Spain has taken a significant stride towards its goal of installing 1 to 3 GW of floating offshore wind capacity by 2030. This was achieved through the implementation of a Maritime Spatial Planning (MSP) covering 19 designated areas, where it is expected the installation of offshore wind farms in the upcoming years. Therefore, it is of interest analysing the impact of climate change on offshore wind resource in these areas. To achieve a sufficiently high spatial resolution for this study, a dynamic downscaling of a multi-model ensemble from the 6th phase of the Coupled Model Intercomparison Project (CMIP6) was conducted using the Weather Research and Forecasting (WRF) model in the Spanish territorial waters, encompassing the Iberian Peninsula, Balearic Islands, and Canary Islands. Thus, wind data were obtained from the latest climate projections, with a 10-km spatial resolution and a 6-hour temporal resolution. The results were compared, for a historical period from 1985 to 2014, with data from the ERA5 reanalysis database and with observational data from buoys. The results of this validation process showed a great accuracy in the dynamical downscaling performed, generally better than when using data from the Coordinated Regional Climate Downscaling Experiment (CORDEX), which performed dynamical downscaling on data from several CMIP5 climate models. Future projections, from 2015 to 2100, were assessed under the Shared Socioeconomic Pathways (SSP) 2-4.5 and 5-8.5 scenarios. The findings of this study indicate a projected growth in Spain's offshore wind energy potential, especially in the Atlantic Ocean and around the Canary Islands.

Using wind speed data from simulations carried out with the WRF atmospheric model, the offshore wind energy resource was classified in the 19 areas involved in de Spanish MSP. This classification considered the wind power density but also factors such as resource stability, environmental risks, and installation costs. The results reveal significant diversity in wind resource classification within potential offshore wind farm areas, ranging from "fair" (3/7) to "outstanding" (6/7). The most promising areas for offshore wind farm development in the future are situated in the northwest of the Iberian Peninsula and the Canary Islands.

The identification of the most cost-effective solutions in each area involves determining the optimal combination of rated power and the number of turbines and comparing them across different locations to pinpoint the most economical sites for offshore wind energy exploitation. This economic analysis was done for a 25-year near-future period under the SSP 2-4.5 scenario, aligning with the expected operational lifespan of wind farms. This study includes the calculation of the Levelized Cost of Energy (LCOE) index, which gives an indication of the minimal price at which the electricity should be sold in order for the project to be profitable. The results highlight that the LCOE is lower for farms with a higher number of wind turbines featuring increased rated power. While the Canary Islands exhibit the most economically advantageous prices overall, other regions such as Galicia and Cataluña also boast promising areas.

How to cite: Thomas, B., Costoya, X., deCastro, M., Insua-Costa, D., Senande-Rivera, M., and Gómez-Gesteira, M.: Downscaling CMIP6 climate projections to classify the future offshore wind energy resource and calculate the Levelized Cost of Energy of various wind farm designs in the Spanish territorial waters., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-523, https://doi.org/10.5194/egusphere-egu24-523, 2024.

Floating offshore solar farms (OSF) are an attractive option for solar energy generation, as they help avoid land competition with other uses. Planning the deployment of OSF requires assessments for site selection, which are primarily based on energy yields, in addition to other considerations. The yields are determined through evaluations using climate and oceanic variables. Uncertainty in these variables can propagate to further uncertainty in yield estimations, which ultimately can lead to significant consequences in the cost of energy. In this work, we propose the development of a novel method to assess the viability of OSF considering insolation uncertainty, with a focus on the United Arab Emirates (UAE) region. Open-source satellite data is utilized to conduct initial site assessments, and produce a set of viable locations based on parameters that include solar irradiance, ambient temperature, sea surface temperature, wind speed and precipitation. Validation of the viable locations will be done through the deployment of meteorological instrumentation, to collect in-situ measurements for a minimum of one year. Machine learning techniques are examined to quantify the uncertainty, followed by determination of impacts on levelized cost of electricity (LCOE) and savings based on uncertainty reduction.

How to cite: Medina-Lopez, E., Lazic, J., and Yousef, L.: Offshore Solar Farm Assessment and Uncertainty Determination for the United Arab Emirates, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8655, https://doi.org/10.5194/egusphere-egu24-8655, 2024.

Development pathways for Sub-Saharan Africa project a substantial increase in population and living standards and, correspondingly, in the regional energy demand. To accommodate future energy needs, power and energy system communities have been developing least-cost optimization models to support long-term transformational energy system planning and the transition to carbon neutrality at the African continental scale.  

