate net load to assess energy system stress without relying on perfect foresight, while maintaining the temporal and spatial correlations of the climate system. Net load is calculated from hourly historical and future climate model data translated to energy variables. To scope the extent of plausible energy systems, we consider eight different design scenarios inspired by the European Ten-Year Network Development Plan (TYNDP) and different levels of transmission expansion.
We find that climate change impacts on net load are highly sensitive to the energy system design, implying that energy systems can be designed so that they are either hindered or helped by climate change. Furthermore, within an energy system scenario, climate change can change the frequency and seasonality of high net load events and their technological and meteorological composition. Wind-dominated systems with currently electrified heating levels, for instance, feature a 30% increase of high net load events under climate change, mostly in summer and fall, while fully electrified net zero systems are impacted by high net load events in winter and spring, which decrease by 50% with climate change. Our work thus calls for a wider perspective on energy-climate stress that captures the non-linear interactions of climate change and energy system design uncertainty, thereby overcoming the current focus on cold "Dunkelflauten".
How to cite: Bloin-Wibe, L., Fischer, E., Göke, L., Knutti, R., de Marco, F., and Wohland, J.: Climate change impacts on net load under technological uncertainty in European power systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1638, https://doi.org/10.5194/egusphere-egu26-1638, 2026.
Hydroelectric power, particularly from run-of-river power plants, is one of the oldest forms of electricity generation from renewable energies. In the context of the rapidly increasing share of variable renewables, such as photovoltaics and wind power [1], the continuous supply of electricity from run-of-river power plants will become an important factor for a successful energy transition [2]. In order to study the effects of this renewable energy on power systems, particularly its contribution to grid stability, spatiotemporally resolved electricity generation data from run-of-river power plants are very useful. The lack of publicly accessible hydroelectric power feed-in data for Germany, especially from the many existing small power plants with installed capacities below 0.25 MW, makes it necessary to estimate the electricity generation for a geographical area and time period under investigation with the help of numerical simulations.
This contribution shows how such a simulation model, which also belongs to the Renewable Spatial-Temporal Electricity Production (ReSTEP) model collection [3], can be created using freely available power plant data and so-called plant load factors as input information. The plant load factors, which relate the hydroelectric power feed-in produced in a certain Transmission System Operator (TSO) region to the average installed capacity of run-of-river power plants in that region, consist of hourly resolved values to ensure the numerical simulations have a high temporal resolution. Once such load factors are calculated for the German TSO regions, the electricity generation can be straightforwardly simulated using the installed capacities and operating times of the investigated run-of-river power plants. After successful model validation, this ReSTEP model was applied to an ensemble of 7,687 run-of-river power plants, with a total installed capacity of 4.73 GW, to simulate their electricity generation for the year 2020. Using such disaggregated simulation results, the electricity generation from run-of-river power plants can be studied on various spatiotemporal scales and presented as highly resolved maps for Germany.
References
[1] Lehneis, R., Manske, D., Schinkel, B., Thrän, D. Modeling of the power generation from wind turbines with high spatial and temporal resolution. EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19913, 2020. https://doi.org/10.5194/egusphere-egu2020-19913
[2] Harnisch, F., Lehneis, R. The power grids need to be made ready for a circular and bio-based economy. Next Sustainability 2023, 2, 100010. https://doi.org/10.1016/j.nxsust.2023.100010
[3] Lehneis, R. Effects of climate change on wind power generation: A case study for the German Bight. Energies 2025, 18, 3287. https://doi.org/10.3390/en18133287
How to cite: Lehneis, R.: Modeling of the German electricity generation from run-of-river power plants with high spatiotemporal resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14144, https://doi.org/10.5194/egusphere-egu26-14144, 2026.
Reanalysis datasets are widely used in wind energy modelling and power system analysis, particularly in regions where long-term observational records are unavailable. However, their application at wind-farm scale remains challenging, as reanalysis wind fields often exhibit systematic biases linked to simplified physical representations, observational uncertainty, and coarse spatial resolution. In particular, limited spatial resolution restricts the ability of reanalysis data to represent local variability that is critical for accurate wind power simulation.
To address this challenge, we investigate a spatially differentiated bias correction strategy that differs from conventional nationally uniform adjustment schemes. The method adopts a cluster-based representation of wind farm locations, allowing bias correction factors to vary across groups of spatially coherent sites. This framework is applied to a large fleet of UK wind farms to assess its performance under realistic modelling conditions.
Using multi-year operational data from UK wind farms, we evaluate monthly wind power simulations driven by corrected and uncorrected reanalysis winds. The spatially differentiated correction yields reductions in simulation error exceeding 30% relative to baseline ERA5-driven results, demonstrating clear improvements over uniform correction approaches. To assess robustness across reanalysis products, the same methodology is applied to MERRA-2, where comparable performance gains are observed.
Beyond aggregate error metrics, the analysis reveals pronounced regional structure in reanalysis wind speed errors across the UK. Underestimation is most evident in areas of complex terrain, including the Scottish Highlands and mountainous regions of Wales, whereas wind farms located on flat inland plains and offshore sites exhibit relatively minor and more spatially consistent biases. These spatial patterns highlight the importance of accounting for geographic variability when correcting reanalysis wind speeds and demonstrate the value of spatially resolved correction strategies for wind energy applications.
How to cite: Wang, Y., C. Warder, S., Wynn, A., R. H. Buxton, O., and D. Piggott, M.: Improving wind power modelling in the UK through spatially resolved bias correction of reanalysis winds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4288, https://doi.org/10.5194/egusphere-egu26-4288, 2026.
Accurate spatiotemporal wind fields at wind turbine hub heights (80–200 m) are essential for renewable energy resource assessment and grid integration studies, yet observational constraints typically limit measurements to sparse station networks at ideally 10-meter reference height. In complex Alpine terrain and semi-complex northern and eastern Austrian terrain, both horizontal interpolation and vertical extrapolation pose exceptional challenges due to orographic flow acceleration, valley channelling, and stability-driven wind shear variations.
We present a comprehensive two-stage framework that transforms sparse concise and quality controlled surface observations into high-resolution, multi-level wind fields and wind power potential suitable for wind energy applications over Austria. The framework processes 25 years (1996–2020), 30 possible if considering all available data, of hourly wind observations from approximately 280 stations and produces gridded wind fields at 1 km horizontal resolution across multiple hub heights.
For horizontal interpolation, an Empirical Orthogonal Function (EOF) decomposition reduces computational complexity by factor ~250× while retaining >95% of spatiotemporal variance. We compare six interpolation approaches: Inverse Distance Weighting, Kriging with External Drift (KED), Random Forest, Bayesian Additive Models for Location, Scale and Shape (BAMLSS), and a Deep Neural Network. Eight terrain-aware covariates capture orographic effects, including topographic position indices, wind exposure indices, surface roughness from CORINE land cover, and ERA5 reanalysis as large-scale atmospheric forcing. Terrain covariates prove essential, with largest gains in complex topography.
For the vertical extrapolation, seven complementary methods extrapolate 10-meter wind fields, for every interpolation method separately, to hub heights of 80, 100, 120, 140, 160, 180, 200, 220, and 250 m: enhanced logarithmic and power law profiles with spatially-variable roughness lengths, stability-dependent extrapolation using surface wind speed as atmospheric stability proxy, directional-terrain correction accounting for orographic sheltering and acceleration, roughness-adaptive method selection, a deep learning model, and a multi-method ensemble providing uncertainty quantification through ensemble spread. GPU acceleration enables efficient processing of massive datasets (~200 million grid points per monthly file).
Preliminary validation against the New European Wind Atlas demonstrates that both the ensemble approach and the machine learning approach captures diurnal wind shear variations and reproduces known orographic patterns, with largest improvements over traditional single-method extrapolation in areas of complex topography.
The resulting multi-decadal, hourly wind speed dataset at multiple hub heights provides a novel resource for Austrian wind energy resource assessment, capacity factor estimation, and renewable integration studies. The modular framework design supports both retrospective climate analysis and operational nowcasting applications.
How to cite: Schicker, I., Lexer, A., and Andre, K.: From surface observations to hub-height wind fields: A two-stage framework combining ML-based interpolation and terrain-aware vertical extrapolation for wind energy applications in the Austrian Alps, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9984, https://doi.org/10.5194/egusphere-egu26-9984, 2026.
Long-distance transmission of wind and solar power often exposes a mismatch between renewable availability at the sending end and demand requirements at the receiving end. This study examines how such spatiotemporal differences influence capacity planning for a large wind-photovoltaic-pumped hydro storage (WP-PV-PHS) energy base connected to an external load center via an ultra-high-voltage direct current (UHVDC) line. A two-layer planning framework is used, combining capacity optimization with an 8,760h operational simulation. Three operating conditions are assessed. In the free-transmission (FT) case, exported power follows the natural variability of WP-PV resources, constrained only by UHVDC limits and PHS operation. When an agreed transmission curve (ATC) case must be followed, the temporal misalignment between supply and demand becomes evident: exported energy drops, curtailment increases, and periods of insufficient supply emerge. Introducing a small gas turbine (GT) in the ATC-GT case mainly supports hours in which PHS alone cannot restore the required output, improving the system’s ability to meet the transmission target. Results indicate that enforcing the ATC reduces annual exported electricity by roughly 15-20% and increases curtailment by more than 10%. Adding 1 GW of gas turbine capacity markedly improves the supply guarantee with limited cost impact. The analysis shows that planning large WP-PV-PHS energy bases requires explicit consideration of both renewable output patterns and receiving-end demand constraints, especially when UHVDC systems impose strict operational limits.
How to cite: Sun, J. and Jurasz, J.: Capacity planning of large-scale wind-photovoltaic-pumped hydro storage energy bases under sending–receiving-end spatiotemporal mismatches, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-198, https://doi.org/10.5194/egusphere-egu26-198, 2026.
Security of supply is a common and important concern when integrating renewables in net-zero power systems. Extreme weather affects both demand and supply leading to power system stress; in Europe this stress spreads continentally beyond the meteorological root cause. We use an approach based on shadow prices to identify periods of elevated stress called system-defining events and analyse their impact on the power system. By classifying different types of system-defining events, we identify challenges to power system operation and planning. Crucially, we find the need for sufficient resilience back-up (power) capacities whose financial viability is precarious due to weather variability and weather-induced risk. Furthermore, we disentangle short- and long-term resilience challenges (from multi-day to annual scale) with distinct metrics and stress tests to incorporate both into future energy modelling assessments. Our methodology and implementation in the open model PyPSA-Eur can be re-applied to other systems and help researchers and policymakers in building more resilient and adequate energy systems.
