Considerable uncertainty remains in projecting long-term spatio-temporal changes in renewable sources, demand and low probability extremes that can disrupt the energy system. Since demand must be balanced by generation from largely renewable sources, there is an urgent need for deeper dialogue between climate science, climate risk, and energy transition research communities.
This session invites contributions spanning pathways to accelerate renewable energy transitions under climate change; approaches to just and equitable energy transitions; insights from climate modeling for demand or supply side challenges; approaches for balancing renewable generation with demand management across timescales; innovative concepts or tools to address uncertainty in energy-climate interactions; insights how climate risks impacts the energy transition. We encourage model-based, empirical, and conceptual studies alike, incluing:
* Impacts of climate variability (including extremes) and change on energy systems, and associated uncertainties
* Effects of present and projected renewable resource variability on energy systems, and technical approaches to balance supply and demand
* Climate-related drivers of energy demand, and the role of demand reduction and management in supporting low-carbon transitions
* Influences of extreme events and spatio-temporal complementarities on both demand and supply within energy systems
* Integrated assessments combining supply and demand side approaches to low-carbon transitions
* Spatio-temporal data needs from climate science and modelling to advance understanding of renewable energy supply and demand under climate change
Orals: Thu, 7 May, 16:15–18:00 | Room 0.51
Understanding how the energy needs of the different sectors will evolve in the future is key to informing climate policy design. For the buildings sector, this involves considering heterogeneity in technological, socioeconomic and climatic conditions. In this work, we develop a building energy sectoral model for Europe that is able to consider technology adoption dynamics at the subnational scale, with high (0.5° x 0.5°) resolution. The model focuses on energy efficiency, by considering building renovation, space heating and space cooling, and the interplay between them. We simulate wide-scale renovation waves in Europe, understanding where we may expect more households renovation efforts, and where policy support will be most needed in the following decades. We develop three scenarios: the Reference one, where no public policy is in place, the Historical Renovation Rate one, where we reproduce the renovation trends observed in the last years in the EU countries, and the Low Energy Demand one, where a high renovation rate is achieved, along with a cap on floorspace and strong electrification of end uses. In these scenarios, although space cooling energy demand is expected to increase, most efforts target reducing space heating energy demand and electrifying end-uses. This study implies that energy renovation investments in EU27 need to increase by roughly 16%, in respect with historical data, to significantly reduce energy demand and emissions. Spatial clustering of renovation activities, which this work uncovers with unprecedented detail across the EU both between and within countries, should be anticipated and explicitly accounted for in the design of European-level policy instruments. To increase renovation rates through subsidies, we find that public government support should roughly match private investments, especially in those regions where the conditions of the building stock, construction costs and energy expenditures might not motivate households to renovate. This is a significant departure from the current situation where private investments are about 20 times higher than public ones.
How to cite: Cofler, E., Colelli, F., Falchetta, G., and Tavoni, M.: Modeling policies for the EU building stock decarbonization at sub-national resolution, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22564, https://doi.org/10.5194/egusphere-egu26-22564, 2026.
The usage of renewable energies is essential for achieving climate neutrality, as outlined in the Paris Agreement and the European Green Deal's target of net-zero emissions by 2050. However, the electricity grid faces significant challenges due to the temporal variability and uneven spatial distribution of renewable energy production. Periods of particularly high or low generation present problems for grid operators. So-called “Dunkelflauten”, periods of little sunshine and low wind speeds, result in a low power supply. These fluctuations emphasise the importance of examining how climate change itself may affect renewable energy sources. Understanding such impacts is crucial for developing effective adaptation strategies. This study investigates changes in the frequency and duration of Dunkelflauten in Germany under climate change by utilising the CMIP6-based high-resolution NUKLEUS ensemble. Unlike previous studies, we do not consider specific installations in order to focus on the change across the entire country. We evaluate changes in wind and photovoltaic capacity factors under 2 K and 3 K global warming scenarios, using two representative wind turbines to illustrate sensitivity to technical specifications. The results show a moderate decline in average wind speed, particularly in northern Germany, which results in a lower wind capacity factor. In contrast, only minimal changes in the photovoltaic capacity factor are projected. Consequently, we conclude that the frequency of Dunkelflauten will increase slightly in the context of a stronger climate change signal. This work highlights the value of high-resolution climate model ensembles for assessing the resilience of renewable energies sources under climate change.