However, these models usually focus on annual or seasonal energy balances and overlook higher time resolution dynamics that can actually lead to short but impactful events when considering the expansion of renewable energy share. Indeed, the variability of renewable generation and power demand can lead to significant risks, including elevated electricity prices, transmission line overload, and power generation deficits. 

In this work, focusing on the Southern African Power Pool, we couple an energy system planning model, OSeMOSYS-TEMBA, and a power system model, PowNet, to obtain higher temporal resolution characterization of the energy system evolution in the future.

 OSeMOSYS-TEMBA is a long-term energy system planning model producing cost-optimal trajectories of capacity expansion for different technologies for all the countries in continental Africa with a seasonal resolution from 2015 to 2070. Yet, OSeMOSYS-TEMBA is not resolved enough to account for power grid reliability under high penetration of renewables, where flexible operations and power grid reliability are crucial, and might substantially affect model projections.

PowNet is a least-cost optimization model running on an annual horizon with hourly resolution and optimizes the dispatch of power from each source as well as the usage of transmission lines, constrained to the power capacity available according to the long-term energy planning provided by the OSeMOSYS-TEMBA model. We assess the difference in generation mix, the impact on transmission lines overloading, power generation deficits in 2030 under three climate policy scenarios: no climate policy, and constrained to 2.0°C and 1.5°C warming constraining emissions to a consistent pathway. 

Results indicate power generation deficits and transmission lines overloading are observed in many countries, especially during the night. These impacts are to be associated with insufficient total power system capacity to meet power demand due to the low time and spatial resolution of the energy system model. Indeed, the increased dependency on variable renewable resources, and a higher resolution demand profile emphasize the need to further expand total capacity, the importance of flexible generation adopting a diverse energy portfolio, and the potential benefits of increasing transmission lines’ capacity. Finally, the storage of unused water for future power generation within the available reservoirs might potentially reduce the power deficit. These results show the importance of the assumptions embedded in the energy system model and motivate methodological improvements to design coupled energy and power system pathways that remain reliable at high spatial and time resolution.

How to cite: Leoni, A., Stevanato, N., Carlino, A., Castelletti, A. F., and Giuliani, M.: From multi-decadal energy planning to hourly power dispatch: evaluating the reliability of energy projections in the Southern African Power Pool, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6025, https://doi.org/10.5194/egusphere-egu24-6025, 2024.

Humans change the climate in many ways, for example, by emitting greenhouse gases or by changing land-use. While studies typically investigate the joint effects of human activity, we here isolate the impact of afforestation and deforestation on winds in the lowermost 350 m of the atmosphere to better understand the role of forests in large-scale wind energy assessments. We use vertically resolved sub-daily output from two regional climate models and compare two extreme scenarios from the LUCAS simulations (Davin et al., 2020). Our results show that afforestation lowers wind speeds by more than 1 m/s in many locations across Europe even 300 m above ground and thus matters at wind turbine hub heights. While adapting the parameters in standard extrapolation allows to capture long-term mean winds well, it remains insufficient to compute wind energy potentials as it fails to capture essential spatio-temporal details, such as changes in the daily cycle. We therefore follow an alternative approach that leverages the vertical resolution of the regional climate models to account for wind profile complexity. Doing so, we report strong changes in wind energy capacity factors due to afforestation and deforestation: they change by up to 50 % in relative terms. Our results confirm earlier studies that land use change impacts on wind energy can be severe and that they are generally misrepresented with common extrapolation techniques.

References:

Davin, E. L., Rechid, D., Breil, M., Cardoso, R. M., Coppola, E., Hoffmann, P., Jach, L. L., Katragkou, E., de Noblet-Ducoudré, N., Radtke, K., Raffa, M., Soares, P. M. M., Sofiadis, G., Strada, S., Strandberg, G., Tölle, M. H., Warrach-Sagi, K., and Wulfmeyer, V: Biogeophysical impacts of forestation in Europe: First results from the LUCAS Regional Climate Model intercomparison, Earth Syst. Dynam., 11, 183–200, 2020, https://doi.org/10.5194/esd-11-183-2020, 2020

Preprint:

Wohland, J., Hoffmann, P., Lima, D. C. A., Breil, M., Asselin, O., and Rechid, D.: Extrapolation is not enough: Impacts of extreme land-use change on wind profiles and wind energy according to regional climate models, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2023-2533, 2023

How to cite: Wohland, J., Hoffmann, P., Lima, D. C. A., Breil, M., Asselin, O., and Rechid, D.: Impacts of extreme land-use change on wind profiles and wind energy according to regional climate models  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1851, https://doi.org/10.5194/egusphere-egu24-1851, 2024.