How to cite: Grochowicz, A., Bloomfield, H., and Victoria, M.: Preparing for the worst: Resilience metrics to guide necessary back-up investments during extreme weather, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6510, https://doi.org/10.5194/egusphere-egu26-6510, 2026.
Achieving the temperature control and net-zero targets remains the one of the most urgent global challenges in addressing climate change since Paris Agreement. Offshore wind power is expanding rapidly worldwide, however, the increasing frequency and intensity of extreme high wind events (EHWEs) driven by climate change are exposing growing physical and economic vulnerabilities of offshore wind infrastructure. Current planning and policy frameworks primarily rely on long-term mean wind statistics and socio-economic indicators, and typically assume that turbines operate reliably throughout their design lifetime. This neglects the cumulative impacts of extreme wind events on turbine integrity, lifetime energy production, and project economics, leading to a systematic underestimation of climate-related risks.
Here we propose an integrated meteorological–engineering–economic framework to quantify the impacts of EHWE on offshore wind energy systems. The framework introduces an Extreme-wind-adjusted levelized cost of energy (EW-LCOE) that links extreme wind hazards, turbine vulnerability, and lifecycle economic performance. Historical EHWEs are identified as the primary risk drivers, and probabilistic damage relationships are used to estimate turbine lifetime and energy losses under extreme wind forcing. Applying this framework to China’s coastal exclusive economic zone reveals strong spatial heterogeneity in EHWE-driven offshore wind risk. The frequency and intensity of extreme wind events generally increase from nearshore to offshore regions, leading to marked spatial differences in turbine lifetime and economic performance. In EHWE-prone regions, recurrent extreme wind events substantially shorten the turbine lifetimes and reduce the lifetime energy yields. Accounting for extreme-wind effects reveals that EW-LCOE in the most vulnerable areas is 5–6 times higher than conventional LCOE estimates, indicating a severe overestimation of offshore wind economic viability when extreme-wind risks are ignored.
By explicitly translating extreme climate hazards into turbine failure risk, lifetime energy losses, and economic costs, this study provides a physically and economically consistent basis for offshore wind planning under climate change. Our results demonstrate that average wind resource metrics alone are insufficient for evaluating offshore wind viability in extreme wind–prone regions, and that turbine resilience to extreme events should be integrated into next-generation offshore wind deployment and investment decisions.
How to cite: Zhao, W., Wang, J., Huang, W., Li, Y., Zhou, M., and Huang, Y.: Revisiting offshore wind energy economics through risk-adjusted LCOE under extreme winds, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15308, https://doi.org/10.5194/egusphere-egu26-15308, 2026.
Austria has committed to achieving Net Zero greenhouse gas emissions by 2040, with an intermediate target of attaining a 100% renewable electricity supply by 2030 in national balance. These goals demand a comprehensive transformation of the Austrian energy sector, calling for tailored energy system models that can effectively support planning and policy decision-making.
For Austria, existing national energy system models are limited in spatial or temporal resolution or lack consistent integration within the European energy system. Conversely, European sector-coupled models typically do not provide the level of detail required to tackle Austrian-focused questions. These include future energy infrastructure needs (e.g. pipelines and transmission lines), the implementation of national policy frameworks and the realistic representation of transition pathways from today’s energy system towards net zero.
We propose to close these gaps by developing an Austria-focused energy system model embedded within a broader European context. Building on the PyPSA-EUR framework and methodologies introduced in PyPSA-DE, we develop PyPSA-AT, a model tailored to Austria’s specific requirements. Key features include high spatial and temporal resolution of Austria’s energy system, inter-connection into Europe’s energy system, extensive inclusion of country-specific input data and national policies as well as model calibration on Austria’s energy balance.
Compared to the pan-European PyPSA-EUR model, first outputs from PyPSA-AT indicate higher domestic infrastructure requirements. Additionally, the Austrian model results in more extensive deployment of wind, and distinct patterns of energy imports and exports. By providing a granular, Austria-centric representation within a European framework, PyPSA-AT supports robust and actionable policy insights on Austria’s pathway towards net zero.
How to cite: Nutz, M., Grabner, I., Scheiber, F., Schmidt, J., Awetisjan, V., Wernhart, H., and Worschischek, P.: Towards an Open Austrian Sector-Coupled Energy System Model within a European context , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13091, https://doi.org/10.5194/egusphere-egu26-13091, 2026.
Positive Energy Districts (PEDs) are a promoted sustainable pathway for urban energy systems, characterised by low-carbon renewable energy, energy self-sufficiency and improved energy equity. By integrating renewable energy sources, energy storage, and demand-side management, PEDs aim to generate at least as much energy as they consume. Buildings increasingly integrate solar photovoltaics (PV) to power heat pumps; however, high PV-penetration can result in excess electricity generation during summer periods, whereas electrified heating significantly increases winter peak electricity demand. This creates a pronounced temporal mismatch between local electricity supply and demand in a neighbourhood, which is further exacerbated by poorly insulated buildings in social housing. Although this temporal mismatch is a recognised challenge in PED implementation, it remains unclear whether the feasibility constraints vary primarily with neighbourhood characteristics, such as housing typologies, or whether similar limitations emerge across neighbourhoods. Here, we examine four social housing communities experiencing energy vulnerability in three Northern European countries. These include neighbourhoods in Texel (Netherlands), Orebro and Grythyttan (Sweden), and Kongsvinger (Norway). We mainly assess and compare the technical feasibility of transitioning them to PEDs.
We analyse electricity and heat demand for the neighbourhoods using a bottom-up approach. We simulate electricity demand profiles for each building in the neighbourhood, combining typical electricity usage and potential electricity demand from heat pumps. Using a 5R1C building thermal model, we simulate the heat demand profiles for residential neighbourhoods, incorporating local weather data, building geometries, and occupancy patterns. We model three levels of insulation: existing, basic and advanced, based on TABULA database. To evaluate renewable energy potential, we simulate solar PV generation with varying PV-penetration levels. We use the Time Series Initialization for Buildings (tsib) Python package, with local weather inputs from COSMO-REA6 reanalysis data. The overall modelling framework employs the methodology presented in Joshi et al. (2025), enabling a comparison across neighbourhoods. We compute technical indicators, including variability, unfulfilled demand, loss of power supply probability, excess energy, and storage capacity requirements for neighbourhoods.
All neighbourhoods meet the PED definition of meeting annual demand at 100% PV-penetration. Comparing scenarios at various PV-penetrations reveals consistent trends across neighbourhoods. The heating demand is significantly reduced by advanced insulation; however, PED feasibility remains constrained by temporal mismatches between the demand and supply. Despite meeting annual targets, unfulfilled demand remains high (around 80%), with slightly lower values observed in the denser Swedish neighbourhood. While solar PVs can contribute to local energy generation, achieving temporal alignment would require extremely large storage. Improving insulation, therefore, emerges as a critical step in addressing the energy vulnerability, although grid support remains necessary. Overall, the neighbourhoods face similar constraints, with multi-family housing showing a reduced temporal mismatch. Across all cases, full independence from the electricity grid remains unattainable, while local generation can significantly support summer electricity supply.
Reference:
Joshi, M. Y., Ricker, B., & Camargo, L. R. (2025). Technical challenges in transitioning vulnerable neighbourhoods to solar photovoltaic-driven positive energy districts with integrated heat pumps. In Journal of Physics: Conference Series (Vol. 3140, No. 3, p. 032002). IOP Publishing.
How to cite: Joshi, M. Y., Ricker, B., and Ramirez Camargo, L.: Comparing technical feasibility of transitioning vulnerable neighbourhoods into Positive Energy Districts (PEDs) across Northern Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21163, https://doi.org/10.5194/egusphere-egu26-21163, 2026.
This study investigates the role of grid-integrated offshore wind (OSW) in coastal regions of an archipelagic country, focusing on the implications for system stability and inertia constraints. Conventional energy modeling frameworks often neglect the temporal and spatial complexities that characterize archipelagic nations such as the Philippines, where fragmented grids and limited inter-island connectivity create significant operational constraints. To address these challenges, several scenarios were developed to assess the integration of floating and fixed OSW technologies within the Philippine power grid under varying inertia conditions. The results reveal the growing advantage of floating platforms, with capacities expanding from 430 MW in low-growth conditions to nearly 4,500 MW in high-growth cases when supported by Battery Energy Storage Systems (BESS). The inclusion of decarbonization measures further enhances OSW deployment potential. Findings present an optimization-based approach for supporting national climate objectives and advancing the global transition toward low-carbon, resilient, and sustainable energy systems. The results indicate that floating offshore wind leads in capacity expansion, particularly under high-growth conditions, reaching 1,262 MW. In contrast, fixed offshore wind plays a smaller role due to limitations related to depth and site accessibility. High-growth scenario with and without CO₂ mitigation and BESS shows that both the addition of BESS and CO₂ reduction policies substantially increase offshore wind deployment. In this case, floating offshore wind capacity reaches 11,258 MW, while fixed offshore wind rises to 1,837 MW. This highlights the significance of energy storage and climate policies in enhancing offshore wind energy production. We also compared the levelized cost of electricity (LCOE). Without CO₂ constraints, fixed offshore wind often proves to be unviable. However, when BESS and CO₂ reduction measures are implemented, the LCOE decreases significantly. In the most favorable scenario, the LCOE drops to 0.18 USD/kWh for fixed offshore wind and 0.15 USD/kWh for floating offshore wind.
How to cite: Gonocruz-Abe, R. A., Silava, N., Maguindayao, E. J., Ozawa, A., and Aguirre, R.: The inertia transition of grid-integrated floating and fixed offshore wind energy in the Philippines, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2353, https://doi.org/10.5194/egusphere-egu26-2353, 2026.
The United Kingdom (UK) has ambitious targets to transform its energy system away from fossil fuels towards sustainable alternatives. Today, the predominant share of the primary energy supply is covered by gas and oil, but with the UK government’s Clean Power 2030 Action Plan, the UK aims for a fast acceleration towards renewables: By 2030, clean sources should constitute at least 95% of the UK’s electricity generation. The ambitions were recently substantiated with a record-breaking 8.4 GW offshore wind action, being the biggest ever in Europe. The UK government expects approximately 50 GW offshore wind, 30 GW onshore wind, and 50 GW solar PV installed by 2030. To accommodate such high penetration of renewable electricity generation capacities, energy storage and adequate energy reserves are essential to ensure a stable power supply.
