How to cite: Minz, J., Kleidon, A., Imberger, M., Branch, O., Badger, J., and Wulfmeyer, V.: Evaluating the physical limits to technical wind energy potential over onshore Germany in 2050, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10896, https://doi.org/10.5194/egusphere-egu23-10896, 2023.

In a rapidly changing climate, changes in the frequency and intensity of future extreme weather events is expected to pose complex impact on future
weather-dependent energy systems. The contribution by Working Group I to the latest IPCC report (AR6) includes for the first time a dedicated chapter
on weather extremes. It demonstrates that even relatively small incremental increases in global mean temperature (+0.5^◦C) cause statistically significant
changes in extremes on the global scale and for large regions. In 2021, Europe experienced the worst wind drought in 60 years, followed by extreme heatwave episodes and a national daily maximum temperature record of 40.3^◦C in the UK in 2022.

The transition of the energy sector to renewable energy is key to reach the Paris Agreement goal of limiting global warming, but this also increases the energy sector’s exposure to future weather extremes in a changing climate. The impacts of climate variability and climate change on present and near future national energy systems are increasingly well documented within the literature. However, within this interdisciplinary field of research there has been little attention on impacts of extreme weather events on the energy system operations. Modellers usually use historic weather data and therefore do not consider ”new” extreme weather risks, posing problems on both the operational and infrastructural side. Omitting those risks can lead to designing systems that are operationally inadequate, i.e. prone to (long) power outages leading to major social, economic and health consequences.

The present work aims to study the variability of renewable energy generation for weather years under different future climates over Europe. We will use different future climate scenarios from the Coupled Model Intercomparison Project 6th Phase (CMIP6), to assess future projections of wind energy capacity factors. The global datasets of climate data will be adapted for Europe where we will interpolate the wind speed based on model-level raw data and further create energy capacity factors. We expect this study to be a first step in exploring the future changes in and occurrences of extreme weather events, and how these will impact the generation of renewable energy over Europe. This will in turn contribute to the knowledge on how we can plan for climate resilient renewable energy systems robust to future extreme weather events over Europe.

How to cite: Mostue, I. A., Zeyringer, M., Storelvmo, T., and Ruiz Baños, D.: Future projections of wind energy capacity factors over Europe using CMIP6, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7150, https://doi.org/10.5194/egusphere-egu23-7150, 2023.

Chile has proven to be a country with great potential for renewable energies, favoring in the last decades the development of renewable energy projects, increasing its solar and wind capacities from 202 MW (1.1%) in 2012 to 6522 MW (21%) in 2021, and increasing further by 2022 to a total of 10476 MW (32%). According to the Chilean Energy Ministry, this trend will continue to increase to cover 70% of energy generation by 2030 with renewable energy sources and to meet the carbon neutrality scenario by 2050. However, since the electricity generation from renewable energy sources is affected by future extreme weather events caused by climate change, this possible uncertainty and variability should be considered when new energy projects are designed and developed. The present work aims to study the variability of renewable energy generation under future climate scenarios in Chile.

To capture the future variability of the weather conditions, we use the global climate models from Coupled Model Intercomparison Project Phase 6 (CMIP), which assess future climate change projections considering climate variability and uncertainties scenarios. We adapt these datasets for Chile, interpolate the wind speed based on model-level raw data proposed by Hahmann et al., and calculate capacity factors per renewable technology.

Through this study, we expect to explore the changes in renewable energy generation in Chile, estimating the impact of climate changes in this area. This work will help decision-makers decide on the future energy transition of Chile in the face of extreme changes in the climate condition of the country.

How to cite: Valenzuela-Venegas, G. and Zeyringer, M.: Studying renewable energy generation variability under future climate scenarios in Chile, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-7130, https://doi.org/10.5194/egusphere-egu23-7130, 2023.

When modelling possible future renewable electricity systems, a strong focus needs to be directed to the input weather variables driving any such system. Since we cannot know the exact weather in any slightly distant future, a probabilistic approach is usually chosen, modelling the system over many possible scenarios, typically all of the past recorded weather data available. However, this narrows the range of situations considered to about 40 years, placing fundamental limits on the analysis, e.g. of rare, extreme scenarios.