Wind energy has become one of the most important mitigation options for climate change over the last decades. However, variability and availability of wind are also expected to be changed due to climate change. For this purpose, the KliWiSt project has been initiated to determine the influence of climate change on wind energy site assessments in Germany. Within the scope of the project, many aspects of climate change impacts on wind energy have been studied to determine uncertainties about the topic and to develop recommendations for actions. Although there are many studies in literature which evaluate the effects of climate change on wind in the upcoming decades, low wind events are scarcely investigated so far. However, low wind events pose a risk for achieving long term renewable energy targets and ensuring stability of the electricity grids. Wind drought is increasingly becoming a significant phenomenon which determines low wind energy production due to extreme low wind resources.

In this study, we have investigated the frequency of low wind events in Germany due to climate change until the end of the 21st century by using an ensemble of high-resolution regional climate model simulations available from the EURO-CORDEX initiative. We also evaluated the performance of the regional climate models with data from different observation stations and with re-analysis data sets. For our investigation we used thresholds of 2 m/s and 3 m/s for wind speed at 10 m and 100 m – respectively for – calculating “calm wind” climate indices. The threshold is determined as 3 m/s (at 100 m height) for low wind events since most of the wind turbines starts wind energy production at this value (“cut in” wind speed). We used two different benchmark data sets (ERA5 and CERRA) to determine historical variation of “calm days” over Germany and to evaluate the performance of the high-resolution regional climate models. Moreover, seasonal, annual, and spatial distributions of low wind events are investigated for Germany where the country has already a high installed wind energy capacity and ambitious renewable energy targets. The study aims to determine trend and frequency of low wind events in the past and future at different terrain conditions at different time scales from different regional climate models. The anticipated results of the study and the project are expected to give insight for policy makers and stakeholders from the renewable energy sector.* 

*This study is part of the project "The influence of climate change on wind energy site assessments (KliWiSt)" which is funded by the German Federal Ministry for Economic Affairs and Climate Action (BMWK).

How to cite: Isik Cetin, I., Frisius, T., Keup-Thiel, E., and Rechid, D.: Investigation of low wind events over Germany from high resolution regional climate models, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5810, https://doi.org/10.5194/egusphere-egu24-5810, 2024.

The wind resource in the Iberian Peninsula has been analyzed using wind climate projections from the XX century to the end of the XXI century of the SSP5-8.5 scenario obtained from the MRI-ESM2.0 global climate numerical model. Six-hour wind speed and direction are seasonally grouped and from them, both the production of electrical power and the intensity of wind energy have been estimated throughout the temporal record. Two periods are considered in the dataset: the historical (1950 – 2014) and the future (2015-2100) periods. The non-parametric Mann-Kendall trend test is applied to identify significant wind energy intensity trends and the non-parametric Mann-Whitney test is applied over the entire domain's all-grid points to statistically evaluate the significant differences between wind energy intensity of different time periods. For an estimation of the evolution of the electrical power throughout the XXI century, the latest generation wind turbine SG 7.0-170 from Siemens-Gamesa has been used as a reference. Considering winter as the season of maximum wind energy production, the results show a future higher electricity production compared to the selected historical period in almost the entire Iberian Peninsula, although there is a decreasing production trend throughout the century. The remainder seasonal results indicate a general drop in electrical power due to a decrease of wind resource in the whole Peninsula throughout the century, especially in autumn with significant losses of more than 2 MW of electricity production in many Portuguese areas on the western coast of the peninsula.

How to cite: Luna, M. Y., Díaz-Fernández, J., García-Miguel, A., Calvo-Sancho, C., Castedo, R., Ortega, J. J., Bolgiani, P., Sastre, M., and Martín, M. L.: A wind energy resource analysis in the Iberian Penindula under climate projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6374, https://doi.org/10.5194/egusphere-egu24-6374, 2024.