Using the open energy system model PyPSA-Eur, we optimize the transition pathway for the UK energy system towards a net-zero emissions system in 2050. For this, we use high spatiotemporal resolution, allowing us to derive energy strategies on a regional level. For an integrated energy system, with electricity, heating, industry, shipping, aviation, and land transport sectors coupled, we inspect the aspect of energy reserves from a wider perspective. Replacing fossil fuels with e-fuels at the sectoral end-users also brings additional benefits, since the conversion from electricity also eases storability and enables long-duration energy storage, which can be exploited in the power sector.
Inspired by recent market trends and research studies, our study investigates whether cost-efficient alternative strategies to a future hydrogen infrastructure exist, to link the power sector with industry sectors and for a provision of long-duration energy storage in a highly renewable energy system. In this work, we evaluate a ladder of energy storage solutions. The first step covers technologies seemingly preferred today, e.g., Li-ion batteries, which have seen high learning rates in combination with low energy conversion losses. The second step includes technologies that are cheap due to their low complexity, e.g., electrical boilers in large hot water tanks, but require more centralization of the supply. The further steps represent technologies with increasingly conversion losses and expenses for the conversion links but offering a medium more suitable to store at large volumes. Using a techno-economic optimization approach, we evaluate the cost of distinct systems that rely on either battery, hydrogen, e-methane, or methanol storage, while we assess their operational and practical benefits. To address meteorological uncertainties, the pathway optimization is performed for a range of reanalysis years.
From our study, strategic allocation of storage and energy reserves on a regional level for the UK can be derived, and our results contribute to the planning of a resilient and sustainable national energy system.
How to cite: Gøtske, E. K. and Hawkes, A.: Beyond hydrogen: The long-duration energy storage potential of emerging renewable fuels in UK , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20442, https://doi.org/10.5194/egusphere-egu26-20442, 2026.
Solar photovoltaic costs have fallen far faster than anticipated since 2010, driving an unforeseen expansion of solar generation; similar cost declines have occurred for wind and are expected to occur for battery storage. Many European planning studies still rely on conservative cost projections that may misrepresent the future role and value of battery storage. This raises a key question for high-renewables power systems: how do faster-than-expected renewable cost reductions and uncertain battery cost trajectories interact to shape the cost-effective deployment and siting of battery storage?
This study addresses this question using the open-source PyPSA-Eur model to represent the European power system with hourly resolution and isolating the arbitrage value under uncertain costs to identify spatially explicit battery capacity. Current battery storage projects often rely on revenue stacking, combining energy arbitrage with frequency services such as fast frequency response and dynamic containment, but these ancillary markets are likely to saturate as the deployment grows, motivating a focus solely on arbitrage value. The analysis draws on recent empirical work that derives national solar and wind cost floors from a comprehensive panel of historical deployment and cost data, showing that global solar costs are likely to continue declining and remain substantially below wind, whose costs approach a moderate floor by mid-century (Baumgärtner & Farmer, 2025). These cost floors are used as boundary conditions for 2030 and 2050 scenarios that explore five discrete cost levels for utility-scale batteries and wind, while treating solar as already very low-cost.
By systematically varying these inputs, the analysis quantifies the spatial patterns of battery capacity location, duration mixes, and use across Europe. The scenario ensemble highlights the temporal complementarity between cheap solar/wind and storage arbitrage, identifying locations where batteries robustly mitigate renewable droughts and where deployment remains highly sensitive to costs. These insights inform resilient spatial planning for high-renewable energy systems under deep technological uncertainty.
How to cite: Schirren, L. and Hawkes, A.: Spatial Battery Siting under Renewable Cost Floors: A PyPSA-Eur Analysis for Europe to 2050, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1840, https://doi.org/10.5194/egusphere-egu26-1840, 2026.
With the European Green Deal the EU aims for net-zero greenhouse gas emissions by 2050. Achieving this involves a shift from fossil fuels to renewable sources of energy. This transition places increasing pressure on the resources needed for renewable energy technologies, such as solar cells and wind turbines, and storage technologies, like batteries, used to facilitate high shares of variable renewable energy. Demand side measures present an opportunity to mitigate the resource demand by supporting the integration of renewables. In extension this could contribute to a more sustainable electricity system by alleviating environmental and social impacts of resource extraction.
The electrification of the transport sector presents an opportunity for one such demand side measure. With increasing deployment of electric vehicles, vast amounts of batteries are distributed throughout the energy system. While the main purpose of these batteries is to store and supply the energy needed for driving, there is usually more storage capacity available than what is utilised on a daily basis. This excess capacity could be used to support the integration of large shares of variable renewable energy. The flexibility from electric vehicles, known as vehicle-to-grid or bidirectional charging, has the potential to provide much of the expected future storage needs in the electricity system.
This work investigates the potential of electric vehicle flexibility to reduce the resource demand of European net-zero electricity systems in 2050. We apply the high spatial and temporal resolution electricity system model for Europe, highRES-Europe, optimising capacity expansion and operation for least cost. Three electric vehicle charging modes with increasing degree of flexibility are considered: immediate, flexible, and bidirectional. A post-analysis is performed to assess the resource demand of the system across electric vehicle flexibility scenarios. First results show that flexible and bidirectional charging can support the integration of large shares of variable renewable energy in future European electricity systems, reducing the need for stationary battery storage. This lessens the resource demand of the energy transition, especially related to batteries, contributing to a more sustainable system.
How to cite: Hvidsten, T. V., Benth, F. E., Price, J., and Zeyringer, M.: The potential of electric vehicle flexibility to reduce resource demand in future net-zero European electricity systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19532, https://doi.org/10.5194/egusphere-egu26-19532, 2026.
Operating large offshore wind farms reduces wind speeds within the farms and in the downwind areas, a phenomenon known as wind farm wake. This can significantly impact annual energy production, especially in regions with densely packed wind turbines. In a project funded by the Danish Energy Agency[1] and the EuroWindWakes Project[2], we analyse this in Danish waters. We use a mesoscale model to simulate atmospheric conditions and apply two wind farm parameterisations (the Fitch and EWP schemes) to evaluate their effects. We model the flow to estimate wind resources for the North Sea, South Baltic Sea, and Kattegatover a typical year, considering several wind farm scenarios: (i) no wind farms, (ii) existing farms as of November 2021, (iii) planned installations in 2030, and (iv) projected setups for 2050. The 2050 scenario includes four wind farm configurations, each with the same total installed capacity. We simulate reductions in wind speed and other climate conditions caused by wind farm operations. We evaluate the mesoscale simulations of the 2021 wind farm scenario using the two schemes with wind measurements from the Northand Baltic Seas. Additionally, we examine how changes in wind speed affect capacity factors, energy yields,and full-load hours for each turbine and for the overall Danish power system.
Our findings show that the wind farms planned for 2025 could reduce average wind speeds within and downwind of clusters, with deficits reaching up to -2.5 m/s in the North Sea Danish EEZ. About half of the Danish sea could experience reductions of at least -0.25 m/s. Wake losses in future scenarios are estimated to be between 13–24% in 2050, slightly higher than the 11–20% expected in 2030 due to larger capacity and clustering, driven by low-capacity density and placement in high-wind areas. Despite these wake effects, projected annual energy production in Denmark shows a significant increase—from around 24 TWh in 2021 to 84–94 TWh in 2030, and 204–232 TWh in 2050—and full load hours rise from roughly 3009 to over 3430. A scenario with many small, dense farms optimises the balance between wake losses and energy output, though infrastructure costs may be higher for small, spread-out farms. The wind speed deficits and capacity factors from the mesoscale model are consistent across two grid resolutions tested, confirming the robustness of the mesoscale wake impact analysis.
1 https://orbit.dtu.dk/en/publications/environmental-mapping-and-screening-of-the-offshore-wind-potentia/
2 https://www.iwes.fraunhofer.de/en/research-projects/current-projects/eurowindwakes.html
How to cite: Hahmann, A., Alonso-de-Linaje, N. G., Imberger, M., Fischereit, J., and Badger, J.: Mapping the future offshore wind potential in Denmark: Assessment of 2050 wind farm scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23045, https://doi.org/10.5194/egusphere-egu26-23045, 2026.
The expansion of onshore wind energy is central to decarbonizing electricity systems, yet it creates localized burdens, such as visual intrusion and noise, that are spatially heterogeneous and unevenly distributed. These patterns raise concerns of spatial distributional justice. Previous analyses of spatial burden distributions face two main limitations. First, local burdens are often approximated using simple infrastructure-based measures. However, experienced impacts also depend on distance to turbines as well as the density and value of affected populations and assets. These dimensions can be captured more directly through changes in residential property values as an impact-based burden measure. Second, spatial assessments typically rely on a single selected approach to distributional justice, although multiple valid approaches exist.
Here, we assess spatial distributive justice by combining two measures of localized burdens, infrastructure-based and impact-based, with multiple approaches to spatial distributional justice. We hypothesize that the diagnosed degree of spatial distributive justice depends critically on both the burden measure and the justice approach applied.
We analyse wind-energy-related burdens in Germany using two complementary measures. The first is an infrastructure-based measure based on installed wind turbines. The second is an impact-based measure derived from spatially modelled property value losses associated with turbine proximity. The impact-based measure uses a multi-arm causal forest to estimate distance-based, heterogeneous price effects at a 1 km² resolution. Treatment is defined for distances of 0–1 km, 1–2 km, and 2–3 km, with locations beyond 3 km serving as the control group. Estimation relies on an unconfoundedness assumption supported by AIPW diagnostics. Effects are extrapolated using GAM smoothing to obtain continuous spatial coverage for aggregation. Both burden measures are related to five variables, the number of inhabitants, electricity demand, land area, energy potential, and gross domestic product. These variables represent different approaches to spatial distributive justice. Spatial distributive patterns are evaluated using Lorenz curves and Gini coefficients at the federal state (NUTS-1) and district (NUTS-3) levels.