In our work, we explore the possibility of using past expired ensemble forecasts from the ECMWF [1] to drastically increase the number of scenarios considered to up to 10 000 years of data. These ensemble forecasts are physical models that are regularly initialized from the same slightly perturbed snapshot, but due to the chaotic nature of weather, their predictions diverge from each other. The later stages of their predictions are thus entirely independent predictions of what the weather could have been, including the correct spatial correlations. We analyze the data from the operational archive of ECMWF to assess their suitability for modelling renewable systems of the future and demonstrate how this wealth of additional weather scenarios can enable the utilization of otherwise heavily data-dependent machine learning techniques in energy modelling. 

 [1] European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Model Ensemble extended forecast https://www.ecmwf.int/en/forecasts/datasets/set-vi

How to cite: Dolezal, P., Keshav, S., and Shuckburgh, E.: Using expired weather forecasts to supply up to 10 000 years of weather data, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17356, https://doi.org/10.5194/egusphere-egu23-17356, 2023.

Wind droughts, or prolonged periods of low wind speeds, can be a severe issue for electricity systems that are largely reliant on wind generation. Therefore, it is important for energy system planning to understand the depth and distribution of wind droughts and their trends over time. In this study, we analyze the ERA5 weather reanalysis from 1979 through 2021, using an energy deficit metric that integrates the depth and duration of wind droughts over an annual temporal scale. Our analysis shows that the most severe wind droughts in many places occurred well before wind power generation started to penetrate power systems. This prevalence of wind droughts in the historical record, combined with little evidence for strong trends in their prevalence, suggests a statistical analysis of weather reanalysis products could provide valuable guidance in designing electricity systems reliant on wind power that are robust to wind droughts.

How to cite: Antonini, E., Virguez, E., Ashfaq, S., Duan, L., Ruggles, T., and Caldeira, K.: Historical analysis of global distribution of and trends in wind droughts, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-5419, https://doi.org/10.5194/egusphere-egu23-5419, 2023.

Solar panels of utility scale are a rapidly growing contribution to the renewable energy supply with increasing efficiency and steeply reducing costs of photovoltaics (PV). Although there is consensus on long term reduction of greenhouse gas emissions and advantages of avoided emissions by using PV systems, there is need to understand the climatic effects of land surface modifications of such systems.

We first review studies focusing on effects of utility-scale solar farms and compare their results in accordance to their characterization of a photovoltaic and the type of model used to evaluate effects. Then, we perform simulations of larger than current utility-scale solar farms but also comparatively small localized farms which are both characterized by modified land surface properties. These regions are either identified on the basis of solar insolation and desert-like criteria (low precipitation) or are proposed in existing studies for potential deployment of solar farms. The solar farm deployments are characterized by static or gradual changes in respective boundary conditions of albedo, soil roughness and outgoing longwave thermal properties. Separate experiments are conducted with non-dynamic and dynamic vegetation components to investigate potential feedbacks originating from the interplay of vegetation and precipitation. To this end, we use a comprehensive model (MPI-ESM-LR; Mauritsen et al., 2019), and the Earth System Model of Intermediate Complexity PlaSim (Fraedrich et al., 2005) to understand, and cross-validate, the effects in two models of different complexity model.

We examine the results considering changes in radiative forces and surface energy budget and, therefore, the surface temperatures which can likely lead to atmospheric circulation patterns, precipitation and vegetation feedbacks. If lower complexity models provide results in an acceptable ballpark of comprehensive ESMs, then they could be further used for future quick compute simulations with various deployment scenarios and experimentation with variable technical aspects. Spatially explicit deployment of PV with focus on parameterized thermal properties dependent on the simulated climate would also allows us to look at the complementary effect of climate on panels and could help constrain the effect of different pathways on PV technology in the future.

How to cite: Samanta, A., Adam, M., May, M. M., and Rehfeld, K.: Comparing the effects of large scale solar farms on climate and regional surface energy budget in different climate models, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-10553, https://doi.org/10.5194/egusphere-egu23-10553, 2023.

The European Union has set ambitious greenhouse gas reduction targets, stimulating renewable energy production and accelerating the deployment of offshore wind energy in northern European waters, mainly the North Sea. We investigate if the set targets are achievable given the wind climate of the North Sea and efficiency loss resulting from large-scale extraction of kinetic energy.

We utilise the wind climate of the North Sea estimated from ERA5 and the New European Wind Atlas (NEWA) to evaluate the offshore energy potential of this region. We consider three scenarios of wind turbine technologies: wind farms in operation today, existing plus wind farms in the construction and planning stage, and all wind farms, which include all possible areas where offshore wind farms could be built in the future, which are determined from current exclusions zones in the North Sea. We estimate the annual energy production and capacity factors per country for the various scenarios under free-stream conditions, considering wind farm wakes from engineering models and the loss of efficiency of huge wind farms. We study the sensitivity of the energy potential to the source of wind climate (ERA5 versus NEWA), whether the data is bias-corrected or not, and the method used to apply the wake losses to the wind farm considered. We also evaluate the possible year-to-year variability of these estimates.