How to cite: Heinrich, P. and Geyer, B.: Changes in Simultaneous Low-Wind and Low-Solar Events (Dunkelflauten) under Global Warming: A High-Resolution Simulation Study for Germany, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-1730, https://doi.org/10.5194/egusphere-egu26-1730, 2026.
As the global energy transition accelerates, planning for resilient and reliable power systems increasingly depend on the spatiotemporal dynamics of variable renewable energy (VRE) generation. However, climate projections often lack the necessary high temporal resolution required to balance supply and demand, limiting their utility in robust energy system planning and risk assessment.
In this work, we present a novel and computationally inexpensive temporal disaggregation approach to generate plausible hourly time series from coarse daily climate model projections over multiple sites or regions, with a focus on wind power generation. The approach picks an analogue day from a bank of historical observations for the candidate day to disaggregate. The choice of analogue is based on squared Euclidean distance between candidate day and historical observations, taking into account all sites and conditions before and after the candidate day. Hourly values from the analogue day are then employed across sites and rescaled to match the daily data to disaggregate. Wind speed values are then converted into hourly capacity factor time series.
We validate the framework using a 71-year open-source ERA5 reanalysis record for onshore near-surface wind speed and wind power generation across the twelve NUTS1 regions of the United Kingdom, which we split between training and validation data sets (15 years).
Our applications shows that the model is highly efficient, requiring less than one minute to downscale 15 years of daily mean data into hourly series. Our approach successfully captures the full probability distribution of the real hourly data and preserves high autocorrelation – up to 0.95 – at midnight when the analogue day changes, which has previously been a challenge for these downscaling methods. Resulting hourly wind power time series also successfully reproduce key energy-modelling-relevant characteristics. For wind drought analysis, the reconstructed time series closely follow the observed event-duration distribution, particularly for the longer, system-critical events. Similarly, the model accurately reproduces the observed rapid change distribution, confirming its ability to capture both the frequency and magnitude of wind power ramp events across different timescales. These results hold for both uniform and area-proportional spatial weights, and for different values of the algorithm’s hyperparameters.
The proposed analogue-based approach provides an efficient, reliable, and stochastically consistent tool for generating the high-resolution VRE time series needed to assess energy-climate interactions and inform critical investment and policy decisions for future decarbonized energy systems.
How to cite: Benseddik, Z., Bloomfield, H., and Rougé, C.: Hourly disaggregation of daily wind projections: an analogue-based, spatially coherent approach to support energy applications, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3327, https://doi.org/10.5194/egusphere-egu26-3327, 2026.
Energy systems across the world are rapidly evolving to meet climate mitigation targets. This requires lower reliance on fossil fuels and more weather-dependent renewable generation (such as wind power, solar power, and hydropower). This increased weather dependence adds a new set of challenges for balancing supply and demand due to the inherent variability of weather, increasing the need for investment in storage and flexible technologies. Both in terms of security of supply risks from system level challenges (e.g., energy shortfall events, where existing generation is insufficient to meet demand) or from smaller-scale infrastructure challenges (e.g., extreme weather impacting the operability of energy system components) there is the need for stress testing of new power system configurations.
A challenge for this stress testing is existing power system models are often limited to running single-year simulations, and there is therefore a need to be able to subset years of different challenge levels (e.g. different return period levels of weather-driven stress) that may cause weather-driven stress. Existing methodologies to explore weather-driven stress translate large volumes of gridded meteorological data into demand and renewable generation timeseries which are analysed, often in terms of demand-net-renewables. However, this involves significant interdisciplinary training in energy and climate impacts modelling and large volumes of storage space to convert many decades of data into demand-net-renewables for a robust stress test selection.
In this study we extend previous work where weather-driven risks to the European energy sector in both a present and future climate have been explored, with a particular focus on the timing, duration, and severity of energy shortfall events [1]. We consider three methods of choosing a stressful year based on demand-net-renewables. These are:
1. A year with a short duration extreme event.
2. A challenging year.
3. A year of challenging large-scale weather.
The first two types of stress test are defined using weather-driven demand, wind power and solar power timeseries, whereas the final type of stressful year involves matching the most commonly occurring weather-patterns [2] from a historically challenging year to those that are occurring in a climate model, therefore bypassing the need to convert all of the climate model data into energy system variables.