Results show that both the chosen burden measure and the distributive justice approach materially affect inferred spatial disparities. Infrastructure-based measures foreground deployment intensity. Impact-based measures emphasize locations where exposure overlaps with dense and high-value housing markets, resulting in larger absolute economic losses. Rural districts tend to appear more burdened under infrastructure-based measures. Urban districts account for a larger share of impact-based burdens. Turbine counts exhibit only a very weak linear correlation with modelled property value losses, with Pearson r close to zero at the NUTS-3 level. This indicates that infrastructure intensity and monetized local impacts capture distinct dimensions of burden. The resulting distributive patterns vary systematically across justice approaches. Relating burdens to the number of inhabitants, electricity demand, or land area yields broadly North–South contrasts. Relating burdens to gross domestic product or energy potential emphasizes West–East differences.
Overall, the results demonstrate that assessments of spatial distributional justice in wind energy deployment are highly sensitive to both the burden measure and the distributive justice approach applied. We provide a transferable workflow for integrating impact-based burden surfaces into spatial planning metrics. This enables more transparent and robust interpretations of regional burden distributions.
How to cite: Vallapurackal, J. and Lehmann, P.: Spatial distribution of local burdens from onshore wind energy deployment in Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17198, https://doi.org/10.5194/egusphere-egu26-17198, 2026.
Wind energy is a crucial renewable energy source for reducing greenhouse gas emissions. In Europe, there is still significant onshore wind energy potential. At the same time, most countries use setback distance regulations for wind energy planning, and only a few have introduced shadow flicker (SF) rules. This research aims to examine whether the setback distance rule is sufficient to protect citizens affected by SF in reality. Below, for Denmark, we compare setback-distance regulations with the SF guideline.
For the methodology, we chose Denmark as the case study, grouped the existing 4775 turbines into 604 windfarms based on location, height, and commission date, calculated the SF-affected areas for these windfarms, and overlaid them with the population grid map to estimate the SF-affected population. Then we calculated the population covered by the setback-distance regulation-affected area around wind turbines and aggregated them by windfarms. In the end, comparisons are made between the two affected areas and populations. The model being used here to calculate SF impacts is the WIMBY_SF tool, an open-source SF simulation model written in Python that takes into consideration complex terrain and estimated turbine operation times. The JRC-CENSUS population1 grid 2021 (JRC, 2024) with a 100 m x 100 m resolution is used to estimate the affected populations.
As a result, the total affected population due to physical impacts from>30 hours/year SF exposure is 16,514. At the same time, by regulation, the suggested distance from residences is four times the turbine tip height, resulting in a population of 16,334. The 30 hours/year is chosen because many EU countries have regulations or guidelines that follow the German guideline, which sets a shadow flicker limit of 30 hours per year for the astronomical maximum possible shadow duration (worst-case scenario). Despite similar affected population sizes, the areas affected by the two assessments vary considerably. The overlapping affected population from the two assessments is 8,939 (36.8% of the union-affected population), and the non-overlapping affected population from both methods is 24,278.
The Intersection over Union (IoU= Area(Model∪Reg)/Area(Model∩Reg)) shows spatial agreement from the setback distance and SF assessments. Across the studied 604 wind farms in Denmark, the IoU distribution is intensely concentrated between 0.50 and 0.70 (56.46%), indicating moderate spatial agreement between the modelling-based and regulation-based affected areas. Only 24 wind farms (3.97%) achieved high agreement (IoU ≥ 0.70), while a notable 85 wind farms (14.07%) exhibit near-zero overlap (IoU ≤ 0.05), implying mismatches in affected area alignment for certain farms.
Furthermore, 319 windfarms show that the physical SF affected population is smaller than the population that lives in the area defined by regulation, 258 windfarms with physical SF affected population larger than the regulation concerned, and only 27 windfarms show 0 affected population for both assessments.
In conclusion, the physically affected population and the regulation-affected population are of similar sizes but differ in geography. A similar analysis will be extended for further European countries
How to cite: Chen, H.-H., Bucha, M., and Ramirez Camargo, L.: Evaluating the Adequacy of Wind Turbine Setback Distances for Limiting Shadow Flicker Impacts, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-23107, https://doi.org/10.5194/egusphere-egu26-23107, 2026.
In recent years, the deployment of wind energy has grown rapidly due to cost reductions and the need to decarbonize energy systems. However, the increase in wind energy projects has exposed significant local obstacles, leading to resistance to the development of new wind capacity, which could in turn affect the European energy transition and the achievement of climate targets at the national and continental levels.
Concerns about social and environmental impacts, such as landscape visual impacts, the vulnerability of birds and bats, and the avoidance of protected conservation areas, have become increasingly relevant to the acceptance of wind energy projects. These considerations have been studied extensively over the past decade to understand their influence on the social acceptance of wind energy projects. Additionally, recent studies have focused on incorporating these aspects into national energy system models to examine the potential implications and trade-offs of future wind capacity. However, few energy system model studies at the continental scale have accounted for different levels of acceptance of wind energy deployment and examined the system design implications and their trade-offs.
In this work, we examine how different levels of acceptance of social and environmental impacts of wind energy can shape the technology’s role in Europe’s net-zero ambitions and what implications this may have for the design of continental electricity systems in 2050. Using a high-spatial and temporal resolution electricity system model for Europe, coupled with a long-term whole-energy system model, we limit land availability for wind energy deployment by defining different levels of acceptance to social (setback distances, noise, shadow flicker, and landscape visual impact) and environmental (protected conservation areas and the vulnerability of birds and bats to wind turbines) considerations, and then determine the cost-optimal electricity system design subject to them.
Our results indicate that as acceptance of social and environmental impacts decreases, land availability and installed onshore wind capacity decline. To compensate for this consequence, solar PV and offshore wind play a more important role across the continental electricity system, supported by increased battery storage. In the more restrictive scenario (high social and high environmental: high-high), some countries, such as the Czech Republic and France, also install nuclear capacity as part of this shift. In terms of total European system costs, all the scenarios show a 2-14% rise compared with the less restrictive scenario (low-low). However, these costs are not distributed evenly across all countries: some, such as the Netherlands, show decreases in costs in some scenarios, whereas expenditure in Germany (by far the costliest electricity system in our modelling) increases by only 7% in the most restrictive case.
Our findings can help the general public understand the potential consequences of different local-scale decisions for the wider European electricity system. Moreover, this study can serve as a basis for decision-makers to develop local-level policies that mitigate and compensate for the impacts of wind energy deployment and, in so doing, increase the social acceptance of future projects.
How to cite: Valenzuela-Venegas, G., Price, J., Zeyringer, M., Vågerö, O., Zhang, M., Panos, E., Chen, R., Etard, A., Hahmann, A., Ramirez Camargo, L., Lohrmann, A., Visconti, P., McKenna, R., Mikovits, C., and Bucha, M.: Balancing local values and energy system implications: social and environmental impacts from wind deployment in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14615, https://doi.org/10.5194/egusphere-egu26-14615, 2026.
This paper explores agrivoltaic deployment scenarios in Europe based on land-use constraints, ecological sensitivity, and grid decarbonisation. Land across Europe is increasingly contested, for example between energy infrastructure, food production, and habitat conservation. With solar PV deployment accelerating, the need to identify land-use strategies that enhance renewable electricity generation without compromising agricultural productivity or biodiversity has become critical. Agrivoltaics offer a potential solution to these constraints by integrating PV within farming systems such that farming and energy outputs are produced concurrently.
The paper investigates two novel research questions to inform agrivoltaic site selection and determine potential synergies:
(1) how do land-use constraints and agrivoltaic implementation affect the renewable energy mix in a decarbonised European energy system?
(2) to what extent do agrivoltaics mitigate land competition and reduce reliance on ecologically sensitive sites?
The highRES model of Europe will be used to address these questions. The model is designed to analyse the effects of high shares of variable renewable energy on the energy system. Deployment scenarios will be modelled with varying land protection constraints to assess whether farmland can be leveraged to relieve pressure on contested land. The findings will demonstrate whether integrating dual-use and alternative PV technologies enables stricter land protection policies whilst maintaining energy system performance.
How to cite: Richards, T., Zeyringer, M., Price, J., and Randle-Boggis, R.: Dual-Use Solar Strategies: Modelling Land-Constrained PV Deployement in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-17769, https://doi.org/10.5194/egusphere-egu26-17769, 2026.
Posters on site: Tue, 5 May, 14:00–15:45 | Hall X1
Off-grid hybrid renewable energy systems are critical for decarbonizing remote regions, yet their long-term design is strongly shaped by climate-driven variability and change in wind and solar resources. This study investigates how multi-decadal weather variability shapes the optimal capacity mix and the trade-offs between cost, reliability, and emissions in an off-grid solar–wind–diesel–pumped-hydro system. Using 45 years of hourly meteorological data for an island power system, we perform multi-objective capacity optimisation under different curtailment constraints and storage capacities.
Results show that designs based on a single “typical” year systematically mis-estimate optimal sizing, especially for high-renewable, high-curtailment configurations. Across the 45-year ensemble, mean optimal capacities remain broadly stable but exhibit pronounced inter-annual deviations that intensify as curtailment limits are relaxed, with wind and solar capacities fluctuating much more than diesel backup. Relaxing curtailment simultaneously lowers levelised cost of electricity and carbon intensity by enabling higher variable renewable penetration, but with diminishing marginal benefits beyond moderate curtailment levels and storage sizes.
These findings demonstrate that capacity planning for off-grid hybrid systems cannot be decoupled from long-term climate variability. Robust, cost-effective decarbonisation requires multi-decadal resource assessment, explicit treatment of curtailment and storage design, and integrated techno-economic-environmental evaluation rather than single-year analyses. We explicitly compare single-year and multi-year designs to quantify these impacts and discuss implications for planning resilient low-carbon island and remote microgrids.
How to cite: Cui, Y. and Jurasz, J.: Multi-decadal climate variability and capacity planning of off-grid hybrid renewable energy systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-339, https://doi.org/10.5194/egusphere-egu26-339, 2026.