How to cite: Hahmann, A., Mitsakou, A., Alonso De Linaje, N. G., García-Santiago, O., Badger, J., van Uden, J., Rennuit-Mortensen, A., and den Haan, M.: Estimating the Offshore Wind Energy potential of the North Sea considering exclusion zones and the efficiency of large wind farms clusters, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13185, https://doi.org/10.5194/egusphere-egu23-13185, 2023.

Deploying renewable electricity generators comes with significant benefits but can also bring about social costs, for example, through interference with landscapes or the biosphere. 
We develop a novel methodology for quantifying the local social costs of renewable energy technologies. Through zoning decisions, authorities implicitly value spatial characteristics by trading-off different traits, such as wind resource quality or distance to settlements. 
We develop a simple theoretical model of renewable zoning and implement a corresponding discrete choice model that allows us to estimate the implied valuation from observed data in high spatial resolution. The wind power zoning in the federal state of Lower Austria, home to Austria's most significant wind resources, serves as our case study. 
According to our preliminary estimates, local social costs are non-negligible and significantly affect wind turbines' socially optimal spatial distribution. These results can inform optimal capacity choice in power system models and support wind turbine siting. Moreover, spatially highly resolved assessments of social cost are a significant improvement over conventional potential assessments based on binary exclusion criteria.

How to cite: Wehrle, S. and Schmidt, J.: Inferring local social cost from renewable zoning decisions. Evidence from Lower Austria's wind power zoning., EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-4191, https://doi.org/10.5194/egusphere-egu23-4191, 2023.

Wind power onshore is one of the key technologies in the process of transitioning to a climate-neutral energy system. Yet, negative externalities of wind power for people and nature can occur. These externalities are usually regulated with spatial planning instruments that exclude certain areas from wind power development, like forest bans or increased setback distances to settlements, which were introduced across many regions in recent years. However, this regulatory practice can cause trade-offs between the regulated externalities. We use a multi-criteria GIS-based model of the potential areas for wind power onshore in Germany to identify and quantify these trade-offs for forest bans and setback distances to settlements.  Our results show that relevant trade-offs exist between a forest ban and the proximity of the remaining potential areas to settlements as well as between setback distances and the share of forest in the remaining potential area. Further, we find that individual and simultaneous implementations of the described regulations reduce the potential area of and production potential from wind power to an extent that future expansion targets for onshore wind power in Germany are no longer achievable.

How to cite: Tafarte, P., Geiger, C., and Lehmann, P.: The opportunity cost of land use restrictions and their impact on the energy transition - a case study for Germany´s onshore wind power, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-2855, https://doi.org/10.5194/egusphere-egu23-2855, 2023.

Bulgaria suffers significantly from energy poverty. With the EU goal to reduce 55% of its emissions by 2030, careful considerations are needed in the decarbonization policies within the energy system to avoid redistributive consequences that affect vulnerable groups.

Decarbonizing the building sector through photovoltaic (PV) solar technology is a viable option and supported by EU directives. PV technology offers financial benefits for the public and reduces energy dependency from the grid. Mapping the solar potential of a building is needed to determine whether an investment in PV is viable. Solar potential mapping that incorporates socio-economic factors can inform policymakers on alleviating energy poverty.

In this paper, a solar potential mapping approach using ArcGIS Pro is developed that allows physical, technical, as well as socio-economic aspects, including social considerations. As a case study for Bulgaria, the city of Plovdiv was chosen.  Open datasets have been used. Energy affordability is used which can be determined on the basis of energy consumption and energy prices.

The results show that there is a high solar energy potential in Plovdiv, while the actual potential depends on which irradiance dataset is used. The potential is estimated to supply the entire city's electricity needs by 20-58%, which translates to providing electricity needs of about 90,000 to 135,000 inhabitants. The solar potential exceeds the building energy consumption needs of 200-300 kWh/m² in all the buildings in Plovdiv. The estimated potential savings after PV installation from utility bills is between €21-32 million annually. The socio-economic factors help place the potential values in perspective, thus visualizing the benefits of distributed PV systems. It strengthens the argument of developing policies including energy poverty indicators.