We demonstrate results from recent stress testing exercises using state-of-the-art outputs from the UK climate projections (UKCP18) 2.2km convection permitting model. We show how from an energy-systems perspective the most challenging short duration extremes are often not contained within the most challenging year, and that this distinction between types of stress needs to be driven by user needs.
We also show that using traditional large-scale weather to subset the stress test event does not lead to the highest impact energy-stress events contained within a large sample of climate data. It does save a significant amount of processing time for users wishing to stress test a system for a ‘reasonably challenging’ event.
[1] Bloomfield, H. (2025). Reasonable worst-case stress-test scenarios for the UK energy sector in the context of the changing climate. Available at: https://www.theccc.org.uk/publication/reasonable-worst-case-stress-test-scenarios-for-the-uk-energy-sector-in-the-context-of-the-changing-climate/
[2] Pope, J. O., Brown, K., Fung, F., Hanlon, H. M., Neal, R., Palin, E. J., & Reid, A. (2022). Investigation of future climate change over the British Isles using weather patterns. Climate Dynamics, 58(9), 2405-2419.
How to cite: Bloomfield, H., Burton, L., Tate, M., Manning, C., and Pope, J.: Designing weather-informed stress tests scenarios for net-zero Energy systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5462, https://doi.org/10.5194/egusphere-egu26-5462, 2026.
Modern electricity grids are introducing more converter-connected renewable energy generators, which help combat climate change. These generators have zero carbon emissions of Scope 1 (instant emissions from burning fossil fuels) and have only Scope 3 carbon emissions (life-cycle emissions due to manufacturing, transportation, deployment and decommissioning). They can connect to the grid almost instantly, which is a great benefit for customers. However, most converter-connected generators lack the inherent grid stabilising functions of conventional generators, which have high inertia in their hardware (steam turbines powered by fossil fuels or nuclear power). As a result of this lower inertia, regular grid disturbances can lead to fast-changing grid frequency variations, potentially cascading to blackouts if uncontrolled. The goal of the modern energy systems is to combine the benefits of heterogeneous energy network with large penetration of renewable generators and highest possible stability of the grid at the level of the standard frequency 50Hz.
We analyse frequency data of the UK grid and demonstrate how the machine learning tools help automate anomaly detection. We apply Bayesian change point analysis and tipping point analysis (early warning signals) to identify and align anomalies, which require additional processing for precise timing of the anomaly events [1]. We also apply clustering to more than 300 datasets of the UK frequency anomaly events and identify several types of such anomalies.
Our results provide the initial grounds for automated preventive management of the grid stability under the increasing number of renewable generators in the national grid. The methodology is generic and can be applied to other types of datasets across Europe.
References
[1] Livina et al, Geoscientific Instrumentation, Methods and Data Systems 14, 541-564, 2025
How to cite: Livina, V. N., Ritzmann, D., Wright, P., and Wilkins, F.: Analysing grid stability under increasing penetration of renewables using machine learning techniques, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8427, https://doi.org/10.5194/egusphere-egu26-8427, 2026.
Wind-solar power is a pillar of renewable energy transition and climate mitigation. However, intrinsic volatility threats electricity supply reliability, which could be further jeopardized by projected climate change. Complementarity of wind-solar power has been introduced to suppress this volatility. However, it has not been translated into a formal mathematical objective in optimization models. Especially, influences of considering complementarity on cost-effectiveness and supply reliability during long-term operation remains unknow. Here, these knowledge gaps are closed through developing a Daily Complementarity Index of wind–solar generation (DCI) and a nuanced analysis. The results of the comparison of our index with existing indices and site validation con-firm the reasonability of the DCI and its improvements in interpretability. Further, although introducing complementarity into the objectives could increase total initial invest, considering gains from declined supply shortage, increase in initial cost could be offset by effectiveness of trans-regional interconnection in enhancing supply reliability.
How to cite: Wu, C. and Yang, D.: Beyond flexible regulation: building resilient renewable power system through spatiotemporal complementarity of wind-solar power, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8893, https://doi.org/10.5194/egusphere-egu26-8893, 2026.