The rapid expansion of large-scale photovoltaic (PV) power plants in arid regions is an important strategy for global carbon neutrality. Extensive PV coverage also fundamentally alters land surface properties and induces feedback to the regional climate. However, the climate responses to different deployment scales remain insufficiently quantified for desert environments. This study utilizes the Weather Research and Forecasting (WRF) model coupled with a PV parameterization scheme. We simulate the climate effects of PV deployment in the Kubuqi Desert, China. Four scenarios were simulated including a baseline without PV, the current actual deployment, two future projections based on energy policies. And we assess impacts on temperature, wind speed, atmospheric moisture, and surface energy balance. The results reveal a scale-dependent photovoltaic heat island effect. As the deployment scale expands, a distinct daytime heat island effect intensifies due to reduced albedo and enhanced sensible heat flux, with the 2 m air temperature increasing by up to 0.40°C in the maximum scenario. The nighttime temperature exhibited warming (+0.11°C) in winter, in contrast to slight cooling in summer. Furthermore, the physical structure of PV arrays creates a strong aerodynamic drag. This reduces 10 m wind speed by over 1.0 m·s-1 in PV area. Additionally, the interaction between surface warming and wind reduction generates a heat-moisture coupling pump. This mechanism promotes vertical mixing and increases mid-tropospheric water vapor (600–800 hPa). These findings explain the complex interactions of desert PV plants and regional climate, providing scientific support for the sustainable planning of renewable energy bases in desert regions.
How to cite: Li, S. and Cong, Z.: Climate effects of photovoltaic power plant in desert based on the WRF Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3768, https://doi.org/10.5194/egusphere-egu26-3768, 2026.
High-density river-valley cities face intensifying heat stress and rising electricity demand, making energy conservation and effective use of renewable resources increasingly imperative. However, wind and solar resources are highly heterogeneous within the urban canopy due to complex terrain and dense morphology, which complicates practical deployment. This study applies a WRF–PALM multiscale modeling framework to quantify future renewable climate resources in Chongqing and to deliver planning-ready micro-siting guidance for hybrid wind–solar streetlights. Bias-corrected CMIP6 forcing under SSP2-4.5 drives mesoscale WRF simulations, and the resulting time-evolving fields are used to force meter-scale PALM large-eddy simulations for both winter and summer. We derive street-level wind and irradiance fields, compute wind and PV capacity factors, and evaluate hybrid energy output across three surface–morphology regimes. Model evaluation indicates that the coupled framework improves near-surface wind and temperature simulations relative to WRF alone. Results show strong seasonality in solar resources and systematic contrasts across regimes: compact high-rise areas exhibit weak within-canopy winds and strong shading, whereas open mid-rise and river-adjacent areas achieve higher PV capacity factors and larger hybrid yields. Using an energy-balance criterion for street lighting, 783 candidate sites in the open mid-rise regime can meet net-zero daily consumption in both seasons. The proposed deployment provides substantial co-benefits, with estimated life-cycle electricity cost savings of about 56.2 million CNY and avoided CO₂ emissions of about 28.1 kt. Overall, we demonstrate that the framework is feasible and offers a transferable pathway from future climate scenarios to actionable, street-scale renewable micro-siting in complex urban terrain.
How to cite: Li, Q., Li, L., Wang, F., and Dong, W.: A WRF-PALM Multiscale Approach to Assessing Renewable Climate Resources in High-Density Urban River Valleys, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4472, https://doi.org/10.5194/egusphere-egu26-4472, 2026.
Wind variability poses challenges for planning and designing reliable power system with high share of renewables. Their representation in reanalysis dataset such as ERA5 remains an active research area, as it can provide reliable long-term mean wind speed estimates but is missing high frequency variability. The aim of this study is to build a machine-learning model that can correct the representation of variability in the ERA5 wind speed time series. The objective is to reduce not only the averaged error (e.g. the root mean squared error or the mean bias), but also the time series variability characteristics (e.g. the autocorrelation error). Our study focuses on five sites in the North and Baltic Seas. Wind speed measurement were collected using floating lidar systems and a meteorological mast at 100-m height, and these were selected at hourly timestamps to be used as the ground truth. From this observed wind speed dataset, our first objective is to find the periods of high variability that must have been presented in the ERA5 wind speed time series, which we refer to as ramp events. The predictors are constructed from physically motivated variables in the ERA5 dataset, and their relevance and redundancy are evaluated through a process of feature selection. This approach provides insight into the atmospheric mechanisms that drive variability in ERA5 wind speed and offers an explainable basis for improving variability representation and, ultimately, long-term mean estimates derived from ERA5 reanalysis data.
How to cite: Dhata, E. F., Kim, C. K., Kim, H.-G., and Hahmann, A. N.: Enhancing ERA5 wind speed time series variability through ramp events detection and correction, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4610, https://doi.org/10.5194/egusphere-egu26-4610, 2026.
The decarbonisation of energy use in agriculture remains a substantial challenge for energy system transitions. Mobile machinery in agriculture still largely depends on fossil fuels, such as diesel. To develop effective substitution strategies, it is necessary to take into consideration the geospatial and temporal variability and dependencies of energy demands, as well as the potential for substitution. Identifying distributional patterns, limitations, displacement effects and specific opportunities is of particular importance for practical application and the implementation of renewable energy systems.
The presentation will demonstrate our approach that has been developed for assessing spatial heterogeneity in agricultural structures, energy demand, renewable resource availability and infrastructure shapes. This approach will be used to identify feasible substitution options and evaluate the CO2 emissions and the climate mitigation potentials. We will demonstrate its application using the Bavarian case study “EigenKraftBayern” for a geospatial and temporal analysis of fuel consumptions and options for substituting fossil diesel in agricultural mobile machinery to enhance renewable energy self-sufficiency in agriculture.
We will describe our modelling approach including required data, regionalised consumption modelling, scenario analysis and assessment of GHG (greenhouse gas) emissions to evaluate regional fuel consumption in agricultural mobile machinery. Within this approach we explicitly account for geospatial differences in production systems and heterogeneity of Bavarian agriculture. Based on this assessment, we deduce alternative drive and fuel options, e.g. electrification, vegetable oil fuel, biomethane, biodiesel and hydrotreated vegetable oil. Our structured, multi-step approach links regional fuel consumption with different substitution pathways, estimates self-supply potentials from locally available renewable energy sources, and compares substitution requirements with technical, spatial and resource-related constraints. Scenario analyses have been used to explore how different assumptions regarding renewable energy availability and infrastructure provision influence substitution outcomes and affect the amount of greenhouse gas emissions.
We will show that fuel consumption and substitution potentials vary markedly across Bavaria. For the reference year 2024, total diesel consumption in agricultural mobile machinery is estimated at approximately 399 million liters, with around 75 % attributable to crop production and 25 % to cattle farming. We will demonstrate that a substantial share (58 %) of this consumption can, in principle, be met by regionally produced renewable energy, while the remaining share would rely on fuels that are not regionally producible.
Finally, we will describe the climate protection effects associated with different substitution scenarios. We will show that the replacement of diesel by renewable alternatives could reduce greenhouse gas emissions by up to 78 %, corresponding to savings of around one million tons of CO₂ equivalents compared to continued fossil fuel use. We will conclude by discussing how spatially explicit and temporally resolved modelling can support the development of resilient, regionally adapted renewable energy systems and policies for the agricultural energy transition.
How to cite: Mallast, J., Siebrecht, N., and Dressler, D.: Regional modelling of fuel consumption for agricultural machinery and potential substitutes for drive energy – use case Bavaria, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5132, https://doi.org/10.5194/egusphere-egu26-5132, 2026.
Offshore wind power plays a vital role in the global energy transition. The escalating demand for green energy has necessitated the development of computationally efficient and accurate wind farm assessment systems. Existing assessment methods, based on numerical simulations or in-situ observations, are often constrained by high costs and limited spatiotemporal resolution when applied to large-scale studies. Thus, by integrating historical meteorological data with machine learning algorithms, this research aims to establish a framework for assessing wind farm potential and develop a corresponding predictive model.
This study utilizes ERA5 global atmospheric reanalysis data and GEBCO bathymetric datasets. First, K-means cluster analysis is employed to identify high-potential development areas in the offshore waters of Taiwan, considering both wind resource potential and bathymetric constraints. Subsequently, this research combines wavelet analysis and principal component analysis for feature extraction to build optimized machine learning models. Furthermore, the predictive performance of various models is evaluated, and the correlations among key variables are examined.
Results indicate that the proposed assessment framework effectively identifies optimal locations for offshore wind farms and enables precise forecasting of future wind energy potential. Additionally, the analysis reveals a weakening temporal correlation between the Southern Oscillation Index and local wind speeds—a phenomenon that may be attributed to global climate change. These findings offer significant practical value for engineering; not only do they provide decision-making support for offshore wind farm site selection, but they also serve as a scientific basis for optimizing power generation strategies and grid dispatching.
How to cite: Liao, H.-C. and Lin, Y.-C.: Evaluation of Offshore Wind Power Potential Using Large-Scale Spatiotemporal Data Mining, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6370, https://doi.org/10.5194/egusphere-egu26-6370, 2026.
The transition to power systems with high shares of variable renewable energy demands high-fidelity scenario ensembles capable of accurately capturing the spatiotemporal characteristics of wind and photovoltaic (PV) generation, including multi-scale variability, persistence, ramping behavior, and inter-technology complementarity. However, existing data-driven generative models often face a trade-off among realism, controllability, and computational efficiency. To address this, we propose the Foundation-Adapted Diffusion Framework for Renewable Scenarios (FADFRS). FADFRS employs a foundation and specialist diffusion model architecture. A shared foundation model is first trained on multi-year capacity factor time series to learn generic temporal patterns. Then, lightweight technology-specific adapters are fine-tuned for wind and PV to capture domain-specific dynamics, such as diurnal/seasonal cycles for PV and persistence regimes with extreme ramps for wind. The framework supports conditional generation based on calendar variables and spatial metadata, enabling the creation of spatially coherent multi-site scenarios and the targeted sampling of low-probability, high-impact events (e.g., renewable droughts). Model fidelity is rigorously assessed with a comprehensive suite of diagnostics. This includes established power system metrics (e.g., duration curves, ramp distributions, spectral signatures) as well as advanced probabilistic scores such as the Energy Score, Variogram Score, and FID. Case studies demonstrate that FADFRS consistently outperforms conventional generative baselines in preserving key statistical and dynamical features while maintaining scenario diversity. The work provides a powerful and practical tool for both retrospective analysis and prospective planning of high-renewable power systems.
How to cite: Li, C., Qin, H., Xu, X., and Yang, L.: A Foundation-Specialist Diffusion Framework for High-Fidelity Wind and Solar Scenario Generation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8854, https://doi.org/10.5194/egusphere-egu26-8854, 2026.