How to cite: van Sark, W., Koster, G., and Ricker, B.: Solar potential mapping to address energy poverty in a data poor region: A case study in Plovdiv, Bulgaria, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-13628, https://doi.org/10.5194/egusphere-egu23-13628, 2023.

What constitutes socially just or unjust energy systems or transitions can be derived from the philosophy and principles of justice. Assessments of justice and modelling outputs leads to great differences based on which justice principles are applied. From the little research so far published in the intersection between energy systems modelling and justice, we find that comparisons between the two principles of utilitarianism and egalitarianism dominate in assessments of distributive justice, with the latter most often considered representing a 'just energy system'. Not recognising alternative and equally valid principles of justice, resting on e.g. capabilities, responsibilities and/or opportunities, may contribute to a narrow understanding of justice that fail to align with the views of different individuals, stakeholders and societies. More importantly, it can lead to the unjust design of future energy systems and energy systems analysis. 

In this work, we contribute to the growing amount of research on justice in energy systems modelling by assessing the implications of different philosophical views on justice on modelling results. Through a modelling exercise with a power system model for Europe (highRES Europe), we explore different designs of a future net-zero European energy system, and its distributional implications based on different justice principles. In addition to the utilitarian and egalitarian approach, we include, among others, principles of 'polluters pay' and 'ability-to-pay', which take historical contributions of greenhouse gas emissions and the socio-economic conditions of a region into account. 

We find that socially just energy systems look significantly different depending on the justice principles applied. Key output metrics include costs, technology mixes and spatial deployment of electricity generation infrastructure. The results should contribute to a greater discussion among researchers on the implications of different constructions of justice in modelling, expansion of approaches, and demonstrate the importance of transparency and assumptions when communicating such results. 

How to cite: Vågerö, O., Zeyringer, M., and Inderberg, T. H. J.: Modelling the just allocation of energy infrastructure - Implications of assumptions and definitions of justice on model results, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-12347, https://doi.org/10.5194/egusphere-egu23-12347, 2023.

There are 770 million people without access to electricity globally, and 77% live in Sub-Sahara Africa (SSA). If the current electrification trends continue, IEA projections indicate that still 670 million people will lack electricity access by 2030, meaning that the SDG7.1 goal of achieving universal electricity access will not be achieved with current policies. Most of the research on optimal solutions for achieving SDG7.1 is focused on SSA. However, a nonnegligible number of people still lack access in other regions; therefore, a global perspective is important. This work aims to spatially analyze the least-cost strategies for achieving universal electricity access globally, the investment needed, and the synergies with climate change mitigation. Optimal least-cost solutions vary depending on the local situation. For instance, the cost of in-situ systems depends on the spatial spread of households, local energy demand and resource availability. Therefore, high-resolution (HR) spatial assessment is needed, also for integrated global analysis.

For this research, we build upon the work of Dagnachew et al. for SSA and expand the scope to global. The model is updated and re-coded for open-source access, and the spatial resolution has been increased from 30’ x 30’ to 5’x 5’. The levelized cost (LCOE) for eleven plausible electrification solutions is assessed per grid cell worldwide to select the least-cost option. They can be summarized in three categories: central grid extension and two off-grid options, stand-alone and mini-grid systems. A central grid connection is the solution that usually offers the largest security of supply. However, for remote areas, the high cost of grid extension justifies prioritizing off-grid solutions. Mini-grids consist of small powerplant (s) that feed electricity into a distribution grid. It is the most reliable off-grid option and can be built ready for future grid connection.

The main factors influencing LCOE are socio-geographic conditions and potential local energy resources for wind and solar. The socio-geographic factors are annual electricity use per household, obtained from the integrated assessment model IMAGE, population density translated into the number of households per grid cell, population dispersion within the grid cells and urban/rural rates. Another important factor is the distance to the central grid, assessed per grid cell (5’x5’ resolution) and determines the cost of grid extension. Preliminary results indicate that after optimizing for the lowest cost, central grid densification is the most suitable option for most people currently lacking access. Photovoltaic systems are used the most for the off-grid options, combined with diesel for mini-grids and in solar home systems. Total investment for the SSA region for achieving SDG7.1 is estimated at around 600 billion.

How to cite: Zapata, V., Dagnachew, A., Edelenbosch, O., and van Vuuren, D.: Global pathways to achieve universal electricity access in 2030, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-17364, https://doi.org/10.5194/egusphere-egu23-17364, 2023.