The deployment of renewable energy technologies is accelerating globally as part of climate mitigation strategies, with capacities projected to triple by 2030 compared to 2023 levels. To be effective, this rapid expansion should be guided by long-term planning that accounts for a changing and varying climate. Such planning efforts are usually supported by energy system models, but these models often lack integration of future climate information. The SPEED2ZERO project addresses this gap by bridging energy and climate research to develop climate-resilient energy transition pathways for Switzerland. To achieve this, a Swiss electricity systems model with high spatial resolution is bounded by outputs of a coarser European model. This multi-scale approach requires coherent climate-informed inputs that align with both spatial resolutions.
To generate these multi-scale inputs, we develop climate-driven projections for hydropower, solar, and wind power, as well as heating and cooling demand for Switzerland and Europe. This comprehensive dataset covers the Representative Concentration Pathways (RCPs) 2.6 and 8.5. We leverage high-resolution regional climate model simulations from the EURO-CORDEX archive that include transient aerosols and bias-correct the relevant variables using CERRA and CERRA-Land reanalysis data. We additionally perform dedicated hydrological simulations for Switzerland using the PREVAH model and route the climate model runoff for Europe using the mRM model. In total, this study considers 3 climate model chains for RCP2.6 and 9 for RCP8.5. These chains consist of global climate model-regional climate model pairs spanning 1991 to 2100. The modeling pipeline employs open-source tools, such as pvlib and windpowerlib, along with technical specifications provided by energy system modelers, to convert the processed climate data into energy quantities.
Results from the modelling pipeline indicate a consistent increase in PV potential over time, a trend mainly attributable to declining aerosols concentrations over most of Europe. In contrast, wind power and water availability for hydropower exhibit substantial spatial variability and model disagreement, including opposing wetting and drying trends across different climate model chains. As these climate signals are spatially heterogeneous, high resolution climate simulations are essential to explore the nuances that coarser datasets might overlook. This granularity allows, for example, the analysis of climate impacts at individual hydropower plants or reservoirs. First results for Switzerland indicate a decline in hydropower generation in the second half of the century, with nationwide trends not always coinciding with trends at single sites. These findings highlight the need to consider energy system planning both from a big picture and local perspective lens to ensure the system's resilience.
How to cite: Haddad, Y. Y., Bloin-Wibe, L., Seubert, P., Zappa, M., Sieber, P., Gudmundsson, L., and Seneviratne, S. I.: Under the Magnifying Glass: multi-scale climate impacts on renewable energy supply and demand in Europe, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-11820, https://doi.org/10.5194/egusphere-egu26-11820, 2026.
Here we examine the interrelationships between 30 natural hazard types and 13 energy system components. Energy systems are increasingly exposed to natural hazards that are becoming more frequent, intense, and interconnected under climate change. These hazards rarely occur in isolation. Single events and compound events can trigger cascading impacts that spread across energy generation, transmission, distribution, storage, and end-use demand, often resulting in large-scale power outages and long recovery times. However, many existing studies focus on individual hazards or isolated energy components, which limits understanding of system-wide risk. This study presents a structured synthesis of multi-hazard impacts on energy systems using a hazard–energy interrelationship framework. Based on a systematic review of 219 sources, including peer-reviewed literature, technical reports, and documented real-world events, we examine 30 natural hazard types across atmospheric, hydroclimatic, geophysical, environmental, and space-related categories. For each natural hazard type, we examine its potential influence on 13 different key energy system components, including power generation, transmission and distribution networks, storage systems, and energy demand. Each of the 390 (30x13) potential hazard–energy interrelationships is classified with their potential to cause one of the following: direct physical damage, increased probability of failure, both, or neither. We include both interrelationships that are evidenced by those that have occurred and evidenced in the literature, as well as those that theoretically might occur. Of the potential 390 natural hazard-energy system component interrelationships, we find 5 (1.3%) interrelationships as direct impacts, 11 (2.8%) with increased probability of influencing an impact, 181 (46.4%) with both direct and increased probability, and 193 (49.5%) with no interrelationships. We find that all energy system components are exposed to at least three hazard types, except cooling demand, which is exposed to only two hazard types, and that cascading impacts are common across the energy supply chain. We found that, by hazard group, the following percentages of interrelationships were identified (expressed as a proportion of the total possible hazard–energy component interrelationships within each hazard group): geophysical (65%), atmospheric (52%), environmental (50%), hydroclimatic (44%), space (28%). Case studies of catastrophic power outages, such as the February 2021 Texas (USA) cold wave, which included a storm and floods, illustrate how failures in power generation can rapidly propagate through transmission and distribution networks and interact with extreme demand conditions. Beyond single-hazard perspectives, this framework highlights key interdependencies and vulnerabilities in energy systems and supports integrated approaches for early warning, resilience planning, and decision support. The findings are directly relevant to initiatives and broader discussions on multi-hazard risk and energy system resilience.