The rapid expansion of solar infrastructure necessitates innovative solutions to mitigate land-use conflicts between renewable energy production and traditional agriculture. The SoLAgri project addresses this challenge by investigating the sustainable design and management of agrivoltaics (APV) as multifunctional landscapes. As extreme weather events increase in frequency, the role of APV extends beyond energy production, serving as a critical tool for agricultural adaptation. Focusing on the practical integration of PV into arable farming, grazing, and fruit production, this research investigates the environmental impact of APV system design. We analyse how specific panel layouts reconfigure precipitation and water distribution, affecting soil moisture dynamics and availability. By prioritizing practicable field management, the project demonstrates how these systems can buffer microclimatic extremes and stabilize agricultural output.
The project employs an advanced monitoring framework combining in-situ sensor networks for real-time soil and climate data with UAV-based multispectral and RGB imaging plus photogrammetry for 3D models. This approach enables high-resolution spatio-temporal modelling of vegetation health, growth patterns, plant species distribution, and key environmental factors such as shadow dynamics and light attenuation. These data support the development of predictive yield models that account for spatial variability in light and water distribution, as well as energy-yield complementarity across various technologies.
The environmental impacts of APV are compared against single-functional landscapes using Life Cycle Assessment (LCA), with the goal of achieving a low ecological footprint. By integrating biodiversity-enhancing habitats directly into system design, SoLAgri demonstrates how optimized APV configurations can harmonize food security, water protection, and nature conservation within the energy transition.
Acknowledgments: This contribution was supported and financed within the framework of the departmental research program via dafne.at with funds from the Austrian Federal Ministry of Agriculture, Forestry, Regions and Water Management (BML). The BML supports applied, problem-oriented and practice-oriented research in the department's area of competence (Project ID 101971).
How to cite: Obriejetan, M., Foelser, M., Kern, T., Koenig, M., Loder, B., Wiberg, H., and Bauer, A.: Multifunctional Agrivoltaics: Spatio-Temporal Modelling of Environmental and Agroecological Dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11482, https://doi.org/10.5194/egusphere-egu26-11482, 2026.
It is expected for climate change to significantly alter surface solar radiation, air temperature, and cloud regimes across the Baltic States, directly impacting the long-term viability of photovoltaic (PV) energy production. While previous research has mainly focused on historical climatology, only few studies have addressed the combined influence of future radiation shifts and temperature-dependent PV efficiency at a regional scale. This study quantifies projected changes in solar energy yield in the Baltic States using a multi-model ensemble from the CMIP6 framework under various SSP scenarios.
To ensure physical consistency, surface downwelling shortwave radiation is used to estimate plane-of-array irradiance, accounting for optimal panel tilt and orientation. Furthermore, PV module efficiency is adjusted based on projected near-surface air temperature to reflect real-world operational conditions. A key focus of the analysis is the spatial and seasonal contrast between coastal and inland regions. Specifically, the study investigates the moderating role of the Baltic sea in suppressing convective cloud formation and providing thermal cooling, which potentially enhances PV performance in coastal areas compared to the interior areas.
Uncertainty is addressed through the analysis of inter-model spread, identifying regions where projected trends in PV yield are strong. The findings provide regionally specific, climate-resilient insights necessary for strategic solar energy planning and decarbonization efforts in the Baltic States.
How to cite: Poļs, R., Priedītis, J., Bethers, P., Bethers, U., and Seņņikovs, J.: Assessment of Future Solar Energy Potential in the Baltic States: Spatial Variability and Coastal Effects, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12730, https://doi.org/10.5194/egusphere-egu26-12730, 2026.
The positive interaction between new energy industry development and comprehensive land consolidation is vital for integrating ecologically fragile areas into the national carbon peaking and carbon neutrality goals, while also supporting regional high-quality development. This study analyzes the challenges and opportunities of new energy development in ecologically fragile areas, investigates the mechanism connecting new energy industry growth and land consolidation, and explores integrated pathways for their coordinated development. The results show that: 1) The development of the new energy industry in ecologically fragile areas faces several challenges, including environmental vulnerability, underdeveloped infrastructure, mismatched resource supply and demand, and land use conflicts. However, with the energy transition, technological breakthroughs, and national spatial planning, the value of renewable energy resources such as wind and solar is increasingly prominent, offering strong prospects for the new energy development. 2) The development of new energy industries and comprehensive land consolidation are mutually supportive, with resource endowments, ecological constraints, new-quality productive forces, and financing mechanisms interacting to form differentiated and coordinated development pathways. 3) Based on new energy industry development and restoration priorities, five restoration models are identified: ecological restoration-led, resource development-led, industry collaboration-led, technology innovation-led, and integrated development-led. Each model has its specific focus and applicable scenarios. This study provides actionable guidance for aligning new energy development with land consolidation in ecologically fragile areas, such as deserts, mining subsidence zones, and regions rich in renewable resources, thereby fostering sustainable regional transformation.
How to cite: Liu, Y. and Tang, L.: Research on the mechanism and models of land consolidation for ecologically fragile areas supported coordinated new energy development, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16206, https://doi.org/10.5194/egusphere-egu26-16206, 2026.
The search for new energy solutions aims to provide reliable, sustainable, and cost-effective energy to communities worldwide. Among emerging green-energy alternatives, the use of low-amplitude mechanical vibrations as a renewable energy source has gained increasing attention. Mechanical vibrations can be converted into electrical energy, offering a clean and potentially continuous power supply. The energy yield from such vibrations depends primarily on the amplitude and frequency of different natural and anthropogenic sources, which vary according to local conditions.
The aim of this presentation is to introduce the E-VIBES project, an ambitious initiative focused on investigating the energy harvesting potential of ground mechanical vibrations. Within the E-VIBES project, we examine several types of natural and anthropogenic ground-motion sources, such as earthquakes, blasts, and microseisms, together with human-made, industry-related sources such as traffic circulation and CO2 injection, assessing their energy potential in terms of amplitude and frequency content. Based on this assessment, we have planned, designed, and constructed two prototypes of energy harvesters using piezoelectric and electromagnetic mechanisms specifically tailored to the characteristics of the selected vibration sources. The main goal of these prototypes is to harvest energy from highly vibrating environments (e.g., areas with high seismicity rates or mining environments) to supply low-consumption lighting or monitoring sensors. In addition, optimization work has been carried out by modeling interconnected harvesting devices, enabling the scaling of generated energy through modular configurations.
The project is currently in the testing and socio-economic assessment phase, during which the resulting device will be deployed under field conditions in Cuya, northern Chile, to evaluate its efficiency and feasibility for electricity generation from mechanical vibrations. In parallel, socio-economic analyses and community educational activities are being planned and will be conducted to assess the broader societal impact and potential benefits of the proposed energy-harvesting solution in low-income territories. A key element of this process will be the identification of strategies to reduce costs and improve accessibility, to enable adoption across a wide range of applications and communities.
How to cite: Barbosa, N., Pavez-Orrego, C., Comte, D., and Kuchler, M. and the EVIBES Consortium: EVIBES Energy Harvesting from Natural and Anthropogenic Vibrations: Modelling, Prototype and Community Testing Stages, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16713, https://doi.org/10.5194/egusphere-egu26-16713, 2026.
Detailed characterization of solar energy resources is increasingly needed to guide national and regional energy transition strategies, support grid planning, and reduce uncertainty for investors. Yet in many European countries the spatial and temporal resolution of publicly available solar datasets remains insufficient for detailed planning. In Poland this gap is further amplified by the limited number of actinometric stations, which makes ground-based climatological series spatially incomplete and unsuitable for characterizing finer temporal dynamics relevant for modern power systems.
To address this, we introduce the concept of the Solar Energy Atlas for Poland (Atlas Energetyki Solarnej PL; AES-PL) - a new high-resolution atlas derived from satellite-based LandSAF products. The atlas provides continuous solar resource data for the period 2015–2024, with 15-min temporal resolution and 3 km spatial resolution, covering the entire territory of Poland. From these data we derive core irradiance metrics (GHI, DNI, GTI) and compute usable PV resource indicators including energy yield (kWh/kWp), capacity factor, and characteristic diurnal profiles for the nine tilt–azimuth configurations most relevant to PV deployment in Poland.
Beyond standard resource climatology, AES-PL explicitly addresses temporal variability as a key dimension of the energy transition. This includes: intra-day variability using a satellite-based ramp-rate metric; inter-annual variability from 10-year continuous time series, and local identification of solar energy droughts understood as prolonged periods of below-median solar input affecting PV production and system balance. Identifying such droughts is important for evaluating system adequacy, storage needs, and interactions with other variable renewables such as wind.
The atlas thus responds to several emerging needs: for system operators, to assess variability, forecastability, and operational flexibility requirements, for investors, to reduce resource and revenue uncertainty at the feasibility study stage, for planners and regulators, to support spatial planning, auction design, and grid reinforcement strategies, and for researchers, by providing an openly documented dataset suitable for integration into energy system models.
By bridging the gap between ground-based point measurements and national policy needs, AES-PL aims to provide a transparent, spatially continuous, and reproducible resource that supports evidence-based decision-making in the context of Poland’s accelerating energy transition.
How to cite: Jurasz, J., Bochenek, B., and Wieczorek, J.: Towards a Solar Resource Atlas for Poland (AES-PL): high-resolution assessment of PV potential, variability, and local energy droughts from satellite data, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18233, https://doi.org/10.5194/egusphere-egu26-18233, 2026.
Accurate simulation of regional photovoltaic (PV) electricity production is essential for energy system planning, grid operation, and climate-energy assessments. At regional and national scales, PV production is often modeled using simplified representations of PV systems due to the limited availability of system orientation and deployment information. However, system characteristics such as tilt, azimuth, and spatial distribution significantly affect daily generation patterns and seasonal energy output. Simplifying these characteristics can introduce systematic biases, not only in total energy estimates but also in the timing of generation. Despite the common use of such simplifications, the sensitivity of regional PV simulations to individual and combined assumptions remains poorly quantified, making it difficult to determine which modeling choices are acceptable and which may lead to significant errors.
We developed a regional PV modeling framework over France, combining a high-resolution inventory of PV installations with a physics-based production model driven by ERA5 reanalysis data. The framework was validated against transmission system operator (TSO) measurements for medium- and large-scale installations. Using this validated framework, we constructed a reference simulation preserving observed PV system characteristics and compared it to progressively simplified scenarios reflecting common modeling assumptions—uniform orientation, fixed tilt angles, and homogeneous spatial distribution.