In a steadily decarbonizing electricity system, it becomes increasingly important to explore cost-effective wind-solar-storage combinations to replace conventional fossil-fuelled power generation without compromising grid reliability. For a renewable-rich state in Southern India (Karnataka), we systematically assess the economics of various wind-solar-battery energy mixes given decreasing fossil-fuelled base generation and hydropower availability using Pareto frontiers. Our approach considers hourly load data, simulates generation based on hourly weather reanalysis products, and models the effects of battery charging and discharging on battery lifetime. We find that the allowed curtailment level limits the achievable grid reliability. Given declining baseload generation and available hydropower in the state electricity grid, the wind-solar-battery combined system can provide limited reliability, which declines as the grid is progressively decarbonized. A fully decarbonized grid with 2 GW of hydropower and a stringent 10% curtailment threshold can achieve maximum reliability of 66%. These values are sensitive to available hydropower capacity, baseload generation from fossil fuel, and the curtailment threshold. For a fully decarbonized grid, increasing the allowed curtailment threshold of renewable generation (during times of excess) to 80% would ensure 99% grid reliability. However, such a solution would be costly, requiring large wind-solar installations that exceed officially assessed potential, constrained by land allocation. Furthermore, these calculations show that adding storage capacity without concomitant expansion of renewable generation capacity is inefficient. The findings highlight the importance of a fresh examination of curtailment thresholds, renewable potential, and possibilities of demand-side management to evaluate pathways to the decarbonization of the electricity grid while maintaining reliability.

How to cite: Gangopadhyay, A., K. Seshadri, A., and Patil, B.: Wind-solar-storage trade-offs in a decarbonising electricity system, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-500, https://doi.org/10.5194/egusphere-egu23-500, 2023.

Ensuring a universal access to a reliable, affordable, and sustainable energy by 2030 would require electrifying around 600 million people in Sub-Saharan Africa. The International Energy Agency estimates that one third of the future electricity connections would be met by mini-grids (MG). This electrification must be compatible with the objective of the Paris agreement and global pathways expected to limit warming to 1.5°C have to reach net-zero CO2 emission as soon as 2050. Autonomous MGs based on solar PV are there a promising solution to electrify rural areas. They have a low cost and allow to significantly reduce greenhouse gases (GHG) emissions compared to diesel generators.

Many different MG configurations hybridizing solar PV, diesel genset and batteries can supply the production required for a given community, and the sizing of MG is usually done by minimizing criteria such as the levelized cost of electricity (LCOE) and/or the carbon footprint (CFP) of the system. The goal of this study is to quantify the distance between the CFP optimum and the LCOE optimum configurations, and the potential to find compromising configurations between.

To do so, we consider fictitious hybrid MG for a large range of configurations (PV and diesel share, storage capacities) to supply typical load profiles for 93 different locations over Africa. The solar PV production is simulated using meteorological data at a 15min resolution (ERA5, Heliosat SARAH2) and we ensure that the electrical consumption is fully supplied with simple dispatch rules for the batteries and the genset.

We show that the least LCOE (LCOE*) and the least CFP (CFP*) configurations and values are mainly driven by the mean capacity factor of the solar resource and by its co-variability with the electric load profile. The larger the capacity factor, the lower the LCOE and the CFP, and the nighttime energy consumption strongly influences the CFP values for both configurations.

We show that, even in a configuration where all the production is obtained from solar, the CFP of a MG is non-negligible. If the CFP of a MG is obviously determined by the direct greenhouse gases (GHG) emissions related to fuel combustion, it indeed also results from the indirect GHG emissions obtained for solar PV and batteries production. For the studied locations, the CFP* values cannot go below a minimum threshold between 180 𝑔𝐶𝑂₂/𝑘𝑊ℎ and 250 𝑔𝐶𝑂₂/𝑘𝑊ℎ depending on the climatic zone and the load profile considered.

For almost all locations, the LCOE* configuration is obtained with a hybrid MG. We also show that this configuration usually allows a CFP reduction by more than 50% compared to a genset only configuration, and that, relatively high increases (often >20%) of the LCOE are needed to reduce further MG emissions at the level of the CFP* configuration. We however also show that significant CFP reduction can be obtained at low cost by choosing a configuration between the CFP* and the LCOE* configurations.

How to cite: Chamarande, T., Hingray, B., and Mathy, S.: Reducing the carbon footprint of mini-grids in Africa: the value of solar PV, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-3367, https://doi.org/10.5194/egusphere-egu23-3367, 2023.