How to cite: Aschale, T. M., Malamud, B. D., and Donoghue, D. N.: Multi-Hazard Impacts and Cascading Risks in Energy Systems , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12258, https://doi.org/10.5194/egusphere-egu26-12258, 2026.
Power infrastructure, like power plants, power stations, overhead lines, etc., might be strongly altered or destroyed by the effect of surrounding air temperature. Either affecting the efficiency of power generation, in case of power plants, or its subsequent transport by influencing the thermal rating of power lines. Extreme temperature events, e.g., the annual maximum surface air temperature, are of interest, since they are representative of the maximum thermal stress from the environment that infrastructure should ideally be capable of withstanding. Additionally, they are usually events that coincide with exceptionally high energy demand, too, due to cooling by air conditioning or electric heating in case of annual temperature minima.
As a result of a changing climate towards hotter average air temperatures, knowledge of the statistics of temperature extremes is relevant to ensure reliable operation of existing infrastructure and to asses the operation environment of potential future assets. The methodology followed in this study is purely statistical [1]. It is the best available methodology for predicting the statistics of extremely rare events based on both observation datasets and the best available climate model outputs. It involves the fit of a non-stationary generalized extreme value distribution (GEV) using the software package ANKIALE [2] using a Bayesian setup. The parameters of the GEV distribution are determined as follows in this setup: First, an a priory distribution from data of 28 CMIP6 models is created. Next, using measurement records, namely the E-OBS dataset in version 31.0e [3], this a priori estimate is then constrained by observations to obtain the final a posteriori distribution of the GEV parameters. It is worth noting that the employed Bayesian approach provides uncertainty or error estimates on the obtained parameters, too, allowing to make statements about the reliability of the predictions.
Using this method, a comprehensive dataset for the non-stationary GEV distribution parameters over Europe was created encompassing the period from 1850 to 2099. For future years, data for the climate scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 is available. To facilitate an easy evaluation of the data, a complementary data viewer software was developed. The software visualizes spacial maps of Europe for summary statistics of the GEV distribution including their uncertainty. A relevant quantity for power infrastructure of the GEV distribution is for example the upper bound, which can be interpreted as the most extreme temperature that is statistically possible.
[1] Robin, Y. and Ribes, A.: Nonstationary extreme value analysis for event attribution combining climate models and observations, Adv. Stat. Clim. Meteorol. Oceanogr., 6, 205–221
[2] Robin, Y., Vrac, M., Ribes, A., Barbaux, O., and Naveau, P.: A Bayesian statistical method to estimate the climatology of extreme temperature under multiple scenarios: the ANKIALE package, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-1121, 2025.
[3] Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. M., & Jones, P. D. (2018). An ensemble version of the E-OBS temperature and precipitation data sets. Journal of Geophysical Research: Atmospheres, 123, 9391–9409.
How to cite: Wener, P., Robin, Y., Dubus, L., and Bouchet, F.: Non-stationary Generalized Extreme Value Distribution Analysis of Temperature Extremes for the Effects on Electrical Power Infrastructure, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19503, https://doi.org/10.5194/egusphere-egu26-19503, 2026.
The electricity grid of the Netherlands is nearing its limits, making short-term load forecasts central to active congestion management. At the same time, the rapid rise of variable renewables has increased the grid’s sensitivity to the weather. Weather forecasts inherently carry some degree of uncertainty, which can be incorporated in energy forecasts in multiple ways. We explore how ensemble weather forecasts can improve probabilistic day-ahead and intraday energy predictions by coupling data-driven load forecasting models with physical Numerical Weather Prediction (NWP) ensembles.
Using the open-source OpenSTEF framework, we train asset-specific forecasting models that predict grid load from calendar, weather, historical load, and market price features. Our approach replaces deterministic meteorological inputs with ensemble quantiles during inference. Tests on real grid assets show improved accuracy, calibration, peak detection and forecast stability. We also identify two key operational challenges: managing dependencies between weather variables and combining different types of specialized weather forecasts with ensembles.