The modeling framework reproduces the temporal variability of regional PV production with high correlations (0.95–0.98) relative to TSO measurements, with a moderate positive bias observed in most regions. Seasonal analysis confirms accurate capture of daily production timing (morning ramp-up, peak, evening decline). Remaining magnitude discrepancies are likely attributable to differences in installed capacity coverage between the PV inventory and TSO observations.
The sensitivity analysis demonstrates that the impact of modeling simplifications depends strongly on their combination. Individual assumptions—such as uniform south-facing orientation or fixed tilt angles—produce moderate deviations from the reference simulation and remain acceptable when broadly consistent with the underlying fleet characteristics. However, combining multiple simplifications (uniform orientation, fixed tilt, and homogeneous spatial distribution) yields substantially larger errors, particularly during winter and low-irradiance periods. These compound errors primarily affect the magnitude of the diurnal cycle, especially during morning and evening hours.
These findings provide practical guidance for modelers who must simulate future PV production without detailed information on system characteristics. By quantifying the sensitivity to common modeling choices, this framework establishes the minimum level of system detail required for reliable scenario modeling. While individual assumptions on system orientation or spatial distribution may be acceptable for large-scale scenario analyses, combining multiple simplifications can substantially reduce reliability. This framework helps interpret the implications of modeling choices and limits uncertainty in climate change projections of regional PV production and energy transition pathways.
How to cite: Botello, C., Oueslati, B., Pol-Tireau, K., Badosa, J., Dupuis, J., and Drobinski, P.: Sensitivity of Regional PV Production Simulations to System Characteristics and Spatial Distribution Assumptions: A Case Study over France., EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19893, https://doi.org/10.5194/egusphere-egu26-19893, 2026.
1. Introduction
Reliable and computationally affordable forecasts of renewable energy production values are necessary for effective grid management and energy market integration and thus for a fast and sustainable transition of the power sector. State-of-the-art Machine Learning based weather prediction (MLWP) models are getting cheaper and better continuously nowadays, making them the perfect option to provide the needed weather forecasts. On the other hand, they lack the variables for solar power generation (solar capacity factor or irradiance or at least cloud cover). This study aims to answer the question of whether MLWP is suitable for deriving solar energy values from weather forecasts and at the same time providing a suitable post-processing pipeline.
2. Methodology
A comprehensive ML-based post-processing technique is developed to predict the solar capacity factor using weather data from forecasts or reanalysis datasets. In addition to basic calculation and data processing steps, the methodology consists of a Convolutional Neural Networks (CNN) trained on ERA5 and the “C3S operational energy dataset”. From ERA5 only the variables wind, humidity, pressure, and temperature were used in the training, making the model suitable for use with MLWP data. From the energy dataset, the solar capacity factor is used as ground truth.
With this architecture, weather forecasts of MLWP models are used to predict the solar capacity factor for up to 10 days lead time.
3. Current (and Upcoming) Results
Compared to a simple persistence baseline, the CNN consistently yields a lower RMSE, with the error reduction ranging from approximately 51% for a one-day lead time to 11% for a lead time of 10 days. Similar results can be achieved by comparing the model against a climatology baseline.
Future work will include comparing the CNN's performance across different MLWP model forecasts to identify the optimal models for energy sector predictions.
4. Conclusions
This research demonstrates the potential of ML-based post-processing for transforming raw MLWP model model outputs into usable, reliable capacity factor forecasts for the energy sector. The developed post-processing pipeline provides a vital tool for energy trading and grid operators to manage risk, optimize renewable energy resource deployment, and support grid stability.
How to cite: Kraayvanger, J.-P. and Quinting, J.: Post-Processing of ML-Based Weather Prediction for Solar Capacity Factor Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21491, https://doi.org/10.5194/egusphere-egu26-21491, 2026.
Electricity considered one of the most important forms of energy and is essential for the development of societies and for daily human life, serving as a way of living. In the economic and industrial sectors, it is a fundamental element. However, the lack of electricity in isolated areas where it is unavailable poses a significant challenge and a real obstacle to the development and growth of these regions. In this context, solar energy emerges as an alternative source of electricity and a viable solution to conventional power grids, which are often expensive to install and technically complex, especially in desert regions. Solar energy represents an excellent and reliable option due to its wide availability, low operational costs, and its safe and environmentally friendly nature.
This theoretical study aims to analyze the use of solar energy through a hybrid system and its application in powering telecommunication equipment. The system provides a reliable electricity supply and operates electrical devices, helping to overcome the isolation of remote areas and connecting them to the wider world.
This research adopts and is based on an analytical and theoretical methodology for the use of solar energy through this hybrid system, specifically designed to power telecommunications equipment in remote areas and to ensure the continuity and reliability of electricity supply. The study relies on a conceptual and analytical approach to guarantee the efficiency and long-term operational reliability of this hybrid system.
This hybrid system consists of several integrated components that work together to ensure continuous power supply, including:
-Solar panels, which must have high efficiency (e.g., 300 W / 39 V / 10 A). The number of panels depends on the required energy demand, as they represent the primary and main energy source for operating equipment during daytime.
-Batteries, which ensure energy storage and enable equipment operation during nighttime or in periods of prolonged solar radiation unavailability. These batteries should have high capacity (e.g., 2 V / 650 Ah), and their number depends on the required load consumption.
Electric generator, which guarantees power supply in the event of a system failure. It must have sufficient capacity, for example 35 kVA or higher.
-Power cabinet, considered one of the most critical components, as it manages and controls the entire system and regulates the operation and consumption of all components. It is a smart unit containing electronic equipment, intelligent control interfaces, protection circuit breakers, and power distribution units. The most important component within it is the FSU (Field Supervision Unit), which acts as a communication controller. It includes digital inputs and outputs (DI/DO), communication ports such as RS485, CAN, RS232, USB ports, and 5G/4G antennas.
The FSU collects data, enables monitoring and remote control via an IP address, and connects these isolated systems to the Internet.
This entire system ensures continuous electricity availability in remote areas, reduces fuel consumption and the use of environmentally unfriendly resources, and minimizes harmful greenhouse gas emissions.
This research is of significant importance as it addresses a major problem and provides a solution to economic and social development challenges in remote and isolated regions.
How to cite: Manghassa, A. and saber, K.: Solar-Based Hybrid Energy Systems for Continuous Power Supply in Isolated Regions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22085, https://doi.org/10.5194/egusphere-egu26-22085, 2026.
Energy system models are crucial tools for planning the transition to low-carbon power systems, particularly in the context of increasing renewable energy sources (RES) integration. However, the inherent variability and uncertainty of RES generation pose challenges for ensuring system reliability. As of today, capacity expansion models often enforce strict reliability constraints, leading to conservative investment decisions that may not reflect real-world risk tolerances, ultimately leading to overdimensioned systems. Reframing planning around acceptable risk can therefore have the potential to shift investment decisions from expensive and rarely used capacity to more valuable assets, such as targeted storage or transmission.
Here, we implement an allowance for shortage tolerance in an electricity system model at NUTS2 resolution, using a multi-objective optimisation approach that minimises both total system cost and energy shortage risk. We explore the trade-offs between cost and reliability by analysing solutions on the Pareto front, and how they could influence the design of a future European power system with high RES penetration. Our findings expose which regions are most vulnerable to shortages, and allow for policy makers to make informed decisions on how to balance decarbonization, affordability, and local robustness.
How to cite: Fordham, D., Verheugen Hvidsten, T., Valenzuela, G., and Zeyringer, M.: The Impact of Shortage Risk Tolerance on a Future European Net-Zero Electricity System – A Multi-Objective Optimisation Approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20904, https://doi.org/10.5194/egusphere-egu26-20904, 2026.
In Europe, buildings are among the biggest producers of greenhouse gas emissions and consumers of useful energy. To tackle this issue, many building energy models were developed with varying levels of complexities analyzing energy demand and supply options. Currently, not many of them fit the purpose of investigating renewable and citizen energy communities (RECs and CECs), which are continually expanding throughout Europe. These communities have their own set of spatial planning and policies measures and regulations to achieve regional and national energy-related goals. This necessitates the use of a regional planning and policy-support tool for analyses of these RECs and CECs from an energy system modeling and regional policy planning perspectives, which is not well-researched in the current literature. We addressed these issues by integrating an existing RECs and CECs-based building model named Building Energy Model, BuEM, with a regional spatial planning and decision-support tool, EnerPlanET. The integration activities between models involve the following: aligning ontological differences, defining a shared data exchange structure and format, creating a common database, and implementing to a systematic workflow. The interaction between models is bidirectional. To illustrate, EnerPlanET provides BuEM building information, such as archetypes, physical characteristics, and transmittance values. This information ranges from highly specific building components-related information towards less detailed-information such as building types, year of construction, communities information, and even country level information. BuEM uses these information as inputs to produce spatially resolved building level energy-related information on annual heat, cooling, and electricity demands with hourly resolution. BuEM additionally provides parameterized information on building-level heat supply options and the impacts of occupants on energy demand and supply. EnerPlanET uses these information to optimize the overall energy system at a community level. BuEM uses internal application programing Interfaces (APIs) to communicate between its own modules and external APIs to communicate with EnerPlanET. The overall modeling platform capabilities is enriched by incorporating user inputs via a graphical user interface on aspects, such as insulation levels, specific supply options, and comfort temperature ranges. Users can also add constraints related to factors such as ventilation, shading, and existing and expected or possible heat supply options. Additionally, users can override input information on aspects such energy consumptions and occupants’ behavior. We validated the overall model with a case study from one of the energy communities- in the Netherlands, Loenen. The overall model provides results on energy balance at individual buildings and community level, technology options’ investment choices, and spatial planning aspects. The overall tool can be applied for regional decision making related to energy use, efficiency, and sharing in multiple RECs and CECs within Europe having differences in geographical scopes, building characteristics, supply options and resources, and regional policies and regulations. The overall method is generic and can be applied for integrating other similar models.
How to cite: Sahoo, S., Ravani, J., Valdes, J., and Ramirez Camargo, L.: Integration of a building energy model with a regional planning tool for energy communities analysis , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5363, https://doi.org/10.5194/egusphere-egu26-5363, 2026.