Critical infrastructure, such as telecommunications networks and hospitals, are in many cases required to have reserve power systems in place, mitigating transmission network failures and protecting against national power grid outage. A previous case study of Great Britain (GB) telecommunications assets implemented a temperature-driven model of infrastructure electricity demand (Fallon et al., 2023), used to plan reserve capacity installation sufficient to meet the highest anticipated 5-day periods of energy consumption (or other regulatory targets). Extending this work with climate models (UKCP18), we demonstrate that the capacity planning framework reliant upon reanalysis observations underestimates capacity installation appropriate to meet historic weather risk, while assessments are improved using historic period climate model outputs. Additionally, climate projections simulating future periods support further upgrading the installed reserve capacity beyond historic requirements.

Quantile-correcting bias adjustments of climate model outputs can address significant discrepancy between the model world and observations temperature distributions across the historic period (model timespan where global climate matches recent observations). Uncorrected, this climate model error leads to an exaggerated frequency of extreme temperature events, hence overestimating the reserve capacity requirement. But under a quantile-correcting approach, assuming a consistent underlying representation of the weather dynamics, the temperature distribution is adjusted to match the reanalysis distribution.

Temperature delta-shifts are calculated to represent the GB historic period climate variability observed across model ensemble members. The resulting infrastructure electricity demand timeseries are compared against timeseries produced from historic period temperature data adjusted by quantile delta mapping, demonstrating that reanalysis data alone is insufficient to capture the greater reserve capacity requirements predicted by quantile delta-mapping of climate model outputs in the historic time period.

Using future period climate model outputs, we compare three alternative treatments of model temperature timeseries simulating future climate: a delta-shift adjustment of reanalysis data, a regional trend-preserving mean bias adjustment, and quantile delta mapping. In each case, reserve capacity requirements increase (5% to 10% increase in a world 2.0°C above pre-industrial temperatures). There is significant variability across different model ensemble members, and sensitivity to individual weather years.

Reserve system operators can use the approaches outlined to make an informed assessment of the need for upgrading or installing new reserve systems, ensuring the stability and resilience of critical infrastructure assets. The consistent trajectories across different approaches and model ensemble members may improve confidence in results, whilst individual model ensemble members can be investigated to identify potential ‘worst case’ outcomes.

How to cite: Fallon, J., Brayshaw, D., Methven, J., Jensen, K., and Krug, L.: Simulating Reserve Power Systems in Future Climates: Bias Adjustment Approaches for Regional Climate Projections, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7974, https://doi.org/10.5194/egusphere-egu24-7974, 2024.

The European electricity system is becoming increasingly dependent on weather conditions, which influence both electricity demand and production. Non-linear dependence of the electricity system on the weather conditions can lead to energy droughts – high demand coinciding with low renewable energy production – even under non-extreme meteorological conditions. Weather conditions driving energy droughts can transcend national boundaries, which leads to the possibility that multiple countries experience concurrent energy droughts, potentially leading to a widespread energy crisis. We examine the interplay between large-scale weather conditions and the risks of co-occurrence and opportunities of disjoint occurrence of energy droughts in renewable electricity systems in European countries. We analyse 1600 years of modelled energy data against meteorological conditions from large ensemble climate model simulations to identify patterns of co-variability of energy droughts in the present-day climate.

We find a strong spatial variability in the risk for concurrent energy droughts within Europe, depending on a country’s renewable energy mix and the region's response to specific large-scale meteorological patterns (weather regimes). Some countries, such as Latvia and Slovenia, mostly experience energy droughts isolated from their neighbouring countries. However, we also find clusters of countries that experience concurrent energy droughts. This is the case for the North Sea region, and many countries in central/eastern Europe. Here, there is limited potential for cooperation, putting these countries more at risk of energy crises. Finally, we differentiate between moderate and extreme energy droughts, which have different co-occurrence signatures. This implies that an interconnected electricity grid has potential to resolve some moderate events, but is less effective in the extreme events.

How to cite: van Duinen, B., van der Most, L., Baatsen, M., and van der Wiel, K.: Strength of co-variability of energy droughts highly region dependent , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8178, https://doi.org/10.5194/egusphere-egu24-8178, 2024.

The deployment of low carbon affordable energy generation technologies plays a crucial role to achieve the Paris Agreement long-term goal to reduce global greenhouse gas emissions to limit global temperature increase well below 2ºC above pre-industrial levels, and pursue efforts to limit it to 1.5º C above pre-industrial levels. In particular wind power installed capacity is projected to increase exponentially over the next few decades. Wind power generation is, however, weather-dependent. Therefore, understanding the variability of wind, how it might be affected by climate change, and how this affects the economics of wind projects seems vital as countries continue to invest in wind energy.