Propagating weather uncertainty into energy forecasts improves the efficiency of grid operation during the energy transition. We invite discussion on hybrid modelling strategies, calibration techniques and validation, and practical aspects such as optimal resolution. We look forward to exchanging ideas and experiences that advance robust and open probabilistic forecasting practices.
How to cite: Reeze, M., van Es, D., and Schilders, L.: Incorporating Weather Uncertainty in Energy Forecasts: Using Ensembles for Intraday and Day‑Ahead Congestion Management, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9383, https://doi.org/10.5194/egusphere-egu26-9383, 2026.
Climate change is expected to alter both the magnitude and variability of renewable energy resources, with key implications for climate-resilient power system planning across the world, especially in rapidly growing and evolving energy systems such as India’s. This study investigates how projected climate change may affect wind and solar photovoltaic (PV) generation potential across India in terms of changes in the mean and variability of power capacity factors across various spatial and temporal scales, and the occurrence of low-generation events.
In particular, for a subset of CMIP6 (Coupled Model Intercomparison Project Phase 6) global climate models, we derive wind and solar PV capacity factor fields (CFs) for the historical and three future scenario experiments (SSP1-2.6, SSP2-4.5, and SSP5-8.5). Future changes in renewable generation potential at grid-point and regional levels are found by analysing changes in mean CFs, and interannual and seasonal variability. We also identify and characterise “renewable droughts”, that is periods during which wind and/or solar PV CFs fall below given thresholds, and assess their frequency, duration, and severity under various climate scenarios.
To evaluate the robustness of CMIP6-derived projections, we first compare the historical CFs with those derived from the ERA5 reanalysis in order to understand the model biases and spread, and assess their ability to represent present-day renewable resources.
The results then highlight regions, seasons, and climate scenarios that may pose amplified risks to renewable energy supply, and may inform discussions on climate risks and resilience in long-term energy system planning for India. The derived CF timeseries will also support future work on system-level power system modelling.
How to cite: Calladine, M., Greenwood, D., Hunt, K., Ali, H., and Bloomfield, H.: The impacts of climate change on wind and solar PV power generation in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6975, https://doi.org/10.5194/egusphere-egu26-6975, 2026.
Disclosure of physical climate risks is increasingly emphasized by investors, regulators, and energy system stakeholders, but current reporting remains limited in transparency and comparability. This challenge is particularly evident for renewable energy companies with geographically dispersed assets exposed to extreme weather events, as electricity generation is inherently dependent on climatic conditions. Although recent regulatory initiatives such as IFRS S2 require the disclosure of climate-related physical risks, they offer limited guidance on how climate hazards can be translated into standardized, quantitative risk metrics at the asset and portfolio levels.
In this study, we propose a standardized, event-based probabilistic framework to assess physical climate risks for renewable energy infrastructure. The framework integrates synthetic tropical cyclone hazard scenarios with simplified representations of energy assets to quantify direct physical impacts and aggregated portfolio-level risk indicators. To illustrate the applicability of the approach, we construct a representation of offshore wind assets inspired by publicly available information from a global renewable energy operator.
By combining event-based hazard modeling with spatially explicit asset exposure, the framework captures the effects of spatial diversification, correlated hazards, and risk aggregation across energy portfolios. The results show how extreme weather events can affect multiple energy assets, shaping both site-level vulnerability and firm-level risk exposure. Overall, the proposed framework highlights how transparent, standardized, event-based climate risk metrics can support investment decision-making, energy system resilience planning, and the development of climate adaptation strategies and cost–benefit analyses in the renewable energy sector.
In future work, the framework can be extended to jointly consider climate-related losses and revenue from energy generation, as well as the identification of new potential sites, enabling assessments that capture not only physical damage but also impacts on operational performance.
How to cite: Li, C.-H.: Event-based assessment of physical climate risk for energy infrastructure using CLIMADA and synthetic tropical cyclone hazards, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8620, https://doi.org/10.5194/egusphere-egu26-8620, 2026.