Achieving climate neutrality targets requires consideration of biomass sourcing and utilization strategies. As biomass is commonly considered a carbon-neutral feedstock in the energy sector and the chemical industry, it could help both sectors achieve their climate targets. The projected demand in these areas varies in the literature and cross-sectoral perspectives are rarely considered. Furthermore, the potential for additional supply in Europe is rather limited, which necessitates careful planning of future sourcing and particularly utilization pathways.
This work aims to develop viable scenarios for biomass sourcing under different sectoral demand scenarios with the goal of achieving climate neutrality in Europe by 2050. The scenarios we discuss are based on a review of the relevant literature and extrapolation of demand based on current demand levels. We focus on assessing trade-offs between usage in energy and chemical industry scenarios in terms of carbon emissions, taking into account alternative decarbonization pathways for sub-sectors, in particular for phasing out biomass from heat generation. Additionally, we compile supply scenarios from the literature to establish guidelines for total available biomass quantities.
We aim to base this work on openly available data and we are committed to taking an open source approach regarding any relevant data generated during the research process. The proposed scenarios provide a foundation for future model based research and inform policy decisions on subsidizing the allocation of biomass resources for attaining climate neutrality.
How to cite: Grabner, I., Nitsch, F., Nutz, M., Scheiber, F., and Schmidt, J.: Biomass Sourcing and Cross-Sectoral Utilization Strategies for Achieving Climate Neutrality in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13094, https://doi.org/10.5194/egusphere-egu26-13094, 2026.
As the global transition towards renewable energy (RE) accelerates, the integration of wind and solar infrastructure into diverse landscapes has become a central theme in multicriteria analyses and spatial planning. This research seeks to contribute to the mitigation of landscape-related pressures and the corresponding societal concerns. To this aim, a data-driven atlas of global "landscape stress" levels will be created, representing the landscape saturation or sensitivity of each country globally. This framework will be then used to propose the right site-selection strategies for different RE technologies and countries, combining their landscape protection requirements with other technical and economic considerations.
Over the last several decades, a wide variety of planning approaches for RE siting has been developed, ranging from site-specific analyses to macro-scale spatial exclusion zones; however, there remains a lack of a selection procedure for appropriate approaches for each country. The methodological objective of the proposed framework is to facilitate a strategic choice between the diverse planning methodologies available to policymakers. This effort culminates in a global atlas for renewable energy landscape planning strategies, which assigns a specific rating to each country and classifies them into strategic groups based on their combined landscape-energy profile. We argue that planning measures should be commensurate with both a country's RE potential and its level of landscape saturation: nations with lower stress can benefit from "loose," streamlined planning frameworks, whereas those with higher saturation might require more thorough planning with stricter criteria—such as higher-resolution landscape scenicness analyses and stricter thresholds in visibility analyses.
The research undertakes an interdisciplinary approach, combining expertise in landscape planning, energy systems analysis, and energy economics. Thus, a dual set of global indicators is utilized; firstly, covering electricity generation, installed RE capacities, and future technical potentials for wind and solar energy and secondly, societal and landscape-related metrics, such as development indices, income averages and terrain ruggedness and landscape scenicness. We present a first-order utilization of these datasets to categorize countries based on their current "landscape saturation" versus their remaining techno-economic RE potential. This preliminary analysis serves as a proof-of-concept for a global "Landscape Stress Index," mapping how different nations are positioned relative to their energy transition targets and landscape constraints and challenges.
With the renewable energy transition scaling up, the spatial integration of infrastructure becomes increasingly complex as the availability of sites with potential for low or mild landscape impacts is gradually depleted. By identifying the national combined landscape-energy profiles of countries, this research establishes a foundation for the selection of evidence-based tools and policy directions. Moreover, it is recognized that the deployment of new energy conversion technologies leads to both positive and negative environmental externalities. The public's skeptical attitude toward the latter can create discrepancies between private and social costs and benefits, which can justify government intervention or regulation to enhance the energy transition process. Overall, this work contributes to a more socially acceptable and efficient global energy transition that integrates landscape concerns along with the major technical and economic criteria that define RE planning.
How to cite: Ioannidis, R., Lohrmann, A., Glensk, B., Weinand, J. M., McKenna, R., and Madlener, R.: Towards a global atlas for renewable energy landscape planning strategies based on energy potentials and landscape saturation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13615, https://doi.org/10.5194/egusphere-egu26-13615, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 4
EGU26-18202 | ECS | Posters virtual | VPS19
Material Selection for Vortex-Induced Vibration Energy Harvesting in Water Systems: Environmental and Performance Insights from the Verona Case Study in ItalyTue, 05 May, 14:27–14:30 (CEST) vPoster spot 4
Wastewater treatment plants are essential environmental infrastructures that operate continuously and require considerable electrical energy, while simultaneously conveying persistent flows that dissipate low-grade hydraulic energy. Recovering even a fraction of this overlooked resource could support decarbonisation targets and provide autonomous power for environmental monitoring and digital water services, without additional land take or large hydropower installations. Within the Horizon Europe project H-HOPE – Hidden Hydro Oscillating Power for Europe – this study investigates how the selection of structural materials affects the performance of vortex-induced vibration energy harvesters (VIV-EH) deployed in controlled water environments. Rather than optimising device geometry or control strategies, the analysis focuses on how broad material classes influence feasibility, energy potential, and environmental suitability when integrating harvesters into existing wastewater infrastructure. Operational records from a municipal wastewater treatment plant in northern Italy were analysed. A validated one-dimensional modelling framework was used as a comparative tool to estimate annual energy production for harvesters manufactured from widely available metallic and composite materials under realistic operating conditions.
Results show a consistent trend: lighter materials with favourable stiffness-to-mass ratios generate larger oscillation amplitudes and substantially higher harvested energy. Fibre-reinforced composites achieve the highest performance, with an estimated annual production of approximately 800–875 kWh/year for the specific case study. Aluminium alloys produce slightly lower yields (≈800 kWh/year) while retaining advantages in recyclability and manufacturability. In contrast, high-density metals such as structural and stainless steel, typically yield 450–480 kWh/year, highlighting how increased mass suppresses the vortex-induced response. These differences arise solely from material choice, without modifying hydraulic conditions, device geometry, or plant operation.
From a renewable-energy perspective, these results indicate that material-driven design is a practical lever for scaling small, autonomous generators across water networks, providing reliable power for sensors, process control and digital water management. Because devices exploit existing hydraulic infrastructure, they can be replicated modularly and integrated alongside other renewables as part of distributed energy portfolios, supporting resilience and local self-sufficiency. However, performance advantages must be considered alongside environmental trade-offs. Composites show limited recyclability and higher embodied energy compared with metals such as aluminium and stainless steel, which favour circularity but offer lower energy conversion. The study relies on a simplified modelling framework and a single representative site, broader validation under different hydraulic regimes and long-term material ageing will require pilot-scale deployment. Despite this, the comparative trends provide robust guidance for design and prioritisation.
Overall, the study demonstrates that targeted material selection can unlock “hidden hydropower” within wastewater systems, delivering incremental yet scalable renewable generation aligned with European decarbonisation goals while enhancing the sustainability and reliability of essential water services.
How to cite: Siviero, M., Guðlaugsson, B., Nascimben, F., Finger, D. C., Benato, A., and Cavazzini, G.: Material Selection for Vortex-Induced Vibration Energy Harvesting in Water Systems: Environmental and Performance Insights from the Verona Case Study in Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18202, https://doi.org/10.5194/egusphere-egu26-18202, 2026.
EGU26-5058 | ECS | Posters virtual | VPS19
When Does Better Scenario Modelling Improve Financeability? A Decision-Coupled Evaluation for Offshore Wind-to-HydrogenTue, 05 May, 14:30–14:33 (CEST) vPoster spot 4
Investment decisions for offshore wind-to-hydrogen (W2H) projects are often framed as “better forecasts reduce uncertainty,” but it is less clear when higher-fidelity scenario modelling meaningfully changes a financing decision versus merely narrowing outcome ranges. We address this question using a decision-coupled evaluation that scores forecast skill on propagated economic distributions and links it directly to financeability metrics.
Using 61 years of ERA5 wind data at 150 m hub height, we generate 1000 synthetic 23-year hourly wind scenarios per method and propagate them through a techno-economic model of a 375 MW offshore W2H project (development in 2024, operation in 2026-2050, base hydrogen price €8/kg, discount rate 7%). We compare three probabilistic scenario generators: historical bootstrapping, parametric Weibull fitting, and a calibrated probabilistic long short-term memory (LSTM) sequence model (used as a benchmark rather than architectural novelty).
We evaluate (a) continuous ranked probability score (CRPS) of levelized cost of hydrogen (LCOH), net present value (NPV), and internal rate of return (IRR), (b) decision bandwidths W(Y) = P95(Y) – P5(Y), (c) threshold-crossing probabilities Pr(NPV>0) and Pr(IRR>10%), and (d) a local elasticity E(Y) = dW(Y)/dCRPS that maps marginal forecast skill to risk-band compression. Finally, we run a financing price sweep to identify the minimum hydrogen offtake price that achieves a 90% probability target for NPV > 0 and the joint target NPV > 0, IRR > 10%.
Results show that improved scenario modelling can substantially reduce economic distribution error and compress risk bands: the LSTM lowers CRPS by 30% for LCOH and NPV and by 25% for IRR versus the best bootstrap/Weibull configurations. However, under base assumptions the financeability thresholds are nearly invariant across methods: the 90%-target required hydrogen price is €7.76-7.78/kg for Pr(NPV>0) and €9.16-9.18/kg for Pr(NPV>0 and IRR>10%), with cross-method spread below €0.02/kg indicates a threshold-saturated regime where better modelling mainly narrows uncertainty rather than shifting the decision boundary. Sensitivity analysis indicates decision value is highest in moderate-margin regimes (roughly €5.5-8/kg) and diminishes at high profitability where models converge.
This work reframes “better scenarios” into an investment-relevant diagnostic: use elasticity and threshold behaviour to identify when modelling improvements will shift financeability versus only compress risk bands, supporting more defensible screening and policy design.
How to cite: Aditama, P. and Zia, A. W.: When Does Better Scenario Modelling Improve Financeability? A Decision-Coupled Evaluation for Offshore Wind-to-Hydrogen, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5058, https://doi.org/10.5194/egusphere-egu26-5058, 2026.