At the global level, the last IPCC report (IPCC AR6 WGIII, 2022) states that the climate change impact on future wind resources will be limited. Regional studies, however, show that wind resources are projected to increase for instance over Northern Europe and decrease over Southern Europe. In North America, various studies have low agreement for the changes on future resources, in part because the inter-annual variability is larger than the projected changes due to climate change. For South America, some studies find increases in wind resources in windy areas. In general, the compounding of the anthropogenic climate change signal with high spatial and temporal wind variability can lead to large uncertainties in the projected impacts of climate change on wind resources, and as a result, on the economics of a project. 

In this study we showcase a methodology to analyze the impact of climate change on the economic indicators of a portfolio of wind farm projects across Europe. Projections of changes in wind resources are obtained using an ensemble of Coupled Model Intercomparison Project 6 (CMIP6) global climate models statistically downscaled to correct biases and increase the spatial resolution. Uncertainties in climate projections are taken into account by considering an ensemble of climate models and different emissions scenarios as represented by the Shared Socioeconomic Pathways (SSPs) . A series of assumptions about the features of a representative wind farm and its key economic parameters (e.g. capital expenditures and operational costs) are made to compute two common economic indicators: the Internal Rate of Return (IRR) and the Levelized Cost of Energy (LCOE).

By varying the production following the different climate scenarios, we analyze the impacts of climate change on the economics of the portfolios for different time horizons in the future. We find that the effect of changes of resource on IRR and LCOE depend on the region, emissions scenario and projection period. In the short term, changes are often masked by the internal variability of the resource on the site.

We also discuss, from the point of view of our role as climate services provider for the wind industry, the limitations of the data provided in the CMIP6 experiment, and some of the data needs we have identified.

How to cite: Lopez, A., Lochbihler, K., and Lizcano, G.: Uncertainties in climate projections and the economics of wind farm portfolios, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17572, https://doi.org/10.5194/egusphere-egu24-17572, 2024.

With steep elevation gradients and an abundance of water, Nepal is one of the leading countries in hydropower capacity. This potential is largely unutilised; representing a significant untapped renewable energy resource that could help Nepal achieve its emissions target and improve its energy security. However, future climate projections suggest changes in discharge seasonality, which will impact the hydropower potential. Hence, we provide an estimate of current and future theoretical hydropower potential in the four large basins in Nepal, namely Mahakali, Karnali, Gandaki, and Koshi. We use current and future discharge simulated in the Spatial Processes in Hydrology (SPHY) model from a previous study to force the theoretical potential module in the Hydropower Potential Exploration (HyPE) model. The HyPE model set up for Nepal is run for 48 combinations of future climate scenarios, combining temperature change in the range of 3°C to 8°C and precipitation change in the range of -30% to 40%. Average monthly discharge components (baseflow, rainfall runoff, snowmelt, and glacier melt) are analysed separately for the reference period (1979-2018), mid-century (2036-2065), and end of century (2071-2100). For each time horizon, we evaluate the relative contribution of the discharge components to the theoretical hydropower potential and quantify the impact of future changes in discharge seasonality.

The Indian summer monsoon dominates the discharge patterns in Nepal. The historical water balance shows an overlap in the peak contributions from rainfall and glacier melt to discharge with both occurring in July and August. A shift in the peaks from these components is not apparent for the climate scenarios considered. However, the peak from snow melt contribution shifts one to two months earlier for most climate scenarios in all basins. Such shift in the seasonal discharge composition could prove promising for stabilizing year-round hydropower generation. At 5 km resolution, we estimate the total theoretical hydropower potential for the four Nepalese basins to be 1170 TWh/yr during the reference period. While challenges remain in accurately simulating discharge in mountainous and data-scarce basins in Nepal contexts using SPHY, the majority of the projections suggest a promising increase in the monthly average discharge and the subsequent monthly theoretical hydropower potential. We observe an increase in total Nepalese hydropower potential up to 22% for the mid-century and 36% by the end of the century. Variations across the basins occur and a decrease in hydropower potential is also observed for the dry future climate scenarios. However, it is important to note that theoretical potential may not be a realistic indicator for hydropower development. Only a small part of the theoretical potential may be technically and financially feasible and an even smaller part may be sustainable. Nonetheless, our research provides a first step to the identification of hydropower project sites considered within the context of a changing climate.

How to cite: van der Veen, V., Dhaubanjar, S., Khanal, S., and Immerzeel, W.: The Nepalese theoretical hydropower potential in a changing climate, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18490, https://doi.org/10.5194/egusphere-egu24-18490, 2024.