This paper presents an investment analysis of a land-based solar photovoltaic power generation project in a coastal region of South Korea by using real options theory (ROT) to address the limitations of traditional discounted cash flow (DCF) analysis under market uncertainty. Because volatility in the system marginal price (SMP) and renewable energy certificate (REC) prices can significantly affect the profitability of renewable energy investments, incorporating managerial flexibility through ROT is essential for reliable economic assessments. We estimate the critical investment price (CIP) using both a traditional DCF approach and a real options model, and further examine how a government’s long-term fixed price contract influences investment outcomes. The CIP by ROT is derived within a Hamilton-Jacobi-Bellman (HJB) framework. Results indicate that the CIP obtained from ROT is generally higher than that from DCF, reflecting option value from managerial flexibility; moreover, this difference increases as price volatility increases. We also find that long-term fixed-price contracts can reduce revenue uncertainty, thereby lowering required rate of return, the investor’s equity share, and the CIP, ultimately improving project attractiveness to investors. Under the long-term fixed-price contract, the project achieves an internal rate of return (IRR) of 5.75%, exceeding the weighted average cost of capital (WACC) of 3.58%, suggesting that the project is economically feasible.
How to cite: Lee, D., Park, C., Jang, I., and Lee, K.: Investment Analysis under Uncertainty: Real Options Valuation of Solar Photovoltaic Projects in South Korea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8767, https://doi.org/10.5194/egusphere-egu26-8767, 2026.
The optimal wind-solar ratio (WSR) is crucial for ensuring the stability and cost-effectiveness of renewable energy systems, yet climate change may exacerbate WSR mismatches, leading to increased system costs and capacity demands. This study develops a climate-driven WSR optimization framework, integrating global climate models (GCMs) with a dispatch optimization model to assess the impacts of climate change on WSR under different scenarios. Our findings reveal that historical preference pathways often misalign with evolving climate conditions, causing sharp increases in both costs and capacity requirements. Notably, mismatched WSRs inflate electricity supply costs by an average of 23% across low-latitude countries, far exceeding the direct effects of climate change itself. By contrast, optimization WSR can effectively mitigate cost risk and enhance the resilience of power systems to climate change. Our results uncover a distinct latitudinal divergence in WSR, with enhanced solar dominance in low-latitude regions and a systematic shift toward wind reliance at higher latitudes driven by climate-induced resource variability. Our results underscore the necessity of region-specific WSR optimization strategies to ensure an economically viable and climate-resilient energy transition.
How to cite: Li, J. and Tong, D.: Navigating optimal wind-solar trade-offs under climate change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-4372, https://doi.org/10.5194/egusphere-egu26-4372, 2026.
The replacement of fossil fuels with renewable energy is central to mitigating human-induced climate change and achieving climate neutrality. However, this transition depends on the reliability of renewable energy systems that are increasingly exposed to climate-driven variability. Climate change is expected to alter not only mean renewable energy potentials but also their extremes such as renewable energy droughts, defined as periods of simultaneous low wind and solar power generation. These events pose a growing challenge to energy security in highly decarbonized systems with limited flexibility and storage capacity. This study presents a global assessment of projected changes in renewable energy droughts under 1.5 °C, 2 °C, and 3 °C global warming scenarios using CMIP6 multi-model simulations. Wind and solar energy potentials are first estimated from wind speed and solar radiation using physics-based empirical approaches. These potentials are then combined into a Standardized Renewable Energy Index (SREI) constructed with copula functions to capture their joint dependence. Changes in renewable energy drought severity, duration, and frequency are subsequently evaluated across warming levels. The results reveal strong regional heterogeneity alongside an overall intensification of renewable energy drought characteristics with increasing warming. Based on a machine learning–based assessment of the drivers of changes in renewable energy droughts, solar radiation emerges as the dominant factor, with its influence strengthening at higher warming levels. By anticipating compound extremes in renewable energy supply and identifying hotspot regions, this work underscores the need to incorporate these intensifying events under global warming into energy system planning and risk-informed strategies, particularly in highly renewable power systems, to support a resilient low-carbon transition.
How to cite: Aminimehr, A., Hellinckx, P., and Tabari, H.: Renewable Energy Droughts Under Global Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13163, https://doi.org/10.5194/egusphere-egu26-13163, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 4
EGU26-3742 | ECS | Posters virtual | VPS19
Designing cost-effective storage portfolios in decarbonizing power systems: a deficit stretch approachTue, 05 May, 14:33–14:36 (CEST) vPoster spot 4
High wind and solar penetrations would make bulk energy storage increasingly important for electricity system reliability. We introduce a deficit stretch framework that relates the temporal structure of generation shortfalls to optimal storage configurations in a decarbonizing grid and links the intensity, duration, and frequency of deficits to storage needs and cost–reliability trade-offs. Using Karnataka (India) as a case study, we simulate wind–solar–demand scenarios to examine (i) drivers of deficit-stretch emergence, (ii) which wind–solar–storage portfolios align with available storage technologies, and (iii) how these choices map onto Pareto frontiers of cost versus reliability. We cluster deficit stretches to identify characteristic storage durations (across hours to seasons) enabling a direct mapping from variability patterns to feasible technology options. Results indicate that solar share largely controls the deficit stretch duration spectrum. The proposed framework offers an empirical approach leading from analysis of renewables variability to consideration of bulk energy storage portfolios amidst cost–reliability tradeoffs and is extendable to other regions as well.
How to cite: Gangopadhyay, A. and K Seshadri, A.: Designing cost-effective storage portfolios in decarbonizing power systems: a deficit stretch approach, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3742, https://doi.org/10.5194/egusphere-egu26-3742, 2026.
EGU26-8851 | ECS | Posters virtual | VPS19
Unfolding the rise in cooling demand from residential buildings sector in IndiaTue, 05 May, 14:36–14:39 (CEST) vPoster spot 4
India’s buildings sector contributed to about 36% of total electricity consumption, with residential buildings comprising nearly 79% of this demand in 2025 [1]. Within residential electricity use, cooling alone accounted for about 31% of the consumption and has seen a rise by 50% over the past decade [1]. India has one of the highest cooling gaps in the world primarily driven by population growth and affordability constraints [2]. India energy security scenario (IESS) 2047 suggests that, with rising per capita income, the residential air conditioner ownership expected to increase by 1.3 folds in the next decade [3]. India Cooling Action Plan has projected that cooling electricity consumption will be doubled by 2038, however passive design strategies on building envelop can reduce the consumption by 15% [4]. V. Chaturvedi et al., (2020) and R. Khosla et al., (2021) suggested that along with passive design interventions, promoting consumer awareness also plays a crucial role in reducing the cooling energy demand [5, 6]. Despite rising cooling demand, the combined quantitative influence of consumer behaviour, climate, technology, and building characteristics on cooling electricity demand in India remains insufficiently explored.
To address this research gap, the authors have developed a bottom-up generic model to estimate the residential cooling energy demand based on variation in ambient temperature, appliance ownership, and relative humidity. The model is applied to India as a case study and with parameters calibrated using context-specific empirical data. Cooling degree days (CDD) serve as a metric to quantify ambient temperature rise relative to a base temperature of 24ºC. The analysis estimates the sensitivity of cooling demand to ambient temperature variations, expressed as a percentage increase in electricity consumption per degree rise. By varying the base temperature from 18ºC to 26ºC, model also captures the influence of consumer behaviour on cooling energy demand. The developed model is soft linked to SAFARI, a system dynamics model, developed by Centre for Science, Technology, and Policy (CSTEP) to design low carbon pathways for India. SAFARI explores the interlinkages between demand sectors such as buildings, transport, agriculture, forest and other land use (AFOLU), industry and supply sector, i.e., power. Soft-linking will enable to generate scenarios of different combinations of climatic conditions, behavioural aspects, varying appliance penetration rate, low carbon interventions in residential building sector such as, cool roof, wall insulation, alternate construction materials etc. These scenarios will allow understand the potential possibilities of reducing the energy demand for the country and can inform policy making on demand side management measures.
References:
1. CSTEP. https://safari.cstep.in/safari/
2. Debnath, B. K. https://doi.org/10.3390/buildings10040078
3. NITI Aayog. https://iess.gov.in
4. Government of India. India-Cooling-Action-Plan.pdf
5. Chaturvedi, V. https://doi.org/10.1016/j.heliyon.2020.e05749
6. Khosla, R. https://doi.org/10.1088/1748-9326/abecbc
How to cite: Davis, D. and Saraf, N.: Unfolding the rise in cooling demand from residential buildings sector in India, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8851, https://doi.org/10.5194/egusphere-egu26-8851, 2026.
